DETAILED ACTION
This action is in reply to the Amendments filed on 11/19/2025.
Claims 1 and 13-21 are cancelled.
Claims 2-12 are rejected.
Claims 2-12 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 11/19/2025, has been entered. Claim 2 has been amended.
Double Patenting
The double patenting rejection has been withdrawn as U.S. Patent No. 11,907,989 and the currently amended claims are distinct inventions with divergent subject matter.
Priority
This patent Application is a continuation of U.S. Patent No. 11,907,989 filed 10/28/2021. This benefit has been received and acknowledged and therefore, the instant claims receive the effective filing date of 10/28/2021.
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 2-12 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 2 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-obtaining, through an interface, a selection of at least one candidate food item;
-obtaining a first list comprising ingredients in a recipe associated with the at least one candidate food item;
-searching a product catalog of a store for the ingredients;
-filtering the ingredients based on inventory data for the store to determine availability of each ingredient at the store;
-generating, based at least in part on the searching and the filtering, a second list comprising ingredients from the first list that are present in the store;
-generating, using a planogram of the store, a navigable route through the store to retrieve the ingredients of the second list;
-providing, through the interface, a clickable route option, wherein responsive to a selection of the clickable route option, the navigable route is graphically displayed within the interface;
-organizing an order of the ingredients within the second list based on a route to pick the at least one candidate food item within the store; and
-training a machine learning model with [utilizing] ratings associated with the at least one candidate food item to rank future generated candidate food items in accordance with the ratings
The above limitations recite the concept of obtaining a first list of ingredients in a recipe of a selected candidate food item, generating a second list of the ingredients based on searching a product catalog of a store, and providing a navigable route through the store to retrieve the ingredients of the second list. 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 obtaining a first list comprising ingredients in a recipe associated with the at least one candidate food item; searching a product catalog of a store for the ingredients; filtering the ingredients based on inventory data for the store to determine availability of each ingredient at the store; generating, based at least in part on the searching and the filtering, a second list comprising ingredients from the first list that are present in the store; generating, using a planogram of the store, a navigable route through the store to retrieve the ingredients of the second list; and organizing an order of the ingredients within the second list based on a route to pick the at least one candidate food item within the store are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “obtaining,” “searching,” “filtering,” “generating,” “generating,” and “organizing” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of obtaining, through an interface, a selection of at least one candidate food item; providing, through the interface, a clickable route option, wherein responsive to a selection of the clickable route option, the navigable route is graphically displayed within the interface; and training a machine learning model with [utilizing] ratings associated with the at least one candidate food item to rank future generated candidate food items in accordance with the ratings are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the selection is obtained through the interface, that the providing is through the interface, that the route option is clickable, that the navigable route is graphically displayed within the interface, and the ranking being done by training a machine learning model with ratings, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “interface,” “clickable,” “graphically,” and “training a machine learning model” language, “obtaining,” “providing,” and “rank” 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).
-obtaining, through an interface, a selection of at least one candidate food item;
-obtaining a first list comprising ingredients in a recipe associated with the at least one candidate food item;
-searching a product catalog of a store for the ingredients;
-filtering the ingredients based on inventory data for the store to determine availability of each ingredient at the store;
-generating, based at least in part on the searching and the filtering, a second list comprising ingredients from the first list that are present in the store;
-generating, using a planogram of the store, a navigable route through the store to retrieve the ingredients of the second list;
-providing, through the interface, a clickable route option, wherein responsive to a selection of the clickable route option, the navigable route is graphically displayed within the interface;
-organizing an order of the ingredients within the second list based on a route to pick the at least one candidate food item within the store; and
-training a machine learning model with ratings associated with the at least one candidate food item to rank future generated candidate food items in accordance with the ratings
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 2 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 [0016] of Applicant’s specification – “Each customer-operated device 120 comprises a processor 121 and a non-transitory computer-readable storage medium 122.Medium 122 comprises executable instructions for a mobile app 123. The executable instructions when executed by processor 121 from medium 122cause processor 121 to perform operations discussed herein and below with respect to app 123.” Specifically, the additional elements of an interface, a clickable route option, graphically displaying, and training a machine learning model are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of obtaining data, searching data, filtering data, generating data, providing data, organizing data, and training 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 2, taken individually or as a whole, the additional elements of claim 2 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.
Dependent claims 3-12, 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 3-12 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 5 and 9-10 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 3-4, 6-8, and 11-12 recite the additional elements of the interface, a device, training a machine learning model (model), an existing retailer application, and an application programming interface 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 3-12 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 3-12 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claim 2, dependent claims 3-12 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. obtaining a first list of ingredients in a recipe of a selected candidate food item, generating a second list of the ingredients based on searching a product catalog of a store, and providing a navigable route through the store to retrieve the ingredients of the second list) 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 amounts to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Subject Matter Allowable Over the Prior Art
In the present application, claims 2-12 would be allowable if rewritten or amended to overcome the rejections under 35 USC § 101 set forth in this Office action. The following is the Examiner's statement of reasons of allowance:
Regarding 35 U.S.C. §103, upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the applicant’s invention. Claims 2-12 are allowable over the prior art as follows:
Claims 2-12 are allowable over 35 U.S.C. §103 as follows:
The most relevant prior art made of record includes previously cited Choi et al. (US 2020/0118461 A1), previously cited Chachek et al. (US 2020/0302510 A1), previously cited Kremen et al. (US 2017/0316488 A1), and newly cited Neumann et al. (US 10,861,076 B1). Choi teaches obtaining, through an interface, a selection of at least one candidate food item (Choi, see at least: [0054]); obtaining a first list comprising ingredients in a recipe associated with the at least one candidate food item (Choi, see at least: [0055]); searching for the ingredients (Choi, see at least: [0054] and [0096]); generating, based at least in part on the searching, a second list comprising ingredients from the first list that are present in the store (Choi, see at least: [0056] and [0054]); the ingredients being of the second list (Choi, see at least: [0056]); and the ingredients within the second list and the at least one candidate food item (Choi, see at least: [0054] and [0055]).
Choi is deficient in a number of ways. As written, the claims require searching a product catalog of a store; filtering the ingredients based on inventory data for the store to determine availability of each ingredient at the store; generating, based at least in part on the searching and the filtering, a second list comprising ingredients from the first list that are present in the store; generating, using a planogram of the store, a navigable route through the store to retrieve the ingredients of the second list; providing, through the interface, a clickable route option, wherein responsive to a selection of the clickable route option, the navigable route is graphically displayed within the interface; organizing an order of the ingredients within the second list based on a route to pick the at least one candidate food item within the store; and training a machine learning model with ratings associated with the at least one candidate food item to rank future generated candidate food items in accordance with the ratings.
Regarding Chachek, Chachek teaches searching a product catalog of a store (Chachek, see at least: [0091] and [0082]); generating, using a planogram of the store, a navigable route through the store to retrieve the products (Chachek, see at least: [0101] and [0087]); providing, through the interface, a clickable route option, wherein responsive to a selection of the clickable route option, the navigable route is graphically displayed within the interface (Chachek, see at least: [0087]); and organizing an order of the products within the second list based on a route to pick food items within the store (Chachek, see at least: [0078] and [0079]).
Though disclosing these features, Chachek does not disclose or render obvious the features discussed above.
Regarding Kremen, Kremen teaches training a machine learning model with ratings associated with the at least one candidate food item to rank future generated candidate food items in accordance with the ratings (Kremen, see at least: [0028]).
Though disclosing these features, Kremen does not disclose or render obvious the features discussed above.
Regarding Neumann, Neumann teaches filtering the ingredients based on inventory data for the store to determine availability of each ingredient at the store (Neumann, see at least: Col. 16 Ln. 58-67 & Col. 17 Ln. 1-27); and generating, based at least in part on the searching and the filtering, a second list comprising ingredients from the first list that are present in the store (Neumann, see at least: Col. 16 Ln. 58-67 & Col. 17 Ln. 1-27).
Though disclosing these features, Neumann does not disclose or render obvious the features discussed above.
Ultimately, the particular combination of limitations as claimed, is not anticipated nor rendered obvious in view of Choi, Chachek, Kremen, and Neumann, and the totality of the prior art. While certain references may disclose more general concepts and parts of the claim, the prior art available does not specifically disclose the particular combination of these limitations.
Choi, Chachek, Kremen, and Neumann, however, do not teach or suggest, alone or in combination the claimed invention. Examiner emphasizes that the prior art/additional art would only be combined and deemed obvious based on knowledge gleaned from the applicant’s disclosure. Such a reconstruction is improper (i.e. hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Cited NPL Silva (i.e. reference U cited 03/03/2026 in PTO-892) teaches applying machine learning algorithms to prepare personalized grocery shopping lists, but does not teach or suggest alone or in combination the claimed invention.
The Examiner further emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. The combination of features as claimed would not be obvious to one of ordinary skill in the art as combining various references from the totality of evidence to reach the combination of features as claimed would be a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
It is thereby asserted by Examiner that, in light of the above and further deliberation over all of the evidence at hand, that the claims are allowable as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that the specification identifies specific technological problems in real-time inventory management and data integration for recipe-based shopping systems. As stated in the specification: "Consumers often express a willingness to try new types of food dishes but are frustrated by the lack of perceived available choices, difficulty associated with shopping for the food items needed, lack of skill believed to be needed to prepare the dishes, and the expense believed to be needed to eat something new or eat healthy." More specifically, the specification explains that "these various tasks can be very time consuming and costly, especially for people who are uncertain where to find certain ingredients or are unclear about which brand or type of ingredients to buy." The technical problem extends to integrating disparate computer systems: "the transaction system 133 further provides access to each store's current inventory of items, such that list/feedback manager 115 can determine when a given store is out of a needed ingredient for a user chosen food dish and alert the user through app 123 to select a different store because of the missing ingredient at the user's selected store." This requires real-time API-based communication between multiple computer systems to filter data based on dynamic inventory availability (Remarks, pages 6-7).
Examiner respectfully disagrees. Customer frustrations with shopping for food items and these shopping tasks being time consuming and costly are not technical problems. Additionally, the claims do not recite the integration of disparate computer systems, nor do the claims recite real-time API-based communication between multiple computer systems to filter data based on dynamic inventory availability (see MPEP 2106.05(a) “After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology”). Accordingly, the claims are not integrated into a practical application.
Applicant further argues that the claims provide a specific technological solution that improves how computer systems integrate and process inventory data in conjunction with planogram-based navigation. The technical improvement is in how the computer system processes and filters ingredient data. The amended claims represent a specific improvement in how computer systems process and filter data by integrating real-time inventory checks before generating shopping lists. The system improves data processing efficiency: The system prevents wasted computational resources and user time by filtering unavailable ingredients before route generation. The filtering step improves the technical functioning of the shopping list generation system by ensuring only available items are processed in subsequent navigation algorithms. The system improves API-based data integration. The claims integrate multiple data sources (recipe data, inventory data, and planogram data) in a specific technical sequence to optimize computer-based navigation generation. The system improves route optimization algorithms. This represents a specific improvement in how computer systems generate optimized navigation routes using spatial data structures (planograms) to algorithmically determine efficient paths through physical spaces (Remarks, pages 7-8).
Examiner respectfully disagrees. The claims do not recite using an API and merely utilizing a API doesn’t improve the API technology. The claims do not recite real-time inventory checks and utilizing real-time inventory checks using generic computer components does not improve the technology or technological field itself. Merely processing less data by filtering data does not improve the technology or technological field itself (i.e. “the judicial exception alone cannot provide the improvement” MPEP 2106.05(a)). Organizing a shopping list based on an order in which the customer will pick the ingredients from the store using the planogram and the locations of each of the ingredients in the list does not describe any way in which an algorithm is improved (i.e. “if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology” MPEP 2106.05(a)). Accordingly, the claims are not integrated into a practical application.
Applicant further argues that the amended claims align with the USPTO's 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence. The guidance emphasizes that AI-related claims are patent eligible when they "provide specific steps for using the output of the [ AI system] to solve a technological problem amounts to an improvement to the functioning of a computer or technology." This machine learning integration improves how the computer system ranks and presents food item data by learning from user feedback to optimize future recommendations. The guidance states: "Claims that reflect the improvements discussed in the disclosure would integrate a recited abstract idea into a practical application, thereby rendering the claim eligible." The specification demonstrates a specific technical application of machine learning to improve computer functionality in data ranking and presentation. Furthermore, the specification represents a specific improvement in data ranking algorithms that optimize computer processing efficiency by learning user preferences (Remarks, page 8).
Examiner respectfully disagrees. Improving data by including feedback does not improve the computer or the machine learning technology itself, rather, it improved the data. Unlike the examples described in the 2024 Guidance Update on Patent Subject Matter Eligibility, no specific steps are described “for using the output of the [ AI system] to solve a technological problem amounts to an improvement to the functioning of a computer or technology.” Accordingly, the claims are not integrated into a practical application.
Applicant further argues that the Examiner incorrectly characterized the claims as reciting certain methods of organizing human activity. The claims do NOT recite abstract ideas but rather recite specific technological improvements to computer data processing systems. The claims recite a specific technical process for: Real-time inventory data filtering; Multi-system API integration; Planogram-based spatial data processing; Dynamic list generation based on filtered data; Algorithmic route optimization; Machine learning-based ranking improvements. These are not Mathematical Concepts, Mental Processes, or Methods of Organizing Human Activity (Remarks, pages 8-10).
Examiner respectfully disagrees. The limitations recite the abstract concept of obtaining a first list of ingredients in a recipe of a selected candidate food item, generating a second list of the ingredients based on searching a product catalog of a store, and providing a navigable route through the store to retrieve the ingredients of the second list. This falls within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a), in that it covers a commercial interaction (i.e. advertising, and marketing or sales activities). Filtering ingredients, generating a route using a planogram, generating a list based on filtered data, and organizing the order of the ingredients within a list are sales activities and do not provide any technological improvements nor do they even recite any additional elements. Regarding “Multi-system API integration,” the claims do not recite multi-system API integration (see MPEP 2106.05(a) “After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology”). Regarding “Machine learning-based ranking improvements,” merely utilizing machine learning to improve ranking does not improve the machine learning technology itself, rather, it does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. machine learning). Accordingly, the claims are ineligible.
Applicant further argues that the claims are similar to PEG Example 1, PEG Example 23, PEG Example 36, and PEG Example 40 (Remarks, page 10).
Examiner respectfully disagrees. Unlike PEG Example 1, the current claims don’t provide any technical improvement; shopping list generation and navigation are not technical fields and merely utilizing generic computer components without any improvements to the computer components themselves does not provide any improvement to technology.
Unlike PEG Example 23, the current claims recite the interface 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).
Unlike PEG Example 36, using planogram spatial data and inventory filtering to improve recipe-based shopping list generation fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field as recipe-based shopping list generation is not a technical field and planogram spatial data and inventory filtering are not additional element. For instance MPEP 2106.05(a) states “the judicial exception alone cannot provide the improvement.”
Unlike PEG Example 40, which improves the performance of the actual network in the network monitoring, the recited limitations fail to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field.
Accordingly, the claims are ineligible.
Applicant further argues that the computer functionality is improved through data filtering as "filtering the ingredients based on inventory data for the store to determine availability of each ingredient at the store" it represents a specific improvement in how computer systems process ingredient data by integrating real-time inventory checks and that the claims are analogous to Enfish (Remarks, pages 10-11).
Examiner respectfully disagrees. Improving how data is processed does not improve the computer technology itself and the claims do not recite “real-time inventory checks.” The limitation of "filtering the ingredients based on inventory data for the store to determine availability of each ingredient at the store" does not recite any additional elements and, as stated by MPEP 2106.05(a), “the judicial exception alone cannot provide the improvement.” Additionally, merely accessing inventory data without reciting the specific technological elements that are utilized to accomplish this does not provide any technical improvements. Furthermore, in Enfish the storing of tabular data is specifically directed to a self-referential table. Thus, the claims were “directed to a specific improvement to the way computers operate,” rather than utilizing a computer as a means for implementing an abstract idea. Id at 1336. Accordingly, the claims are ineligible.
Applicant further argues that the multi-system integration through API communication represents an improvement in computer-to-computer data communication technology, not merely applying an abstract idea on a generic computer and is analogous to BASCOM (Remarks, pages 11-12).
Examiner respectfully disagrees. Multi-system integration through API communication is not recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Unlike the current claims, in BASCOM, the inventive concept is found in the unconventional and non-generic combination of known elements, providing individually customizable filtering at the remote ISP server. The claim while "involving" an abstract idea is not "directed" to that idea standing alone. It is not simply directed to the abstract idea of filtering content on the internet or on generic computer components performing conventional activities. Instead, claim 1 "carve[s] out a specific location for the filtering system (a remote ISP server) and require the filtering system to give users the ability to customize filtering for their individual network accounts." Accordingly, the claims are ineligible.
Applicant further argues that the claim recites "generating, using a planogram of the store, a navigable route through the store to retrieve the ingredients of the second list" and "organizing an order of the ingredients within the second list based on a route to pick the at least one candidate food item within the store." The specification explains the technical implementation: "Using the API, list/feedback manager 115 may also obtain a planogram from transaction system 133 for the store. The planogram shows the aisles, shelves, displays and overall layout of the store." The system then processes this spatial data: "list/feedback manager 115 organizes the shopping list based on an order in which the customer will pick the ingredients from the store using the planogram and the locations of each of the ingredients in the list." This represents a specific improvement in how computer systems generate optimized navigation routes using spatial data structures. It is analogous to McRO and Thales Visionix (Remarks, page 12).
Examiner respectfully disagrees. Generating optimized navigation routes using a planogram is not a technical improvement. In McRO the claims “focused on a specific asserted improvement in computer animation, i.e., the automatic use of a particular type” [see McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, 120 U.S.P.Q.2d 1091 (Fed. Cir. 2016) page 24]. The claims were found eligible because of a specific improvement in computer animation (i.e. a problem rooted in technology). This is not the case with the claimed invention. Unlike in McRO, the claimed invention fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field. In the case of Thales, the claims “are directed to systems and methods that use inertial sensors in a non-conventional manner” [see Thales Visionix Inc. v. United States, Case no. 2015-5150, March 8, 2017 (Fed. Cir.)(slip op.)]. The instant claims, however, recite functions without specifying even arguably new physical components or specifying processes defined other than by the functions themselves. The claimed functions can be carried out in existing computers long in use, no new machinery being necessary. Additionally, the additional elements are merely recited in a generic manner. Accordingly, the claims are ineligible.
Applicant further argues that the recited claims improve user interface technology. The claims recite "providing, through the interface, a clickable route option, wherein responsive to a selection of the clickable route option, the navigable route is graphically displayed within the interface." The specification describes: "When the customer arrives at a store for the shopping list of ingredient items, the app provides a navigation interface and a path for the customer to optimally traverse the store and pick the ingredient items." Applicant further argues that the claims are analogous to Core Wireless and Trading Technologies (Remarks, pages 12-13).
Examiner respectfully disagrees. Merely displaying a clickable route option recites the interface at a high level of generality (i.e. as generic computing hardware) such that it amounts 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). The interface technology itself is not improved.
In Core Wireless, the claims were found eligible because the actual interface, which is part of a technological field, is improved. Core Wireless describes that “The asserted claims in this case are directed to an improved user interface for computing devices…Claim 1 of the '476 patent requires “an application summary that can be reached directly from the menu,” specifying a particular manner by which the summary window must be accessed. The claim further requires the application summary window list a limited set of data, “each of the data in the list being selectable to launch the respective application and enable the selected data to be seen within the respective application.” This claim limitation restrains the type of data that can be displayed in the summary window. Finally, the claim recites that the summary window “is displayed while the one or more applications are in an un-launched state,” a requirement that the device applications exist in a particular state. These limitations disclose a specific manner of displaying a limited set of information to the user, rather than using conventional user interface methods to display a generic index on a computer. Unlike Core Wireless, the current claims merely describe a clickable option displayed on an interface.
In the case of Trading Tech, the courts concluded the claims are "directed to improvements in existing graphical user interface devices that have no "pre-electronic trading analog," and recite more than "’setting, displaying, and selecting' data or information that is visible on the [graphical user interface] device,” and further compares the limitations to DDR in that the claims are “necessary rooted in computer technology” and “overcome a problem specifically arising in the realm of computer networks” [see Trading Techs. Int'l, Inc. v. CQG, Inc., __ F.3d __, slip op. (Fed. Cir. Jan. 18, 2017)]. Unlike Trading Techs., the current claims recite no such technical improvement.
Accordingly, the claims are ineligible.
Applicant further argues that machine learning integration improves data ranking. The claims integrate "training a machine learning model with ratings associated with the at least one candidate food item to rank future generated candidate food items in accordance with the ratings." The specification describes the technical implementation: "This feedback is used to train MLM 113 to rank food dishes for a particular user or a group of users as a whole. When list/feedback manager 115 finds potential food dishes that meet the user provided values and selections for the initial options, list/feedback manager 115 provides the potential food choices to MLM 113 along with the user's choices and the output of MLM 131 is a ranked order listing of the potential food choices." This improves how the computer system processes and ranks food item data by integrating machine learning to optimize future recommendations based on learned user preferences. Applicant further argues that the claims are analogous to PEG Example 39 (Neural Network Training) and SRI International (Remarks, pages 13-14).
Examiner respectfully disagrees. Improving data ranking improves the abstract idea, it does not improve the machine learning technology itself. Merely utilizing machine learning does not improve the machine learning, nor does it improve the computer technology itself.
Unlike PEG Example 39, which was found to be eligible as it does not recite an abstract idea (see page 9 of the Subject Matter Eligibility Examples), the claims recite the abstract idea of obtaining a first list of ingredients in a recipe of a selected candidate food item, generating a second list of the ingredients based on searching a product catalog of a store, and providing a navigable route through the store to retrieve the ingredients of the second list which falls within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas.
Unlike SRI International, the current claims do not solve a technological problem arising in computer networks, constituting an improvement in computer network technology; improving ranking in recipe recommendation is not a technical solution to a problem that arises in technology.
Accordingly, the claims are ineligible.
Applicant further argues that the recited elements in the above arguments, considered individually and as an ordered combination, integrate any abstract idea into a practical application by: Improving Computer Data Processing, Improving System Integration, Improving Route Generation Technology, Improving User Interface Technology, and Improving Machine Learning Ranking. The claims do NOT merely: Link an abstract idea to a technological environment, Apply an abstract idea using generic computer components, and Add insignificant extra-solution activity. The claims DO: Improve the functioning of computer systems themselves, Provide specific technological solutions to technological problems, and Recite unconventional combinations of technical elements (Remarks, page 14).
Examiner respectfully disagrees. Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually. Additionally, as detailed in response to the arguments above with regards to Improving Computer Data Processing (i.e. Improving how data is processed does not improve the computer technology itself), Improving System Integration (i.e. integration through API communication is not recited in the current claims), Improving Route Generation Technology (i.e. generating optimized navigation routes using a planogram is not a technical improvement), Improving User Interface Technology (i.e. merely displaying a clickable route option recites the interface at a high level of generality (i.e. as generic computing hardware) such that it amounts 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 Improving Machine Learning Ranking (i.e. Improving data ranking improves the abstract idea, it does not improve the machine learning technology itself), the additional elements 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). Accordingly, the claims are ineligible.
Applicant further argues that, under Step 2B Analysis, even if the Examiner were to find an abstract idea at Step 2A Prong 2 (which Applicant disputes), the claims provide significantly more by improving the functioning of computer systems. The ordered combination of: Searching product catalogs for recipe ingredients, Filtering based on real-time inventory data to determine availability, Generating lists based on filtered, available ingredients, Using planogram spatial data structures to generate optimized routes, Organizing ingredient order based on spatial navigation algorithms, Providing graphical interface displays with clickable route options, and Training machine learning models with user feedback for improved future ranking provides an inventive concept that goes beyond conventional computer implementation (Remarks, page 15).
Examiner respectfully disagrees. The courts consider the determination of whether a claim is eligible (which involves identifying whether an exception such as an abstract idea is being claimed) to be a question of law, not a question of novelty. While the second step of the Alice analysis does consider whether the functions being performed are well-understood, routine, or conventional (and therefore, not considered to amount to significantly more), the question of whether a claim is eligible under 101 is based on determining whether an abstract idea has been claimed, and whether the additional elements amount to significantly more than the abstract idea. Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually. Accordingly, the claims are ineligible.
Applicant further argues that the Examiner has not established, nor can establish, that the specific combination of elements recited in the claims was well-understood, routine, or conventional in the relevant field at the time of the invention (Remarks, page 15).
Examiner respectfully disagrees. The additional elements are insufficient to integrate the abstract idea into a practical application because the 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) and 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). Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually. Additionally, as is described in the MPEP 2106.05(II) (i.e. “Thus, in Step 2B, examiners should: … Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant”), step 2B considers whether additional elements concluded to be insignificant extra-solution activity in Step 2A are more than well-understood, routine, conventional activity in the field. Examiner did not identify any of the additional elements as insignificant extra-solution activity in Step 2A so there weren’t elements to be evaluated in terms of whether they are more than well-understood, routine, conventional activity in the field. Accordingly, the claims are ineligible.
Applicant further argues, regarding Inventory-Based Filtering Before Route Generation, the specific step of filtering ingredients based on real-time inventory availability BEFORE generating shopping lists and routes is not a well-understood, routine, or conventional data processing step. The specification describes this as a solution to the problem that consumers are "uncertain where to find certain ingredients"-a problem that would not exist if inventory-based pre-filtering were routine and conventional (Remarks, page 15).
Examiner respectfully disagrees. Being uncertain of where to find ingredients isn’t a technical problem. Additionally, as is described in the MPEP 2106.05(II) (i.e. “Thus, in Step 2B, examiners should: … Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant”), step 2B considers whether additional elements concluded to be insignificant extra-solution activity in Step 2A are more than well-understood, routine, conventional activity in the field. Examiner did not identify any of the additional elements as insignificant extra-solution activity in Step 2A so there weren’t elements to be evaluated in terms of whether they are more than well-understood, routine, conventional activity in the field. Accordingly, the claims are ineligible.
Applicant further argues that using planogram spatial data to organize ingredient lists in a specific order based on optimal store traversal routes is not generic data organization. The specification describes the technical architecture: "Cloud/Server 110 comprises at least one processor 111 and a non-transitory computer-readable storage medium 112. Medium 112 comprises executable instructions for one or more machine-learning models (algorithms) 113, a recipe manager 114, and a list/feedback manager 115" (Remarks, page 15).
Examiner respectfully disagrees. A server/cloud comprising at least one processor and a non-transitory computer-readable storage medium, one or more machine-learning models being run by instructions of a medium (i.e. generic computer software used to implement generic machine learning technology), a recipe manager (i.e. generic computer software), and a list/feedback manager (i.e. generic computer software) are all generic computer components. Utilizing generic computer components to organize list data for shopping routes does not amount to significantly more than an abstract idea. Accordingly, the claims are ineligible.
Applicant further argues that the specific integration of machine learning ranking with inventory-filtered, planogram-optimized ingredient lists is not a conventional combination. Each component may be known separately, but their specific integration as claimed represents a non-conventional arrangement (Remarks, pages 15-16).
Examiner respectfully disagrees. Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually. The machine learning is 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). Additionally, a planogram is a diagram, it’s not an additional element. Furthermore, as is described in the MPEP 2106.05(II) (i.e. “Thus, in Step 2B, examiners should: … Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant”), step 2B considers whether additional elements concluded to be insignificant extra-solution activity in Step 2A are more than well-understood, routine, conventional activity in the field. Examiner did not identify any of the additional elements as insignificant extra-solution activity in Step 2A so there weren’t elements to be evaluated in terms of whether they are more than well-understood, routine, conventional activity in the field. Accordingly, the claims are ineligible.
Applicant further argues that the current claims are similar to BASCOM (Remarks, page 16).
Examiner respectfully disagrees. In BASCOM, the inventive concept is found in the unconventional and non-generic combination of known elements, providing individually customizable filtering at the remote ISP server. The claim while "involving" an abstract idea is not "directed" to that idea standing alone. Unlike the current claims, it is not simply directed to the abstract idea of filtering content on the internet or on generic computer components performing conventional activities. Instead, claim 1 "carve[s] out a specific location for the filtering system (a remote ISP server) and require the filtering system to give users the ability to customize filtering for their individual network accounts." Accordingly, the claims are ineligible.
Applicant further rebuts Examiner’s previous response to arguments. Applicant argues that Examiner mischaracterizes the technical problem. The problem is not merely that tasks are time-consuming in a business sense, but rather how computer systems inefficiently process inventory data and fail to integrate real-time availability checks before generating shopping lists and navigation routes. The specification describes the technical integration: "the transaction system 133 further provides access to each store's current inventory of items, such that list/feedback manager 115 can determine when a given store is out of a needed ingredient." This is a computer data processing and system integration problem. This is a technical problem in computer system architecture and data processing, not a business problem. It is analogous to network latency (a technical problem) even though reducing latency has business benefits (Remarks, pages 16-17).
Examiner respectfully disagrees. Examiner reiterates that tasks being time consuming and costly is not a technical problem and merely processing less data is not a technical improvement. Additionally, the claims don’t recite “Real-time API communication between disparate systems,” “Dynamic database queries for inventory status,” or “Synchronization of recipe data with inventory data,” and using multiple sources of data encompasses advertising, and marketing or sales activities. Accordingly, the claims are ineligible.
Applicant further rebuts Examiner’s previous response to arguments. Applicant argues the Generating routes using planogram data is a specific technological process involving spatial data structures and algorithmic route optimization, not merely a sales activity. The specification describes: "Using the API, list/feedback manager 115 may also obtain a planogram from transaction system 133 for the store. The planogram shows the aisles, shelves, displays and overall layout of the store." This is a spatial data structure-a technical computing concept. Again, the claims do not recite any API technology. The system then algorithmically processes this spatial data: "list/feedback manager 115 organizes the shopping list based on an order in which the customer will pick the ingredients from the store using the planogram and the locations of each of the ingredients in the list." This algorithmic processing of spatial data to optimize traversal paths is analogous to: McRO and GPS Navigation Systems. The mere fact that the technology is applied in a commercial context (shopping) does not render it abstract, any more than digital image processing is abstract because images can be used in advertising (Remarks, pages 17-18).
Examiner respectfully disagrees. Examiner reiterates that "generating, using a planogram of the store, a navigable route through the store to retrieve the ingredients of the second list" is part of the abstract idea as it is directed to a sales activity. A planogram is a diagram, it’s not an additional element and utilizing a diagram of a store to create a route is a sales activity.
In McRO the claims “focused on a specific asserted improvement in computer animation, i.e., the automatic use of a particular type” [see McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, 120 U.S.P.Q.2d 1091 (Fed. Cir. 2016) page 24]. The claims were found eligible because of a specific improvement in computer animation (i.e. a problem rooted in technology). This is not the case with the claimed invention. Unlike in McRO, the claimed invention fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field.
A GPS utilizes actual technology as it utilizes satellite technology; a diagram is cot comparable to satellite technology.
Accordingly, the claims are ineligible.
Applicant further rebuts Examiner’s previous response to arguments. Applicant argues that the Examiner's analysis overlooks the specific technical implementation described in the specification. The machine learning model improves how the computer system processes and ranks food item data by integrating multiple data sources The specification describes: "list/feedback manager 115 provides the potential food choices to MLM 113 along with the user's choices and the output of MLM 131 is a ranked order listing of the potential food choices." This describes a specific data flow and processing architecture. Furthermore: "a list of priorities or preferences may be maintained for each user that allows the user to rank in the user's priority order the importance of the 6 options, these priorities may be supplied to the MLM 113 with the potential food dishes, such that the user's priorities are reflected in the user's ranked order." This represents a specific improvement in data ranking algorithms. Applicant further argues that the current claims are similar to SRI and PEG Example 39 (Remarks, page 18).
Examiner respectfully disagrees. Examiner reiterates that merely training and utilizing a machine learning model with ratings data to rank items fails to actually improve the technology. Improving the data does not improve the machine learning technology itself. Accordingly, the claims are ineligible.
Unlike SRI International, the current claims do not solve a technological problem arising in computer networks, constituting an improvement in computer network technology; improving ranking in recipe recommendation is not a technical solution to a problem that arises in technology.
Unlike PEG Example 39, which was found to be eligible as it does not recite an abstract idea (see page 9 of the Subject Matter Eligibility Examples), the claims recite the abstract idea of obtaining a first list of ingredients in a recipe of a selected candidate food item, generating a second list of the ingredients based on searching a product catalog of a store, and providing a navigable route through the store to retrieve the ingredients of the second list which falls within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas.
Accordingly, the claims are ineligible.
Applicant further rebuts Examiner’s previous response to arguments. Applicant argues that the claims are not merely directed to "store navigation" in the abstract sense of physically walking through a store. They are directed to computer-implemented navigation systems that use planogram data structures, API-based inventory integration, and algorithmic route optimization-all squarely within the technical field of computer science and data processing. This is computer-based spatial data processing and algorithmic route optimization established technical fields within computer science. It is analogous to: GPS Navigation Technology, Robotics Navigation, and Computer Graphics and Game AI. The fact that the navigation occurs in a retail environment does not make it non-technical, just as GPS navigation used to find stores is still technical, and network routing used for ecommerce is still technical (Remarks, page 19).
Examiner respectfully disagrees. Examiner reiterates that store navigation is not a technical field. A planogram is a diagram and a optimizing a route for picking up items is a sales activity (i.e. not additional elements). Examiner again points out that the claims do not recite any API technology. The current claims do not recite the technology included in GPS Navigation Technology, Robotics Navigation, and Computer Graphics and Game AI (e.g. satellite technology, sensors, and specific image processing technology); the current claims are not comparable to these technologies. Accordingly, the claims are ineligible.
Applicant further rebuts Examiner’s previous response to arguments. Applicant argues that the Examiner's characterization is overly broad and ignores the specific technological improvements recited in the claims. The specification describes the technical architecture: "Cloud/Server 110 comprises at least one processor 111 and a non-transitory computer-readable storage medium 112. Medium 112 comprises executable instructions for one or more machine-learning models (algorithms) 113, a recipe manager 114, and a list/feedback manager 115." This is not generic computer implementation-it is a specific technical architecture for multi-system data integration involving: Recipe manager modules, List/feedback manager modules, Machine learning model modules, API interfaces for system-to-system communication, and Spatial data processing for planogram analysis. Applicant further argues that the current claims are similar to Amdocs (Remarks, pages 19-21).
Examiner respectfully disagrees. Examiner reiterates that the claims merely recite a generic processor performing the generic computer functions of obtaining data, searching data, generating data, providing data, organizing data, and training data. Examiner has analyzed the claims individually and as whole. Recipe manager modules and List/feedback manager modules are generic computer software, the machine learning is 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), the API interfaces for system-to-system communication are recited in the claims, and utilizing planogram data is a sales activities. The claim does not recite a “specific technical architecture for multi-system data integration.”
In Amdocs the “claim entails an unconventional technical solution (enhancing data in a distributed fashion) to a technological problem (massive record flows which previously required massive databases)…the claim’s enhancing limitation necessarily requires that these generic components operate in an unconventional manner to achieve an improvement in computer functionality” (See pages 22-23 of Amdocs). In the case of the present application, the claims do not overcome a technical problem or propose a technical solution, but merely perform the present abstract idea using generic computer structure, performing generic computer functions without improvements to a technology or the computer itself does not transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself.
Accordingly, the claims are ineligible.
Applicant further argues that the claims are similar to DDR (Remarks, page 21).
Examiner respectfully disagrees. In the case of DDR, the claims overcome a problem or propose a solution “specifically arising in the realm of computer [technology]” DDR Holdings, 773 F.3d at 1257. No such technical solution is present in the current claims.
Applicant further argues that the claims are similar to Enfish (Remarks, pages 21-22).
Examiner respectfully disagrees. In Enfish the storing of tabular data is specifically directed to a self-referential table. Thus, the claims were “directed to a specific improvement to the way computers operate,” rather than utilizing a computer as a means for implementing an abstract idea. Id at 1336. In the case of the present claims, the claims are not directed toward any technological improvement, but merely perform the present abstract idea using generic computer structure, performing generic computer functions without improvements to a technology or the computer itself does not transform the abstract idea into a patent eligible application such that the claim is integrated into a practical application.
Applicant further argues that the claims are similar to BASCOM (Remarks, page 21).
Examiner respectfully disagrees. In BASCOM, the inventive concept is found in the unconventional and non-generic combination of known elements, providing individually customizable filtering at the remote ISP server. The claim while "involving" an abstract idea is not "directed" to that idea standing alone. Unlike the current claims, it is not simply directed to the abstract idea of filtering content on the internet or on generic computer components performing conventional activities. Instead, claim 1 "carve[s] out a specific location for the filtering system (a remote ISP server) and require the filtering system to give users the ability to customize filtering for their individual network accounts." Accordingly, the claims are ineligible.
Applicant further argues that the claims are similar to McRO (Remarks, pages 22-23).
Examiner respectfully disagrees. In McRO the claims “focused on a specific asserted improvement in computer animation, i.e., the automatic use of a particular type” [see McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, 120 U.S.P.Q.2d 1091 (Fed. Cir. 2016) page 24]. The claims were found eligible because of a specific improvement in computer animation (i.e. a problem rooted in technology). This is not the case with the claimed invention. Unlike in McRO, the claimed invention fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field. Accordingly, the claims are ineligible.
Applicant further argues that the claims are similar to Visual Memory (Remarks, page 23).
Examiner respectfully disagrees. In Visual Memory, the claims were "directed to an improved computer memory system, not an abstract idea." This is not the case with the claimed invention. Unlike in Visual Memory, the claimed invention fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field. Accordingly, the claims are ineligible.
Applicant further argues that the claims are similar to Ancora (Remarks, page 23).
Examiner respectfully disagrees. In Ancora, the claims were directed to an improvement in computer security technology, not an abstract idea." This is not the case with the claimed invention. Unlike in Ancora, the claimed invention fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field. Accordingly, the claims are ineligible.
Applicant further argues that under Step 2A, Prong 1, the claims do NOT recite a judicial exception (Remarks, pages 23-24).
Examiner respectfully disagrees. As detailed in the responses to the arguments above, the claimed invention fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field. Accordingly, the claims are ineligible.
Applicant further argues that Example 1, Example 23, Example 36, and Example 40 provide support for eligibility (Remarks, page 24).
Examiner respectfully disagrees. As detailed in the responses to the arguments above, the current claims are unlike Example 1, Example 23, Example 36, and Example 40. Accordingly, the claims are ineligible.
Applicant further argues that, under Step 2A, Prong 2, even if any abstract idea were present (which is disputed), the claims integrate it into a practical application as the current claims improve the functioning of computer functioning and provides Enfish, Visual Memory, Finjan, and SRI International (Remarks, page 24).
Examiner respectfully disagrees. Unlike Enfish, Visual Memory, Finjan, and SRI International, the claimed invention fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field. Accordingly, the claims are ineligible.
Applicant further argues that the claims are specific machine configurations for data integration and processing (Remarks, pages 24-25).
Examiner respectfully disagrees. The additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim. Accordingly, the claims are ineligible.
Applicant further argues that the claims are Meaningful Limitation Beyond Technological Environment and do not merely link an abstract idea to "use a computer." Applicant cites to DDR Holdings, McRO, and Ancora (Remarks, page 24).
Examiner respectfully disagrees. Unlike DDR Holdings, McRO, and Ancora, 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). Accordingly, the claims are ineligible.
Applicant further argues that, under Step 2B, the claims recite significantly more than any judicial exception as the claims recite a Non-Conventional Data Processing Sequence that is not well-understood, routine, or conventional (Remarks, page 25).
Examiner respectfully disagrees. The additional elements are insufficient to integrate the abstract idea into a practical application because the 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) and 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). Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually. Additionally, as is described in the MPEP 2106.05(II) (i.e. “Thus, in Step 2B, examiners should: … Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant”), step 2B considers whether additional elements concluded to be insignificant extra-solution activity in Step 2A are more than well-understood, routine, conventional activity in the field. Examiner did not identify any of the additional elements as insignificant extra-solution activity in Step 2A so there weren’t elements to be evaluated in terms of whether they are more than well-understood, routine, conventional activity in the field. Accordingly, the claims are ineligible.
Applicant further argues that, under Step 2B, the claims recite an Unconventional System Integration (Remarks, page 25).
Examiner respectfully disagrees. As detailed in the response to the augments above, some of the systems listed by Applicant (e.g. API architecture, Recipe databases, inventory systems, etc.) ae not recited in the claims. Additionally, the courts consider the determination of whether a claim is eligible (which involves identifying whether an exception such as an abstract idea is being claimed) to be a question of law, not a question of novelty. While the second step of the Alice analysis does consider whether the functions being performed are well-understood, routine, or conventional (and therefore, not considered to amount to significantly more), the question of whether a claim is eligible under 101 is based on determining whether an abstract idea has been claimed, and whether the additional elements amount to significantly more than the abstract idea. Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually. Accordingly, the claims are ineligible.
Applicant further argues that, under Step 2B, the specification describes: "Cloud/Server 110 comprises at least one processor 111 and a non-transitory computer-readable storage medium 112. Medium 112 comprises executable instructions for one or more machine-learning models (algorithms) 113, a recipe manager 114, and a list/feedback manager 115." This is not generic computing but a specific technical architecture (Remarks, page 26).
Examiner respectfully disagrees. As detailed in the response to the augments above, A server/cloud comprising at least one processor and a non-transitory computer-readable storage medium, one or more machine-learning models being run by instructions of a medium (i.e. generic computer software used to implement generic machine learning technology), a recipe manager (i.e. generic computer software), and a list/feedback manager (i.e. generic computer software) are all generic computer components. Utilizing generic computer components to organize list data for shopping routes does not amount to significantly more than an abstract idea. Accordingly, the claims are ineligible.
Applicant further cites to BASCOM, Amdocs, and Ancora (Remarks, page 26).
Examiner respectfully disagrees. As detailed in the response to the augments above, the current claims are not similar to BASCOM, Amdocs, and Ancora.
Applicant further cites to Ex Parte Avery: "Specific layered GUI arrangement eligible - improvement to Technology," Ex Parte Rogers: "Unconventional indexing of data clusters eligible - not merely organizing data," and Ex Parte Boldt: "Integration into practical application through improved printing efficiency" (Remarks, page 26).
Examiner respectfully disagrees. Unlike Ex Parte Avery, the current claims fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field.
Unlike Ex Parte Roger, the current claims do not recite additional elements 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).
Unlike Ex Parte Boldt, the current claims fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field.
Accordingly, the claims are ineligible.
Applicant further argues that, independent Claim 2 is eligible under Step 2A Prong 1 as it does not recite a judicial exception and recites specific improvements to computer technology, is eligible under Step 2A Prong 2 as it integrates any concepts into practical application, and is eligible under Step 2B provides inventive concept through unconventional arrangement of technical elements (Remarks, pages 26-27).
Examiner respectfully disagrees. As detailed in response to the arguments above, claim 2 recites an abstract idea under Step 2A Prong 1, is not integrated into a practical application under Step 2A Prong 2, and does not amount to significantly more than the abstract idea under Step 2B. Accordingly, the claims are ineligible.
Applicant further argues, regarding dependent claims 3-12, that each dependent claim adds additional technical limitations that further integrate the invention into practical applications and provide additional inventive concepts. Examiner failed to provide claim-by-claim analysis as required. Each dependent claim adds meaningful technical limitations that independently support eligibility (Remarks, page 27).
Examiner respectfully disagrees. As detailed in the current a previous rejections, Dependent claims 3-12, 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 3-12 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 5 and 9-10 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 3-4, 6-8, and 11-12 recite the additional elements of the interface, a device, training a machine learning model (model), an existing retailer application, and an application programming interface 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 3-12 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 3-12 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claim 2, dependent claims 3-12 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. obtaining a first list of ingredients in a recipe of a selected candidate food item, generating a second list of the ingredients based on searching a product catalog of a store, and providing a navigable route through the store to retrieve the ingredients of the second list) 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 amounts to significantly more than the judicial exception itself.
Accordingly, the claims are ineligible.
Applicant further concludes that the claims are patent-eligible under 35 U.S.C. § 101 because they do NOT recite judicial exceptions but rather recite specific improvements to computer technology in data processing, system integration, spatial processing, UI technology, and machine learning. They integrate any concepts into practical applications. They provide inventive concepts. They are analogous to patent-eligible precedent. They align with USPTO AI Guidance for machine learning implementations that improve computer functionality. They are supported by PEG Examples showing similar technical improvements are eligible (Remarks, pages 27-28).
Examiner respectfully disagrees. Examiner disagrees with Applicant’s conclusions for the reasons detailed in response to the arguments above. Accordingly, the claims are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Wood et al. (US 2017/0262931 A1) teaches providing a recipe service.
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.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689