DETAILED ACTION
This action is in reply to the Amendments filed on 01/29/2026.
Claims 7 and 17 are cancelled.
Claims 1-6, 8-16, and 18-20 are rejected.
Claims 1-6, 8-16, and 18-20 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 01/29/2026, has been entered. Claims 1, 4-6, 8-11, 14-16 and 18-20 have been amended.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-sending, from an online system to a client device associated with a user of the online system, instructions causing the client device to display a user interface for placing an order comprising one or more items to be collected from a retailer location;
-responsive to receiving a request from the [user] client device to place the order via the user interface, the order associated with a future timeframe, retrieving a set of data associated with each item of the one or more items;
-accessing first a machine-learning model trained [used] to predict a likelihood that an item is a predictable availability item, wherein a predictable availability item is an item that is associated with at least a threshold measure of fluctuation of availability at a retailer location throughout a day, and wherein the machine-learning model is trained by:
-receiving data associated with a plurality of items included among an inventory of the retailer location,
-receiving, for each of the plurality of items, a label indicating whether a measure of fluctuation of an availability of each item at different times of day at the retailer location is at least a threshold measure, and
-training [utilizing] the machine-learning model based at least in part on the data and the label for each of the plurality of items;
-for each item of the one or more items, applying the first machine-learning model to generate a value representing a likelihood that the item is a predictable availability item, wherein the predicted likelihood is based at least in part on the set of data associated with the item;
-identifying a set of predictable availability items included among the one or more items, wherein the identifying is based at least in part on the likelihood predicted for each item of the one or more items;
-for each predictable availability item of the set of predictable availability items, applying a second machine-learning model to predict the availability of the predictable availability item at the retailer location during the future timeframe, wherein the availability is predicted based at least in part on the set of data associated with the predictable availability item;
-for each predictable availability item predicted by the second machine-learning model to be unavailable during the future timeframe, generating a user interface element that indicates to the user that the item is likely to be unavailable; and
-updating the [display] user interface to include the generated user interface elements, wherein the updating causes the client device to display [of] the update[] user interface
The above limitations recite the concept of predicting the likelihood that an item in an order is a predictable availability item and, for each predictable availability item, predicting the availability of the item during a future timeframe. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations of receiving data associated with a plurality of items included among an inventory of the retailer location, receiving, for each of the plurality of items, a label indicating whether a measure of fluctuation of an availability of each item at different times of day at the retailer location is at least a threshold measure; and identifying a set of predictable availability items included among the one or more items, wherein the identifying is based at least in part on the likelihood predicted for each item of the one or more items are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “receiving,” “receiving,” and “identifying,” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of sending, from an online system to a client device associated with a user of the online system, instructions causing the client device to display a user interface for placing an order comprising one or more items to be collected from a retailer location; responsive to receiving a request from the [user] client device to place the order via the user interface, the order associated with a future timeframe, retrieving a set of data associated with each item of the one or more items; accessing first a machine-learning model trained [used] to predict a likelihood that an item is a predictable availability item, wherein a predictable availability item is an item that is associated with at least a threshold measure of fluctuation of availability at a retailer location throughout a day, and wherein the machine-learning model is trained by: training [utilizing] the machine-learning model based at least in part on the data and the label for each of the plurality of items; for each item of the one or more items, applying the first machine-learning model to generate a value representing a likelihood that the item is a predictable availability item, wherein the predicted likelihood is based at least in part on the set of data associated with the item; for each predictable availability item of the set of predictable availability items, applying a second machine-learning model to predict the availability of the predictable availability item at the retailer location during the future timeframe, wherein the availability is predicted based at least in part on the set of data associated with the predictable availability item; for each predictable availability item predicted by the second machine-learning model to be unavailable during the future timeframe, generating a user interface element that indicates to the user that the item is likely to be unavailable; and -updating the [display] user interface to include the generated user interface elements, wherein the updating causes the client device to display [of] the update[] user interface are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the user placing the order is by sending, from an online system to a client device associated with a user of the online system, instructions causing the client device to display a user interface for placing an order, that the user is using a client device, that the order is placed via the user interface, that the first model is a first machine-learning model that is trained, that the second model is a second machine-learning model, that the generated element is a generated user interface element, that the updating is of the user interface, and that the client device to display the updated user interface, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “online system,” “client device,” “instructions causing the client device to display a user interface,” “user interface,” “first machine-learning model,” “trained,” “training,” “updated user interface,” “second machine-learning model,” and “user interface element[s]” in the context of this claim encompasses advertising, and marketing or sales activities.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-sending, from an online system to a client device associated with a user of the online system, instructions causing the client device to display a user interface for placing an order comprising one or more items to be collected from a retailer location;
-responsive to receiving a request from the client device to place the order via the user interface, the order associated with a future timeframe, retrieving a set of data associated with each item of the one or more items;
-accessing first a machine-learning model trained to predict a likelihood that an item is a predictable availability item, wherein a predictable availability item is an item that is associated with at least a threshold measure of fluctuation of availability at a retailer location throughout a day, and wherein the machine-learning model is trained by:
-receiving data associated with a plurality of items included among an inventory of the retailer location,
-receiving, for each of the plurality of items, a label indicating whether a measure of fluctuation of an availability of each item at different times of day at the retailer location is at least a threshold measure, and
-training the machine-learning model based at least in part on the data and the label for each of the plurality of items;
-for each item of the one or more items, applying the first machine-learning model to generate a value representing a likelihood that the item is a predictable availability item, wherein the predicted likelihood is based at least in part on the set of data associated with the item;
-identifying a set of predictable availability items included among the one or more items, wherein the identifying is based at least in part on the likelihood predicted for each item of the one or more items;
-for each predictable availability item of the set of predictable availability items, applying a second machine-learning model to predict the availability of the predictable availability item at the retailer location during the future timeframe, wherein the availability is predicted based at least in part on the set of data associated with the predictable availability item;
-for each predictable availability item predicted by the second machine-learning model to be unavailable during the future timeframe, generating a user interface element that indicates to the user that the item is likely to be unavailable; and
-updating the user interface to include the generated user interface elements, wherein the updating causes the client device to display the updated user interface
The additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0097] of Applicant’s specification – “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In some embodiments, a software module is implemented with a computer program product comprising one or more computer-readable media storing computer program code or instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.” Specifically, the additional elements of a computer system, a processor, a computer-readable medium, an online system, a client device, instructions causing the client device to display a user interface, the user interface, first machine-learning model, the first machine-learning model being trained, training the first machine-learning model, a second machine-learning model, user interface element[s], and an updated user interface are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of sending data, retrieving data, accessing data, receiving data, training data, predicting data, identifying data, generating data, and updating and displaying data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) 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.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 11 is a computer program product reciting similar functions as claim 1. Examiner notes that claim 11 recites the additional elements of a computer program product, a non-transitory computer-readable storage medium, a processor, an online system, a client device, instructions causing the client device to display a user interface, the user interface, first machine-learning model, the first machine-learning model being trained, training the first machine-learning model, a second machine-learning model, user interface element[s], and an updated user interface, however, claim 11 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Claim 20 is a computer system reciting similar functions as claim 1. Examiner notes that claim 20 recites the additional elements of a computer system, a processor, a non-transitory computer-readable storage medium, an online system, a client device, instructions causing the client device to display a user interface, the user interface, first machine-learning model, the first machine-learning model being trained, training the first machine-learning model, a second machine-learning model, user interface element[s], and an updated user interface, however, claim 20 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Therefore, claims 1, 11, and 20 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-6, 8-10 and 12-16, 18-19, 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 claims 2-10 and 12-19 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 2-3, 8-10, 12-13, and 19 do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 4-7 and 14-16 and 18 recite the additional elements of a second machine-learning model that is trained, training the second machine-learning model, the client device, a user interface element, the user interface, the computer-readable storage medium, and the processor, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 2-10 and 12-19 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-6, 8-10 and 12-16, 18-19 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 11, and 20, dependent claims 2-6, 8-10 and 12-16, 18-19 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. predicting the likelihood that an item in an order is a predictable availability item and, for each predictable availability item, predicting the availability of the item during a future timeframe) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 10-15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. (US 2019/0236740 A1), hereinafter Rao, in view of Yien et al. (US 11,295,322 B1), hereinafter Yien.
Regarding claim 1, Rao discloses a method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
-sending, from an online system to a client device associated with a user of the online system, instructions causing the client device to display a user interface for placing an order comprising one or more items to be collected from a retailer location (Rao, see at least: “Process 700 may be carried out by the online concierge system 102 (e.g., the order fulfillment engine 206) communicating with a customer via the CMA 106 [i.e. from an online system to a client device associated with a user of the online system]. The order fulfillment engine 206 provides a customer interface 702. The customer interface [i.e. sending instructions causing the client device to display a user interface] includes an ordering interface through which a customer may make item selections, and add items to a delivery order [i.e. for placing an order comprising one or more items to be collected from a retailer location]” [0053]);
-responsive to receiving a request from the client device to place the order via the user interface, the order associated with a future timeframe, retrieving a set of data associated with each item of the one or more items (Rao, see at least: “The customer interface receives 704 an item to be included in a delivery order. This item may be any item selected for purchase by the customer through the customer interface [i.e. responsive to receiving a request from the client device to place the order via the user interface]. The customer may also provide a delivery time associated with the order [i.e. the order associated with a future timeframe], which the online concierge system 102 can use to determine or approximate a picking time for the order. In response to the customer inputs, the online concierge system 102 (e.g., the order fulfillment engine 206) determines 706 a probability that the item received at 704 is available at a warehouse, e.g., a warehouse selected by the customer, or a warehouse selected by the online concierge system 102 for fulfilling an order from the customer. The probability is determined by inputting the item, warehouse, and timing characteristics for the item received [i.e. retrieving a set of data associated with each item of the one or more items] and the warehouse into a machine-learned item availability model 216” [0053]);
-accessing first a machine-learning model trained to predict a likelihood that an item is a predictable availability item, wherein a predictable availability item is an item that is associated with at least a threshold measure of fluctuation of availability at a retailer location throughout a day (Rao, see at least: “The item characteristics may be determined by the machine-learned item availability model 216 [i.e. accessing a machine-learning model trained] to be statistically significant factors [i.e. associated with at least a threshold measure] predictive of the item's availability. For different items, the item characteristics that are predictors of availability may be different. For example, an item type factor might be the best predictor of availability for dairy items, whereas a time of day may be the best predictive factor of availability for vegetables [i.e. to predict a likelihood that an item is a predictable availability item]” [0027] and “The product type may affect the item availability, since certain product types may have a higher turnover and re-stocking rate than others [i.e. wherein a predictable availability item is an item that is associated with at least a threshold measure of fluctuation of availability at a retailer location throughout a day], or may have larger inventories in the warehouses” [0029]), and wherein the machine-learning model is trained by:
-receiving data associated with a plurality of items included among an inventory of the retailer location (Rao, see at least: “The training datasets 220 [i.e. wherein the machine-learning model is trained by:] include item characteristics [i.e. receiving data associated with a plurality of items]. In some examples, the item characteristics include a department associated with the item. For example, if the item is yogurt, it is associated with the dairy department. The department may be the bakery, beverage, nonfood and pharmacy, produce and floral, deli, prepared foods, meat, seafood, dairy, the meat department, or dairy department, or any other categorization of items used by the warehouse [i.e. included among an inventory of the retailer location]. The department associated with an item may affect item availability, since different departments have different item turnover rates and inventory levels” [0029]),
-receiving, for each of the plurality of items, a label indicating whether a measure of fluctuation of an availability of each item at different times of day at the retailer location is at least a threshold measure (Rao, see at least: “The product type may affect the item availability, since certain product types may have a higher turnover and re-stocking rate than others [i.e. receiving, for each of the plurality of items, a label indicating whether a measure of fluctuation of an availability of each item at different times of day at the retailer location], or may have larger inventories in the warehouses” [0029] and “The item characteristics may be determined by the machine-learned item availability model 216 to be statistically significant factors [i.e. is at least a threshold measure] predictive of the item's availability. For different items, the item characteristics that are predictors of availability may be different. For example, an item type factor might be the best predictor of availability for dairy items, whereas a time of day may be the best predictive factor of availability for vegetables [i.e. a label indicating whether a measure of fluctuation of an availability of each item at different times of day at the retailer location is at least a threshold measure]” [0027]), and
-training the machine-learning model based at least in part on the data and the label for each of the plurality of items (Rao, see at least: “The training datasets 220 [i.e. training the machine-learning model] include item characteristics [i.e. ased at least in part on the data]. In some examples, the item characteristics include a department associated with the item. For example, if the item is yogurt, it is associated with the dairy department. The department may be the bakery, beverage, nonfood and pharmacy, produce and floral, deli, prepared foods, meat, seafood, dairy, the meat department, or dairy department, or any other categorization of items used by the warehouse. The department associated with an item may affect item availability, since different departments have different item turnover rates and inventory levels … In some examples, the item characteristics include a product type associated with the item. For example, if the item is a particular brand of a product, then the product type will be a generic description of the product type, such as “milk” or “eggs.” The product type may affect the item availability, since certain product types may have a higher turnover and re-stocking rate than others [i.e. based at least in part on the label for each of the plurality of items], or may have larger inventories in the warehouses” [0029] and “For different items, the item characteristics that are predictors of availability may be different. For example, an item type factor might be the best predictor of availability for dairy items, whereas a time of day may be the best predictive factor of availability for vegetables [i.e. based at least in part on the label for each of the plurality of items]” [0027]);
-for each item of the one or more items, applying the first machine-learning model to generate a value representing a likelihood that the item is a predictable availability item, wherein the predicted likelihood is based at least in part on the set of data associated with the item (Rao, see at least: “The item characteristics [i.e. wherein the predicted likelihood is based at least in part on the set of data associated with the corresponding item] may be determined by the machine-learned item availability model 216 to be statistically significant factors predictive of the item's availability [i.e. for each item of the one or more items, applying the first machine-learning model to generate a value representing a likelihood that the item is a predictable availability item]. For different items, the item characteristics that are predictors of availability may be different. For example, an item type factor might be the best predictor of availability for dairy items, whereas a time of day may be the best predictive factor of availability for vegetables” [0027]);
-identifying a set of predictable availability items included among the one or more items, wherein the identifying is based at least in part on the likelihood predicted for each item of the one or more items (Rao, see at least: “The item characteristics may be determined by the machine-learned item availability model 216 to be statistically significant factors predictive of the item's availability [i.e. wherein the identifying is based at least in part on the likelihood predicted for each item of the one or more items]. For different items, the item characteristics that are predictors of availability may be different. For example, an item type factor might be the best predictor of availability for dairy items, whereas a time of day may be the best predictive factor of availability for vegetables [i.e. identifying a set of predictable availability items included among the one or more items]” [0027]);
-for each predictable availability item of the set of predictable availability items, predicting the availability of the predictable availability item at the retailer location during the timeframe, wherein the availability is predicted based at least in part on the set of data associated with the predictable availability item (Rao, see at least: “For example, the online concierge system 102 may input the item, warehouse, and timing characteristics for each item-warehouse pair into the machine-learned item availability model 216 to assess the availability of each item in the delivery order at each potential warehouse at a particular day and/or time [i.e. during a timeframe]. The machine-learned item availability model 216 predicts 408 the probability that one of the set of items in the delivery order is available at the warehouse [i.e. for each predictable availability item of the set of predictable availability items, predicting the availability of the predictable availability item at the retailer location]” [0034] and “The item characteristics may be determined by the machine-learned item availability model 216 to be statistically significant factors predictive of the item's availability [i.e. wherein the availability is predicted based at least in part on the set of data associated with the corresponding predictable availability item]. For different items, the item characteristics that are predictors of availability may be different. For example, an item type factor might be the best predictor of availability for dairy items, whereas a time of day may be the best predictive factor of availability for vegetables [i.e. for each predictable availability item of the set of predictable availability items]” [0027]);
-for each predictable availability item predicted to be unavailable, generating a user interface element that indicates to the user that the item is likely to be unavailable (Rao, see at least: “If the probability that an item is available is below a threshold, then the order fulfillment engine 206 notifies the customer 712 through an ordering interface of the customer interface provided [i.e. for each predictable availability item predicted to be unavailable, generating a user interface element that indicates to the user that the item is likely to be unavailable]. The notification may be a warning or other message transmitted to the customer through the ordering interface. For example, the notification may be a message saying “item frequently not found” provided through the ordering interface [i.e. generating a user interface element that indicates to the user that the item is likely to be unavailable]” [0055]); and
-updating the user interface to include the generated user interface elements, wherein the updating causes the client device to display the updated user interface (Rao, see at least: “If the probability that an item is available is below a threshold, then the order fulfillment engine 206 notifies the customer 712 through an ordering interface of the customer interface provided [i.e. updating the user interface to include the generated user interface elements]. The notification may be a warning or other message transmitted to the customer through the ordering interface. For example, the notification may be a message saying “item frequently not found” [i.e. the generated user interface elements] provided through the ordering interface [i.e. wherein the updating causes the client device to display the updated user interface]” [0055]).
Rao does not explicitly disclose applying a second machine-learning model to predict the availability of the predictable availability item at the retailer location during the future timeframe; and each predictable availability item being predicted by the second machine-learning model to be unavailable during the future timeframe.
Yien, however, teaches an online order management service that interfaces between merchants and order/delivery services (i.e. abstract), including the known technique of applying a second machine-learning model to predict the availability of the item at the retailer location during the future timeframe (Yien, see at least: “the recommendation services 236 and 432 may utilize various types of automation and/or machine-learning (ML) techniques to perform various functions described herein. For instance, many of the techniques for analyzing data to make intelligent decisions and/or recommendations may be performed using one or more ML algorithms [i.e. applying a second machine-learning model] to identify patterns in data, learn or train models to analyze data, and/or make predictions based on observed characteristics in data” Col. 39 Ln. 49-57 and “By tracking and analyzing sales of different items by multiple merchants, the inventory system 404 may detect patterns, such as that certain items sell better in certain seasons, certain weather, certain times of week, certain locations [i.e. at the retailer location], and so forth. Information regarding sales patterns of different items can be utilized in order to make recommendations to merchants regarding which items should be ordered and/or stocked at any given time [i.e. to predict the availability of the item]” Col. 25 Ln. 50-57 and “techniques described herein leverage access to data that is not available to conventional techniques to make intelligent recommendations for improving the selection of different vendors as well as improving the ability of a merchant to forecast the rates at which products should be ordered [i.e. during the future timeframe]” Col. 6 Ln. 21-26); and
the known technique of each item being predicted by the second machine-learning model to be unavailable during the future timeframe (Yien, see at least: “the recommendation services 236 and 432 may utilize various types of automation and/or machine-learning (ML) techniques to perform various functions described herein. For instance, many of the techniques for analyzing data to make intelligent decisions and/or recommendations may be performed using one or more ML algorithms to identify patterns in data, learn or train models to analyze data, and/or make predictions [i.e. each predictable availability item being predicted by the second machine-learning model] based on observed characteristics in data” Col. 39 Ln. 49-57 and “By tracking and analyzing sales of different items by multiple merchants, the inventory system 404 may detect patterns, such as that certain items sell better in certain seasons, certain weather, certain times of week, certain locations, and so forth. Information regarding sales patterns of different items can be utilized in order to make recommendations to merchants regarding which items should be ordered and/or stocked at any given time [i.e. to be unavailable during the future timeframe]” Col. 25 Ln. 50-57 and “techniques described herein leverage access to data that is not available to conventional techniques to make intelligent recommendations for improving the selection of different vendors as well as improving the ability of a merchant to forecast the rates at which products should be ordered [i.e. during the future timeframe]” Col. 6 Ln. 21-26). These known techniques are applicable to the method of Rao as they both share characteristics and capabilities, namely, they are directed to an online order management service that interfaces between merchants and order/delivery services.
It would have been recognized that applying the known techniques of applying a second machine-learning model to predict the availability of the item at the retailer location during the future timeframe; and each item being predicted by the second machine-learning model to be unavailable during the future timeframe, as taught by Yien, to the teachings of Rao would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modifications of applying a second machine-learning model to predict the availability of the item at the retailer location during the future timeframe; and each item being predicted by the second machine-learning model to be unavailable during the future timeframe, as taught by Yien, into the method of Rao would have been recognized by those of ordinary skill in the art as resulting in an improved method that would improve the ability of a merchant to forecast the rates at which products should be ordered (Yien, Col. 6 Ln. 21-26).
Regarding claim 2, Rao in view of Yien teaches the method of claim 1. Rao further discloses:
-wherein retrieving the set of data associated with each item of the one or more items comprises retrieving one or more of: whether a refund issued for a corresponding item is associated with an issue associated with a previous order, whether a refund issued for a corresponding item is associated with a cancellation of a previous order, whether a refund issued for a corresponding item is associated with a complaint associated with a previous order, whether a refund issued for a corresponding item is associated with a low rating associated with a previous order, whether a refund issued for a corresponding item is associated with one or more additional items being removed from a previous order, whether a corresponding item was a first item added to a shopping list, whether a corresponding item is categorized as a loss leader item, or whether a corresponding item is categorized as a fresh made item (Rao, see at least: “the item characteristics [i.e. wherein retrieving the set of data associated with each item of the one or more items comprises retrieving one or more of:] include a department associated with the item. For example, if the item is yogurt, it is associated with the dairy department. The department may be the bakery, beverage, nonfood and pharmacy, produce and floral, deli, prepared foods [i.e. whether a corresponding item is categorized as a fresh made item], meat, seafood, dairy, the meat department, or dairy department, or any other categorization of items used by the warehouse. The department associated with an item may affect item availability, since different departments have different item turnover rates and inventory levels” [0029]).
Regarding claim 3, Rao in view of Yien teaches the method of claim 1. Rao further discloses:
-wherein receiving the label indicating the measure of fluctuation of the availability of each item at different times of day at the retailer location includes receiving one or more of: a standard deviation, a range, a difference, a ratio, or a rate (Rao, see at least: “The product type may affect the item availability, since certain product types may have a higher turnover and re-stocking rate than others [i.e. wherein receiving the label indicating the measure of fluctuation of the availability of each item at different times of day at the retailer location includes receiving one or more of: a standard deviation, a range, a difference, a ratio, or a rate], or may have larger inventories in the warehouses” [0029]).
Regarding claim 4, Rao in view of Yien teaches the method of claim 1. Rao further discloses:
-wherein the machine-learning model is trained by:
-receiving historical availability data associated with a plurality of predictable availability items included among the inventory of the retailer location (Rao, see at least: “The training datasets 220 [i.e. wherein the machine-learning model is trained by:] relate a variety of different factors to known item availabilities from the outcomes of previous delivery orders (e.g. if an item was previously found or previously unavailable) [i.e. receiving historical availability data associated with a plurality of predictable availability items included among the inventory of the retailer location]. The training datasets 220 include the items included in previous delivery orders, whether the items in the previous delivery orders were picked, warehouses associated with the previous delivery orders, and a variety of characteristics associated with each of the items (which may be obtained from the inventory database 204). Each piece of data in the training datasets 220 includes the outcome of a previous delivery order (e.g., if the item was picked or not) … the training datasets 220 are historic delivery order information used to train the machine-learned item availability model 216” [0027] and “The item characteristics may be determined by the machine-learned item availability model 216 to be statistically significant factors predictive of the item's availability. For different items, the item characteristics that are predictors of availability may be different. For example, an item type factor might be the best predictor of availability for dairy items, whereas a time of day may be the best predictive factor of availability for vegetables [i.e. associated with a plurality of predictable availability items included among the inventory of the retailer location]” [0027]); and
-training the machine-learning model based at least in part on the historical availability data (Rao, see at least: “The training datasets 220 relate a variety of different factors to known item availabilities from the outcomes of previous delivery orders (e.g. if an item was previously found or previously unavailable) [i.e. training the machine-learning model based at least in part on the historical availability data]. The training datasets 220 include the items included in previous delivery orders, whether the items in the previous delivery orders were picked, warehouses associated with the previous delivery orders, and a variety of characteristics associated with each of the items (which may be obtained from the inventory database 204). Each piece of data in the training datasets 220 includes the outcome of a previous delivery order (e.g., if the item was picked or not) … the training datasets 220 are historic delivery order information used to train the machine-learned item availability model 216” [0027]).
Rao does not explicitly disclose the machine-learning model being the second machine-learning model.
Yien, however, teaches an online order management service that interfaces between merchants and order/delivery services (i.e. abstract), including the known technique of the second machine-learning model is trained by receiving historical data (Yien, see at least: “the recommendation services 236 and 432 may utilize various types of automation and/or machine-learning (ML) techniques to perform various functions described herein. For instance, many of the techniques for analyzing data to make intelligent decisions and/or recommendations may be performed using one or more ML algorithms [i.e. the second machine-learning model is trained by] to identify patterns in data, learn or train models to analyze data, and/or make predictions based on observed characteristics in data” Col. 39 Ln. 49-57 and “the system implements a machine learning model by analyzing training data, such as historical data [i.e. by receiving historical data], current order data, etc.” Col. 3 Ln. 16-18); and
the known technique of training the second machine-learning model based at least in part on the historical data (Yien, see at least: “the recommendation services 236 and 432 may utilize various types of automation and/or machine-learning (ML) techniques to perform various functions described herein. For instance, many of the techniques for analyzing data to make intelligent decisions and/or recommendations may be performed using one or more ML algorithms [i.e. second machine-learning model] to identify patterns in data, learn or train models [i.e. training the second machine-learning model] to analyze data, and/or make predictions based on observed characteristics in data” Col. 39 Ln. 49-57 and “the system implements a machine learning model by analyzing training data, such as historical data [i.e. based at least in part on the historical data], current order data, etc.” Col. 3 Ln. 16-18). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Rao with Yien for the reasons identified above with respect to claim 1.
Regarding claim 5, Rao in view of Yien teaches the method of claim 4. Rao further discloses:
-wherein training the second machine-learning model is further based at least in part on historical supply and demand data associated with the online system (Rao, see at least: “the training datasets 220 [i.e. wherein training the second machine-learning model is further based at least in part on] include a time of day at which each previous delivery order was placed. Time of day may impact item availability, since during high-volume shopping times, items may become unavailable that are otherwise regularly stocked by warehouses. In addition, availability may be affected by restocking schedules, e.g., if a warehouse mainly restocks at night, item availability at the warehouse will tend to decrease over the course of the day. Additionally or alternatively, the training datasets 220 include a day of the week previous delivery orders were placed. The day of the week may impact item availability, since popular shopping days may have reduced inventory of items [i.e. historical supply and demand data associated with the online system], or restocking shipments may be received on particular days” [0028]).
Regarding claim 10, Rao in view of Yien teaches the method of claim 1. Rao further discloses:
-wherein retrieving the set of data associated with the item comprises retrieving information describing the measure of fluctuation of the availability of the item at different times of day at the retailer location (Rao, see at least: “the item characteristics [i.e. wherein retrieving the set of data associated with the corresponding item comprises] include a department associated with the item … The product type may affect the item availability, since certain product types may have a higher turnover and re-stocking rate than others [i.e. retrieving information describing the measure of fluctuation of the availability of the item at different times of day at the retailer location], or may have larger inventories in the warehouses” [0029] and “The item characteristics may be determined by the machine-learned item availability model 216 to be statistically significant factors predictive of the item's availability. For different items, the item characteristics that are predictors of availability may be different. For example, an item type factor might be the best predictor of availability for dairy items, whereas a time of day may be the best predictive factor of availability for vegetables [i.e. retrieving information describing the measure of fluctuation of the availability of the item at different times of day at the retailer location]” [0027]).
Claims 11-15 recite limitations directed towards 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 (Rao, see at least: “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described” [0060]). The limitations recited in claims 11-15 are parallel in nature to those addressed above for claims 1-5, respectively, and are therefore rejected for those same reasons set forth above in claims 1-5, respectively.
Claim 20 recites limitations directed towards a computer system comprising: a processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions (Rao, see at least: “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described” [0060]). The limitations recited in claim 20 are parallel in nature to those addressed above for claim 1, and are therefore rejected for those same reasons set forth above in claim 1.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Rao, in view of Yien, in further view of Seaward et al. (US 2016/0307265 A1), hereinafter Seaward, in further view of Phatak et al. (US 2023/0186332 A1), hereinafter Phatak.
Regarding claim 6, the combination of Rao/Yien/Seaward teaches the method of claim 1. Rao further discloses:
-for each predictable availability item of the set of predictable availability items:
-applying the machine-learning model to predict the availability of the predictable availability item at the retailer location during one or more timeframes, wherein the machine-learning model is applied to the set of data associated with the predictable availability item (Rao, see at least: “For example, the online concierge system 102 may input the item, warehouse, and timing characteristics [i.e. wherein the machine-learning model is applied to the set of data associated with the corresponding predictable availability item] for each item-warehouse pair into the machine-learned item availability model 216 [i.e. applying the machine-learning model] to assess the availability of each item in the delivery order at each potential warehouse at a particular day and/or time [i.e. during one or more timeframes]. The machine-learned item availability model 216 predicts 408 the probability that one of the set of items in the delivery order is available at the warehouse [i.e. for each predictable availability item of the set of predictable availability items, applying the machine-learning model to predict the availability of the corresponding predictable availability item at the retailer location]” [0034] and “The item characteristics may be determined by the machine-learned item availability model 216 to be statistically significant factors predictive of the item's availability. For different items, the item characteristics that are predictors of availability may be different. For example, an item type factor might be the best predictor of availability for dairy items, whereas a time of day may be the best predictive factor of availability for vegetables [i.e. associated with the corresponding predictable availability item]” [0027]); and
-updating the user interface to include a user interface element that displays, information describing the predicted availability of each predictable availability item of the set of predictable availability items at the retailer location (Rao, see at least: “If the probability that an item is available is below a threshold, then the order fulfillment engine 206 notifies the customer 712 through an ordering interface of the customer interface provided. The notification may be a warning or other message transmitted to the customer through the ordering interface. For example, the notification may be a message saying “item frequently not found” provided through the ordering interface [i.e. updating the user interface to include a user interface element that displays, information describing the predicted availability of each predictable availability item of the set of predictable availability items at the retailer location]” [0055]).
Rao does not explicitly disclose does not explicitly teach the machine-learning model being a second machine-learning model.
Yien, however, teaches an online order management service that interfaces between merchants and order/delivery services (i.e. abstract), including the known technique of the second machine-learning model to predict the availability of an item and the second machine-learning model being applied to the set of data (Yien, see at least: “the recommendation services 236 and 432 may utilize various types of automation and/or machine-learning (ML) techniques to perform various functions described herein. For instance, many of the techniques for analyzing data to make intelligent decisions and/or recommendations may be performed using one or more ML algorithms [i.e. the additional machine-learning model] to identify patterns in data, learn or train models to analyze data, and/or make predictions based on observed characteristics in data [i.e. being applied to the set of data]” Col. 39 Ln. 49-57 and “By tracking and analyzing sales of different items by multiple merchants, the inventory system 404 may detect patterns, such as that certain items sell better in certain seasons, certain weather, certain times of week, certain locations, and so forth. Information regarding sales patterns of different items can be utilized in order to make recommendations to merchants regarding which items should be ordered and/or stocked at any given time [i.e. to predict the availability of an item]” Col. 25 Ln. 50-57). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Rao with Yien for the reasons identified above with respect to claim 1.
Rao in view of Yien does not explicitly teach predict the availability of the predictable availability item at the retailer location during one or more additional future timeframes; and updating the user interface to include information describing the predicted availability of each predictable availability item of the set of predictable availability items at the retailer location during the one or more additional future timeframes.
Seaward, however, teaches managing fulfillment of orders (i.e. [0004]), including the known technique of predict the availability of the predictable availability item at the retailer location during one or more additional future timeframes (Seaward, see at least: “the server 120 may determine whether the fulfillment center can fulfill the order by the fulfillment time based on scheduled shipments to the fulfillment center. For example, the server 120 may determine that fulfillment center A has orders for fulfillment before 10 AM for all remaining apples at fulfillment center A, but that fulfillment center A will have two hundred additional apples arriving at 11 AM, and in response, determine that apples are available for sale after 11 AM” [0047]); and
the known technique of updating the user interface to include information describing the predicted availability of each predictable availability item of the set of predictable availability items at the retailer location during the one or more additional future timeframes (Seaward, see at least: “FIG. 2A is an example interface 210 used in managing orders. The interface 210 may be displayed on a user device. The interface 210 includes a notification 220 that an order cannot be fulfilled and multiple alternate suggestion 230, 232, 234, 236. The notification 220 is “Sorry, coffee is not available at 9:00 AM from Fulfillment Center A.” The alternate suggestions include “Order tea instead for 9 AM from Fulfillment Center A” 230, “Order coffee for 9 AM from Fulfillment Center B, which is a block away instead” 232, “Order coffee for 9:10 AM instead from Fulfillment Center A” [i.e. updating the user interface to include information describing the predicted availability of each predictable availability item of the set of predictable availability items at the retailer location during the one or more additional future timeframes] and “Cancel” 236” [0080]). These known techniques are applicable to the method of Rao in view of Yien as they both share characteristics and capabilities, namely, they are directed to managing fulfillment of orders.
It would have been recognized that applying the known techniques of predicting the availability of the predictable availability item at the retailer location during one or more additional future timeframes; and updating the user interface to include information describing the predicted availability of each predictable availability item of the set of predictable availability items at the retailer location during the one or more additional future timeframes, as taught by Seaward, to the teachings of Rao in view of Yien would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modifications of predicting the availability of the predictable availability item at the retailer location during one or more additional future timeframes; and updating the user interface to include information describing the predicted availability of each predictable availability item of the set of predictable availability items at the retailer location during the one or more additional future timeframes, as taught by Seaward, into the method of Rao in view of Yien would have been recognized by those of ordinary skill in the art as resulting in an improved method that would enable users to avoid waiting for orders that would not be able to be fulfilled by particular fulfillment times and enable users to place alternate orders (Seaward, [0022]).
The combination of Rao/Yien/Seaward does not teach displaying information describing the predicted availability as a graph.
Phatak, however, teaches a customer order prediction system (i.e. abstract), including the known technique of displaying, as a graph, information describing prediction data (Phatak, see at least: “User interfaces 218 can enable user interaction with the predictor computing device 102. For example, user interface 218 can be a user interface that allows an operator to interact, communicate, control and/or modify different features or parameters of the predictor computing device 102. The user interface 218 can, for example, display the predicted order sizes of customers [i.e. information describing prediction data] or the performance of the predictor computing device 102 using different textual, graphical or other types of graphs [i.e. displaying, as a graph], tables or the like” [0041] and “FIG. 2 illustrates an example computing device 200. The predictor computing device 102, the retailer order management system 112, the central ordering computing device 124, the external information source 126, and/or the customer computing devices 104, 106 may include the features shown in FIG. 2” [0033]). The known technique is applicable to the method of the combination of Rao/Yien/Seaward as they both share characteristics and capabilities, namely, they are directed to a customer order prediction system.
It would have been recognized that applying the known technique of displaying, as a graph, information describing prediction data, as taught by Phatak, to the teachings of the combination of Rao/Yien/Seaward would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of displaying, as a graph, information describing prediction data, as taught by Phatak, into the method of the combination of Rao/Yien/Seaward would have been recognized by those of ordinary skill in the art as resulting in an improved method that would decrease costs, increase sales and improve customer satisfaction (Phatak, [0003]).
Claim 16 recites limitations directed towards a computer program product. The limitations recited in claim 16 are parallel in nature to those addressed above for claim 6, and are therefore rejected for those same reasons set forth above in claim 6.
Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rao, in view of Yien, in further view of Seaward.
Regarding claim 8, the combination of Rao/Yien/Seaward teaches the method of claim 1. Rao further discloses:
Rao in view of Yien does not explicitly disclose receiving information describing a current availability of each predictable availability item of the set of predictable availability items at the retailer location.
Seaward further teaches managing fulfillment of orders (i.e. [0004]), including the known technique of receiving information describing a current availability of each predictable availability item of the set of predictable availability items at the retailer location (Seaward, see at least: “the server 120 may determine whether the fulfillment center can fulfill the order by the fulfillment time based at least on inventory of the fulfillment center. For example, the server 120 may determine that a fulfillment center's inventory management system indicates that the fulfillment center has eight hundred bagels to sell that day, determine that orders for six hundred bagels have been placed for users, and in response, determine that bagels are still available for sale from the fulfillment center [i.e. receiving information describing a current availability of each predictable availability item of the set of predictable availability items at the retailer location] at any desired time that day. In another example, the server 120 may determine that a fulfillment center's inventory management system indicates that the fulfillment center has eight hundred bagels to sell that day, determine that orders for eight hundred bagels have been placed for users, and in response, determine that bagels are not available for sale from the fulfillment center at any desired time that day” [0046]). This known technique is applicable to the method of Rao in view of Yien as they both share characteristics and capabilities, namely, they are directed to managing fulfillment of orders.
It would have been recognized that applying the known technique of receiving information describing a current availability of each predictable availability item of the set of predictable availability items at the retailer location, as taught by Seaward, to the teachings of Rao in view of Yien would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of receiving information describing a current availability of each predictable availability item of the set of predictable availability items at the retailer location, as taught by Seaward, into the method of Rao in view of Yien would have been recognized by those of ordinary skill in the art as resulting in an improved method that would enable users to avoid waiting for orders that would not be able to be fulfilled by particular fulfillment times and enable users to place alternate orders (Seaward, [0022]).
Regarding claim 9, the combination of Rao/Yien/Seaward teaches the method of claim 8. Rao further discloses:
Rao in view of Yien does not explicitly disclose retrieving the set of data associated with the predictable availability item comprising retrieving information that describes the current availability of the predictable availability item at the retailer location.
Seaward further teaches managing fulfillment of orders (i.e. [0004]), including the known technique of retrieving the set of data associated with the predictable availability item comprising retrieving information that describes the current availability of the predictable availability item at the retailer location (Seaward, see at least: “the server 120 may determine whether the fulfillment center can fulfill the order by the fulfillment time based at least on inventory of the fulfillment center. For example, the server 120 may determine that a fulfillment center's inventory management system indicates that the fulfillment center has eight hundred bagels to sell that day, determine that orders for six hundred bagels have been placed for users, and in response, determine that bagels are still available for sale from the fulfillment center [i.e. wherein retrieving the set of data associated with the predictable availability item comprises retrieving information that describes the current availability of the predictable availability item at the retailer location] at any desired time that day. In another example, the server 120 may determine that a fulfillment center's inventory management system indicates that the fulfillment center has eight hundred bagels to sell that day, determine that orders for eight hundred bagels have been placed for users, and in response, determine that bagels are not available for sale from the fulfillment center at any desired time that day” [0046]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Rao in view of Yien with Seaward for the reasons identified above with respect to claim 8.
Claims 18-19 recite limitations directed towards a computer program product. The limitations recited in claims 18-19 are parallel in nature to those addressed above for claims 8-9, respectively, and are therefore rejected for those same reasons set forth above in claims 8-9, respectively.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that the claims do not recite a judicial exception under Step 2A Prong 1 of the revised Alice/Mayo eligibility test. The Office Action groups the present claims within the "certain methods of organizing human activity" grouping of abstract ideas of the Alice/Mayo test. In particular, the Office Action says that the claims are abstract because they recite the concept of "commercial interaction." The claims are directed to a specific technological process of dynamically rearranging information on a graphical user interface using two trained machine learning models such that the user can be provided with the most relevant content about future item availability generated based on user item selections. Rearranging content items on a graphical user interface is not the same as commercial interactions or organizing activity of humans (Remarks, pages 14-15).
Examiner respectfully disagrees. The claims recite the concept of predicting the likelihood that an item in an order is a predictable availability item and, for each predictable availability item, predicting the availability of the item during a future timeframe. This falls within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a), as the recited limitations encompass advertising, and marketing or sales activities. Additionally, providing a user with relevant content is not a technological process, it’s part of the abstract idea. Furthermore, the claims don’t recite “dynamically rearranging information on a graphical user interface” they merely recite updating a user interface to include ‘user interface elements’ that indicate to the user that the item is likely to be unavailable. While, the claims recite additional elements such as the machine learning models, the GUI, and the user interface elements, these additional elements amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea). Accordingly, the claims are ineligible.
Applicant further argues that the elements of claim I are similar to Claim 2 of Example 37 of the Revised Subject Matter Eligibility Guidance. Example 37 Claim 2 recites a method of rearranging icons in a graphical user interface that includes action by a processor to track memory allocation to various applications associated with the icons. The Guidance explains that the example claim is not directed to an abstract idea and in particular does not recite any method of organizing human activity because it is about using a computer processor to analyze icon usage and rearrange the locations of icons on the screen, which does not implicate fundamental economics or managing interactions between people. The present claim similarly uses a computing processor to run machine learning models that inform appropriate times and locations to display user interface elements about predicted item availability in a limited screen space. Therefore, the present claims are not directed to an abstract idea for the same reasons as applied in Example 37 claim 2 (Remarks, page 15).
Examiner respectfully disagrees. Example 37 Claim 2 is directed to determining the amount of use of each icon by tracking how much memory has been allocated to each application associated with each icon over a predetermined period of time, which cannot be done in the human mind, and, unlike the current claims, does not recite “Certain Methods of Organizing Human Activity.” The current claims fall within the “Certain Methods of Organizing Human Activity,” grouping not the “Mental Processes” grouping. Additionally, the claims merely recite updating a user interface to include ‘user interface elements’ that indicate to the user that the item is likely to be unavailable; unlike Example 37 Claim 2 they do not recite any technical details regarding this step other than displaying ‘user interface elements’ on a GUI. Accordingly, the claims are ineligible.
Applicant further argues that, even if the present claims were directed to a judicial exception, that judicial exception would be integrated into a practical application under Step 2A, Prong 2 of the revised Alice/Mayo eligibility test. The claims present a technical solution to a problem of users being presented with options to schedule requests at times in which the requests may not be able to be fulfilled. Dynamically predicting and displaying information about future availability of items relevant to the user using the claimed machine learning models is a technical solution that integrates the supposed judicial exception into a practical idea (Remarks, page 15).
Examiner respectfully disagrees. Users being presented with options to schedule requests at times in which the requests may not be able to be fulfilled is not a technical problem. Additionally, the recited additional elements re recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) and do no more than mere instructions to apply the exception using generic computer components. Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application. Accordingly, the claims are ineligible.
Rejections under 35 U.S.C. §103
Applicant argues that the cited references fail to teach or disclose “accessing first a machine-learning model trained to predict a likelihood that an item is a predictable availability item, wherein a predictable availability item is an item that is associated with at least a threshold measure of fluctuation of availability at a retailer location throughout a day” and “for each item of the one or more items, applying the first machine-learning model to generate a value representing a likelihood that the item is a predictable availability item, wherein the predicted likelihood is based at least in part on the set of data associated with the item” as Rao does not teach a model that outputs a likelihood that a particular item is a "predictable availability item," i.e., the type of item that normally has fluctuating availability at a retailer throughout a day. Rather, Rao discusses predicting a probability that an item is available at a warehouse. The claimed step of predicting whether an item is a predictable availability item is separate from the prediction of whether the item is currently available or not, and requires a separate model, not taught or suggested by Rao. The claim amendments further clarify this distinction by emphasizing the difference between the first model that is trained to predict a likelihood that an item is a predictable availability item and the second model that is trained to predict whether an item will be available in a future timeframe (Remarks, pages 16-17).
Examiner respectfully disagrees. Rao discloses that a machine learning model is used to determine statistically significant characteristics that are predictive of the item's availability such as time of day significantly effecting the availability of vegetables (i.e. determining that vegetables have a high rate of turnover throughout the day) (see Rao, [0027] and [0029]). In other words, if the characteristics such as the item being certain product types (e.g. vegetables) are statistically significant, then the item is a "predictable availability item." Additionally, newly cited reference Yien teaches that the availability is predicted by a separate machine learning model. Accordingly, the cited references teach the amended claims.
Applicant further argues that independent claims 11 and 20 have been amended similarly to independent claim 1, and therefore they are distinguishable over the cited references for at least the same reasons. Additionally, the claims depending from the independent claims are patentably distinguishable over the cited art at least by virtue of their dependency (Remarks, page 17).
Examiner respectfully disagrees. As detailed in response to the arguments above, claim 1 is not allowable. Accordingly, claims 11 and 20 as well as the dependent claims are not allowable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Tenneti et al. (US 2022/0237679 A1) teaches predicting availability of items.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARIELLE E WEINER whose telephone number is (571)272-9007. The examiner can normally be reached M-F 8:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Maria-Teresa (Marissa) Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
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