Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This communication is a Final Office Action in response to Applicant’s amendment for application number 18/201,958 received on 04/08/2026.
In accordance with Applicant’s amendment, claims 1, 4-5, 7-15, 17-18, and 20-25 are amended. Claims 1, 4-5, 7-15, 17-18, and 20-25 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Response to Arguments
Response to §101 arguments – Applicant’s arguments with respect to the §101 rejections previously applied to the claims have been considered and are unpersuasive.
Applicant argues (Remarks at pg. 13): “For example, the claims include "updating, in at least one database, order data associated with an order system of the food establishment based on output data including the first predicted value and the plurality of further predicted values of the target variable." These features, in addition to the various features recited in the present claims, at least apply any purported judicial exception to a practical application.”. In response, Examiner respectfully disagrees and notes that the mentioned step constitutes an additional element that does not integrate the abstract idea into a practical application, add significantly more, or otherwise results in an improvement to technology because it amounts to insignificant extra-solution activity (e.g., insignificant application) as noted in MPEP 2106.05(g). See In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential).
Applicant argues (Remarks at pg. 14): “Furthermore, the patentability of the present claims is supported by Federal Circuit case law. For example, Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1338 (Fed. Cir. 2016) holds that "[s]oftware can make non-abstract improvements to computer technology just as hardware improvements can, and sometimes the improvements can be accomplished through either route. We thus see no reason to conclude that all claims directed to improvements in computer-related technology, including those directed to software, are abstract." Enfish further holds that claims "directed to a specific improvement to computer functionality" do not recite an abstract idea. Id. at 1344. Enfish supports the patentability of the present claims because the present claims recite specific improvements to computer functionality. For example, as described in the originally filed application, "it may be challenging to predict food item demand using conventional techniques, e.g., due to the large number of variables a food establishment has to consider." Specification, 17. The present claims recite specific improvements to computer functionality because the claims "obviate a need for certain efforts or resources that otherwise would be involved in demand forecasting systems." Specification, 28. The claims further provide that "[c]omputing resources used by one or more machines, databases, or networks may be more efficiently utilized or even reduced, e.g., as a result of more accurate predictions, automated operations, enhanced scalability, or integration of tools." Id. Thus, the present claims recite specific improvements to computer functionality and do not recite an abstract idea.”. In response, Examiner respectfully disagrees and notes the present claims do not provide an analogous improvement to the computer to that of Enfish, specifically because the present claims do not improve the computer itself. The improvements of a self-referential table provide a specific benefit to the functioning of the computer, which is not the case in the claims of the instant application. The present claims are directed to forecasting demand for food establishments, which is not an improvement to the computer itself. Rather, this is an improvement to the abstract idea associated with “Mental Processes.
Response to §103 arguments – Applicant’s arguments (Remarks at pgs. 14-16) with respect to the §103 rejections previously applied to the original claims are primarily raised in support of the amendments to independent claims 1, 15, and 18. The amendments and supporting arguments are believed to be fully addressed in the updated §103 rejections below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4-5, 7-15, 17-18, and 20-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of those findings is provided below, as further set forth in MPEP 2106.
Step 1: The claimed invention is analyzed to determine if it falls outside one of the four statutory categories of invention. See MPEP 2106.03
Claims 1, 4-5, and 7-14 are directed to a system (i.e., Machine), claims 15, 17, and 21-22 are directed to a method (i.e., Process), and claims 18, 20, and 23-25 are directed to a computer-readable medium (i.e., Manufacture). Therefore, claims 1, 4-5, 7-15, 17-18, and 20-25 are directed to patent eligible categories of invention. Accordingly, the claims satisfy Step 1 of the eligibility inquiry.
Step 2A, Prong 1: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether they recite a judicial exception. See MPEP 2106.04
Independent claim 1 recites a system for predicting demand of food items. As drafted, the limitations recited by claim 1 fall under the “Mental Processes” abstract idea grouping by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion). Claim 1 recites a system comprising a memory, and one or more processors with limitations for: “enriching first user data of the food establishment to obtain a first input data set, the first user data comprising a sequence of first data points defining a time series representing at least changes in demand over time for an observed period, each first data point comprising, for a respective time step in the time series, an observed value of a target variable and a value of each of a plurality of establishment input features, the target variable being related to a food item, and the first user data being enriched, for each first data point, using the values of the plurality of complementary features corresponding to the first data point; enriching second user data of the food establishment to obtain a second input data set, the second user data comprising a second data point for a first prediction period, the second data point comprising a value of each of the establishment input features, and the second user data being enriched using the values of the plurality of complementary features corresponding to the second data point; generating, a first predicted value of the target variable for the first prediction period, the generating of the first predicted value comprising: processing, the first input data set to generate a first vector representation representative of the changes in demand over time in the target variable and the plurality of complementary features for the observed period, processing of the first input data set comprising processing, the time series by passing hidden state vectors between the respective time steps in the time series to determine the first vector representation; processing, the second input data set to generate a second vector representation representative of the values of the plurality of complementary features for the first prediction period, the first component and the second component separately processing the first input data set and the second input data set, respectively; concatenating, the first vector representation and the second vector representation to generate at least one concatenated vector representative of the plurality of complementary features for the observed period and the first prediction period and the changes in demand over time in the target variable for the observed period; and processing, the at least one concatenated vector to generate the first predicted value for the first prediction period; generating, a plurality of further predicted values of the target variable, the generating of the plurality of further predicted values including using the first predicted value as input an updated observed value for the target variable for the first prediction period to generate a second predicted value of the target variable for a second prediction period that follows the first prediction period based on a first updated vector representation, the first updated vector representation representative of the changes in demand over time in the target variable and the plurality of complementary features for the observed period and the first prediction period;”. As recited, the limitations recited by independent claim 1, but for the recitation of additional elements including generic computing components, can be accomplished mentally such as via human observation, evaluation, judgement, opinion or with the help of pen and paper. Claims 15 and 18 recite a method and a non-transitory computer-readable medium with limitations that are substantially similar to those recited by claim 1. Therefore, the same analysis applies.
The dependent claims 4, 5, 7, 9, 10, 11, 17, 20, 21, 22, 23, and 25 further narrow the abstract idea and introduce the following additional elements for consideration. Dependent claims 8, 12, 13, 14, 24 further narrow the abstract idea and do not introduce further additional elements for consideration.
Step 2A, Prong 2: An evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the judicial exception into a practical application of the exception. See MPEP 2106.04(d).
Regarding the computing additional elements from the independent claims, namely memory from claim 1, non-transitory computer-readable medium from claim 18, and one or more processors from claims 1/18, these additional elements have been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent). Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
With respect to the additional elements for via one or more application programming interfaces (APIs), by a multi-input machine learning model, by a first component of the multi-input machine learning model comprising a Recurrent Neural Network (RNN), by the RNN, by a second component of the multi-input machine learning model comprising one or more dense layers, by a third component of the multi-input machine learning model, by a fourth component of the multi-input machine learning model, and generated by the first component of the multi-input machine learning model from independent claims 1/15/18, these additional elements fail to integrate the abstract idea into a practical application because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
With respect to the limitations for crawling a plurality of external data sources for review data, location data, and online trend data via one or more application programming interfaces (APIs) to obtain values of a plurality of complementary features of a food establishment from claims 1/10/18, these limitations fail to integrate the abstract idea into a practical application because they amount to insignificant extra-solution activity (e.g., mere data gathering, insignificant application), which does not integrate the abstract idea into a practical application, as noted in MPEP 2106.05(g).
With respect to the limitations for updating, in at least one database, order data associated with an order system of the food establishment based on output data including the first predicted value and the plurality of further predicted values of the target variable from claims 1/10/18, these limitations fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent). Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
With respect to the additional elements wherein the second component comprises a feedforward network from claim 4, wherein the RNN of the first component comprises one or more long short-term memory (LSTM) layers from claim 5, wherein the multi-input machine learning model is trained on a linked sequence of training data sets, each training data set in the linked sequence of training data sets comprising training data covering a respective training data period from claims 7/17/20, and wherein enriching the first user data comprises invoking an auto-enrichment function of an online data aggregator component from claims 11/23, these additional elements fail to integrate the abstract idea into a practical application because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
With respect to the limitations for retraining the multi-input machine learning model on the updated linked sequence of training data sets from claim 9, retraining the multi-input machine learning model on the modified sequence of training data sets from claim 10, retraining the multi-input machine learning model on the updated linked sequence of training data sets from claim 21, retraining the multi-input machine learning model on the modified sequence of training data sets from claim 22, and retraining the multi-input machine learning model on the updated linked sequence of training data sets from claim 25, these additional elements fail to integrate the abstract idea into a practical application because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Dependent claims 8, 12, 13, 14, 24 recite the same abstract ideas (“Mental Processes”) as the independent claims along with further steps/details falling under the scope of the abstract idea itself, along with the same or substantially same additional elements addressed above.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
Regarding the computing additional elements from the independent claims, namely memory from claim 1, non-transitory computer-readable medium from claim 18, and one or more processors from claims 1/18, these additional elements have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which does not amount to significantly more than the abstract idea itself. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not add significantly more. See MPEP 2106.05(f). Therefore, the computing additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the additional elements for via one or more application programming interfaces (APIs), by a multi-input machine learning model, by a first component of the multi-input machine learning model comprising a Recurrent Neural Network (RNN), by the RNN, by a second component of the multi-input machine learning model comprising one or more dense layers, by a third component of the multi-input machine learning model, by a fourth component of the multi-input machine learning model, and generated by the first component of the multi-input machine learning model from independent claims 1/15/18, these additional elements fail to add significantly more to the abstract idea because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the limitations for crawling a plurality of external data sources for review data, location data, and online trend data via one or more application programming interfaces (APIs) to obtain values of a plurality of complementary features of a food establishment from claims 1/10/18, these additional elements at most amount to insignificant extra-solution activity (e.g., mere data gathering), which does not add significantly more to the abstract idea, as noted in MPEP 2106.05(g). Additionally, the crawling a plurality of external data sources for review data, location data, and online trend data via one or more application programming interfaces (APIs) extra-solution activity have been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
With respect to the limitations for updating, in at least one database, order data associated with an order system of the food establishment based on output data including the first predicted value and the plurality of further predicted values of the target variable from claims 1/10/18, these additional elements fail to add significantly more to the abstract idea because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the additional elements wherein the second component comprises a feedforward network from claim 4, wherein the RNN of the first component comprises one or more long short-term memory (LSTM) layers from claim 5, wherein the multi-input machine learning model is trained on a linked sequence of training data sets, each training data set in the linked sequence of training data sets comprising training data covering a respective training data period from claims 7/17/20, and wherein enriching the first user data comprises invoking an auto-enrichment function of an online data aggregator component from claims 11/23, these additional elements fail to add significantly more to the abstract idea because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the limitations for retraining the multi-input machine learning model on the updated linked sequence of training data sets from claim 9, retraining the multi-input machine learning model on the modified sequence of training data sets from claim 10, retraining the multi-input machine learning model on the updated linked sequence of training data sets from claim 21, retraining the multi-input machine learning model on the modified sequence of training data sets from claim 22, and retraining the multi-input machine learning model on the updated linked sequence of training data sets from claim 25, these additional elements fail to add significantly more to the abstract idea because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Dependent claims 8, 12, 13, 14, 24 recite the same abstract ideas (“Mental Processes”) as the independent claims along with further steps/details falling under the scope of the abstract idea itself, along with the same or substantially same additional elements addressed above, which does not add significantly more to the judicial exception.
Accordingly, claims 1, 4-5, 7-15, 17-18, and 20-25 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-5, 7, 11-15, 18, and 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Mimassi (US 20220198586 A1, hereinafter “Mimassi”), in view of Love et al. (US 20240273558 A1, hereinafter “Love”), in further view of Kivatinos et al. (US 20200034707 A1, hereinafter “Kivatinos”), in further view of O’Donoghue et al. (US 20230119186 A1, hereinafter “O’Donoghue”).
Regarding claims 1/15/18: Mimassi teaches a system ([Abstract] A system and method for image-based personalized food item search, design, and culinary fulfillment), comprising a memory that stores instructions; ([0011] engine comprising a first plurality of programming instructions stored in the memory which… a prediction engine comprising a second plurality of programming instructions stored in the memory; [0114] Memory 25 may be random-access memory having any structure and architecture known in the art, for use by processors 21, for example to run software.), and one or more processors configured by the instructions to perform operations ([0114] Computing device 20 includes processors 21 that may run software that carry out one or more functions or applications of aspects, such as for example a client application 24. Processors 21 may carry out computing instructions under control of an operating system 22 such as, for example, a version of MICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operating systems, some variety of the Linux operating system, ANDROID™ operating system, or the like.), a method ([Abstract] A system and method for image-based personalized food item search, design, and culinary fulfillment), and computer-readable medium ([0113] at least some network device aspects may include non-transitory machine-readable storage media) with limitations comprising:
enriching first user data of the food establishment to obtain a first input data set, ([0055] A culinary fulfilment engine 300 then determines the patron's location by querying the patron's mobile device for location information (e.g., provided by the mobile device's GPS hardware, Wi-Fi location applications, etc.) and gathers information from external resources 180.; Fig. 1: External Resources (nutrition data, health data, rating data, maps, etc.) 180. For example, system may access a publicly-available mapping website such as Google maps.; [0055] The patron may further enter additional food item preferences and a destination or select a pre-entered destination presented from the patron's preferences through patron real-time update engine 211, which will allow the system to better customize its restaurant suggestions. One of ordinary skill in the art would reasonably consider the patron as a first user of the system.);
the first user data comprising a sequence of first data points defining a time series representing at least changes in demand over time for an observed period, each first data point comprising, for a respective time step in the time series, an observed value of a target variable and a value of each of a plurality of establishment input features, the target variable being related to a food item, and the first user data being enriched, for each first data point, using the values of the plurality of complementary features corresponding to the first data point; ([0012] patron profile database comprising a plurality of patron profiles, each patron profile comprising: a patron preference; and a patron review for one or more food item recommendations, each food item recommendation comprising a second list of required ingredients and a second required culinary skill; [0024] FIG. 5 is a flow diagram showing the steps of an exemplary method for an optimized food item recipe generation process based on a particular patron current food preferences, historical culinary transactions, current geographic location, and the restaurant's ingredients on hand and culinary skills.; [0055The patron may further enter additional food item preferences and a destination or select a pre-entered destination presented from the patron's preferences through patron real-time update engine 211; A culinary fulfilment engine 300 then determines the patron's location by querying the patron's mobile device for location information and gathers information from external resources 180 about restaurant options located nearby and along the route from the patron's currently location to the patron's destination, as well as traffic information related to the patron's location, intended route, and identified restaurant options.; Fig. 2: Patron Culinary Transactions 213); [0091] An analysis (as further exemplified in FIG. 5) is performed on patrons historical and real-time food item requirements and compared to menu options and culinary capabilities of restaurants in proximity of patron 404 from which a consumer specific food item is generated 405. Examiner notes that one of ordinary skill in the art would reasonably interpret historical culinary transactions, as disclosed by Mimassi, as equivalent to the time series comprising observed values at different time steps, as disclosed in Applicant’s claim.);
enriching second user data of the food establishment to obtain a second input data set, (Fig. 1: External Resources (nutrition data, health data, rating data, maps, etc.) 180; [0054] restaurants may connect to restaurant portal 140 to enter information about the restaurant and its menu. The system may be able to determine certain restaurant information by accessing external resources 180 such as mapping websites and applications. For example, system may access a publicly-available mapping website such as Google maps, which may contain information about the restaurant's name, location, types of food offered, hours of operation, phone number, etc. Thus, in some aspects, it is not necessary for the restaurant to enter certain information through portal, as the information may be automatically obtained from external resources 180. One of ordinary skill in the art would reasonably consider the restaurant as a second user of the system.);
the second user data comprising a second data point for a first prediction period, the second data point comprising a value of each of the establishment input features, and the second user data being enriched using the values of the plurality of complementary features corresponding to the second data point; ([0054] Likewise, restaurants may connect to restaurant portal 140 to enter information about the restaurant and its menu. Examples of the types of information that a restaurant may enter include, but are not limited to: restaurant name, location, types of food offered, hours of operation, phone number, specific menu offerings, food preparation times for certain dishes (including adjustments to food preparation times during busy periods for the restaurant), prices, calorie counts, ingredients, side dishes, drinks, and special pricing options like daily “happy hour” specials or seasonal offerings. In some aspects, the system may be able to determine certain restaurant information by accessing external resources 180 such as mapping websites and applications.);
generating, by a multi-input machine learning model, a first predicted value of the target variable ([0061] a recipe generator engine 214 receives the patron's current food item requirements from a patron real time update engine 211 along with a patron profile 213. A recipe generation engine 214 obtains restaurant ingredient data 215 and restaurant recipe data 216 for one or more restaurants either from a database 150 or from external resources 180. A recipe generation engine 214 then uses machine learning algorithms to create a personalized food item optimized to meet the patron preferences and outcomes. See also Fig. 2, which depicts multiple inputs being fed into recipe generator engine 214.);
for the first prediction period, ([0057] In some aspects, culinary fulfilment engine 300, through restaurant portal 140, may also provide information to the restaurant to schedule the restaurant's food preparation activities to coordinate with the patron's arrival. One of ordinary skill in the art would reasonably interpret the patron’s arrival time as the prediction period, as supported by Applicant’s own specification, where in [0021] discloses “The prediction period may be a future period for which the target variable is to be predicted, e.g., a next day or a next week.”.);
for the first prediction period, ([0057] In some aspects, culinary fulfilment engine 300, through restaurant portal 140, may also provide information to the restaurant to schedule the restaurant's food preparation activities to coordinate with the patron's arrival. One of ordinary skill in the art would reasonably interpret the patron’s arrival time as the prediction period, as supported by Applicant’s own specification, where in [0021] discloses “The prediction period may be a future period for which the target variable is to be predicted, e.g., a next day or a next week.”.);
the generating of the first predicted value comprising: processing, by a first component of the multi-input machine learning model comprising a Recurrent Neural Network (RNN), the first input data set to generate a first vector representation ([0011] a first machine learning algorithm configured to identify associations among the patron preferences, the first lists of required ingredients, and the first required culinary skills; [0093] Convert aggregate historical patron food item text documents to corresponding word vectors to represent generalized patron food profile 601 ; [0096] An exemplary recipe optimization method may include deep learning techniques familiar to those skilled in the art. One such form of deep learning that is particularly useful when generating text is Recurrent Neural Networks (“RNN”)).);
processing of the first input data set comprising processing, by the RNN, the time series ([0024] FIG. 5 is a flow diagram showing the steps of an exemplary method for an optimized food item recipe generation process based on a particular patron current food preferences, historical culinary transactions, current geographic location, and the restaurant's ingredients on hand and culinary skills.; [0096] The initial input data will cause the model to learn the weights of connections that influence the activity of these gates which will impact the resultant output. To generate unique personalized recipes for a given patron, standard recipes along with the patron profile data are fed into the input gate of the RNN, in turn the RNN will learn what's important to the patron and create unique recipe outputs.);
processing, by a second component of the multi-input machine learning model… …, the second input data set to generate a second vector representation representative of the values of the plurality of complementary features for the first prediction period, the first component and the second component separately processing the first input data set and the second input data set, respectively; ([0092] FIG. 5 is a flow diagram showing the steps of an exemplary method for an optimized food item recommendation to a particular restaurant patron based upon their preferences and patron profile. Convert patron food item text documents to corresponding word vector 501. Convert restaurant recipe, restaurant ingredient data and culinary preparation skill text documents to corresponding word vectors 502. Using a matrix dimension reduction technique such as principal dimension analysis or others known to those skilled in the art, reduce the input matrix for more effective processing. Compare resultant vectors using semantic term vector space techniques known to one in the art 503. Select restaurant word vector that is most similar to the patron food item requirement 504. Modify restaurant recipe items based on restaurant ingredients, culinary capabilities to most closely align to patron's requirements 505. Output food item description and recipe to patron and restaurant 506.; [0093] FIG. 6 is a flow diagram showing the steps of an exemplary method for an optimized food item based on the restaurants' food ingredients on hand, culinary skills and a predicted preference of a patron. Convert aggregate historical patron food item text documents to corresponding word vectors to represent generalized patron food profile 601. Convert restaurant recipe and culinary preparation text documents to corresponding word vectors 602. Compare resultant vectors using term vector space techniques 603. Select restaurant word vector that is most similar to the generalized patron food item requirement 604. Modify restaurant recipe items based on restaurant ingredients, culinary capabilities to most closely align to generic patron's requirements 605. Output food item menu to patron 606.; [0094] An exemplary semantic comparison method may include term vector space analysis technique to those familiar in the art. Term vector modeling is an algebraic model for representing text and text documents as vectors. Each term or word in a text document typically corresponds to a dimension in that vector. Once a text document is described as a word vector, comparisons between two vectors may be made using vector calculus. One useful technique to determine similarities between documents is by comparing the deviation of angles between each document vector and the original query vector where the query is represented as a vector with same dimension as the vectors that represent the other documents.; [0011] second machine learning algorithm configured to recognize and output a target food item);
…by a third component of the multi-input machine learning model, … ([0018] the third machine learning algorithm is used construct a prediction model);
and processing, by a fourth component of the multi-input machine learning model, the at least one concatenated vector to generate the first predicted value for the first prediction period; ([0067] In operation, recommendation engine 314 will take as inputs a personalized recipe information 241, patron location data 312, traffic data 313, restaurant location data 315, restaurant skill data 316, restaurant review data 317. Using semantic vector space methods familiar to those skilled in the art, the input data is represented as word vector and compared using cosine similarity techniques with the optimized target vector to provide as outputs a culinary preparation information 318 that is used by the restaurant and a patron personalized food item 242 that is displayed to the patron.);
generating, by the multi-input machine learning model, a plurality of further predicted values of the target variable, the generating of the plurality of further predicted values including using the first predicted value as input an updated observed value for the target variable for the first prediction period to generate a second predicted value of the target variable for a second prediction period that follows the first prediction period ([0057] culinary fulfilment engine 300, through restaurant portal 140, may also provide information to the restaurant to schedule the restaurant's food preparation activities to coordinate with the patron's arrival. If the restaurant has entered information such as food preparation times, culinary fulfilment engine 300 may use that information to instruct the restaurant's kitchen staff when to start preparation of the patron's order, such that the order will be ready just prior to arrival of the patron.);
based on a first updated vector representation generated by the first component of the multi-input machine learning model, the first updated vector representation representative of the changes in demand over time in the target variable and the plurality of complementary features for the observed period and the first prediction period; ([Fig. 2] Patron Culinary Transactions 213, Patron Review Data 231, Patron Profile 212, Health Data Retriever 221, Cost Data Retriever 223, Wearable Information 233; [Fig. 5] Convert patron related food item text documents to corresponding word vectors 501; [0010] The system may receive as an input a food item image, perform image recognition on the food item image to identify a target food item, use the identified target food item to predict an ingredient list for the target food item, and generate personalized target food item recommendations for patrons based on a multitude of variables associated with the business enterprises, patrons historic culinary transactions, dietary needs and preferences both explicit and inferred. The system may be accessed through web browsers or purpose-built computer and mobile phone applications.);
and updating, in at least one database, order data associated with an order system of the food establishment based on output data including the first predicted value and the plurality of further predicted values of the target variable. ([0056] In an aspect, culinary fulfilment server 300 will contact the restaurant through restaurant portal 140 to automatically enter an order into the restaurant's computer 141.; [0057] Such food preparation times and scheduling may be adjusted for busy periods at the restaurant (typically around lunch and dinner) either automatically based on the restaurant's history as stored in a database 150, or by retrieving information stored in a database 150 that has been manually entered by the restaurant through restaurant portal 140.).
Mimassi doesn’t teach:
crawling a plurality of external data sources for review data, location data, and online trend data via one or more application programming interfaces (APIs) to obtain values of a plurality of complementary features of a food establishment;
representative of the changes in demand over time in the target variable and the plurality of complementary features for the observed period,
by passing hidden state vectors between the respective time steps in the time series to determine the first vector representation;
… comprising one or more dense layers…
concatenating,… …the first vector representation and the second vector representation to generate at least one concatenated vector
representative of the plurality of complementary features for the observed period and the first prediction period and the changes in demand over time in the target variable for the observed period;
Love teaches:
crawling a plurality of external data sources for review data, location data, and online trend data via one or more application programming interfaces (APIs) to obtain values of a plurality of complementary features of a food establishment; ([0056] Additional channel events 175 can be received one or more external data sources 130, which can include third-party websites, databases, and/or servers that provide information relating to the channels 170 and/or individuals located within the channels 170. Exemplary external data sources 130 can include websites, databases, and/or servers associated with cellular device providers, weather outlets, news outlets, social media sites, and/or the like.; [0057] In many embodiments, the channel events 175 can include various types of location data.; [0064] The merchant data 504 can provide information related to merchants (e.g., businesses, vendors, etc.) or establishments located in the channel 170. For example, the merchant data 504 may identify the locations of the merchants, the vertical associated with the merchants, hours of operation, and products or services offered by the merchants. In some embodiments, the merchant data 504 can be received directly from computing devices 110 operated by the merchants and/or an external data source 130, such as a crowd-sourced business review applications, business information databases, etc.);
representative of the changes in demand over time in the target variable and the plurality of complementary features for the observed period, ([0115] The annotation generation stage can be applied to certain training procedures, such as those that use supervised or semi-supervised training techniques. The annotation generation stage can annotate each channel feature vector (or each set of channel features) with a label, which can identify an output (e.g., demand or other prediction) associated with each training feature vector (or each set of channel features). That is, each channel feature vector can include a set of training channel features that represent the conditions for the channel 170 at a given point in time, and the demand indicator label 321 can identify the demand or prediction based on those conditions. In some cases, the demand indicator labels can be appended to, or included in, the feature vectors.; [0116] In the model training stage 725, one or more predictive models 160 can be trained (using the training datasets 316 generated in the previous stage) to predict demand metrics 513 for the channel 170. The predictive models 160 can involve analyzing time-series data to identify periods of increased demand. The predictive models 160 can use and/or combines anomaly detection for real-time identification of anomalous changes in channel-level demand (i.e., users) and/or forecasting to predict future levels of demand.; [0128] The time series forecasting models 831 can include a time series regression model 832 and/or another suitable time series forecasting model, such as GBM, Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Autoregressive Recurrent Neural Networks, or Long Short-Term Memory (LSTM), among others.).
… comprising one or more dense layers… ([Fig. 7A] 160 – Predictive Model(s));
representative of the plurality of complementary features for the observed period and the first prediction period and the changes in demand over time in the target variable for the observed period; ([0112] In the training feature generation stage 715, channel features are extracted and/or derived from the preprocessed historical channel events 702 to generate a training dataset 716 and/or a validation dataset 717. The channel features can correspond to the various types of channel events 175, or other features derived therefrom, such as device geolocations, within-channel device counts, events, surveys, social media, weather conditions, historical data, POIs, user clicks, conversions, etc. Other types of channel features also can be generated. The channel features can be aggregated and split into training dataset 716 and validation dataset 717, to train and test, respectively, the predictive models 160 in the training stage of FIG. 7A. When used in the context of the training procedure of FIG. 7A, the channel features can be referred to as “training channel features” in some portions of this disclosure. Some of the training channel features can be derived by analyzing and transforming the data corresponding to the channel events 175.; [0113] In some embodiments, separate groups or sets of training channel features can be extracted that represent the demand conditions in the channel 170 at a given point in time. For example, a first set of training channel features can correspond to the demand conditions in Channel A at Time A, a second set of training channel features can correspond to the demand conditions in Channel A at Time B, a third set of training channel features can correspond to the demand conditions in Channel A at Time C, etc.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Mimassi with Love’s features listed above. One would’ve been motivated to do so in order to model historical time series and predict future forecasts (Love; [0128]), include additional information or data related to the activities, individuals, entities, and/or conditions of the channel 170 (Love; [0067]), and collectively model or represent the demand conditions (Love; [0114]). By incorporating the teachings of Love, one would’ve been able to crawl external data sources, generate vector representations representative of changes in demand over time, using one or more dense layers, and represent a plurality of features.
Love doesn’t teach:
by passing hidden state vectors between the respective time steps in the time series to determine the first vector representation;
concatenating,… …the first vector representation and the second vector representation to generate at least one concatenated vector
Kivatinos teaches:
concatenating, … …the first vector representation and the second vector representation to generate at least one concatenated vector, ([0035] The encoded fee schedule and encoded set of recent billing claims may be combined, such as by concatenating the two vectors into a single vector.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Mimassi with Kivatinos’ features listed above. One would’ve been motivated to do so in order to use information from both the physician's fee schedule and recent billing claims to make its prediction. Kivatinos; [0035]) By incorporating the teachings of Kivatinos, one would’ve been able to concatenate the 2 vectors in order to produce a recommendation.
Kivatinos doesn’t teach:
by passing hidden state vectors between the respective time steps in the time series to determine the first vector representation;
O’Donoghue teaches:
by passing hidden state vectors between the respective time steps in the time series to determine the first vector representation, ([0323] In some embodiments, the state processing event recurrent neural network machine learning model is configured to process an event encoding data object to generate a hidden state vector for the encoded event data object, then process the hidden state vector in accordance with the parameters (e.g., weights and/or biases) of the state processing recurrent neural network machine learning model to generate a state processing model output for the encoded event data object.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Mimassi with O’Donoghue’s features listed above. One would’ve been motivated to do so in order to generate the state-level attention weight value for the encoded event data object based at least in part on the state processing model output for the encoded event data object (O’Donoghue; [0323]) By incorporating the teachings of O’Donoghue, one would’ve been able to determine the first vector representation by passing hidden state vectors between the time steps.
Regarding claim 4: Modified Mimassi teaches the system of claim 1. Mimassi doesn’t teach:
wherein the second component comprises a feedforward network.
Lee further teaches:
wherein the second component comprises a feedforward network. (Fig. 1: Neural Network 100).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Mimassi with Lee’s additional features listed above. One would’ve been motivated to do so in order to include organizing a structure of the neural network 100 and “training” the neural network 100 (Lee; [0024]). By incorporating the teachings of Lee, one would’ve been able to use a feedforward neural network in the demand forecasting tool.
Regarding claim 5: Modified Mimassi teaches the system of claim 4. Mimassi further teaches:
wherein the RNN of the first component comprises one or more long short-term memory (LSTM) layers ([0096] One such form of deep learning that is particularly useful when generating text is Recurrent Neural Networks (“RNN”) using long short-term memory (“LSTMs”) units or cells. A single LSTM is comprised of a memory-containing cell, an input gate, an output gate and a forget gate. The input and forget gate determine how much of incoming values transit to the output gate and the activation function of the gates is usually a logistic function. The initial input data will cause the model to learn the weights of connections that influence the activity of these gates which will impact the resultant output. To generate unique personalized recipes for a given patron, standard recipes along with the patron profile data are fed into the input gate of the RNN, in turn the RNN will learn what's important to the patron and create unique recipe outputs.);
Regarding claim 7: Modified Mimassi teaches the system of claim 1. Mimassi further teaches:
wherein the multi-input machine learning model is trained on a linked sequence of training data sets, each training data set in the linked sequence of training data sets comprising training data covering a respective training data period. ([0083] According to some embodiments, machine learning engine 1505 may be configured to use any desirable machine learning techniques to learn or train food item models 1507 using the labeled examples. Examples of machine learning techniques that can be used include, but are not limited to, supervised learning based techniques (e.g., artificial neural networks, Bayesian-based techniques, decision trees, etc.), unsupervised learning based techniques (e.g., data clustering, expectation-maximization algorithms, etc.) reinforcement learning based techniques, deep learning based techniques, and the like. One of ordinary skill in the art would reasonably interpret reinforcement learning as an iterative training process to optimize system performance and improve predictions, done with an ordered sequence of data.)
Regarding claims 11/23: Modified Mimassi teaches the system of claim 1 and the method of claim 15. Mimassi further teaches:
wherein enriching the first user data comprises invoking an auto-enrichment function of an online data aggregator component. ([0054] it is not necessary for the restaurant to enter certain information through portal, as the information may be automatically obtained from external resources 180. [0099] Recommendation engine 314 also receives information from a number of sources to assist with producing a specific recipe recommendation, including (but not limited to) patron location data 312, traffic data 313, restaurant location data 315, restaurant skill data 316 (such as the skills of individual chefs that are working at the time), and restaurant review data 317. This aggregated information may then be used to produce a patron-specific personalized food item 242.
Regarding claims 12/24: Modified Mimassi teaches the system of claim 1 and the method of claim 15. Mimassi further teaches:
wherein the target variable is a number of food items sold by the food establishment. (Fig. 3: Recommendation Engine 314, Patron Personalized Food Item 242. [0067] In operation, recommendation engine 314 will take as inputs a personalized recipe information 241, patron location data 312, traffic data 313, restaurant location data 315, restaurant skill data 316, restaurant review data 317. Using semantic vector space methods familiar to those skilled in the art, the input data is represented as word vector and compared using cosine similarity techniques with the optimized target vector to provide as outputs a culinary preparation information 318 that is used by the restaurant and a patron personalized food item 242 that is displayed to the patron.).
Regarding claim 13: Modified Mimassi teaches the system of claim 1. Mimassi further teaches:
wherein the establishment input features comprise one or more of: date; holiday data; weather data; temperature data; humidity data; establishment type; cuisine type; delivery type; parking availability; establishment geographic area; establishment geographic area income level; establishment rating; peak time; food item price; food item category; or promotion data. ([0099] Recommendation engine 314 also receives information from a number of sources to assist with producing a specific recipe recommendation, including (but not limited to) patron location data 312, traffic data 313, restaurant location data 315, restaurant skill data 316 (such as the skills of individual chefs that are working at the time), and restaurant review data 317. This aggregated information may then be used to produce a patron-specific personalized food item 242, along with a set of culinary instructions for preparing the patron-specific item that may be sent as culinary preparation information 318.)
Regarding claim 14: Modified Mimassi teaches the system of claim 1. Mimassi further teaches:
wherein the complementary features comprise one or more of: competitor data; online trend data; web search data; location data; or social media data ([0014] According to an aspect of an embodiment, the patron preference is based on social media information retrieved from a social media network.; [0040] Food blogs and social media accounts dedicated to the food industry have proliferated in the past decade. As is especially the case with social media accounts, oftentimes a picture of a food item (e.g., a meal displayed on an Instagram account) may be posted online with little context such as, for example, the name of the dish, the location where the dish was eaten/purchased, and the ingredients required to make the meal. Therefore, if an individual wishes to eat to the meal they see in a picture they must expend energy to search for that missing context, which can be time and resources intensive.).
Claims 8-9, 17, 20-21, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Mimassi (US 20220198586 A1, hereinafter “Mimassi”), in view of Love et al. (US 20240273558 A1, hereinafter “Love”), in further view of Kivatinos et al. (US 20200034707 A1, hereinafter “Kivatinos”), in further view of O’Donoghue et al. (US 20230119186 A1, hereinafter “O’Donoghue”) as applied to claims 7, and 15 above, in further view of Quigley et al. (US 20230131603 A1, hereinafter “Quigley”).
Regarding claim 8: Modified Mimassi teaches the system of claim 7. Mimassi doesn’t teach:
enriching third user data of the food establishment to obtain an additional training data set, the third user data comprising a sequence of third data points for an additional training data period, each third data point comprising an observed value of the target variable and a value of each of the establishment input features, and the third user data being enriched, for each third data point, using values of the complementary features corresponding to the third data point;
linking the additional training data set to the linked sequence of training data sets to obtain an updated linked sequence of training data sets; and
storing the updated linked sequence of training data sets.
Lee further teaches:
storing the updated linked sequence of training data sets. ([0063] external system 620 may include any number of servers, hosts, systems, and/or databases that store data to be accessed by the system 610. One of ordinary skill in the art would reasonably interpret the sequence of training data sets as data that can be stored to be accessed by the system 610.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Mimassi with Lee’s additional features listed above. One would’ve been motivated to do so in order to store data (Lee; [0063]). By incorporating the teachings of Lee, one would’ve been able to store linked sequence of training data sets.
Lee doesn’t teach:
enriching third user data of the food establishment to obtain an additional training data set, the third user data comprising a sequence of third data points for an additional training data period, each third data point comprising an observed value of the target variable and a value of each of the establishment input features, and the third user data being enriched, for each third data point, using values of the complementary features corresponding to the third data point;
linking the additional training data set to the linked sequence of training data sets to obtain an updated linked sequence of training data sets;
Quigley teaches:
enriching third user data of the food establishment to obtain an additional training data set, the third user data comprising a sequence of third data points for an additional training data period, each third data point comprising an observed value of the target variable and a value of each of the establishment input features, and the third user data being enriched, for each third data point, using values of the complementary features corresponding to the third data point; [0533] Training can be done using training data, which may be collected or generated for training purposes. [0534] In embodiments, the machine learning system 502 trains a model based on training data. In embodiments, the machine learning system 502 may receive vectors containing user data (e.g., transaction history, preferences, wish list virtual assets, and the like), virtual asset data (e.g., price, color, fabric, and the like), and outcomes (e.g., redemption, exchanges, and the like).; [0550] the analytics system 602 may include one or more analytic agents that are configured to execute a set of processes on collected data to produce an analytic result data structure. Once structured, an analytic agent may query the structured data set with a set of queries (e.g., SQL queries) and may further process the results of the queries (e.g., combine, aggregate, perform statistical analyses, and/or the like) to obtain an analytic result data structure.
linking the additional training data set to the linked sequence of training data sets to obtain an updated linked sequence of training data sets; ([1177] Training a machine-learning model may include supervised learning (for example, based on labelled input data), unsupervised learning, and reinforcement learning. One of ordinary skill in the art would reasonably interpret reinforcement learning as an iterative training process to optimize system performance and improve system predictions, performed with an ordered sequence of data updated over time.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Mimassi with Quigley’s features listed above. One would’ve been motivated to do so in order to improve prediction accuracy, reduce storage space, and increase processing speed (Quigley; [1176]). By incorporating the teachings of Quigley, one would’ve been able to use historical data to train the system.
Regarding claim 9: Modified Mimassi teaches the system of claim 8. Mimassi further teaches:
retraining the multi-input machine learning model… ([0083] Examples of machine learning techniques that can be used include, but are not limited to, supervised learning-based techniques (e.g., artificial neural networks, Bayesian-based techniques, decision trees, etc.), unsupervised learning-based techniques (e.g., data clustering, expectation-maximization algorithms, etc.) reinforcement learning based techniques, deep learning-based techniques, and the like. One of ordinary skill in the art would reasonably interpret reinforcement learning as an approach that includes retraining a model to achieve better performance.).
Mimassi doesn’t teach:
…on the updated linked sequence of training data sets.
Quigley further teaches:
…on the updated linked sequence of training data sets. ([1177] Training a machine-learning model may include supervised learning (for example, based on labelled input data), unsupervised learning, and reinforcement learning. One of ordinary skill in the art would reasonably interpret reinforcement learning as an iterative training process to optimize system performance and improve system predictions, performed with an ordered sequence of data updated over time.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Mimassi with Quigley’s additional features listed above. One would’ve been motivated to do so in order to improve prediction accuracy, reduce storage space, and increase processing speed (Quigley; [1176]). By incorporating the teachings of Quigley, one would’ve been able to use updated data to train the system.
Regarding claim 17/20: Modified Mimassi teaches the method of claim 15 and the non-transitory computer-readable medium of claim 18. Mimassi further teaches:
wherein the multi-input machine learning model is trained on a linked sequence of training data sets, each training data set in the linked sequence of training data sets comprising training data covering a respective training data period. ([0083] According to some embodiments, machine learning engine 1505 may be configured to use any desirable machine learning techniques to learn or train food item models 1507 using the labeled examples. Examples of machine learning techniques that can be used include, but are not limited to, supervised learning-based techniques (e.g., artificial neural networks, Bayesian-based techniques, decision trees, etc.), unsupervised learning-based techniques (e.g., data clustering, expectation-maximization algorithms, etc.) reinforcement learning based techniques, deep learning-based techniques, and the like. One of ordinary skill in the art would reasonably interpret reinforcement learning as an iterative training process to optimize system performance and improve predictions, done with an ordered sequence of data.)
Mimassi doesn’t teach:
enriching third user data of the food establishment to obtain an additional training data set, the third user data comprising a sequence of third data points for an additional training data period, each third data point comprising an observed value of the target variable and a value of each of the establishment input features, and the third user data being enriched, for each third data point, using values of the plurality of complementary features corresponding to the third data point;
linking the additional training data set to the linked sequence of training data sets to obtain an updated linked sequence of training data sets;
and storing the updated linked sequence of training data sets.
Lee further teaches:
storing the updated linked sequence of training data sets. ([0063] external system 620 may include any number of servers, hosts, systems, and/or databases that store data to be accessed by the system 610. One of ordinary skill in the art would reasonably interpret the sequence of training data sets as data that can be stored to be accessed by the system 610.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Mimassi with Lee’s additional features listed above. One would’ve been motivated to do so in order to store data (Lee; [0063]). By incorporating the teachings of Lee, one would’ve been able to store linked sequence of training data sets.
Lee doesn’t teach:
enriching third user data of the food establishment to obtain an additional training data set, the third user data comprising a sequence of third data points for an additional training data period, each third data point comprising an observed value of the target variable and a value of each of the establishment input features, and the third user data being enriched, for each third data point, using values of the plurality of complementary features corresponding to the third data point;
linking the additional training data set to the linked sequence of training data sets to obtain an updated linked sequence of training data sets;
Quigley teaches:
enriching third user data of the food establishment to obtain an additional training data set, the third user data comprising a sequence of third data points for an additional training data period, each third data point comprising an observed value of the target variable and a value of each of the establishment input features, and the third user data being enriched, for each third data point, using values of the plurality of complementary features corresponding to the third data point; [0533] Training can be done using training data, which may be collected or generated for training purposes. [0534] In embodiments, the machine learning system 502 trains a model based on training data. In embodiments, the machine learning system 502 may receive vectors containing user data (e.g., transaction history, preferences, wish list virtual assets, and the like), virtual asset data (e.g., price, color, fabric, and the like), and outcomes (e.g., redemption, exchanges, and the like).; [0550] the analytics system 602 may include one or more analytic agents that are configured to execute a set of processes on collected data to produce an analytic result data structure. Once structured, an analytic agent may query the structured data set with a set of queries (e.g., SQL queries) and may further process the results of the queries (e.g., combine, aggregate, perform statistical analyses, and/or the like) to obtain an analytic result data structure.
linking the additional training data set to the linked sequence of training data sets to obtain an updated linked sequence of training data sets; ([1177] Training a machine-learning model may include supervised learning (for example, based on labelled input data), unsupervised learning, and reinforcement learning. One of ordinary skill in the art would reasonably interpret reinforcement learning as an iterative training process to optimize system performance and improve system predictions, performed with an ordered sequence of data updated over time.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Mimassi with Quigley’s additional features listed above. One would’ve been motivated to do so in order to improve prediction accuracy, reduce storage space, and increase processing speed (Quigley; [1176]). By incorporating the teachings of Quigley, one would’ve been able to use historical data to train the system.
Regarding claims 21/25: Modified Mimassi teaches the method of clam 17 and the non-transitory computer-readable medium of claim 20. Mimassi further teaches:
retraining the multi-input machine learning model… ([0083] Examples of machine learning techniques that can be used include, but are not limited to, supervised learning-based techniques (e.g., artificial neural networks, Bayesian-based techniques, decision trees, etc.), unsupervised learning-based techniques (e.g., data clustering, expectation-maximization algorithms, etc.) reinforcement learning based techniques, deep learning-based techniques, and the like. One of ordinary skill in the art would reasonably interpret reinforcement learning as an approach that includes retraining a model to achieve better performance.).
Mimassi doesn’t teach:
…on the updated linked sequence of training data sets.
Quigley further teaches:
…on the updated linked sequence of training data sets. ([1177] Training a machine-learning model may include supervised learning (for example, based on labelled input data), unsupervised learning, and reinforcement learning. One of ordinary skill in the art would reasonably interpret reinforcement learning as an iterative training process to optimize system performance and improve system predictions, performed with an ordered sequence of data updated over time.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Mimassi with Quigley’s additional features listed above. One would’ve been motivated to do so in order to improve prediction accuracy, reduce storage space, and increase processing speed (Quigley; [1176]). By incorporating the teachings of Quigley, one would’ve been able to use updated data to train the system.
Claims 10 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Mimassi (US 20220198586 A1, hereinafter “Mimassi”), in view of Love et al. (US 20240273558 A1, hereinafter “Love”), in further view of Kivatinos et al. (US 20200034707 A1, hereinafter “Kivatinos”), in further view of O’Donoghue et al. (US 20230119186 A1, hereinafter “O’Donoghue”) as applied to claims 8 and 21 above, in further view of Skeirik (US 5224203 A, hereinafter “Skeirik”).
Regarding claim 10/22: Modified Mimassi teaches the system of claim 8 and the method of claim 21. Mimassi further teaches:
retraining the multi-input machine learning model… ([0083] Examples of machine learning techniques that can be used include, but are not limited to, supervised learning-based techniques (e.g., artificial neural networks, Bayesian-based techniques, decision trees, etc.), unsupervised learning-based techniques (e.g., data clustering, expectation-maximization algorithms, etc.) reinforcement learning based techniques, deep learning-based techniques, and the like. One of ordinary skill in the art would reasonably interpret reinforcement learning as an approach that includes retraining a model to achieve better performance.).
Mimassi doesn’t teach:
wherein each training data set is identified by a training data identifier, the operations further comprising: receiving a user selection of a training data identifier;
generating a modified sequence of training data sets that commences at the training data set corresponding to the user selection and ends at the additional training data set; and
… on the modified sequence of training data sets.
Quigley teaches:
…on the modified sequence of training data sets. ([1177] Training a machine-learning model may include supervised learning (for example, based on labelled input data), unsupervised learning, and reinforcement learning. One of ordinary skill in the art would reasonably interpret reinforcement learning as an iterative training process to optimize system performance and improve system predictions, performed with an ordered sequence of data updated over time.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Mimassi with Quigley’s additional features listed above. One would’ve been motivated to do so in order to improve prediction accuracy, reduce storage space, and increase processing speed (Quigley; [1176]). By incorporating the teachings of Quigley, one would’ve been able to use updated data to train the system.
Quigley doesn’t teach:
wherein each training data set is identified by a training data identifier, the operations further comprising: receiving a user selection of a training data identifier;
generating a modified sequence of training data sets that commences at the training data set corresponding to the user selection and ends at the additional training data set;
Skeirik teaches:
wherein each training data set is identified by a training data identifier, (Fig. 2: Steps 202 and 206 - Store Input Data with Associated Timestamps in Historical Database and Store Training Input Data with Associated Timestamps in Historical Database);
the operations further comprising: receiving a user selection of a training data identifier; (Fig. 4: Step 404 – Retrieve Input Data at Current Time from Historical Database.);
generating a modified sequence of training data sets that commences at the training data set corresponding to the user selection and ends at the additional training data set; ([Page 49, Column 25, Lines 33-37] The sequence of steps described above is the preferred embodiment used when the neural network 1206 can be effectively trained using a single presentation of the training set created for each new training input data 1306. [Page 49, Column 25, Lines 44-50] the neural network 1206 can save the training sets (that is, the training input data and the associated input data which is retrieved in step and module 308) in a database of training sets, which can then be repeatedly presented to the neural network 1206 to train the neural network. The user might be able to configure the number of training sets to be saved.)
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Mimassi with Skeirik’s features listed above. One would’ve been motivated to do so, so that as new training data becomes available, new training sets are constructed and saved (Skeirik; [Page 49, Column 25, Lines 50-52]). By incorporating the teachings of Skeirik, one would’ve been able to use data identifiers for training data sets.
Accordingly, claims 1, 4-5, 7-15, 17-18, and 20-25 are rejected under 35 U.S.C. 103.
Conclusion
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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/G.J.T./Examiner, Art Unit 3625
/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
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