DETAILED ACTION
This is a Non-Final Office Action in response to claims on 10/27/2023. Claims 1-9 are pending. The effective filling date is 10/25/2023.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 1 is objected to because of the following informalities: after paragraph 4 of the first claim, there is a period, which should be a comma. Examiner is interpreting the claim to be a comma, and Applicant should amend to have correct claim construction. Claims 2-9 depend from Claim 1, and therefor follow the same objection. Appropriate correction is required.
Claim Rejections - 35 USC § 101
Claims 1-9 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea.
A. Step 1 — Claim(s) Recite an Abstract Idea
The claims are directed to collecting, organizing, and analyzing information and using mathematical concepts and decision rules to make decisions about business operations. For example, claim 1 recites collecting quantitative data about kitchen personnel movement and courier movement, identifying typical operational scenarios by clustering and classification of the collected data, introducing such data into a simulator where meal preparation and courier travel times are randomly drawn from a statistical distribution, and processing simulator data by at least one decision-making algorithm for application to restaurant traffic management. These steps are akin to fundamental practices of collecting and analyzing information, performing statistical analysis and simulation, and applying decision rules to schedule and dispatch deliveries — i.e., methods of organizing human activity and mathematical concepts. See Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208 (2014) (claims directed to intermediate financial tasks and certain methods of organizing human activity found to be abstract); Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66 (2012).
Similarly, dependent claims recite queuing, machine learning, and reinforcement-learning algorithms such as Temporal Difference learning, DQN, SARSA, and Actor-Critic, and a hybrid DQN+queue structure. These are methods of data analysis, decision making, and mathematical techniques for optimization. The claimed subject matter, taken as a whole, is therefore directed to an abstract idea of collecting data, statistically modeling or simulating operations, and making decisions based on those models.
B. Step 2 — No Inventive Concept Transforming the Abstract Idea into Patent-Eligible Subject Matter
To the extent the claims recite implementations on a “simulator,” a “digital twin,” “kitchen module,” “delivery module,” “configurator,” “status vectors,” event-driven simulation, GPS data acquisition, or generic data processing, the specification shows these are implemented using standard, well-known computing components and conventional programming techniques (e.g., GPS for courier tracking, kernel density estimation, scikit-learn, OSRM routing, OpenAI Gym API, MLflow, known RL algorithms such as DQN/SARSA/TD-AC). The mere use of general-purpose computer components or popular machine learning techniques to perform the abstract idea does not supply an inventive concept. See Alice, 573 U.S. at 222–23; Parker v. Flook, 437 U.S. 584 (1978).
The claims do not recite a specific, unconventional machine or a non-routine software/hardware configuration that provides a concrete technological improvement to computer functionality. The specification describes high-level modules and data structures (status vectors, event queues, matrices of travel times), but the claims do not define novel, particularized data structures, algorithmic techniques, or low-level architectural features sufficient to distinguish them from mere implementation on a generic computer. Absent such specific limitations, the claims are directed to implementing an abstract idea using routine computer components and conventional data-processing techniques. See Alice; Mayo.
Accordingly, the claim limitations, individually and as an ordered combination, do not include an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter under § 101.
Claim Limitations not Found in Prior Art
Claims 1-9 are allowed.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2022/0044307 A1 Xu et al. (hereinafter Xu) teaches optimizing food delivery using data about orders (Xu Abstract, optimizing food service including receiving orders, and preparing for dine in or delivery, sending staff to aid areas of need; [0017] information associated with customers, restaurants merchants, food service assistants an delivery personal are all analyzed; [0023] food preparation information; [0033-0034] the food delivery techniques are optimized by using recuring foods orders and data received about orders to be processed; [0036] the service computing device is used to communicate between networks); but fails to explicitly disclose wherein the simulator consists of the restaurant kitchen, delivery and configurator modules and the restaurant kitchen module is a digital twin of a real restaurant kitchen and simulates its resources, their quantity, the main parameters and their dependencies, the delivery module includes data on the movement of couriers and its factors, and the configurator module includes data on restaurant operating scenarios.
US 2020/0401576 A1 Yerli teaches that there can be a simulator to create a twin reality (Yerli Abstract, virtual twin; [0016] general ‘real world’ data), but fails to teach the simulator consists of the restaurant kitchen, delivery and configurator modules and the restaurant kitchen module is a digital twin of a real restaurant kitchen and simulates its resources, their quantity, the main parameters and their dependencies, the delivery module includes data on the movement of couriers and its factors
EP 3,467,718 A1 Eleftheriadis et al. teaches using a reinforcement learning algorithm to make decisions (Eleftheriadis [0016] using a learning algorithm); but fails to explicitly disclose automation of restaurant traffic management process, including the reception of orders, preparation of meals by the restaurant's kitchen and the delivery of meals by couriers from the restaurant to clients located beyond the premises of the restaurant, characterised in that
quantitative data on the movement of restaurant kitchen personnel and on the movement of couriers is collected and then typical restaurant operational scenarios are identified by way of the clustering and classification of this data,
the data is then introduced into a simulator, where it is used for the purposes of a previously unknown series of orders placed during one day, wherein the individual meal preparation and courier travel times are drawn randomly from a statistical distribution that approximates the restaurant's kitchen personnel and courier movement data, wherein the simulator consists of the restaurant kitchen, delivery and configurator modules and the restaurant kitchen module is a digital twin of a real restaurant kitchen and simulates its resources, their quantity, the main parameters and their dependencies, the delivery module includes data on the movement of couriers and its factors, and the configurator module includes data on restaurant operating scenarios,
data from the simulator is then processed into data for restaurant traffic control with the use of at least one decision-making algorithm and can be applied for the management of restaurant traffic.
The specific limitations not found in prior at include:
the data is then introduced into a simulator, where it is used for the purposes of a previously unknown series of orders placed during one day, wherein the individual meal preparation and courier travel times are drawn randomly from a statistical distribution that approximates the restaurant's kitchen personnel and courier movement data
wherein the simulator consists of the restaurant kitchen, delivery and configurator modules and the restaurant kitchen module is a digital twin of a real restaurant kitchen and simulates its resources, their quantity, the main parameters and their dependencies, the delivery module includes data on the movement of couriers and its factors, and the configurator module includes data on restaurant operating scenarios,
data from the simulator is then processed into data for restaurant traffic control with the use of at least one decision-making algorithm and can be applied for the management of restaurant traffic.
The claims may not be found in the prior art, but the claims remain rejected under 101, see the above reasoning.
Conclusion
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/JESSICA E SULLIVAN/ Examiner, Art Unit 3627
/FAHD A OBEID/ Supervisory Patent Examiner, Art Unit 3627