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 .
DETAILED ACTION
1. This Final Office action is in reply to the Applicant amendment filed on 08 April 2026.
2. Claims 1, 10, 19 have been amended.
3. Claims 1-20 are currently pending and have been examined.
Response to Amendment
In the previous office action, Claims 1-20 were rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter (abstract idea). Applicants have not amended Claims to provide statutory support and the rejection is maintained.
Applicant’s amendments necessitated the new grounds of rejection.
Response to Arguments
Applicant’s arguments filed 08 April 2026 have been fully considered but they are not persuasive. In the remarks regarding the 35 USC § 101 rejection for Claims 1-20, Applicant argues that: (1) the claims are not directed to an abstract idea, and even if they were, they would amount to significantly more than the abstract idea. Examiner respectfully disagrees. Still commensurate to the two-part subject matter eligibility framework decision in the Federal court decision in Alice Corp. Pty. Ltd. V. CLS Bank International et al., (Alice), 2019 revised patent subject matter eligibility guidance (2019 PEG) and the October 2019 Update: Subject Matter Eligibility (“October 2019 Update), and the new “July 2024 Guidance Update on Patent Subject Matter Eligibility Examples, including on Artificial Intelligence”, and the Examiner details the maintained rejection under 35 U.S.C. 101 in the below rejection with further explanation. Applicant argues that as amended, Applicant additionally states: “The claims reflect this disclosed improvement and should not be dismissed at the high level of generality cautioned against Desjardins (re: Ex parte Desjardins, Appeal 2024-000567)” (see Remarks/Arguments pages 7-8). However the Examiner respectfully disagrees. The broadly recited claims still recite Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations- The claim fundamentally relies on mathematical algorithms, supervised regression, and outlier detection models to process numbers. Certain methods of organizing human activity –marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The method targets enterprise/business management—specifically, personnel management, resource allocation, and workflow optimization. Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). The step of comparing actual staff to expected staff (as predictive analytics) can practically be performed in the human mind. In summary as indicated below through Steps 1-2B, the recitation of a computer (one or more processors) to perform the claim limitations amount to no more than mere instruction to apply the exception using generic computer components. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Regarding the argument for the Desjardins decision, as currently recited in the amended claims, Applicants’ claimed invention does not recite additional improvement than just utilizing machine learning to help user input for the claim results. Additionally, “method” claims 1-9 still do not recite any computer architecture components in the body of the at least independent Claim 1 to fully support the step claim limitations to pass Step 1 of this analysis. For at least these reasons, the rejection is maintained.
Regarding the arguments for the previous cited prior art rejection, Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action due to change in claim scope. For at least these reasons, the arguments are moot. It is noted that any citations to specific, pages, columns, paragraphs, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123. The Examiner has a duty and responsibility to the public and to Applicant to interpret the claims as broadly as reasonably possible during prosecution. In re Prater, 415 F.2d 1 393, 1404-05, 162 USPQ 541, 550-51 (CCPA 1969).
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.
Broadly recited Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more. The below representative Claim 1 describes valuating the patent eligibility of the described method for optimizing cashier staffing. The claims as a whole recite certain grouping of an abstract idea and are analyzed in the following step process:
Step 1: Claims 10-20 are each focused to a statutory category of invention, namely a “non-transitory machine-readable medium/system” set. However, “method” claims 1-9 still do not recite any computer architecture components to fully support the step claim limitations. Despite this failure to pass Step 1, the Examiner proceeds to the next steps of the analysis.
Step 2A: Prong One: Claims 1-20 recite limitations that set forth the abstract ideas, namely, the claims as a whole recite the claimed invention as directed to an abstract idea without significantly more. As recited in below Applicants’ representative Claim 1 recite steps for:
“generating a training dataset including input data corresponding to sales data at a store over a time period, the training dataset labeled with a corresponding number of cashiers working at front-end lanes at respective time increments in the time period;
removing outliers from the training dataset using an outlier detection model to generate a clean labeled training dataset;
training a supervised regression machine learning model using the clean labeled training dataset;
generating an inference dataset;
predicting, using the supervised regression machine learning model, an expected number of cashiers for a subset of data from the inference dataset corresponding to a particular time based on transactional activity at the store;
comparing an actual number of cashiers at the particular time to the expected number of cashiers;
and outputting an indication of whether the store was overstaffed, understaffed, or adequately staffed based on a result of comparing the actual number of cashiers at the particular time to the expected number of cashiers”
As detailed in the MPEP 2106 and commensurate to the two-part subject matter eligibility framework decision in the Federal court decision in Alice Corp. Pty. Ltd. V. CLS Bank International et al., (Alice), 2019 revised patent subject matter eligibility guidance (2019 PEG) and the October 2019 Update: Subject Matter Eligibility (“October 2019 Update), and the new “July 2024 Guidance Update on Patent Subject Matter Eligibility Examples, including on Artificial Intelligence”, the 2019 PEG explains that the abstract idea exception includes the following groupings of subject matter. The 35 U.S.C. 101 Step 2A, Prong One analysis focuses on whether a claim recites a judicial exception by evaluating if it falls into one of three specific groupings: mathematical concepts, mental processes, and/or certain methods of organizing human activity. Based on the provided steps above, the analysis for Step 2A Prong One is as follows:
Mathematical Concepts
The claims recite limitations that fall under the Mathematical Concepts category. Specifically, the steps involving: "removing outliers... using an outlier detection model"; "training a supervised regression machine learning model" (e.g., regression algorithms); "outlier detection model" "supervised regression machine learning model" and "comparing" involve mathematical relationships and calculations used to process data. While machine learning is inherently mathematical, USPTO guidance distinguishes between "involving" and "reciting" mathematics.
Mental Processes
The claims also recite Mental Processes. The act of "comparing" and "evaluating" staffing levels (a task a human supervisor traditionally does in their head or on paper). Actions like "comparing; predicting; comparing" and "outputting an indication of whether the store was overstaffed" can be performed as mental processes (judgments or evaluations) could be performed mentally.
Certain Methods of Organizing Human Activity
The overall method of (as best interpreted by the Examiner for one of ordinary skilled in the art of managing store labor of determining the number of cashiers to schedule and assessing whether they are overstaffed or understaffed (calculating cashier staffing) falls under managing human activity, specifically fundamental economic practices or managing interactions between people (staffing management). The core purpose of the representative Claim 1 is to manage human staffing (at a store over a time period; cashiers; a corresponding number of cashiers working at front-end lanes at respective time increments in the time period), which is a common business practice. Federal Circuit decisions, such as Recentive Analytics v. Fox (2025), have held that applying generic machine learning (ML) to optimize human schedules is directed to an abstract idea of organizing human activity. The claims also relate to commercial or legal Interactions, as managing store staffing involves business practices, i.e. “comparing an actual number of cashiers at the particular time to the expected number of cashiers; outputting an indication of whether the store was overstaffed, understaffed, or adequately staffed based on a result of comparing the actual number of cashiers at the particular time to the expected number of cashiers”.
See MPEP § 2106.04(a) III C. Hence, the claims are ineligible under Step 2A Prong one. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception.
Prong Two: Claims 1-20: With regard to this step of the analysis (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 10, 19 recite additional elements directed to “non-transitory machine-readable medium; system; processing circuitry; memory, including instructions, which when executed by the processing circuitry, cause the processing circuitry to perform operations…” (e.g., see Applicants’ un-published Specification ¶’s 16-24). Therefore, the claims contain computer components that are cited at a high level of generality and are merely invoked as a tool to perform the abstract idea. Simply implementing an abstract idea on a computer is not a practical application of the abstract idea. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. See MPEP § 2106.05(f) (h).
Step 2B: As explained in MPEP § 2106.05, Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea nor recites additional elements that integrate the judicial exception into a practical application. The additional elements of “non-transitory machine-readable medium; system; processing circuitry; memory, including instructions, which when executed by the processing circuitry, cause the processing circuitry to perform operations…”, etc. are generically-recited computer-related elements that amount to a mere instruction to “apply it” (the abstract idea) on the computer-related elements (see MPEP § 2106.05 (f) – Mere Instructions to Apply an Exception). These additional elements in the claims are recited at a high level of generality and are merely limiting the field of use of the judicial exception (see MPEP §2106.05 (h) – Field of Use and Technological Environment). There is no indication that the combination of elements improves the function of a computer or improves any other technology. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible.
The Examiner interprets that the steps of the claimed invention both individually and as an ordered combination result in Mere Instructions to Apply a Judicial Exception (see MPEP §2106.05 (f)). These claims recite only the idea of a solution or outcome with no restriction on how the result is accomplished and no description of the mechanism used for accomplishing the result. Here, the claims utilize a computer or other machinery (e.g., see Applicants’ un-published Specification ¶’s 16-24) regarding using existing computer processors as well as program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored. “system 100” in its ordinary capacity for performing tasks (e.g., to receive, analyze, transmit and display data) and/or use computer components after the fact to an abstract idea (e.g., a fundamental economic practice and certain methods of organization human activities) and does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)). Software implementations are accomplished with standard programming techniques with logic to perform connection steps, processing steps, comparison steps and decisions steps. These claims are directed to being a commonplace business method being applied on a general-purpose computer (see Alice Corp. Pty, Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357, 110 USPQ2d 1976, 1983 (2014)); Versata Dev. Group, Inc., v. SAP Am., Inc., 793 D.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) and require the use of software such as via a server to tailor information and provide it to the user on a generic computer. Based on all these, Examiner finds that when viewed either individually or in combination, these additional claim element(s) do not provide meaningful limitation(s) that raise to the high standards of eligibility to transform the abstract idea(s) into a patent eligible application of the abstract idea(s) such that the claim(s) amounts to significantly more than the abstract idea(s) itself. Accordingly, Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e. abstract idea exception) without significantly more.
Claim Rejections - 35 USC § 103
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree et al. (Crabtree) (US 2024/0348663) in view of Golan et al. (Golan) (US 2022/0129821).
With regard to Claims 1, 10, 19, Crabtree teaches a method/system/at least one non-transitory machine-readable medium including instructions, which when executed by processing circuitry, cause the processing circuitry to perform operations/processing circuitry; and memory, including instructions, which when executed by the processing circuitry (platform 100 is configured as a cloud-based computing platform comprising various system or sub-system components configured to provide functionality directed to the execution of epistemic uncertainty quantification and reduction using simulations and an AI system to provide guidance and support. According to the embodiment, the system architecture comprises the following main components: an artificial intelligence computing system 200, a simulation environment computing system 300, a scenario generation computing system 400, a model training and optimization computing system 500, a data filtering computing system 101, a distributed computational graph (DCG) computing system 102, and one or more databases 103. In some embodiments, systems 101-103, and 200-500 may each be implemented as standalone software applications or as a services/microservices architecture which can be deployed (via platform 100) to perform a specific task or functionality) (see at least paragraphs 107-115), cause the processing circuitry to perform operations comprising:
generating a training dataset (platform 100 is configured as a cloud-based computing platform comprising various system or sub-system components configured to provide functionality directed to the execution of epistemic uncertainty quantification and reduction using simulations and an AI system to provide guidance and support. According to the embodiment, the system architecture comprises the following main components: an artificial intelligence computing system 200, a simulation environment computing system 300, a scenario generation computing system 400, a model training and optimization computing system 500, a data filtering computing system 101, a distributed computational graph (DCG) computing system 102, and one or more databases 103. In some embodiments, systems 101-103, and 200-500 may each be implemented as standalone software applications or as a services/microservices architecture which can be deployed (via platform 100) to perform a specific task or functionality) including input data corresponding to sales data (in the grocery store simulation, customer data may contain sensitive information such as personal identifiers, purchase history, or demographic details) at a store (grocery store) over a time (time stamp; iterations or epochs; period; Platform can support model tine-tuning at step 1504. The pre-trained model is fine-tuned using the prepared target dataset. The model may be trained for a few iterations or epochs, allowing it to adapt its learned features to the grocery store domain. As a last step 1505 platform evaluates the fine-tuned model. The fine-tuned model is evaluated on a separate test set from the relevant (e.g., grocery store) domain to assess its performance and generalization ability. Transfer learning allows the simulation and decision platform 100 to leverage existing knowledge and reduce the time and data requirements for training new models), the training dataset labeled with a corresponding number of cashiers (cashiers) working at front-end lanes (workstations) at respective time increments in the time period (the AI component generates a scenario for the grocery store simulation and describes it as follows: “In this scenario, the store operates with 10 cashiers and 5 re-stockers. The average customer arrival rate is 30 per hour, and the average checkout time is 3 minutes. The restocking time for each item is 2 minutes) (see at least paragraphs 70,104-108, 129-133, 140, 168-169, 186);
removing outliers (detecting anomalies, outliers, and edge cases in the simulation results using unsupervised learning algorithms, such as clustering or anomaly detection, to identify potential risks, opportunities, or areas of improvement; the scenario generation computing system 400 may incorporate anomaly detection 403 techniques to identify unusual or unexpected scenarios that may require further investigation or validation. This helps in detecting potential data quality issues, outliers, or rare events that could impact the simulation results. As an example, in the grocery store simulation, the scenario generation component can use unsupervised learning techniques, such as clustering or autoencoders, to detect anomalous scenarios. For instance, if a generated scenario suggests an extremely high customer arrival rate that deviates significantly from the historical patterns, the component can flag it as an anomaly and provide a notification to the user: “Anomaly Detected: The generated scenario suggests a customer arrival rate of 100 customers per hour, which is significantly higher than the typical range of 20-50 customers per hour) from the training dataset using an outlier detection model to generate a clean labeled training dataset (in the grocery store simulation, the scenario generation component can use unsupervised learning techniques, such as clustering or autoencoders, to detect anomalous scenarios. For instance, if a generated scenario suggests an extremely high customer arrival rate that deviates significantly from the historical patterns, the component can flag it as an anomaly and provide a notification to the user: “Anomaly Detected: The generated scenario suggests a customer arrival rate of 100 customers per hour, which is significantly higher than the typical range of 20-50 customers per hour. Please review and validate the scenario inputs to ensure accuracy and feasibility) (see at least paragraphs 64; 129-133,157-165);
training a supervised regression machine learning model using the clean labeled training dataset (the explainable artificial intelligence techniques employed by the simulation and decision platform comprise one or more of: feature importance analysis, counterfactual explanations, rule-based reasoning, and visual analytics; the one or more hardware processors are further configured for detecting anomalies, outliers, and edge cases in the simulation results using unsupervised learning algorithms, such as clustering or anomaly detection, to identify potential risks, opportunities, or areas of improvement) (see at least paragraphs 64; 129-133,157-165);
generating an inference dataset (the quantification and reduction of epistemic uncertainty comprises: representing the uncertainty in the simulation model's parameters, structure, and predictions using probability distributions or fuzzy sets; updating the uncertainty estimates based on the observed simulation results and real-world data using Bayesian inference or belief propagation; identifying the most informative scenarios or experiments to run using active learning, Bayesian optimization, or information-theoretic measures; and adapting the simulation model's complexity, granularity, or scope based on the uncertainty reduction goals and computational constraints; model training and optimization computing system 500 can provide compliance assistance 504 (e.g., data sharing, model sharing, regulatory and legal constraints, etc.). Privacy-preserving machine learning techniques aim to protect sensitive information during the model training and inference processes. These techniques ensure that the model does not leak or expose private data while still allowing for effective learning and prediction. For example, in the grocery store simulation, customer data may contain sensitive information such as personal identifiers, purchase history, or demographic details. Privacy-preserving techniques can be applied to ensure that this sensitive information is not compromised during the model training and optimization process) (see at least paragraphs 67, 91, 169);
predicting, using the supervised regression machine learning model (The model training and optimization computing system can utilize aggregation protocols 502, particularly in the context of federated learning, to securely combine the locally trained models or gradients from multiple participating entities without revealing their individual contributions. Aggregation protocols ensure that the global model is updated based on the collective knowledge of the participants while preserving data privacy and security. Secure aggregation protocols allow multiple parties to compute the sum or average of their individual values without disclosing those values to each other. In the context of federated learning, secure aggregation is used to combine the local model updates or gradients from the participating entities. As an example, in the grocery store simulation, multiple stores participate in federated learning to train a global model for customer behavior prediction. Each store computes the gradients based on their local data and sends them to a central server for aggregation. Secure aggregation protocols ensure that the individual gradients are not exposed, and only the aggregated result is computed), an expected number of cashiers for a subset of data from the inference dataset corresponding to a particular time (the AI component generates a scenario for the grocery store simulation and describes it as follows: “In this scenario, the store operates with 10 cashiers and 5 re-stockers. The average customer arrival rate is 30 per hour, and the average checkout time is 3 minutes. The restocking time for each item is 2 minutes) at the store (For example, in the grocery store simulation, consider a pre-trained model for customer segmentation based on purchase history from a related retail domain. Transfer learning can be used to adapt this model to the specific context of the grocery store simulation; in the grocery store simulation, customer data may contain sensitive information such as personal identifiers, purchase history, or demographic details) (see at least paragraphs 129-133, 165-169);
comparing an actual number of cashiers at the particular time (the AI component generates a scenario for the grocery store simulation and describes it as follows: “In this scenario, the store operates with 10 cashiers and 5 re-stockers. The average customer arrival rate is 30 per hour, and the average checkout time is 3 minutes. The restocking time for each item is 2 minutes) to the expected number of cashiers (In the simulation and decision platform 100, RL may be used for real-time parameter adjustment wherein the AI component acts as an RL agent, observing the state of the simulation and taking actions to adjust parameters in real-time. The agent learns from the feedback it receives in the form of rewards (e.g., improvement in KPIs) or penalties (e.g., violation of constraints). For example, in the grocery store simulation, the RL agent observes the average waiting time and staff cost at regular intervals. If the waiting time exceeds a certain threshold, the agent decides to increase the number of cashiers. If the staff cost exceeds the budget, the agent reduces the number of cashiers or re-stockers. The agent learns to balance the competing objectives over time) (see at least paragraphs 129-133, 140, 165);
outputting an indication of whether the store was overstaffed, understaffed, or adequately staffed (staff allocation) based on a result of comparing the actual number of cashiers at the particular time to the expected number of cashiers (in the grocery store simulation, the scenario generation component can use a combination of rule-based and machine learning techniques to generate human-readable scenarios. For instance, it can utilize decision trees or association rule mining to identify common patterns and relationships in the historical data, such as peak shopping hours, popular product categories, and typical customer behavior. Based on these patterns, the component can generate scenarios that describe different operating conditions, such as: “Scenario 1: Weekend Rush”; “Day: Saturday”; Time: 10:00 AM-12:00 PM”; “Customer Arrival Rate: High (50 customers per hour)”; “Popular Product Categories: Fresh Produce, Bakery, Snacks”; “Staff Allocation: 8 cashiers, 4 stockers”; and “Expected Waiting Time: 5-10 minutes) (see at least paragraphs 153, 181);
Crabtree does not specifically teach based on transactional activity. Golan teaches based on transactional activity (the traffic intelligence system 1 of FIG. 2 is applicable to a retail environment. In such an embodiment, cashier input 23 may include data related to checkout, such as line times, number of operating cashiers, and transaction times. Likewise, transaction records 24 may include receipts or records of purchase, payment types, frequency of individual customer patronage. Store layout information 25 may include information regarding organization and layout of items within a store. Store layout information 25 may be more general, containing only item categories and general locations, or it may be more specific, containing precise locations of exact products, such as brand and product names) in analogous art of retail traffic for the purposes of: “transaction records 24 may include receipts or records of purchase, payment types, frequency of individual customer patronage. Store layout information 25 may include information regarding organization and layout of items within a store. Store layout information 25 may be more general, containing only item categories and general locations, or it may be more specific, containing precise locations of exact products, such as brand and product names” (see at least paragraphs3, 15, 20-21; Abstract).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the retail traffic analysis statistics to actionable intelligence as taught by Golan in the system of Crabtree, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
With regard to Claims 2, 11, 20, Crabtree teaches wherein the respective time increments are hourly or per cashier shift (shopping hours; customers per hour) (see at least paragraph 153).
With regard to Claims 3, 12, Crabtree teaches wherein the outlier detection model is an isolation forest model (isolation forests) (see at least paragraphs 113, 158, 166, 183).
With regard to Claims 4, 13, Crabtree teaches wherein the supervised regression machine learning model is a random forest regressor (see at least paragraphs 113, 158, 166, 183).
With regard to Claims 5, 14, Crabtree teaches wherein the inference dataset includes one or more outliers that are not removed (see at least paragraphs 166, 187).
With regard to Claims 6, 15, Crabtree teaches wherein comparing the actual number of cashiers at the particular time to the expected number of cashiers includes generating an indication of whether the actual number of cashiers at the particular time exceeds, is lower than, or is equal to the expected number of cashiers (see at least paragraphs 140, 153-156, 181).
With regard to Claims 7, 16, Crabtree teaches wherein outputting the indication includes outputting the indication that the store was overstaffed when the actual number of cashiers at the particular time exceeds the expected number of cashiers, understaffed when the actual number of cashiers at the particular time is lower than the expected number of cashiers, and adequately staffed when the actual number of cashiers at the particular time is equal to the expected number of cashiers (see at least paragraphs 140, 153-156, 181).
With regard to Claims 8, 17, Crabtree teaches wherein outputting the indication of whether the store was overstaffed, understaffed, or adequately staffed includes using a tolerance deviation for adequately staffed of up to two cashiers difference between the actual number of cashiers and the expected number of cashiers (in the grocery store simulation, the RL agent observes the average waiting time and staff cost at regular intervals. If the waiting time exceeds a certain threshold, the agent decides to increase the number of cashiers. If the staff cost exceeds the budget, the agent reduces the number of cashiers or re-stockers. The agent learns to balance the competing objectives over time. Reinforcement learning 203 may also be used for adaptive sampling and scenario selection. The AI component uses RL techniques, such as multi-armed bandit algorithms or Bayesian optimization, to adaptively select the most informative scenarios to run based on their expected value of information) (see at least paragraphs 147-156, 181).
With regard to Claims 9, 18, Crabtree teaches wherein the input data from training dataset includes at least one of a number of cashiers in non-front-end lanes, a number of active touchpoints for each group of lanes, a percent idle time of cashiers at front-end lanes, an average time between consecutive transactions at front-end lanes, a total number of items that were processed for each group of lanes, a binary feature indicating whether there was a touchpoint that was open for a time increment shorter than the respective time increments, or a percentage of busy lanes based on a busy lanes rule (These scenarios provide a clear and concise description of the initial conditions, assumptions, and expected outcomes, making them easily understandable by domain experts and stakeholders. As an example, in the grocery store simulation, the scenario generation component can use a combination of rule-based and machine learning techniques to generate human-readable scenarios. For instance, it can utilize decision trees or association rule mining to identify common patterns and relationships in the historical data, such as peak shopping hours, popular product categories, and typical customer behavior. Based on these patterns, the component can generate scenarios that describe different operating conditions, such as: “Scenario 1: Weekend Rush”; “Day: Saturday”; Time: 10:00 AM-12:00 PM”; “Customer Arrival Rate: High (50 customers per hour)”; “Popular Product Categories: Fresh Produce, Bakery, Snacks”; “Staff Allocation: 8 cashiers, 4 stockers”; and “Expected Waiting Time: 5-10 minutes) (see at least paragraphs 147-156, 181).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Drive et al. (US 2016/0283074)
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS L MANSFIELD whose telephone number is (571)270-1904. The examiner can normally be reached M-Thurs, alt. Fri. (9-6).
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THOMAS L. MANSFIELD
Examiner
Art Unit 3623
/THOMAS L MANSFIELD/Primary Examiner, Art Unit 3624
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