DETAILED ACTION
Claims 1-20 are pending. Claims 1-20 are considered in this Office 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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/15/2025 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The initialed and dated copy of Applicant’s IDS form 1449 is attached to the instant Office action.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1, 10, and 19 of the current application (Hereby known as ‘674) are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 11, and 20 of U.S. Patent No. 12,243,008 (Hereby known as ‘008). Although the claims at issue are not identical, they are not patentably distinct from each other because:
Regarding Claims 1, 10, and 19, Claims 1, 10, and 19 of the current application (‘674) recite substantially similar steps of '008 - Claims 1, 11, and 20 respectively.
Claims 1, 10, and 19 of ‘674 recite the steps of:
receiving, from a client device, an image of a food storage area associated with a user;
applying an image processing machine learning model to the image to detect a set of acquired items included in the food storage area;
determining, based on the set of acquired items, a set of remaining items to fulfill one or more recipes, the determining comprising:
identifying one or more candidate available items from the set of acquired items based at least in part on one or more of: a predicted perishability of each acquired item and a predicted amount of each acquired item that has been used;
retrieving a plurality of recipes from a recipe data store, wherein each recipe comprises one or more ingredients;
matching the one or more candidate available items with a set of recipes based at least in part on the one or more ingredients of each recipe and the one or more candidate available items;
identifying the set of remaining items for each recipe of the set of recipes, wherein each remaining item corresponds to an ingredient that is not included among the one or more candidate available items;
retrieving a set of attributes associated with the user and the set of recipes;
computing a suggestion score for each recipe of the set of recipes based at least in part on the set of attributes associated with the user and the set of recipes;
ranking the set of recipes based at least in part on the suggestion score for each recipe; and
selecting, from the set of recipes, the one or more recipes for suggesting to the user based at least in part on the ranking; and
sending, for display to the client device associated with the user, the one or more recipes and the set of remaining items.
Whereas Claims 1, 11, and 20 of ‘008 states:
detecting a set of acquired items associated with a customer of an online concierge system, wherein the set of acquired items is included among an inventory of the customer;
identifying one or more candidate available items from the set of acquired items based at least in part on one or more of: a predicted perishability of each acquired item and a predicted amount of each acquired item that has been used, wherein identifying one or more candidate available items comprises:
accessing a machine learning model that is trained to predict a likelihood that an item is available, wherein the machine learning model is trained by:
receiving a plurality of attributes associated with a plurality of items included among one or more inventories of one or more retailer locations,
receiving, for each item of the plurality of items, a label indicating an availability of the item,
training the machine learning model based at least in part on the plurality of attributes and the label for each of the plurality of items,
applying the machine learning model to each of the plurality of items to determine a difference between the label and the predicted likelihood of the respective item,
updating the label of the respective item based on the determined difference, and
updating the machine learning model for each of the plurality of items using the updated labels; and
applying the machine learning model to a plurality of attributes of each acquired item of the set of acquired items to predict the likelihood that each acquired item is available;
retrieving a plurality of recipes from a recipe data store, wherein each recipe comprises one or more ingredients;
matching the one or more candidate available items with a set of recipes based at least in part on the one or more ingredients of each recipe and the one or more candidate available items;
identifying a set of remaining items for each recipe of the set of recipes, wherein each remaining item corresponds to an ingredient that is not included among the one or more candidate available items;
retrieving a set of attributes associated with the customer and the set of recipes;
computing a suggestion score for each recipe of the set of recipes based at least in part on the set of attributes associated with the customer and the set of recipes;
ranking the set of recipes based at least in part on the suggestion score for each recipe;
selecting, from the set of recipes, one or more recipes for suggesting to the customer based at least in part on the ranking; and
sending, for display to a customer client device associated with the customer, the one or more recipes and the set of remaining items identified for each of the one or more recipes.
These are obvious variants of each other as both recite substantially the same limitations. Further, elimination of an element or its functions is deemed to be obvious in light of prior art teachings of at least the recited element or its functions (see In re Karlson, 136 USPQ 184, 186; 311 F2d 581 (CCPA 1963)), thereby rendering the elimination of any elements recited in the claims of the related patent (that are not recited in the instant claims) obvious.
Thus, Claims 1, 10, and 19 of the current application are obvious variants of claims 1, 11, and 20 in ‘008.
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.
Alice - Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 10, and 19 recite the limitations for receiving an image of a food storage area associated with a user (Collecting Information, an Observation, a Mental Process; Managing Human Behavior, i.e. managing resources/food, a Certain Method of Organizing Human Activity), applying an image processing machine learning model to the image to detect a set of acquired items included in the food storage area (Collecting and Analyzing the Information, an Observation and Evaluation, a Mental Process; Managing Human Behavior, i.e. managing resources/food, a Certain Method of Organizing Human Activity), determining, based on the set of acquired items, a set of remaining items to fulfill one or more recipes, the determining comprising: identifying one or more candidate available items from the set of acquired items based at least in part on one or more of: a predicted perishability of each acquired item and a predicted amount of each acquired item that has been used; retrieving a plurality of recipes from a recipe data store, wherein each recipe comprises one or more ingredients; matching the one or more candidate available items with a set of recipes based at least in part on the one or more ingredients of each recipe and the one or more candidate available items; identifying the set of remaining items for each recipe of the set of recipes, wherein each remaining item corresponds to an ingredient that is not included among the one or more candidate available items; retrieving a set of attributes associated with the user and the set of recipes; computing a suggestion score for each recipe of the set of recipes based at least in part on the set of attributes associated with the user and the set of recipes; ranking the set of recipes based at least in part on the suggestion score for each recipe; and selecting, from the set of recipes, the one or more recipes for suggesting to the user based at least in part on the ranking (Analyzing the Information, through Collecting and Analyzing Information, an Observation and Evaluation, a Mental Process; Managing Human Behavior, i.e. managing resources/food, a Certain Method of Organizing Human Activity), and sending, for display to the client device associated with the user, the one or more recipes and the set of remaining items (Transmitting the Information, a Judgment, a Mental Process; Managing Human Behavior, i.e. managing resources/food, a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of sending recipes to users, but for the recitation of generic computer components. That is, other than reciting a computer system, processor, medium, client device, and computer program product, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of Managing Human Behavior, a Certain Method of Organizing Human Activity. For example, ranking recipes based on suggestions scores, selecting a recipe, and sending the recipe to someone encompasses a family where a child asks a parent for a recipe that would work for Valentine’s day, and the parent thinking about which recipe they would send them by ranking the ones they know in their head, and then sending this to their kid, an observation, evaluation, and judgment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation, evaluation, and judgment. Further, as described above, the claims recite limitations for Managing Human Behavior, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The computer system, processor, medium, computer program product, and client device are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the collecting and transmitting steps above are at best insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). 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.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[0012] The customer client device 100 is a client device through which a customer may interact with the picker client device 110, the retailer computing system 120, or the online concierge system 140. The customer client device 100 can be a personal or mobile computing device, such as a smartphone, a tablet, a laptop computer, or a desktop computer.”
Which shows that any generic computing device can be used to perform the abstract limitations, such as a laptop, phone, desktop, etc., and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the collecting and transmitting steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the computer system, medium, product, etc., nor the collecting and transmitting steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-9, 11-18, and 20 contain the identified abstract ideas, further narrowing them, with the additional elements of a smart refrigerator which is highly generic when considered as part of a practical application or under prong 2 of the Alice analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Allowable Subject Matter
Claims 1-20 have overcome the prior art and would be allowable if amended to overcome the 35 USC 101 rejection.
The closest prior art of record is Byron (U.S. Publication No. 2020/015,9736), Leifer (U.S. Publication No. 2019/022,8855), and Grady (U.S. Publication No. 2017/023,6081). Byron, a system and method to select substitute ingredients in a food recipe, teaches a method comprising, by a computer system comprising one or more processors, detecting a set of acquired items associated with a customer of an online concierge system, wherein the set of acquired items is included among an inventory of the customer, identifying one or more candidate available items from the set of acquired items based at least in part on one or more of: a predicted perishability of each acquired item and a predicted amount of each acquired item that has been used, retrieving a plurality of recipes from a recipe data store, wherein each recipe comprises one or more ingredients, matching the one or more candidate available items with a set of recipes based at least in part on the one or more ingredients of each recipe and the one or more candidate available items, identifying a set of remaining items for each recipe of the set of recipes, wherein each remaining item corresponds to an ingredient that is not included among the one or more candidate available items; retrieving a set of attributes associated with the customer and the set of recipes, computing a suggestion score for each recipe of the set of recipes based at least in part on the set of attributes associated with the customer and the set of recipes, selecting, from the set of recipes, one or more recipes for suggesting to the customer based at least in part on the ranking, sending, for display to a customer client device associated with the customer, the one or more recipes and the set of remaining items identified for each of the one or more recipes, use of a learning feedback mechanism, and ranking the set of recipes based at least in part on the suggestion score for each recipe, it does not explicitly state the ranking is based on scores, nor does it teach training the machine learning with attributes and updating the machine learning model with updated labels. Leifer, a method and system for improving food-related personalization, teaches similarity scores for ingredient types and recipe matching based on ranking and the scores, use of supervised and unsupervised machine learning, among other types of learning, which are trained using training data, and use of a digital shopping cart for recommendation of food items, ingredients, and recipes, but it does not teach training the machine learning with attributes and updating the machine learning model with updated labels. McKean, a system and method for artificial intelligence selection of recipe sets, teaches model being trained using training data for the selection of recipe sets, training the machine learning model with attributes of various ingredients, scoring of recipes using a machine learning technique, and updating the score based on a recalculation, but it does not teach training the machine learning with attributes and updating the machine learning model with updated labels. None of the above prior art explicitly teaches teach this training the machine learning with attributes and updating the machine learning model with updated labels, and these are the reasons which adequately reflect the Examiner's opinion as to why Claims 1-20 are allowable over the prior art of record.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 4/4/2026
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