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 .
Response to Arguments
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “Claims 9-20 stand rejected under 35 U.S.C. §101 as allegedly being directed to non-statutory subject matter. Applicant believes that the clarifying amendments herein render this ground for rejection moot., as they did in the prosecution of related U.S. Patent Application No. 18/108603, issued as U.S. Patent No. 11,954,745 on April 9, 2024. In particular, for example, the clarifying amendments elaborate upon the nature of the training of the AI model that necessarily resides in and improves an electronic computing environment.,” Examiner respectfully disagrees with Applicant’s assertion. Examiner respectfully asserts that each application is examined on its own merits for compliance with pertinent statutory requirements. Even assuming arguendo, the present claims do not positively recite “training” the omnichannel AI restaurant management model. The present claims recite utilizing a “trained” model to perform abstract steps of the claim including predicting. The limitations reciting using a trained model 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. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other 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 process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Accordingly, the present claims are rejected under 35 USC 101.
Regarding the 35 USC 102 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “The Office Action relies on Yim for teaching a transmitting of the predicted fulfillment time to "a restaurant device". See, Office Action, pg. 15, first paragraph. Claims 15 and 20, as amended herein for clarification, require that the predicted fulfillment time be transmitted to "an ordering device."” Applicant’s arguments with respect to the previous prior art combination of the record have been considered but are moot because the new grounds of rejection does not rely on any reference applied in the prior art rejection for any teachings or matter specifically challenged in the argument. The claims are rejected under a new grounds of rejection, which was necessitated by amendment. Examiner has introduced the Neumann reference to cure the deficiencies of the prior art combination of the record.
Regarding Applicant’s assertion of “There is no evidence of record that Yim teaches such a limitation. Applicant further respectfully notes with respect to claim 20, that there is no evidence of record that Yim teaches "adjusting...an ordering channel setting" in response to the computation of the predicted fulfillment time.,” Applicant’s arguments with respect to the previous prior art combination of the record have been considered but are moot because the new grounds of rejection does not rely on any reference applied in the prior art rejection for any teachings or matter specifically challenged in the argument. The claims are rejected under a new grounds of rejection, which was necessitated by amendment. Examiner has introduced the Yang reference to cure the deficiencies of the prior art combination of the record.
Accordingly, the 35 USC 102 rejection has been withdrawn; however, the claims remain rejected under 35 USC 103. See the detailed rejection below.
Regarding the 35 USC 103 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “Applicant respectfully reiterates that Neumann and Koennings, alone or in combination, fail to teach or suggest limitations of claims 9 and 12-14 (before and after amendment herein). For example, Neumann and Koennings fail to teach or suggest computing...a matrix of restaurant operational parameter values that would result in the at least one of the predicted fulfillment time meeting the predetermined threshold.,” Examiner respectfully disagrees. As can be seen in cited paragraph [0027] of Neumann, the reference discloses generating a “feasibility quantifier” that includes a vector or matrix that describes the qualitative and quantitative mathematical probability of completing a task, given a set of constraints and the task’s relationship in time to and ordering with other tasks, within a particular timeframe. Paragraph [0027] further states that each task has an associated probability in relating to the preparing for a customer within a certain period of time, such as a 15-minute, 30-minute, or another timeframe, wherein the timeframe may be determined by an identification of a meal, meal preparation time, expected delivery time, task chain, and the like. Therefore, Examiner respectfully disagrees with Applicant’s assertion in view of Neumann.
Regarding Applicant’s assertion of “b) No Motivation or Reason to Combine Even if, for the sake of argument, the cited references taught every limitation of claims 9 and 12-14, the claims are allowable unless the Office provides evidence that it would have been obvious to combine the cited references, as suggested in the Office Action. In this case, the Office merely cites to a goal accomplished by Koennings alone (Office Action, pg. 22, first paragraph) to allegedly support combining Koennings with Neumann. It is unclear how Koennings, already achieving the stated goal alone, would have triggered any motivation or reason for someone of ordinary skill to look outside of Koennings, much less to have a reason to combine Koennings specifically with Neumann.,” Examiner respectfully disagrees with Applicant’s assertion. MPEP 2141.01(a)(I) discloses
“A reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention). Note that "same field of endeavor" and "reasonably pertinent" are two separate tests for establishing analogous art; it is not necessary for a reference to fulfill both tests in order to qualify as analogous art.”
Examiner respectfully asserts that the Neumann and Koennings references are both related to managing cooking under dynamic conditions. For example, Neumann discloses, in a least paragraphs [0020-0021], that the computing device can determine changes in task chains related to the sequential preparation of a meal. Similarly, Koennings discloses in at least paragraphs [0002-0003], that the system can operate the cooking apparatus under changing operating conditions with reproducible results including various cooking functions related to heating, mixing, boiling etc.
Even assuming arguendo, Examiner respectfully asserts that the Non-Final Office Action dated 01/15/2026 provides a motivation to combine Neumann with the teachings of Koennings. Examiner maintains that it would have been obvious to a person having ordinary skill in the art to modify the teachings of Neumann to incorporate the teachings of Koennings. One would have been motivated to do so in order to automatically adjust the control parameter of the kitchen appliance based on instructional triggers (Koennings, [0014]). By incorporating the teachings of Koennings, one would have been able to provide a multi-function cooking apparatus that adjusts recipe program instruction based on the difference between the predicted and expected value exceeding a predefined threshold (Koennings, [0023]).
Accordingly, the present claims are rejected under 35 USC 103.
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 9-20 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 9-14 are directed to a system and claims 15-20 are directed to a method. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Independent claims 9 and 15 are related to identifying predicted fulfilment times for orders, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to commercial interactions including advertising or marketing sales activities or behaviors, as well as business relations. Independent claim 9 recites limitations including “identifying a plurality of current orders at a restaurant, the plurality of current orders comprising orders from a plurality of ordering channels, the plurality of ordering channels comprising at least an in-store ordering channel and a delivery service ordering channel; computing, a predicted fulfillment time for each order of the plurality of current orders at the restaurant; identifying, by an execution of the restaurant management rules, that at least one of the predicted fulfillment times exceeds a predetermined threshold; computing, a matrix of restaurant operational parameter values that would result in the at least one of the predicted fulfillment time meeting the predetermined threshold; identifying a restaurant device associated with at least one of the parameters from the matrix of restaurant operational parameter values.” Independent claim 15 recites limitations including “identifying a plurality of current orders at a restaurant, the plurality of current orders comprising orders from a plurality of ordering channels, the plurality of ordering channels comprising at least an in-store ordering channel and a delivery service ordering channel; computing, a predicted fulfillment time for each order of the plurality of current orders at the restaurant, wherein the predicting comprises: (i) identifying at least one semantic component from each order; (ii) grouping together orders that have a semantic similarity, thereby defining a semantic order cluster; (iii) identifying a number of items in each order; and (iv) computing, based on a comparison of the semantic order cluster and the number of items in each order to model data, the predicted fulfillment time.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of the preamble, covers an abstract idea but for the recitation of generic computer components. That is, other than the preamble language, nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the preamble language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Similarly, independent claims 9 and 15 are related to identifying predicted fulfilment times for orders, constituting an abstract idea based on “Mental Processes” related to concepts performed in the human mind including observation, evaluation, judgment, and opinion. Independent claim 9 recites limitations including “identifying a plurality of current orders at a restaurant, the plurality of current orders comprising orders from a plurality of ordering channels, the plurality of ordering channels comprising at least an in-store ordering channel and a delivery service ordering channel; computing, a predicted fulfillment time for each order of the plurality of current orders at the restaurant; identifying, by an execution of the restaurant management rules, that at least one of the predicted fulfillment times exceeds a predetermined threshold; computing, a matrix of restaurant operational parameter values that would result in the at least one of the predicted fulfillment time meeting the predetermined threshold; identifying a restaurant device associated with at least one of the parameters from the matrix of restaurant operational parameter values.” Independent claim 15 recites limitations including “identifying a plurality of current orders at a restaurant, the plurality of current orders comprising orders from a plurality of ordering channels, the plurality of ordering channels comprising at least an in-store ordering channel and a delivery service ordering channel; computing, a predicted fulfillment time for each order of the plurality of current orders at the restaurant, wherein the predicting comprises: (i) identifying at least one semantic component from each order; (ii) grouping together orders that have a semantic similarity, thereby defining a semantic order cluster; (iii) identifying a number of items in each order; and (iv) computing, based on a comparison of the semantic order cluster and the number of items in each order to model data, the predicted fulfillment time.” These limitations, as drafted, but for the recitation of the preamble, is a process that covers performance of the limitations in the mind but for the recitation of generic computer components. That is, but for the preamble language, nothing in the claim elements preclude the steps from practically being performed in the human mind. For example, with the exception of the preamble language, the claim steps in the context of the claim encompass a user mentally or manually performing the steps of the claim.
Dependent claims 16-19 further narrow the abstract idea and do not introduce further additional elements for consideration.
Dependent claims 10-14 and 20 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 9 and 15 do not integrate the judicial exception into a practical application. Independent claim 9 is directed to a system comprising “an electronic processing device; and a non-transitory computer-readable memory storing (i) an omnichannel Al restaurant management model, (ii) restaurant management rules, and (ii) instructions that when executed by the electronic processing device result in.” Independent claim 15 is directed to a method. Claim 9 recites the additional elements of “an omnichannel Al restaurant management model trained with a data set of previous omnichannel ordering data that includes both internal and external data sources,” “computing, by a first execution of the omnichannel Al restaurant management model, a predicted fulfillment time for each order of the plurality of current orders at the restaurant” and “computing, by a second execution of the omnichannel Al restaurant management model, a matrix of restaurant operational parameter values that would result in the at least one of the predicted fulfillment time meeting the predetermined threshold.” Similarly, claim 15 recites the additional element of “computing, by an execution of an omnichannel AI restaurant management model trained with a data set of previous omnichannel ordering data that includes both internal and external data sources, a predicted fulfillment time for each order of the plurality of current orders at the restaurant.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. The limitations reciting using a trained model 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. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other 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 process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Claim 9 recites the additional element of “and transmitting a signal to the identified restaurant device that causes the restaurant device to change a current value of the at least one of the parameters from the matrix of restaurant operational parameter values to the value from the matrix of restaurant operational parameter values.” Similarly, claim 15 recites the additional element of “and transmitting, to an ordering device, and for at least one of the plurality of current orders, an indication of the predicted fulfillment time.” This limitation, as drafted, is nothing more than post-solution activity. This type of extra-solution activity is not sufficient to prove integration into a practical application. See MPEP 2106.05(g).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 16-19 further narrow the abstract idea and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 10 introduces the additional element of “wherein the identified restaurant device comprises a POS device.” Dependent claim 11 introduces the additional element of “wherein the identified restaurant device comprises a staffing device.” Dependent claim 12 introduces the additional element of “wherein the identified restaurant device comprises a kitchen device.” Dependent claim 14 introduces the additional element of “wherein the identified restaurant device comprises an inventory device.” Dependent claim 20 introduces the additional element of “further comprising adjusting, in response to the computing of at least one of the predicted fulfillment times, an ordering channel setting.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other 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 process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 13 introduces the additional element of “wherein the kitchen device comprises one or more of an oven, a grill, a cooktop, a warmer, a conveyer, a fryer, a refrigerator, and a freezer.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 9 and 15 do not comprise anything significantly more than the judicial exception. Independent claim 9 is directed to a system comprising “an electronic processing device; and a non-transitory computer-readable memory storing (i) an omnichannel Al restaurant management model, (ii) restaurant management rules, and (ii) instructions that when executed by the electronic processing device result in.” Independent claim 15 is directed to a method. Claim 9 recites the additional elements of “an omnichannel Al restaurant management model trained with a data set of previous omnichannel ordering data that includes both internal and external data sources,” “computing, by a first execution of the omnichannel Al restaurant management model, a predicted fulfillment time for each order of the plurality of current orders at the restaurant” and “computing, by a second execution of the omnichannel Al restaurant management model, a matrix of restaurant operational parameter values that would result in the at least one of the predicted fulfillment time meeting the predetermined threshold.” Similarly, claim 15 recites the additional element of “computing, by an execution of an omnichannel AI restaurant management model trained with a data set of previous omnichannel ordering data that includes both internal and external data sources, a predicted fulfillment time for each order of the plurality of current orders at the restaurant.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other 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 process or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Claim 9 recites the additional element of “and transmitting a signal to the identified restaurant device that causes the restaurant device to change a current value of the at least one of the parameters from the matrix of restaurant operational parameter values to the value from the matrix of restaurant operational parameter values.” Similarly, claim 15 recites the additional element of “and transmitting, to an ordering device, and for at least one of the plurality of current orders, an indication of the predicted fulfillment time.” This limitation, as drafted, is nothing more than post-solution activity. Section 2106.05(d)(II) of the MPEP states that “receiving and transmitting data over a network,” and specifically “sending messages over a network,” is a well-understood, routine, and conventional computer function. Therefore, these limitations are not anything significantly more than the judicial exception.
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 16-19 further narrow the abstract idea and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claim 10 introduces the additional element of “wherein the identified restaurant device comprises a POS device.” Dependent claim 11 introduces the additional element of “wherein the identified restaurant device comprises a staffing device.” Dependent claim 12 introduces the additional element of “wherein the identified restaurant device comprises a kitchen device.” Dependent claim 14 introduces the additional element of “wherein the identified restaurant device comprises an inventory device.” Dependent claim 20 introduces the additional element of “further comprising adjusting, in response to the computing of at least one of the predicted fulfillment times, an ordering channel setting.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other 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 process or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 13 introduces the additional element of “wherein the kitchen device comprises one or more of an oven, a grill, a cooktop, a warmer, a conveyer, a fryer, a refrigerator, and a freezer.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 9-20 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 9 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 20220004955 A1) in view of Koennings et al. (US 20170224148 A1).
Regarding claim 9, Neumann teaches an omnichannel Artificial Intelligence (AI) restaurant management system (Fig. 1 and [0063-0065] teach a computing system, wherein [0038-0039] teach the machine learning models may be generated using artificial intelligence methods), comprising:
an electronic processing device (Fig. 1 and [0063-0065] teach a computing system, wherein [0038-0039] teach the machine learning models may be generated using artificial intelligence methods);
a non-transitory computer-readable memory storing (i) an omnichannel Al restaurant management model trained with a data set of previous omnichannel ordering data that includes both internal and external data sources (Fig. 1 and [0063-0065] teach a computing system comprising a memory that includes one or more machine-readable media with instructions that embody the methodologies, wherein [0038-0039] teach the machine learning models may be generated using artificial intelligence methods, wherein [0028-0029] teach the training data includes a plurality of data entries from previous iterations of the machine-learning process, database, user inputs, and expert inputs, wherein [0020] teaches database information including a history of ingredients for each meal, ordering data, timing data, and more, as well as Fig. 1 and [0017] teach the computing device receives information from an online repository regarding identification of meals to be prepared; see also: [0018-0019]),
(ii) restaurant management rules (Fig. 1 and [0063-0065] teach a computing system comprising a memory that includes one or more machine-readable media with instructions that embody the methodologies, wherein [0021] teaches generating a chronological sequential ordering of tasks based on time constraints of a restaurant, as well as in [0026] teaches the computing device can identify a plurality of constraints as a function of identification of meals, which includes at least a resource constraint and a timing constraint, wherein a constraint is a barrier, limitation, consideration, or any other constraint pertaining to resource utilization during optimizing the combination of a plurality of task chains that may arise during meal preparation, wherein the constraints may be stored and retrieved from a database for orders at a restaurant; see also: [0038-0039]), and
(ii) instructions that when executed by the electronic processing device result in (Fig. 1 and [0063-0065] teach a computing system comprising a memory that includes one or more machine-readable media with instructions that embody the methodologies, wherein [0038-0039] teach the machine learning models may be generated using artificial intelligence methods):
identifYimg a plurality of current orders at a restaurant (Fig. 5 and [0054] teach a computing device is configured to receive a plurality of identifications of meals to be prepared, wherein a plurality of identifications of meals may include computing device that retrieves an ingredients list, as well as in [0021-0022] teach preparing a plurality of meal orders for a restaurant; see also: [0026-0027, 0049]),
the plurality of current orders comprising orders from a plurality of ordering channels ([0026] teaches the received orders can be received via orders input by a restaurant wait staff, logged by a web based application, a mobile application, or other meal ordering service, application, device, or the like, wherein [0022] teaches the ordered tasks are received via courier services via application services, wherein Fig. 5 and [0054] teach a computing device is configured to receive a plurality of identifications of meals to be prepared, wherein a plurality of identifications of meals may include computing device that retrieves an ingredients list, as well as in [0022] teaches preparing a plurality of meal orders; see also: [0017, 0026-0027, 0049]);
the plurality of ordering channels comprising at least an in-store ordering channel and a delivery service ordering channel ([0026] teaches the received orders can be received via orders input by a restaurant wait staff, logged by a web based application, a mobile application, or other meal ordering service, application, device, or the like, wherein [0022] teaches the ordered tasks are received via courier services via application services, wherein Fig. 5 and [0054] teach a computing device is configured to receive a plurality of identifications of meals to be prepared, wherein a plurality of identifications of meals may include computing device that retrieves an ingredients list, as well as in [0022] teaches preparing a plurality of meal orders; see also: [0017, 0026-0027, 0049]);
computing, by a first execution of the omnichannel Al restaurant management model, a predicted fulfillment time for each order of the plurality of current orders at the restaurant ([0050] teaches objectives may include minimization of meal preparation times in excess of estimated or requested arrival times, wherein [0051] teaches the computing device may determine a solution set including the plurality of candidate task chain combinations, wherein optimizing the objective function includes iteratively calculating the difference between expected retrieval time and expected time of completion, wherein the expected time of completion is for when meal is ready for retrieval, wherein the objective function is optimized on minimizing time differences to measure the efficiency of task chains, wherein [0052] teaches generating an output of scoring candidate task chain combinations according to the goal criterion by scoring with regard to difference between expected retrieval time and expected time of completion using an objective function, wherein the objective function may result in re-ranking task chains based on calculating the expected time of completion, wherein the objective function may be optimized to prioritize task chains that will increase the difference between expected retrieval time and expected time of completion, wherein [0053] teaches the objective function related to the expected time of completion is for a task chain that corresponds to the preparation of a plurality of meals at a restaurant, wherein [0027] teaches generating a plurality of candidate task chain combinations by receiving feasibility data that includes a plurality of task combinations with feasibility quantifiers, wherein the feasibility quantifiers are a vector, matrix, numerical value, or the like, which describes a qualitative and/or quantitative mathematical relationship correlating the probability of completing a task, given a set of constraints and the task’s relationship to time to and ordering to other tasks, within a particular timeframe, wherein a timeframe may be determined by an identification of a meal, meal preparation time, expected delivery time, task chain, and the like; see also: [0056]);
identifYimg, by an execution of the restaurant management rules, that at least one of the predicted fulfillment times exceeds a predetermined threshold (Fig. 5 and [0057] teach identifYimg a plurality of constraints as a function of the plurality of identifications of meals based on resource and timing constraints, wherein [0050] teaches objectives may include minimization of meal preparation times in excess of estimated or requested arrival times, wherein the objective function includes a minimization of meal preparation times as a function of constraints, wherein [0021] teaches generating a chronological sequential ordering of tasks based on time constraints of a restaurant, as well as in [0026] teaches the computing device can identify a plurality of constraints as a function of identification of meals, which includes at least a resource constraint and a timing constraint, wherein a constraint is a barrier, limitation, consideration, or any other constraint pertaining to resource utilization during optimizing the combination of a plurality of task chains that may arise during meal preparation, wherein the constraints may be stored and retrieved from a database for orders at a restaurant; see also: [0051-0054, 0058]);
computing, by a second execution of the omnichannel Al restaurant management model, a matrix of restaurant operational parameter values that would result in the at least one of the predicted fulfillment time meeting the predetermined threshold ([0050] teaches objectives may include minimization of meal preparation times in excess of estimated or requested arrival times, wherein the objective function includes a minimization of meal preparation times as a function of constraints, wherein [0051] teaches the computing device may determine a solution set including the plurality of candidate task chain combinations, wherein optimizing the objective function includes iteratively calculating the difference between expected retrieval time and expected time of completion, wherein the expected time of completion is for when meal is ready for retrieval, wherein the objective function is optimized on minimizing time differences to measure the efficiency of task chains, wherein [0052] teaches generating an output of scoring candidate task chain combinations according to the goal criterion by scoring with regard to difference between expected retrieval time and expected time of completion using an objective function, wherein the objective function may result in re-ranking task chains based on calculating the expected time of completion, wherein the objective function may be optimized to prioritize task chains that will increase the difference between expected retrieval time and expected time of completion, wherein [0053] teaches the objective function related to the expected time of completion is for a task chain that corresponds to the preparation of a plurality of meals at a restaurant, wherein [0027] teaches generating a plurality of candidate task chain combinations by receiving feasibility data that includes a plurality of task combinations with feasibility quantifiers, wherein the feasibility quantifiers are a vector, matrix, numerical value, or the like, which describes a qualitative and/or quantitative mathematical relationship correlating the probability of completing a task, given a set of constraints and the task’s relationship to time to and ordering to other tasks, within a particular timeframe, wherein a timeframe may be determined by an identification of a meal, meal preparation time, expected delivery time, task chain, and the like; see also: [0056]);
identifying a restaurant device associated with at least one of the parameters from the matrix of restaurant operational parameter values ([0024] teaches the concurrently performed tasks of the task chains involve a combination of tasks performed at an oven including heating an oven, wherein the temperature can be optimized by the machine learning model and the objective function to batch cooking steps together, wherein [0053] teaches the objective function related to the expected time of completion is for a task chain that corresponds to the preparation of a plurality of meals at a restaurant, wherein [0027] teaches generating a plurality of candidate task chain combinations by receiving feasibility data that includes a plurality of task combinations with feasibility quantifiers, wherein the feasibility quantifiers are a vector, matrix, numerical value, or the like, which describes a qualitative and/or quantitative mathematical relationship correlating the probability of completing a task, given a set of constraints and the task’s relationship to time to and ordering to other tasks, within a particular timeframe, wherein a timeframe may be determined by an identification of a meal, meal preparation time, expected delivery time, task chain, and the like; see also: [0050-0052, 0056]).
However, Neumann does not explicitly teach and transmitting a signal to the identified restaurant device that causes the restaurant device to change a current value of the at least one of the parameters from the matrix of restaurant operational parameter values to the value from the matrix of restaurant operational parameter values.
From the same or similar field of endeavor, Koennings teaches and transmitting a signal to the identified restaurant device that causes the restaurant device to change a current value of the at least one of the parameters from the matrix of restaurant operational parameter values to the value from the matrix of restaurant operational parameter values ([0023] teaches comparing the prediction value with a termination time value expected parameters of the external instruction if the difference between the prediction value and the expected value exceeds a predefined threshold value, wherein the control system where the recipe program adjustment component is configured to adjust one or more internal instructions of the not-yet-executed program instructions and adjust one or more external instructions, wherein [0041] teaches determining the difference between a prediction value and the expected value exceeds a predefined threshold value, wherein [0050] teaches a plurality of data packets including a vector of temperature values with each temperature value corresponding to the temperature of the respective sensor at a given location at a given point in time, wherein [0055] teaches the re-synchronizing a multi-function cooking apparatus to generate appropriate recipe adjustments for complex scenarios like multiple course food products with many food components which are prepared in parallel on a cooking apparatus by using a plurality of kitchen appliances, wherein the heating and cooling functions can be adapted to the level of complexity by using corresponding pre-defined control parameter settings and recipe program adjustment patterns in the databases, and wherein [0014] teaches the control parameters are directly communicated to the remote kitchen appliance and automatically adjust the control parameter setting accordingly; see also: [0016, 0049]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Neumann to incorporate the teachings of Koennings to include and transmitting a signal to the identified restaurant device that causes the restaurant device to change a current value of the at least one of the parameters from the matrix of restaurant operational parameter values to the value from the matrix of restaurant operational parameter values. One would have been motivated to do so in order to automatically adjust the control parameter of the kitchen appliance based on instructional triggers (Koennings, [0014]). By incorporating the teachings of Koennings, one would have been able to provide a multi-function cooking apparatus that adjusts recipe program instruction based on the difference between the predicted and expected value exceeding a predefined threshold (Koennings, [0023]).
Regarding claim 12, the combination of Neumann and Koennings teaches all the limitations of claim 9 above.
Neumann further teaches wherein the identified restaurant device comprises a kitchen device ([0024] teaches the concurrently performed tasks of the task chains involve a combination of tasks performed at an oven including heating an oven, wherein the temperature can be optimized by the machine learning model and the objective function to batch cooking steps together, wherein [0053] teaches the objective function related to the expected time of completion is for a task chain that corresponds to the preparation of a plurality of meals at a restaurant; see also: [0027]).
Regarding claim 13, the combination of Neumann and Koennings teaches all the limitations of claim 12 above.
Neumann further teaches wherein the kitchen device comprises one or more of an oven ([0024] teaches the concurrently performed tasks of the task chains involve a combination of tasks performed at an oven including heating an oven, wherein the temperature can be optimized by the machine learning model and the objective function to batch cooking steps together, wherein [0053] teaches the objective function related to the expected time of completion is for a task chain that corresponds to the preparation of a plurality of meals at a restaurant; see also: [0027]).
Regarding claim 14, the combination of Neumann and Koennings teaches all the limitations of claim 9 above.
Neumann further teaches wherein the identified restaurant device comprises an inventory device ([0020] teaches a computing device comprising a plurality of tables of a meal database that includes ingredient lists, presence of ingredients in preparing a meal, location of the ingredients, and more, as well as in [0022] teaches a computing device comprising a resource list with a list of ingredient identities, amounts, expirations, and more; see also: [0021, 0040]).
Claim(s) 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 20220004955 A1) in view of Koennings et al. (US 20170224148 A1) in view of Jagolta et al. (US 20220351167 A1).
Regarding claim 10, the combination of Neumann and Koennings teaches all the limitations of claim 9 above.
However, Neumann does not explicitly teach wherein the identified restaurant device comprises a Point-Of-Sale (POS) device.
From the same or similar field of endeavor, Jagolta teaches wherein the identified restaurant device comprises a Point-Of-Sale (POS) device ([0040] teaches the mobile POS terminals and kitchen fulfilment terminals in each of the restaurants communicate with the backend server through their gateways to perform the functions of displaYimg of menus, ordering of menu items, routing of ordered items to the kitchen staff, sequencing of ordering items through the kitchen, and more, thus providing efficient operation of the restaurant, wherein [0045] teaches the backend server directs the terminals to perform routing of items to kitchen staff, sequencing of ordering items through the kitchens, and more, wherein the terminals can provide messaging to execute the functions; see also: [0048]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Neumann and Koennings to incorporate the teachings of Jagolta to include wherein the identified restaurant device comprises a Point-Of-Sale (POS) device. One would have been motivated to do so in order to provide efficient operation of the restaurant by sequencing the ordering of items through the kitchen and routing of ordered items to the kitchen staff for preparation (Jagolta, [0040]). By incorporating the teachings of Jagolta, one would have been able to frequently update the preparation time indicator through the use of digital menus available on the restaurant terminals (Jagolta, [0048]).
Regarding claim 11, the combination of Neumann and Koennings teaches all the limitations of claim 9 above.
However, Neumann does not explicitly teach wherein the identified restaurant device comprises a staffing device.
From the same or similar field of endeavor, Jagolta teaches wherein the identified restaurant device comprises a staffing device ([0040] teaches the mobile POS terminals and kitchen fulfilment terminals in each of the restaurants communicate with the backend server through their gateways to perform the functions of displaYimg of menus, ordering of menu items, routing of ordered items to the kitchen staff, sequencing of ordering items through the kitchen, and more, thus providing efficient operation of the restaurant, wherein [0045] teaches the backend server directs the terminals to perform routing of items to kitchen staff, sequencing of ordering items through the kitchens, and more, wherein the terminals can provide messaging to execute the functions; see also: [0048]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Neumann and Koennings to incorporate the teachings of Jagolta to include wherein the identified restaurant device comprises a staffing device. One would have been motivated to do so in order to provide efficient operation of the restaurant by sequencing the ordering of items through the kitchen and routing of ordered items to the kitchen staff for preparation (Jagolta, [0040]). By incorporating the teachings of Jagolta, one would have been able to frequently update the preparation time indicator through the use of digital menus available on the restaurant terminals (Jagolta, [0048]).
Claim(s) 15 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Yim et al. (US 20210174370 A1) in view of Neumann (US 20220004955 A1).
Regarding claim 15, Yim teaches a method for omnichannel Artificial Intelligence (AI) restaurant management (Figs. 5, 8), comprising:
identifYimg a plurality of current orders at a restaurant, the plurality of current orders comprising orders from a plurality of ordering channels (Fig. 8 and [0136] teach a control server controls robots to determine a cooking sequence and controls robots, wherein the control server collects order information on orders received from tables an determines an efficient cooking sequence, wherein [0143] teaches the server may determine the number of orders and recipe information of the food, as well as in [0184-0185] teach a food material pickup robot receives N orders for a predetermined period of time, wherein [0095] teaches the table robot may transmit input order information on an order placed by the customer through voice at the table to the control server or the serving robot or the cooking robot, as well as in [0076-0077] teach the robot can receive menu information from the user using a predetermined mounted display or voice recognition, as well as in [0131] teaches the guide robot may guide a waiting time to a customer who visits a store or a customer who takes out food and the guide robot may directly take an order, wherein the guide robot may include an ordering system to instruct other robots to complete the order/take-out order/delivery order in the store; see also: [0093, 0137-0138, 0206]);
the plurality of ordering channels comprising at least an in-store ordering channel and a delivery service ordering channel (Fig. 8 and [0136] teach a control server controls robots to determine a cooking sequence and controls robots, wherein the control server collects order information on orders received from tables an determines an efficient cooking sequence, wherein [0143] teaches the server may determine the number of orders and recipe information of the food, as well as in [0184-0185] teach a food material pickup robot receives N orders for a predetermined period of time, wherein [0095] teaches the table robot may transmit input order information on an order placed by the customer through voice at the table to the control server or the serving robot or the cooking robot, as well as in [0076-0077] teach the robot can receive menu information from the user using a predetermined mounted display or voice recognition, as well as in [0131] teaches the guide robot may guide a waiting time to a customer who visits a store or a customer who takes out food and the guide robot may directly take an order, wherein the guide robot may include an ordering system to instruct other robots to complete the order/take-out order/delivery order in the store; see also: [0093, 0137-0138, 0206]);
computing, by an execution of an omnichannel AI restaurant management model trained with a data set of previous omnichannel ordering data that includes both internal and external data sources ([0240] teaches the artificial intelligence causes a model constituted by an ANN to learn using learning data that can be used to make a determination which is used for a robot to perform a certain operation, as well as in [0249] teaches the controller may utilize data in the artificial intelligence and control the elements of the robot such that a predicted operation or an operation determined to be preferable out of one or more executable operations is executed, wherein [0143] teaches the control server may also provide a time point at which the food is completed based on temporal characteristics and serving characteristics, such as simultaneous serving and sequential serving, of the ordered food, wherein the server may efficiently control the cooking robot by determining a number of orders and recipe information of food and grouping similar cooking steps, wherein the temporal characteristics of the ordered food contain temporal properties for preparing or cooking the ordered food, wherein the serving characteristics contain serving properties for transferring time for moving the ordered food to the table, wherein [0146] teaches the control server may generate the cooking instructions based on the sequence or the similarity between the foods ordered from the plurality of tables and a food completion time determined using temporal characteristics or serving characteristics of the ordered menu items, as well as in [0185] teaches a food robot can receive “N” orders for a predetermined period of time, wherein the cooking sequence can be rearranged based on similarity among ordered menu items as the plurality of orders are received, wherein the cooking sequence is arranged such that the foods may be cooked based on a time point in which the order is received and an expected serving time; see also: [0144, 0232-233]; Examiner’s Note: See the 35 USC 103 combination below for teachings pertaining to the unbolded claim language.),
a predicted fulfillment time for each order of the plurality of current orders at the restaurant ([0240] teaches the artificial intelligence causes a model constituted by an ANN to learn using learning data that can be used to make a determination which is used for a robot to perform a certain operation, as well as in [0249] teaches the controller may utilize data in the artificial intelligence and control the elements of the robot such that a predicted operation or an operation determined to be preferable out of one or more executable operations is executed, wherein [0143] teaches the control server may also provide a time point at which the food is completed based on temporal characteristics and serving characteristics, such as simultaneous serving and sequential serving, of the ordered food, wherein the server may efficiently control the cooking robot by determining a number of orders and recipe information of food and grouping similar cooking steps, wherein the temporal characteristics of the ordered food contain temporal properties for preparing or cooking the ordered food, wherein the serving characteristics contain serving properties for transferring time for moving the ordered food to the table, wherein [0146] teaches the control server may generate the cooking instructions based on the sequence or the similarity between the foods ordered from the plurality of tables and a food completion time determined using temporal characteristics or serving characteristics of the ordered menu items, as well as in [0185] teaches a food robot can receive “N” orders for a predetermined period of time, wherein the cooking sequence can be rearranged based on similarity among ordered menu items as the plurality of orders are received, wherein the cooking sequence is arranged such that the foods may be cooked based on a time point in which the order is received and an expected serving time; see also: [0144, 0232-233]),
wherein the predicting comprises: (i) identifYimg at least one semantic component from each order ([0095] teaches the table robot may transmit input order information on an order placed by the customer through voice at the table to the control server or the serving robot or the cooking robot, as well as in [0076-0077] teach the robot can receive menu information from the user using a predetermined mounted display or voice recognition, wherein [0104] teaches if the same types of five foods are ordered from several tables and the foods are served independently, wherein [0137-0138] teach the server instructs cooking based on a sequence/similarity among orders placed from tables, wherein the cooking robot may operate sequentially according to the sequence of orders, wherein when similar food is ordered from a plurality of tables, the cooking robot may be controlled to cook the food, as well as in [0143] teaches determining a number of orders and recipe information of food and grouping similar cooking steps, wherein [0146] teaches the control server may generate the cooking instructions based on the sequence or the similarity between the foods ordered from the plurality of tables and a food completion time determined using temporal characteristics or serving characteristics of the ordered menu items; see also: [0139, 0185]);
(ii) grouping together orders that have a semantic similarity, thereby defining a semantic order cluster ([0104] teaches if the same types of five foods are ordered from several tables and the foods are served independently, wherein [0137-0138] teach the server instructs cooking based on a sequence/similarity among orders placed from tables, wherein the cooking robot may operate sequentially according to the sequence of orders, wherein when similar food is ordered from a plurality of tables, the cooking robot may be controlled to cook the food, as well as in [0143] teaches determining a number of orders and recipe information of food and grouping similar cooking steps, wherein [0146] teaches the control server may generate the cooking instructions based on the sequence or the similarity between the foods ordered from the plurality of tables and a food completion time determined using temporal characteristics or serving characteristics of the ordered menu items; see also: [0139, 0185]);
(iii) identifYimg a number of items in each order ([0185] teaches a food robot can receive “N” orders for a predetermined period of time, wherein the cooking sequence can be rearranged based on similarity among ordered menu items as the plurality of orders are received, wherein the cooking sequence is arranged such that the foods may be cooked based on a time point in which the order is received and an expected serving time, as well as in [0105] teaches the robot may change the cooking sequence based on a cooking time period for which the ordered food is cooked, a time point at which the ordered food is served, a number of ordered foods for each table, and types of food, as well as in [0143] teaches determining a number of orders and recipe information of food and grouping similar cooking steps; see also: [0113, 0153, 0184]);
and (iv) computing, based on a comparison of the semantic order cluster and the number of items in each order to model data, the predicted fulfillment time ([0143] teaches the control server may also provide a time point at which the food is completed based on temporal characteristics and serving characteristics, such as simultaneous serving and sequential serving, of the ordered food, wherein the server may efficiently control the cooking robot by determining a number of orders and recipe information of food and grouping similar cooking steps, wherein the temporal characteristics of the ordered food contain temporal properties for preparing or cooking the ordered food, wherein the serving characteristics contain serving properties for transferring time for moving the ordered food to the table, wherein [0146] teaches the control server may generate the cooking instructions based on the sequence or the similarity between the foods ordered from the plurality of tables and a food completion time determined using temporal characteristics or serving characteristics of the ordered menu items, as well as in [0185] teaches a food robot can receive “N” orders for a predetermined period of time, wherein the cooking sequence can be rearranged based on similarity among ordered menu items as the plurality of orders are received, wherein the cooking sequence is arranged such that the foods may be cooked based on a time point in which the order is received and an expected serving time; see also: [0144]);
transmitting, to an ordering device, and for at least one of the plurality of current orders, an indication of the predicted fulfillment time ([0146] teaches the control server may generate the cooking instructions based on the sequence or the similarity between the foods ordered from the plurality of tables and a food completion time determined using temporal characteristics or serving characteristics of the ordered menu items, wherein [0108-0109] teaches when the customer waits for food after ordering, the table robot may display a waiting time until the food is served; see also: [0110]).
However, Yim does not explicitly teach the plurality of ordering channels comprising at least a delivery service ordering channel; an omnichannel AI restaurant management model trained with a data set of previous omnichannel ordering data that includes both internal and external data sources.
From the same or similar field of endeavor, Neumann teaches the plurality of ordering channels comprising at least a delivery service ordering channel ([0026] teaches the received orders can be received via orders input by a restaurant wait staff, logged by a web based application, a mobile application, or other meal ordering service, application, device, or the like, wherein [0022] teaches the ordered tasks are received via courier services via application services, wherein Fig. 5 and [0054] teach a computing device is configured to receive a plurality of identifications of meals to be prepared, wherein a plurality of identifications of meals may include computing device that retrieves an ingredients list, as well as in [0022] teaches preparing a plurality of meal orders; see also: [0017, 0026-0027, 0049]);
an omnichannel AI restaurant management model trained with a data set of previous omnichannel ordering data that includes both internal and external data sources (Fig. 1 and [0063-0065] teach a computing system comprising a memory that includes one or more machine-readable media with instructions that embody the methodologies, wherein [0038-0039] teach the machine learning models may be generated using artificial intelligence methods, wherein [0028-0029] teach the training data includes a plurality of data entries from previous iterations of the machine-learning process, database, user inputs, and expert inputs, wherein [0020] teaches database information including a history of ingredients for each meal, ordering data, timing data, and more, as well as Fig. 1 and [0017] teach the computing device receives information from an online repository regarding identification of meals to be prepared; see also: [0018-0019]);.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Yim to incorporate the teachings of Neumann to include the plurality of ordering channels comprising at least a delivery service ordering channel; an omnichannel AI restaurant management model trained with a data set of previous omnichannel ordering data that includes both internal and external data sources. One would have been motivated to do so in order to improve task efficiency by combining tasks that can be performed concurrently (Neumann, [0024]). By incorporating the teachings of Neumann, one would have been able to generate a task chain that maximizes batching ingredient preparation for a plurality of meals, thus minimizing the time to prepare meals (Neumann, [0050]).
Regarding claim 18, the combination of Yim and Neumann teaches all the limitations of claim 15 above.
However, Yim does not explicitly teach wherein the comparison to the model data comprises an execution of a mathematical regression model.
From the same or similar field of endeavor, Neumann further teaches wherein the comparison to the model data comprises an execution of a mathematical regression model ([0040-0041] teach the machine learning model may generate a plurality of candidate task chains, wherein the task chain may be selected by generating an objective function that is a mathematical function with a solution set that includes a plurality of data elements to be compared, wherein [0024] teaches the task chains can be calculated by a machine learning model and objective function to batch cooking steps together and generate task steps that correspond to ingredient preparation for a plurality of meals with overlap, wherein [0050] teaches optimizing the objective function by generating a solution set that includes the plurality of task chain combinations that meet the objective of maximizing batching gradient preparation for a plurality of meals to minimize time for preparing meals, wherein [0036-0037] teach the machine learning module uses techniques include linear regression models and decision tress; see also: [0031-0033]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Yim and Neumann to incorporate the further teachings of Neumann to include wherein the comparison to the model data comprises an execution of a mathematical regression model. One would have been motivated to do so in order to improve task efficiency by combining tasks that can be performed concurrently (Neumann, [0024]). By incorporating the teachings of Neumann, one would have been able to generate a task chain that maximizes batching ingredient preparation for a plurality of meals, thus minimizing the time to prepare meals (Neumann, [0050]).
Regarding claim 19, the combination of Yim and Neumann teaches all the limitations of claim 15 above.
However, Yim does not explicitly teach wherein the comparison to the model data comprises an execution of a decision tree model.
From the same or similar field of endeavor, Neumann further teaches wherein the comparison to the model data comprises an execution of a decision tree model ([0040-0041] teach the machine learning model may generate a plurality of candidate task chains, wherein the task chain may be selected by generating an objective function that is a mathematical function with a solution set that includes a plurality of data elements to be compared, wherein [0024] teaches the task chains can be calculated by a machine learning model and objective function to batch cooking steps together and generate task steps that correspond to ingredient preparation for a plurality of meals with overlap, wherein [0050] teaches optimizing the objective function by generating a solution set that includes the plurality of task chain combinations that meet the objective of maximizing batching gradient preparation for a plurality of meals to minimize time for preparing meals, wherein [0036-0037] teach the machine learning module uses techniques include linear regression models and decision tress; see also: [0031-0033]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Yim and Neumann to incorporate the further teachings of Neumann to include wherein the comparison to the model data comprises an execution of a decision tree model. One would have been motivated to do so in order to improve task efficiency by combining tasks that can be performed concurrently (Neumann, [0024]). By incorporating the teachings of Neumann, one would have been able to generate a task chain that maximizes batching ingredient preparation for a plurality of meals, thus minimizing the time to prepare meals (Neumann, [0050]).
Claim(s) 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Yim et al. (US 20210174370 A1) in view of Neumann (US 20220004955 A1) in view of Zito et al. (US 20200121125 A1).
Regarding claim 16, the combination of Yim and Neumann teaches all the limitations of claim 15 above.
However, Yim does not explicitly teach wherein the predicting further comprises:(v) identifYimg a number of modifiers for each order.
From the same or similar field of endeavor, Zito teaches wherein the predicting further comprises:(v) identifYimg a number of modifiers for each order ([0028] teaches the robotic kitchen assistant maintains an inventory of similar batch prepared items as the orders arrive, wherein the assistant can use temperature information and incoming orders in order to batch cook fries, as well as determine when to prepare additional fries, wherein [0115] teaches receiving a customer order including eat-in and to-go orders, wherein the orders shall specify a food item, toppings, and a customer identifier, wherein [0116] teaches evaluating whether any part of the customer order can be prepared by the robotic kitchen assistant by comparing the food items in the customer order list to food items that are prepared with at least one step performed by the robotic assistant, wherein [0117-0018] teach calculating the steps to complete the customer order including the time to begin preparation of the food items, what steps to perform and by who, wherein [0119] teaches determining cooking times for each food item, estimated upcoming demand, time to completion of the items already on the grill, and estimated time to completion of items included in the order, wherein [0135] teaches different types of customer order information can be received including a food item, menu item, topping or other modification, wherein the robotic kitchen assistant can help complete the order; see also: [0006-0009, 0120-0121]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Yim and Neumann to incorporate the teachings of Zito to include wherein the predicting further comprises:(v) identifYimg a number of modifiers for each order. One would have been motivated to do so in order to maximize the throughput efficiency by minimizing the time necessary to perform all required actions (Zito, [0168]). By incorporating the teachings of Zito, one would have been able to allow the robotic kitchen assistant to obtain more accurate estimates on when to start new food items based on order history and more (Zito, [0171]).
Regarding claim 17, the combination of Yim and Neumann teaches all the limitations of claim 15 above.
However, Yim does not explicitly teach wherein the computing is further based on a comparison of the number of modifiers for each order to model data
From the same or similar field of endeavor, Zito further teaches wherein the computing is further based on a comparison of the number of modifiers for each order to model data ([0028] teaches the robotic kitchen assistant maintains an inventory of similar batch prepared items as the orders arrive, wherein the assistant can use temperature information and incoming orders in order to batch cook fries, as well as determine when to prepare additional fries, wherein [0115] teaches receiving a customer order including eat-in and to-go orders, wherein the orders shall specify a food item, toppings, and a customer identifier, wherein [0116] teaches evaluating whether any part of the customer order can be prepared by the robotic kitchen assistant by comparing the food items in the customer order list to food items that are prepared with at least one step performed by the robotic assistant, wherein [0117-0018] teach calculating the steps to complete the customer order including the time to begin preparation of the food items, what steps to perform and by who, wherein [0119] teaches determining cooking times for each food item, estimated upcoming demand, time to completion of the items already on the grill, and estimated time to completion of items included in the order, wherein [0135] teaches different types of customer order information can be received including a food item, menu item, topping or other modification, wherein the robotic kitchen assistant can help complete the order, and wherein [0095] teaches the robot kitchen assistant can be fed data to improve performance of the system by allowing the software to adapt to constantly learning as it performs tasks; see also: [0006-0009, 0120-0121]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Yim, Neumann, and Zito to incorporate the further teachings of Zito to include wherein the computing is further based on a comparison of the number of modifiers for each order to model data. One would have been motivated to do so in order to maximize the throughput efficiency by minimizing the time necessary to perform all required actions (Zito, [0168]). By incorporating the teachings of Zito, one would have been able to allow the robotic kitchen assistant to obtain more accurate estimates on when to start new food items based on order history and more (Zito, [0171]).
Claim(s) 20 is rejected under 35 U.S.C. 103 as being unpatentable over Yim et al. (US 20210174370 A1) in view of Neumann (US 20220004955 A1) in view of Yang et al. (WO 2021179906 A1).
Regarding claim 20, the combination of Yim and Neumann teaches all the limitations of claim 15 above.
However, Yim does not explicitly teach further comprising adjusting, in response to the computing of at least one of the predicted fulfillment times, an ordering channel setting.
From the same or similar field of endeavor, Yang teaches further comprising adjusting, in response to the computing of at least one of the predicted fulfillment times, an ordering channel setting (Pg. 6 teaches the target probability estimation model may operate under the condition of insufficient capacity, wherein the model is trained to determine whether the target order meets the insufficient capacity condition, wherein Pg. 7 teaches in a case where the order acceptance probability is less than a reference probability threshold, the system generates an order operation suggestion for the user, wherein when the order probability is less than a reference probability threshold, it indicates that the target order has a low probability of being received by a delivery person in the first reference time period from the current moment, and an order operation suggestion that recommends the order be canceled is generated, wherein Pg. 12 teaches the suggestion generation module is configured to generate service channel for order cancelation and an order cancellation option operation suggestion for presenting to the user when the order acceptance probability is less than a reference threshold; see also: Pgs. 8-11; Examiner’s Note: The page numbers correspond to the translated WIPO publication. Please refer to the page numbers at the bottom center of the translated portion of the document for the citations.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Yim and Neumann to incorporate the teachings of Yang to include further comprising adjusting, in response to the computing of at least one of the predicted fulfillment times, an ordering channel setting. One would have been motivated to do so in order to generate operation suggestions so that the users can better understand the status of the orders, which improves the user experience (Yang, Pg. 13). By incorporating the teachings of Yang, one would have been able to balance the dispatching resources of the takeaway platform for orders, while reducing losses for merchants and improving user experience (Yang, Pg. 7).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Mimassi et al. (US 11521171 B2) discloses obtaining and analyzing information through a mobile device or POS of the restaurant, wherein the system may gather information from the POS receipts and mobile app information, which is related to the independent claim limitations of at least “the plurality of ordering channels comprising at least an in-store ordering channel and a delivery service ordering channels”
Yien et al. (US 11295322 B2) discloses training a machine learning model to manage preparing meals for two separate delivery requests from different third-party delivery services, which is related to the independent claim limitations of at least “an omnichannel Al restaurant management model trained with a data set of previous omnichannel ordering data that includes both internal and external data sources”
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sara G Brown whose telephone number is (469)295-9145. The examiner can normally be reached M-F 8:00 am- 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571) 270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
