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 .
Status of Claims
Applicant’s communications filed on 3/18/2026 have been considered.
Claims 1-16 are currently pending and have been examined.
Response to Arguments
Applicant’s arguments filed with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
Note: Claims 9-16 have been determined as reciting eligible subject matter under 35 U.S.C. 101, as discussed below. Accordingly, this response to 101 arguments pertains only to method claims 1-8. The 101 rejection of claims 1-8 has been maintained in this Office Action, further discussed below.
Applicant argues on page 3 that the claims do not recite a method of organizing human activity or a mental process because the claims “recite a processor configured to [perform the claimed functions]… the claims ‘cannot be practically performed in the human mind’”. This argument has been fully considered but is not persuasive. Claims recite a mental process when they contain limitations that can practically be performed in the human mind, including observations, evaluations, judgements, and opinions, even if they are claimed as being performed on a computer. MPEP 2106.04. While Claim 1 recites that determining whether the first item identifier matches one of a plurality of item identifiers is performed at a mobile computing device, this amounts to performing a comparison (evaluation), using a computer as a tool, since the item identifier is compared against the primary item identifiers to determine potential matches. Applicant’s specification discloses that it is determined whether the item identifier matches one of a list of primary identifiers (see at least [0040]), further indicating that this claimed function amounts to mere comparison. While this limitation is claimed as being performed at the mobile computing device, the claim is merely using a computer as a tool to perform the concept, and therefore is considered to recite a mental process. It is further noted that independent claim 1 does not recite a processor configured to train a model or execute the trained model, but rather recite a mobile computing device implemented as a tool to perform the abstract idea.
The 101 rejection of the Non-Final rejection (filed 11/18/2025, [page 4]) asserted that the claims are directed to the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, in that they recite recommending item sets. This is further supported by Applicant’s specification (see at least Specification [0017-0018]), discussing improved recommendations for item sets that are provided by the invention. This is an abstract idea because it is a concept of business relations between a customer and a retail store, which makes it a method of organizing human activity (i.e., one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2)). Despite Applicant’s assertion to the contrary, the Examiner maintains that claim 1 clearly sets forth or describe abstract idea(s) for those reasons set forth previously. While the claim recites certain limitations falling under mental processes, as discussed above, the claims as a whole further recite Certain Methods of Organizing Human Activity, in that they recite recommending item sets. Furthermore, as discussed above, the claims implement the judicial exception in a technological environment, without effectuating a change or improvement to the computer or other technology, and are accordingly directed to the judicial exception without integration into a practical application.
Accordingly, claim 1 recites an abstract idea, since the claimed limitations amount to activities falling under the “Certain Methods of Organizing Human Activity” grouping of abstract ideas (recommending item sets), while further reciting limitations directed towards “Mental Processes” (performing comparisons of identifiers). The rejection has been maintained.
Applicant’s arguments filed with respect to the rejection of claims under 35 USC 103 have been fully considered but are not persuasive.
Applicant argues on pages 3-8 that the previously cited combination of Ahuja in view of Garner does not disclose the limitations of independent claims 1 and 9. This argument has been fully considered, but is rendered moot in view of the new grounds of rejection set forth in this Office Action. Currently, independent claims 1 and 9 stand rejected in view of the newly cited combination of Garner in view of Gentile, and further in view of Ahuja. Accordingly, the rejection has been maintained.
Note: This action is made as a second non-final rejection, as a new grounds of rejection not previously made under 35 USC 103 have been deemed necessary.
Eligibility Considerations
Claims 9-16 recite eligible subject matter under 35 U.S.C. 101. Specifically, claims 9-16 do more than apply the abstract idea using a generic computer because the recited additional elements of the claim apply or use the judicial exception in some other meaningful way beyond generally linking the user of the judicial exception to a particular technological environment, such that the claims as a whole are more than a drafting effort to monopolize the exception. For example, Claim 9 recites a mobile computing device, comprising: a radio frequency identification (RFID) reader; a display; and a processor configured to: capture, via the RFID reader of the mobile computing device… a first item identifier of a first item retrieved… from an RFID tag affixed to the first item; train a classification model; determine, at the mobile computing device, whether the first item identifier matches one of a plurality of primary item identifiers; determine, via execution of the trained classification model, and item set identifier; ad present the item set identifier at the display. The limitations of claim 9 are directed towards a practical application including a specific technological implementation of a mobile device including a processor and an RFID reader obtaining an item identifier by reading RFID tags affixed to items, and subsequently performing local processing regarding the items to present via a display of the mobile device, such that the claims apply the judicial exception beyond generally linking the user of the judicial exception to a particular technological environment. Thus, claim 9 integrates the judicial exception into a practical application, and claim 9 qualifies as eligible subject matter under 35 U.S.C. 101. Claims 10-16 depend from claim 9, and respectively qualify as eligible subject matter for similar reasons.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 9-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claim 9, the claim have been amended to recite “a mobile computing device, comprising… a processor configured to… train a classification model”. This claimed limitation represents new matter because a review of the originally filed disclosure does not describe the particulars of the mobile device training a classification model. While the originally filed disclosure recites that the server maintains a classification model, and the client devices can be configured to report local activity to the server, which can then update (e.g., re-train) the model based on such activity, and deploy an updated model to the client devices (see at least [0023-0024][0050]), the disclosure does not explicitly disclose that the mobile device itself performs the training of the classification model. Specifically, the amended language is not supported by the originally filed specification.
Therefore, the subject matter of the claim, recited above, does not conform to the disclosure in such a manner in which one of ordinary skill in the art would recognize the claimed limitations as being what the Applicant adequately described as the invention or what the Applicant actually had possession of at the time of the invention. Applicant’s failure to disclose the mobile device comprising a processor configured to train a classification model raises questions as to whether Applicant truly had possession of this feature at the time of filing and thereby fails to comply with the written description requirement. See MPEP 2163.
Dependent claims 10-16 depend from claim 9, and therefore inherit the deficiencies of claim 9, as discussed above.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
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. Claims 1-8 are directed towards a process. Claims 9-16 are directed towards a machine. Therefore, claims 1-16 are directed to one of the four statutory categories (YES).
Under Step 2A of the MPEP, 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. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception.
Note: It has been determined that claims 9-16 are not directed towards a judicial exception, as discussed above, and accordingly an analysis for claims 9-16 has not been provided. However, claims 1-8 have been determined as reciting a judicial exception without integration into a practical application, and accordingly an analysis of claims 1-8 has been provided below.
Taking Claim 1 as representative, claim 1 recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
capture, via a user, a first item identifier of a first item retrieved by the user from a tag affixed to the first item;
classifying one or more item identifiers to determine an item set containing a captured item identifier and a confidence level associated with the item set;
determine whether the first item identifier matches one of a plurality of primary item identifiers;
in response to determining that the first item identifier matches one of the primary item identifiers, provide input data to a model, the input data including the first item identifier;
determine, via execution of the model, an item set identifier corresponding to an item set containing the first item identifier, the item set being indicative of one or more items associated with the first item; and
present the item set identifier at an output.
Claim 1, as exemplary, recites certain methods of organizing human activity, such as performing commercial interactions. See MPEP 2106.04(a)(2). The MPEP defines the “Certain Methods of Organizing Human Activity” grouping 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) (see MPEP § 2106.04(a)(2). The abstract ideas recited in representative claim 1 are certain methods of organizing human activity because capturing a first item identifier of a first item retrieved by a user, determining an item set containing a captured item identifier and a confidence level associated with the item set, determining… whether the first item identifier matches one of a plurality of primary item identifiers, determining an item set identifier corresponding to an item set containing the first item identifier, and presenting the item set identifier is a commercial or legal interaction because it is an advertising, marketing or sales activity, or business relations. This is further supported by Applicant’s specification ([0017-0018]), discussing the improvements in item set recommendations that are provided from the invention. Accordingly, claim 1 recites an abstract idea.
Accordingly, under Prong One of Step 2A of the Alice/Mayo test, claim 1 recites an abstract idea (Step 2A, Prong One: YES).
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception.
Claim 1 recites additional elements beyond the judicial exception(s), including a radio frequency identification (RFID) reader of a mobile computing device; an RFID tag; training a classification model via a plurality of input data sets; and execution of the trained classification model.
These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Claim 1 specifying that the abstract idea of recommending item sets is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the Alice/Mayo test, when considered both individually and as a whole, the limitations of claim 1 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claim 1 recites an abstract idea and fail to integrate the abstract idea into a practical application, claim 1 is “directed to” an abstract idea (Step 2A: YES). Accordingly, the judicial exception is not integrated into a practical application.
Next, under Step 2B, the instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements of a radio frequency identification (RFID) reader of a mobile computing device; an RFID tag; training a classification model via a plurality of input data sets; and execution of the trained classification model amount to no more than mere instructions to apply the exception using generic computer components. For the same reason these elements are not sufficient to provide an inventive concept. Therefore when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible (Step 2B: NO).
Dependent claims 2-8, 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. As for dependent claims 3-4 and 6-8, these claims recite limitations that further define the same abstract idea noted in independent claim 1. Therefore, claims 3-4 and 6-8 are considered patent ineligible for the reasons given above.
As for dependent claims 2 and 5, these claims recite limitations that further define the abstract idea noted in independent claim 1. Additionally, they recite the following additional limitations:
storing, in a memory of the mobile computing device, a list of the primary item identifiers;
in response to presenting the item set identifier at the output, receiving a selection of the item set identifier via an input of the mobile computing device;
The additional elements of a memory of the mobile computing device and an input of the mobile computing device are all recited at a high level of generality such that they amount to no more than instructions to apply the judicial exception in a generic technological environment. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Accordingly, under the Alice/Mayo test, claims 1-8 are ineligible.
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.
Claims 1-4, 6-12, and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Garner (US 2020/0273013 A1), in view of newly cited U.S Patent Application No. 2013/0299569 A1 to Gentile et al., hereinafter Gentile, and further in view of previously cited Ahuja (US 2024/0144173 A1).
Regarding Claim 1, Garner discloses A method, comprising ([0024]):
capturing, via a barcode reader of a mobile computing device associated with a user, a first item identifier of a first item retrieved by the user from a tag affixed to the first item ([0038-0043] an exemplary portable device 102, including an imaging system 220 that captures images, implementing image processing and identifying a product at a retail store… the subset of frames are locally processed on the portable device… trained machine learning modeling applications perform barcode recognition relative to a product image and/or product label);
training a classification model via a plurality of input data sets including one or more item identifiers to determine an item set containing a captured item identifier and a confidence level associated with the item set ([0074-0075][0098] customizing model training based on limited listings including subsets of tens of retail products from the thousands of products that the customer is predicted to attempt to identify… customization of the training of one or more models can be partially or fully performed locally on the portable device… utilizing products identified through the image recognition process; [0107] the portable user device 102 can process each frame of a subset of one or more frames… including a first modeling technique implemented to… obtain a corresponding first product identification probability that an item, captured within each of the subset of at least one frame, is estimated to be a first product of the first subset of tens of retail products; see [0101][0105] filtering a product database to determine a first subset of tens of retail products that the customer is predicted to attempt to identify… products from a corresponding product category are included in the listing of products; [0049] identifying a product and/or whether there is a threshold level of confidence of an accuracy of the identified product) (Note: a product category is an item set);
determining, at the mobile computing device, whether the first item identifier matches one of a plurality of primary item identifiers ([0107] a first modeling technique obtains a corresponding first product identification probability that an item, captured within each of the subset of at least one frame, is estimated to be a first product of the first subset of tens of retail products for which the one or more products are trained… The aggregated first identification probability can be evaluated… to identify when the aggregated first identification probability has a predefined relationship with a collective threshold probability, and display an image of the first product in response to the predefined relationship; see [0063-0064] comparing an aggregated first identification probability to a collective threshold probability; [0042] machine learning modeling applications 320a-d running locally on the portable device);
in response to determining that the first item identifier matches one of the primary item identifiers, providing input data to a classification model, the input data including the first item identifier ([0098] initiation of customization of training may utilize one or more product identifiers, image data and/or other such data obtained based at least in part on the products identified through the image recognition process at least in part to customized training); see [0028] mobile devices 102… can supply image and/or video data to the model training systems to be used in recognizing a product within the images and/or to further train the models based on the application of the models; [0108] By identifying a set of products that the customer is likely to attempt to identify through the image recognition, the system can provide more focused training of models to produce more efficient, smaller trained models);
determining, via execution of the trained classification model, an item set containing the first item identifier, the item set being indicative of one or more items associated with the first item ([0107] the portable user device 102 can use the customized trained models to locally identify products based on images captured by the portable user device… [and] process each frame… including a first modeling technique implemented to… obtain a corresponding first product identification probability that an item, captured within each of the subset of at least one frame, is estimated to be a first product of the first subset of tens of retail products; see [0101][0105] a first subset of tens of retail products that the customer is predicted to attempt to identify… products from a corresponding product category are included in the listing of products); and
presenting the item at an output of the mobile computing device ([0107] The decision control circuit can cause an image of the first product to be displayed in response to identifying that one or more of the aggregated first identification probability and the aggregated second identification probability has the predefined relationship with the collective threshold probability; see [Figs. 12 and 13]).
Garner discloses capturing, via a barcode reader of a mobile computing device associated with a user, a first item identifier of a first item retrieved by the user from a tag affixed to the first item (see at least [0038-0043]). However, Garner does not explicitly disclose capturing via a radio frequency identification (RFID) reader, a first item identifier from an RFID tag.
However, in the field of item identification, (see at least Gentile [0057]), Gentile, on the other hand, teaches capturing via a radio frequency identification (RFID) reader, a first item identifier from an RFID tag ([0060] scanning device 105 is a… mobile phone… including specially adapted scanning devices, capable of reading an RFID tag; [0061] A scan of unique identification tag 104 by scanning device 105 would be sent to primary server 107, which uses the scanned information to retrieve information linked to product 103 from product information database 108; see [Fig. 1] identification tag 104 affixed to product 103; [0058] the unique identification tag could be an RFID tag).
The limitations of Gentile are applicable to the method of Garner, as they share characteristics and capabilities, namely, they are directed to item identification using a mobile device of a customer. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the item identification using a mobile device of a customer as taught by Garner, to include capturing via a radio frequency identification (RFID) reader, a first item identifier from an RFID tag, as taught by Gentile. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Garner in order to assist in the purchase of products that will fit a scanned product, as well as provide additional functionality to a generic scanner by providing for multiple scanner types for specific constituents (Gentile, [0025-0026][0041]).
Garner further discloses determining, via execution of the trained classification model, an item set containing the first item identifier, the item set being indicative of items associated with the first item (see at least Garner [0101][0105][0107]), and presenting the item at an output of the mobile computing device (see at least Garner [0107][Figs. 12 and 13]). However, Garner does not explicitly disclose determining an item set identifier corresponding to an item set containing the first item identifier; and presenting the item set identifier.
Additionally, in the field of item identification/classification (see at least Ahuja [abstract][0009-0016]), Ahuja, on the other hand, teaches determining an item set identifier corresponding to an item set containing the first item identifier ([0072] The recipe scoring module 270 also computes a suggestion score for each recipe included among the set of recipes that the recipe matching module 265 matches with the one or more candidate available items to the customer… The suggestion score for a recipe may account for… the expected value associated with the set of remaining items for the recipe; [0074] selecting one or more recipes for suggesting to a customer from a set of recipes that the recipe matching module 265 matches with one or more candidate available items to the customer… once the recipe ranking module 275 has ranked the set of recipes, the recipe selection module 280 may select one or more recipes for suggesting to the customer based on the ranking); and
presenting the item set identifier ([0093] sending 350 (e.g., using the content presentation module 210) the one or more recipes 500 selected 345 by the online concierge system 140 and a set of remaining items 405 identified 325 for each recipe 500 to the customer client device 100 associated with the customer… each recipe 500 may be presented in association with the set of remaining items 405 identified 325 for the recipe 500).
The limitations of Ahuja are applicable to the method of Garner in view of Gentile, as they share characteristics and capabilities, namely, they are directed to item identification/classification. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the device-based item identification and classification process as taught by Garner in view of Gentile, to include determining and presenting an item set identifier corresponding to an item set, as taught by Ahuja. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Garner in view of Gentile in order to match recipes with items identified by a customer, as well as reduce the waste of grocery items while helping customers discover new recipes based on items in an inventory (Ahuja, [0003][0084-0086]).
Regarding Claim 2, Garner, Gentile and Ahuja teach the limitations of claim 1.
Garner further discloses storing, in a memory of the mobile computing device, a list of the primary item identifiers ([0093] the portable user device maintains a local product database locally storing sets of product imaging data for each set of the filtered subset of tens of retail products in the listing 634 and available for sale from the retail store; see [0032] local product database locally storing sets of product imaging data… and at least image attribute data corresponding to respective products);
wherein determining whether the first item identifier matches one of a plurality of primary item identifiers includes determining whether the list includes the first item identifier ([0107] a first modeling technique implemented relative to a first image attribute… obtains a corresponding first product identification probability that an item, captured within each of the subset of at least one frame, is estimated to be a first product of the first subset of tens of retail products for which the one or more products are trained; see [0032] local product database locally storing sets of product imaging data… and at least image attribute data corresponding to respective products).
Regarding Claim 3, Garner, Gentile and Ahuja teach the limitations of claim 1.
Garner further discloses in response to determining that the first item identifier matches one of the primary item identifiers, prior to providing the classification model with the input data: capturing, via the barcode reader, at least one additional item identifier ([0127-0128] a user can add products to a virtual shopping cart based on scanning of a barcode, where the product is identified based on barcode scanning; see [Fig. 14] depicting multiple products in a virtual cart; [0129] checkout of a virtual cart is initiated via activation of a checkout option 1404; [0088] retraining can be initiated based on a schedule, in response to a detected change, in response to a customer initiating a shopping experience, a notification that a customer is in a store or within a threshold distance of a store, other such triggers, or a combination of two or more of such triggers) (Note: each product is added to the virtual cart after scanning and identification, and accordingly the additional product is added to the cart after identification of the first product).
However, Garner in view of Ahuja does not explicitly teach capturing, via the RFID reader.
Gentile, on the other hand, teaches capturing, via the RFID reader ([0060] scanning device 105 is a… mobile phone… including specially adapted scanning devices, capable of reading an RFID tag; [0061] A scan of unique identification tag 104 by scanning device 105 would be sent to primary server 107, which uses the scanned information to retrieve information linked to product 103 from product information database 108).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the capturing an item identifier via a mobile phone as taught by Garner in view of Ahuja, to include capturing via an RFID reader, as taught by Gentile, for the same reasons discussed above with respect to claim 1.
Regarding Claim 4, Garner, Gentile and Ahuja teach the limitations of claim 3.
Garner further discloses prior to providing the classification model with the input data: determining the first item identifier and the at least one additional item identifier ([0127-0128] a user can add products to a virtual shopping cart based on scanning of a barcode, where the product is identified based on barcode scanning; see [Fig. 14] depicting multiple products in a virtual cart; [0088] retraining can be initiated based on a schedule, in response to a detected change, in response to a customer initiating a shopping experience, a notification that a customer is in a store or within a threshold distance of a store, other such triggers, or a combination of two or more of such triggers). However, Garner does not explicitly disclose determining that a count of the first item identifier and the at least one additional item identifier exceeds a threshold.
Ahuja, on the other hand, teaches determining that a count of the first item identifier and the at least one additional item identifier exceeds a threshold ([0068] the recipe matching module 265 may match the one or more candidate available items to the customer with a recipe if at least a threshold number or percentage of the ingredients of the recipe correspond to the one or more candidate available items to the customer).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the device-based item identification and classification process as taught by Garner in view of Gentile, to include determining that a count of item identifiers exceeds a threshold, as taught by Ahuja. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Garner in view of Gentile in order to match recipes with items identified by a customer (Ahuja, [0003]).
Regarding Claim 6, Garner, Gentile and Ahuja teach the limitations of claim 1.
Garner further discloses wherein an item set includes the first item identifier (see at least [0101][0105] a first subset of tens of retail products that the customer is predicted to attempt to identify… products from a corresponding product category are included in the listing of products). However, Garner does not explicitly disclose wherein obtaining the item set identifier includes obtaining a plurality of item set identifiers corresponding to distinct item sets each including the first item identifier.
Ahuja, on the other hand, teaches wherein obtaining the item set identifier includes obtaining a plurality of item set identifiers corresponding to distinct item sets each including the first item identifier ([0068] The recipe matching module 265 retrieves recipes (e.g., from the recipe store 260) and matches one or more candidate available items to a customer with a set of the recipes. The recipe matching module 265 may do so based on one or more ingredients of each recipe and the one or more candidate available items to the customer… the recipe matching module 265 may match the one or more candidate available items to the customer with the set of recipes based on the quantity of each ingredient required for each recipe).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the device-based item identification and classification process as taught by Garner in view of Gentile, to include obtaining a plurality of item set identifiers corresponding to distinct item sets each including the first item identifier, as taught by Ahuja. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Garner in view of Gentile in order to match recipes with items identified by a customer, as well as reduce the waste of grocery items while helping customers discover new recipes based on items in an inventory (Ahuja, [0003][0084-0086]).
Regarding Claim 7, Garner, Gentile and Ahuja teach the limitations of claim 6.
Garner further discloses presenting items at the output ( [0127-0128] a user can add products to a virtual shopping cart based on scanning of a barcode, where the product is identified based on barcode scanning; see [Fig. 14] depicting multiple products in a virtual cart). However, Garner does not explicitly disclose presenting the plurality of item set identifiers at the output.
Ahuja, on the other hand, teaches presenting the plurality of item set identifiers at the output ([0093] The online concierge system 140 then sends 350 (e.g., using the content presentation module 210) the one or more recipes 500 selected 345 by the online concierge system 140 and a set of remaining items 405 identified 325 for each recipe 500 to the customer client device 100 associated with the customer… each recipe 500 may be presented in association with the set of remaining items 405 identified 325 for the recipe 500).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the device-based item identification and classification process as taught by Garner in view of Gentile, to include obtaining a plurality of item set identifiers corresponding to distinct item sets each including the first item identifier, as taught by Ahuja. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Garner in view of Gentile in order to match recipes with items identified by a customer (Ahuja, [0003]).
Regarding Claim 8, Garner, Gentile and Ahuja teach the limitations of claim 6.
Garner further discloses querying an inventory repository to determine an availability of a set of items included in the item set ([0060] products to be considered in the processing may be limited based on… available product inventory; [0077] The retail product database stores product information for each of tens of thousands of different retail products available for sale from the first retail store; [0084] a set of filtering rules is applied to products based on inventory levels and/or on-hand inventory of one or more of the products at one or more of the retail stores). However, Garner does not explicitly disclose for each of the plurality of item set identifiers, determine an availability of a set of items included in the item set; and when one or more of the set of items is unavailable, discarding the item set identifier without presenting the item set identifier at the output.
Ahuja, on the other hand, teaches for each of the plurality of item set identifiers, determine an availability of a set of items included in the item set ([0057] The item detection module 250 may detect a set of acquired items associated with a customer of the online concierge system 140, in which the set of acquired items is included among an inventory of the customer; [0063] The item availability module 255 identifies one or more candidate available items to a customer from a set of acquired items associated with the customer detected by the item detection module 250; [0068] the recipe matching module 265 may match the one or more candidate available items to the customer with the set of recipes based on the quantity of each ingredient required for each recipe and the quantity of each of the one or more candidate available items to the customer); and
when one or more of the set of items is unavailable, discarding the item set identifier without presenting the item set identifier at the output ([0068] suppose that only one 14-ounce can of tomatoes is included among the items identified by the item availability module 255 likely to be available to the customer and a recipe calls for 28 ounces of canned tomatoes. In this example, if none of the other ingredients of the recipe are included among the candidate available items to the customer, the recipe matching module 265 may not match the candidate available items to the customer with this recipe).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the device-based item identification and classification process as taught by Garner in view of Gentile, to include, determining an availability of items included in each of the plurality of item set identifiers, and discarding item set identifiers for which one or more of the set of items is unavailable, as taught by Ahuja. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Garner in view of Gentile in order to match recipes with items identified by a customer, as well as reduce the waste of grocery items while helping customers discover new recipes based on items in an inventory (Ahuja, [0003][0084-0086]).
Note: The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. MPEP 2111.04. Claim 8 includes a contingent limitation reciting “when one or more of the set of items is unavailable, discarding…”. In the interest of compact prosecution, art has nonetheless been applied to the identified contingent limitation.
Regarding Claim 9, Garner discloses A mobile computing device, comprising ([0032][Fig. 2]):
a radio frequency identification (RFID) reader ([0035] the portable device 102 includes one or more sensors 236 to provide information to the system… sensors include radio frequency identification (RFID) tag reader sensors capable of reading RFID tags in proximity to the sensor);
a display ([0034][Fig. 2] display 230); and
a processor configured to ([0032-0033][Fig. 2] the portable device 102 comprises… a processor-based system with the control circuit 202):
capture, via a barcode reader of the mobile computing device associated with a user, a first item identifier of a first item retrieved by the user from a tag affixed to the first item ([0038-0043] an exemplary portable device 102, including an imaging system 220 that captures images, implementing image processing and identifying a product at a retail store… the subset of frames are locally processed on the portable device… trained machine learning modeling applications perform barcode recognition relative to a product image and/or product label);
train a classification model via a plurality of input data sets including one or more item identifiers to determine an item set containing a captured item identifier and a confidence level associated with the item set ([0074-0075][0098] customizing model training based on limited listings including subsets of tens of retail products from the thousands of products that the customer is predicted to attempt to identify… customization of the training of one or more models can be partially or fully performed locally on the portable device… utilizing products identified through the image recognition process; [0107] the portable user device 102 can process each frame of a subset of one or more frames… including a first modeling technique implemented to… obtain a corresponding first product identification probability that an item, captured within each of the subset of at least one frame, is estimated to be a first product of the first subset of tens of retail products; see [0101][0105] filtering a product database to determine a first subset of tens of retail products that the customer is predicted to attempt to identify… products from a corresponding product category are included in the listing of products; [0049] identifying a product and/or whether there is a threshold level of confidence of an accuracy of the identified product) (Note: a product category is an item set);
determine, at the mobile computing device, whether the first item identifier matches one of a plurality of primary item identifiers ([0107] a first modeling technique obtains a corresponding first product identification probability that an item, captured within each of the subset of at least one frame, is estimated to be a first product of the first subset of tens of retail products for which the one or more products are trained… The aggregated first identification probability can be evaluated… to identify when the aggregated first identification probability has a predefined relationship with a collective threshold probability, and display an image of the first product in response to the predefined relationship; see [0063-0064] comparing an aggregated first identification probability to a collective threshold probability; [0042] machine learning modeling applications 320a-d running locally on the portable device);
in response to determining that the first item identifier matches one of the primary item identifiers, provide input data to a classification model, the input data including the first item identifier ([0098] initiation of customization of training may utilize one or more product identifiers, image data and/or other such data obtained based at least in part on the products identified through the image recognition process at least in part to customized training); see [0028] mobile devices 102… can supply image and/or video data to the model training systems to be used in recognizing a product within the images and/or to further train the models based on the application of the models; [0108] By identifying a set of products that the customer is likely to attempt to identify through the image recognition, the system can provide more focused training of models to produce more efficient, smaller trained models);
determine, via execution of the trained classification model, an item set containing the first item identifier, the item set being indicative of one or more items associated with the first item ([0107] the portable user device 102 can use the customized trained models to locally identify products based on images captured by the portable user device… [and] process each frame… including a first modeling technique implemented to… obtain a corresponding first product identification probability that an item, captured within each of the subset of at least one frame, is estimated to be a first product of the first subset of tens of retail products; see [0101][0105] a first subset of tens of retail products that the customer is predicted to attempt to identify… products from a corresponding product category are included in the listing of products); and
present the item at the display ([0107] The decision control circuit can cause an image of the first product to be displayed in response to identifying that one or more of the aggregated first identification probability and the aggregated second identification probability has the predefined relationship with the collective threshold probability; see [Figs. 12 and 13]).
Garner discloses a mobile computing device comprising an RFID reader (see at least [0032][0035][Fig. 2]), and capturing, via a barcode reader of the mobile computing device, associated with a user, a first item identifier of a first item retrieved by the user from a tag affixed to the first item (see at least [0038-0043]). However, Garner does not explicitly disclose capturing via the RFID reader, a first item identifier from an RFID tag.
However, in the field of item identification, (see at least Gentile [0057]), Gentile, on the other hand, teaches capturing via the RFID reader, a first item identifier from an RFID tag ([0060] scanning device 105 is a… mobile phone… including specially adapted scanning devices, capable of reading an RFID tag; [0061] A scan of unique identification tag 104 by scanning device 105 would be sent to primary server 107, which uses the scanned information to retrieve information linked to product 103 from product information database 108; see [Fig. 1] identification tag 104 affixed to product 103; [0058] the unique identification tag could be an RFID tag).
The limitations of Gentile are applicable to the device of Garner, as they share characteristics and capabilities, namely, they are directed to item identification via a mobile device of a customer. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the mobile device comprising an RFID reader as taught by Garner, to include capturing an item identifier from an RFID tag via an RFID reader, as taught by Gentile. One of ordinary skill in the art at the time of filing would have been motivated to expand the device of Garner in order to assist in the purchase of products that will fit with a scanned product by allowing for faster retrieval of information that does not require connection to a network, and providing capabilities to the person scanning the code (Gentile, [0020-0026]).
Garner further discloses a processor determining, via execution of the trained classification model, an item set containing the first item identifier, the item set being indicative of items associated with the first item (see at least Garner [0033][0101][0105][0107]), and presenting the item at the display (see at least Garner [0033][0107][Figs. 12 and 13]). However, Garner does not explicitly disclose a processor determining an item set identifier corresponding to an item set containing the first item identifier; and presenting the item set identifier.
Additionally, in the field of item identification/classification (see at least Ahuja [abstract][0009-0016]), Ahuja, on the other hand, teaches a processor determining an item set identifier corresponding to an item set containing the first item identifier ([0072] The recipe scoring module 270 also computes a suggestion score for each recipe included among the set of recipes that the recipe matching module 265 matches with the one or more candidate available items to the customer… The suggestion score for a recipe may account for… the expected value associated with the set of remaining items for the recipe; [0074] selecting one or more recipes for suggesting to a customer from a set of recipes that the recipe matching module 265 matches with one or more candidate available items to the customer… once the recipe ranking module 275 has ranked the set of recipes, the recipe selection module 280 may select one or more recipes for suggesting to the customer based on the ranking; see [0095] a computer processor for performing any or all of the steps, operations, or processes described); and
presenting the item set identifier ([0093] sending 350 (e.g., using the content presentation module 210) the one or more recipes 500 selected 345 by the online concierge system 140 and a set of remaining items 405 identified 325 for each recipe 500 to the customer client device 100 associated with the customer… each recipe 500 may be presented in association with the set of remaining items 405 identified 325 for the recipe 500; see [0095] a computer processor for performing any or all of the steps, operations, or processes described).
The limitations of Ahuja are applicable to the device of Garner in view of Gentile, as they share characteristics and capabilities, namely, they are directed to item identification/classification. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the processor-based item identification and classification process as taught by Garner in view of Gentile, to include determining and presenting an item set identifier corresponding to an item set, as taught by Ahuja. One of ordinary skill in the art at the time of filing would have been motivated to expand the device of Garner in view of Gentile in order to match recipes with items identified by a customer, as well as reduce the waste of grocery items while helping customers discover new recipes based on items in an inventory (Ahuja, [0003][0084-0086]).
Claim 10 recites a mobile computing device comprising substantially similar limitations as claim 2. All limitations as recited have been analyzed and rejected with respect to claim 2, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 10 is rejected for the same rationale.
Claim 11 recites a mobile computing device comprising substantially similar limitations as claim 3. All limitations as recited have been analyzed and rejected with respect to claim 3, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 11 is rejected for the same rationale.
Claim 12 recites a mobile computing device comprising substantially similar limitations as claim 4. All limitations as recited have been analyzed and rejected with respect to claim 4, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 12 is rejected for the same rationale.
Claim 14 recites a mobile computing device comprising substantially similar limitations as claim 6. All limitations as recited have been analyzed and rejected with respect to claim 6, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 14 is rejected for the same rationale.
Claim 15 recites a mobile computing device comprising substantially similar limitations as claim 7. All limitations as recited have been analyzed and rejected with respect to claim 7, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 15 is rejected for the same rationale.
Claim 16 recites a mobile computing device comprising substantially similar limitations as claim 8. All limitations as recited have been analyzed and rejected with respect to claim 8, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 16 is rejected for the same rationale.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Garner in view of Gentile in view of Ahuja, and further in view of previously cited Chang (US 2014/0095479 A1).
Regarding Claim 5, Garner, Gentile and Ahuja teach the limitations of claim 1.
Garner further discloses receiving a selection of the item identifier via an input of the mobile computing device ([0128][Fig. 14] virtual cart interface 1400 shows a listing 1402 of the one or more products in the virtual cart, as well as options to adjust quantities, delete products from the virtual cart, or other such options);
identifying at least a second item identifier ([0127-0128] a user can add products to a virtual shopping cart based on scanning of a barcode, where the product is identified based on barcode scanning; see [Fig. 14] depicting multiple products in a virtual cart); and
adding the second item identifier to a list of target items at the mobile computing device ([0127-0128] a user can add products to a virtual shopping cart based on scanning of a barcode, where the product is identified based on barcode scanning; see [Fig. 14] depicting multiple products in a virtual cart).
Garner discloses receiving a selection of the item identifier via in input of the mobile computing device (see at least [0128][Fig. 14]), as well as identifying and adding a second item identifier to a list of target items at the mobile computing device (see at least [0127-0128][Fig. 14]). However, Garner does not explicitly disclose in response to presenting the item set identifier at the output, receiving a selection of the item set identifier; and retrieving an item set definition corresponding to the item set identifier, the item set definition including at least a second item identifier.
In the field of item identification and recommendation (see at least Chang [0027-0035]), Chang, on the other hand, teaches in response to presenting the item set identifier at the output, receiving a selection of the item set identifier ([0044] the mobile computing device 102 displays the selected recipes for the user… based on the ingredient(s) available on hand; [0045] the mobile computing device 102 determines whether the user has selected a recipe from the list of recipe recommendations); and
retrieving an item set definition corresponding to the item set identifier, the item set definition including at least a second item identifier ([0046] If a recipe has been selected by the user in block 812, the method 800 advances to block 816 in which the mobile computing device 102 may provide… suggestions to supplement the selected recipe, including a shopping list including any missing ingredients from the selected recipe).
The limitations of Chang are applicable to the method of Garner in view of Gentile in view of Ahuja, as they share characteristics and capabilities, namely, they are directed to item identification using a mobile device of a customer. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the item identification process as taught by Garner in view of Gentile in view of Ahuja, to include receiving a selection of the item set identifier, and retrieving an item set definition corresponding to the item set identifier, where the item set definition includes at least a second item identifier. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Garner in view of Gentile in view of Ahuja in order to provide recommendations for identified items, while monitoring the shelf life of items for spoilage concerns (Chang, [0017][0021]).
Claim 13 recites a mobile computing device comprising substantially similar limitations as claim 5. All limitations as recited have been analyzed and rejected with respect to claim 5, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 13 is rejected for the same rationale.
Conclusion
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/ZACHARY RYAN DONAHUE/Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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