DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice for all US Patent Applications filed on or after March 16, 2013
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 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.
Status of the Claims
This communication is in response to communications received on 4/6/26. Claim(s) none is/are amended, claim(s) none is/are cancelled, claim(s) none is/are new, and applicant does not provide any information on where support for the amendments can be found in the instant specification as there are no amendments. Therefore, Claims 12-20 is/are pending and have been addressed below.
Election/Restrictions
Claims 1-20 were previously pending and subject to a restriction/election requirement mailed 2/4/26.
Applicant’s election with traverse of claim(s) 12-20 in the reply filed on 4/6/26 is acknowledged. The traversal is on the ground(s) that “The claims of Group I and Group II do not recite scheduling or schedule adjustment operations in any materially different technical sense.” This is not found persuasive because the inventions have acquired a separate status in the art in view of their different classification (Status monitoring or status determination for a person or group which is monitoring work performed) for claims 1-11 and (reassessing an established staff task schedule to ensure that the schedule is still valid and then optionally rescheduling a task to an available individual or group) for claims 12-20. Specifically claims 1-11 include a) self-checkout (SCO) adoption rates (thus monitoring), b) identifying (monitoring), and c) using the monitoring data in machine learning thus d) the focus is not merely reassessing schedules and rescheduling as needed. In contrast claims 12-20 determine a relationship for scheduling prior to use of machine learning and output an assessment on scheduling based on the determined relationship.
The requirement is still deemed proper and is therefore made FINAL.
Response to Arguments
There are no arguments.
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.
Claim(s) 12-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below.
The limitation(s) below for representative claim(s) 12 and 19 that, under its broadest reasonable interpretation, is directed to predicting understaffed terminals of labor optimization and management.
Step 1: The claim(s) as drafted, is/are a process (claim(s) 12-18 recites a series of steps) and system (claim(s) 19-20 recites a series of components).
Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s):
Claim 12: collecting transactional data from point-of-sale (POS) terminals and self-checkout (SCO) terminals;
calculating POS idle times and SCO idle times from the transactional data;
determining a relationship between traffic at the POS terminals and the SCO terminals and a total number of cashiers operating the POS terminals based on the POS idle times and the SCO idle times;
training a machine learning model (MLM) using the relationship and transactional features;
applying the MLM to predict a current POS terminal staffing level for a store; and
outputting an indication as to whether staffing at the POS terminals of the store is busy or non-busy based on the current POS terminal staffing level provided by the MLM.
Claim(s) 19: same analysis as claim(s) 12.
Dependent claims 13-18, 20 recite the same or similar abstract idea(s) as independent claim(s) 12 and 19 with merely a further narrowing of the abstract idea(s): .
The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with predicting understaffed terminals of labor optimization and management.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements terminals, machine learning model (claim(s) 12, 19), a system, comprising: a processor, a non-transitory computer-readable storage medium (claim(s) 19), terminal(s) (claim(s) 15, 17, 20), machine learning model (claim(s) 16).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.
Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0012, 0033, 0041]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0012, 0033, 0041]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
It has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).
Claim(s) 12-13, 15, 17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pachigar et al. (US 2024/0403741 A1) in view of Laserson et al. (US 2023/0068255 A1).
Regarding claim 12 and 19, Pachigar teaches
a method, comprising
{a system, comprising: a processor; a non-transitory computer-readable storage medium comprising instructions; the instructions when executed by the processor cause the processor to perform operations comprising: - claim 19}:
collecting transactional data from point-of-sale (POS) terminals and self-checkout (SCO) terminals;
(original vs citation vs clarification: strikethrough of italicized original followed by plain lettering clarification in bold) calculating data from the transactional data [for the limitations above, see at least Figs. 2 and 13 and [0070-0071, 0128-0129] computer “As shown in FIG. 2 , a system for service location optimization may be employed in retail service location 100 as depicted in FIG. 1 . Specifically, as shown in FIG. 1 , retail service location 100 may include one or more manned POS terminals 110, also referred to a “full service” checkouts, one or more unmanned POS terminals 150, also referred to a “self” checkouts … Data from POS terminals 110 and 150, and from the various sensors, may be provided to service location optimizer 260. …
Service location optimizer 260 may generate metrics, predictions, models, and recommended optimizations of retail service location 100”;
[0072] “Service location metric calculator 210 may use information received from POS terminals 110 and 150, and from the various sensors, to calculate various metrics of retail service location 100 including, for example”];
(original vs citation vs clarification: strikethrough of italicized original followed by plain lettering clarification in bold) determining a relationship between first inputs and a total number of cashiers operating the POS terminals based on the second inputs [see at least [0036] Manned POS has a single clerk or cashier “As shown in FIG. 1 , retail service location 100 may include number of manned, or “full service,” point of sale (POS) terminals 110, such as manned POS terminals 110 a-110 c shown in FIG. 1 . Retail service location 100 may further include number of unmanned, or “self service,” POS terminals 150, such as unmanned POS terminals 150 a-150 b shown in FIG. 1 . … In addition, a manned POS terminal 110 that is in an “open” operational status may be operated by a clerk or cashier 140.”;
[0072-0074] “Service location metric calculator 210 may use information received from POS terminals 110 and 150, and from the various sensors, to calculate various metrics of retail service location 100 including, for example, …
The information received from POS terminals 110 and 150, and from the various sensors, as well as the metrics calculated by service location metric calculator 210 may be stored in database 220.”;
[0075-0078] prediction modeling module and optimization module perform same operation “Prediction modeling module 230 may use the measurements and metrics produced by service location metric calculator 210 to model and then predict one or more metrics of retail service location 100, such as those discussed above, based on actual measured past metrics of retail service location 100, or of other of retail service locations 100, such as may be stored in database 220, and user-supplied settings for one or more operational characteristics of retail service location 100. The user-supplied settings for one or more operational characteristics may include, for example, … a schedule of POS terminals 110 and 150 that are in operation according to time of day, day of the week, and other factors, such as holidays, special events, and retailer or manufacturer promotions, etc. …
Prediction modeling module 230 may produce the predicted metrics by any suitable means, including, for example, queue theory, machine learning”;
[0047, 0075] determining a relationship between inputs including ([0075]) schedule for manned and unmanned POS (where manned is a single clerk per manned as noted in [0036] and there could be no unmanned POS scheduled) and Laserson’s data (traffic at the POS terminals and the SCO terminals and the POS idle times and the SCO idle times) “Queue theory modelling may be used to understand and design workflows for multiple retail service locations 100. This may include using a mathematical approach to capture, define, and optimize, for example, a relationship … . Such a relationship may be used to generate and display desired metrics, such as … recommended store/location configurations, such as the optimized number and mix of manned vs. unmanned service lanes.”];
training a machine learning model (MLM) using the relationship and transactional features [see at least [0034-0035, 0082-0084, 0086, 0101] training a machine learning model may use various data;
[0072-0074] training data such as relationship and transaction features “Service location metric calculator 210 may use information received from POS terminals 110 and 150, and from the various sensors, to calculate various metrics of retail service location 100 including, for example”;
[0047, 0075] training data such as relationship, where determining a relationship between inputs including ([0075]) schedule for manned and unmanned POS (where manned is a single clerk per manned as noted in [0036] and there could be no unmanned POS scheduled) and Laserson’s data (traffic at the POS terminals and the SCO terminals and the POS idle times and the SCO idle times) “Queue theory modelling may be used to understand and design workflows for multiple retail service locations 100. This may include using a mathematical approach to capture, define, and optimize, for example, a relationship … . Such a relationship may be used to generate and display desired metrics, such as … recommended store/location configurations, such as the optimized number and mix of manned vs. unmanned service lanes.”];
applying the MLM to predict a current POS terminal staffing level for a store [see at least [0075, 0072] machine learning used to predict metrics of a store, where metrics [0072] are not limited “Prediction modeling module 230 may use the measurements and metrics produced by service location metric calculator 210 to model and then predict one or more metrics of retail service location 100, such as those discussed above, based on actual measured past metrics of retail service location 100, or of other of retail service locations 100, such as may be stored in database 220, and user-supplied settings for one or more operational characteristics of retail service location 100.”;
[0113, 0105] ([0113]) metrics such as staffing level as manned POS require staff “At operation 615, the method may calculate total POS terminals 110 and 150” such as ([0105]) current operations of terminals such as staffing “An operational level of POS terminals 110 and 150 may be determined based on collected current or historical data, or may be specified by a user.”;
[0042, 0044, 0047] further define machine learning “Such analysis may not only indicate potential performance gains for a particular retail service location 100, but may proactively identify retail service locations 100 that may benefit from such an analysis. …
Queue theory modelling may be used to understand and design workflows for multiple retail service locations 100. … Such analysis may lead to a better understanding of the operation of a retail service locations 100, and may be used to suggest improved configurations and operations of retail service locations 100.”]; and
outputting an indication as to whether staffing at the POS terminals of the store is busy or non-busy based on the current POS terminal staffing level provided by the MLM {provide one or more indications of a proper staffing level or an improper staffing level to the one or more stores based on one or more predicted staffing levels provided by the MLM - claim 19} [see at least [0111, 0113-0114] “FIG. 6 depicts a flowchart of calculating an optimized customer wait time in a method of service location optimization, according to one or more embodiments.” … step 615 current staffing is used to determine step 630 “At operation 625, the method may calculate a predicted service rate of the operational POS terminal 110 or 150. At operation 630, the method may calculate an average waiting time in a queue of the operational POS terminal 110 or 150.” … step 630 is used to determine step 640-650 which determines more or less staff are needed “Otherwise, at operation 645, the method may select the total operational POS terminals 110 and 150 from the second list as an optimal total POS terminals 110 and 150 for the specified conditions.” where “a second list, or data frame D2, of operational POS terminals 110 and 150 having an average wait time greater than the target average waiting time per customer”;
[0119, 0121, 0018] “FIG. 11 depicts a process flow 1100 of a method of service location optimization, according to one or more embodiments. As shown in FIG. 11 , process flow 1100 may include calculation of one or more models 1110, execution of one or more algorithms 1120, and production of one or more outputs 1140.”].
Pachigar teaches store analysis but does not explicitly teach, however in the similar field of store analysis Laserson discloses
calculating POS idle times and SCO idle times from the transactional data [see at least Fig. 1B and [0014, 0031-0035] computer “System 100 comprises a cloud/server 110, one or more retailer servers 120, Self-Service Terminals (SSTs) 130, and Point-Of-Sale (POS) terminals 140.”;
[0001, 0024] store or stores;
[0034-0035] transaction data for SST ([0034]) and POS ([0035]) “Medium 132 also comprises a transaction log 134 that comprises transaction details for transactions, total number of transactions, total number of items per transaction, items per transaction, price of transaction, calendar date, time of day, etc. …
Medium 142 also comprises a transaction log 144 (as discussed above with the SST 130).”;
[0036] collecting transaction data “It is to be noted that transaction logs 134 and 144 may also be stored on the corresponding retailer server 120, such that preference analytic manager 113 receives or obtains transaction logs 134 and 144 from SST 130, POS terminal 140, and/or retailer server 120.”;
[0037, 0061] calculate idle time “Transaction logs 134 and 144 are analyzed by preference analytic manager 113 in view of the 2×2 preference model (FIG. 2A) and in view of the basket sizes (total number of purchased items) of each transaction. Low and high occupancy for the SSTs 130 and POS terminals 140 is calculated by preference analytical manager 113 as a percentage of idle time (no transaction activity being reported) at each of the terminals 130 and 140 in any given configured interval or time. …
In an embodiment of 211 and 230, at 231, the self-checkout analytic service calculates the first occupancy rate based on a first idle time during which the POS terminals 140 are inactive/idle for the interval. The self-checkout analytic service also calculates a second occupancy rate based on a second idle time during which the SSTs 130 are inactive/idle for the interval.”];
(original vs citation) determining a relationship between traffic at the POS terminals and the SCO terminals and a total number of cashiers operating the POS terminals based on the POS idle times and the SCO idle times [see at least Fig. 1A and [0041] traffic at the POS terminals and the SCO terminals “preference analytic manager 113 determines a state of the terminals 130 and 140 that maps to the preference model of FIG. 1A. For example, the bottom left quadrant represents a first state of the checkout terminals 130 and 140 “when a shopper/customer has a choice during checkout,” since analytic manager 113 determines there is low occupancy (traffic) on the SSTs 130 and the POS terminals 140.”;
[0037, 0061] calculate idle time].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pachigar with Laserson to include the limitation(s) above as disclosed by Laserson. Doing so would improve Pachigar’s (Pachigar [0072]) store analysis via additional methods of analysis to determine proper staffing [see at least Laserson [0007, 0037, 0061] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Pachigar and b) Laserson and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Laserson also teaches as noted in the citation above.
a method, comprising
{a system, comprising: a processor; a non-transitory computer-readable storage medium comprising instructions; the instructions when executed by the processor cause the processor to perform operations comprising: - claim 19}:
collecting transactional data from point-of-sale (POS) terminals and self-checkout (SCO) terminals.
Regarding claim 13, modified Pachigar teaches the method of claim 12,
and Pachigar teaches wherein collecting further comprises determining, from the transactional data, particular transactional features that comprise one or more of a total number of item line counts, a completed ticket count, a total SCO terminal sign-on duration, and a total POS terminal sign-on duration [see at least [0072, claim 1] “Service location metric calculator 210 may use information received from POS terminals 110 and 150, and from the various sensors, to calculate various metrics of retail service location 100 including, for example, customer 115 arrivals per time period (minute, hour, day-part, day, etc.), total number of transactions per time period (minute, hour, day-part, day, etc., aggregated for all of retail service location 100, per each POS terminal 110 or 150, per type of POS terminal 110 or 150, etc.), average number of items per transaction (per minute, hour, day-part, day, etc., aggregated for all of retail service location 100, per each POS terminal 110 or 150, per type of POS terminal 110 or 150, etc.), average basket size per customer 115 (such as total amount spent, per minute, hour, day-part, day, etc., aggregated for all of retail service location 100, per each POS terminal 110 or 150, per type of POS terminal 110 or 150, etc.), customer traffic in retail service location 100 per time period (minute, hour, day-part, day, etc.), average number and percentage of POS terminals 110 and 150 in operation per time period (per minute, hour, day-part, day, etc., aggregated for all of retail service location 100, per each POS terminal 110 or 150, per type of POS terminal 110 or 150, etc.), distribution of all transactions by POS terminal type (per minute, hour, day-part, day, etc., per type of POS terminal 110 or 150), percentage of total spent by all customers 115 by POS terminal type (per minute, hour, day-part, day, etc., per type of POS terminal 110 or 150), etc.”].
Regarding claim 17, modified Pachigar teaches the method of claim 12,
as well as (original vs citation vs clarification: strikethrough of italicized original followed by plain lettering clarification in bold) processing the applying and the outputting for the store with terminals.
Pachigar teaches (original vs citation) further comprising: processing the applying and the outputting for a different store that lacks any available SCO terminals [see at least [0042, 0044, 0047] multiple location “Such analysis may not only indicate potential performance gains for a particular retail service location 100, but may proactively identify retail service locations 100 that may benefit from such an analysis. …
Queue theory modelling may be used to understand and design workflows for multiple retail service locations 100. … Such analysis may lead to a better understanding of the operation of a retail service locations 100, and may be used to suggest improved configurations and operations of retail service locations 100.”;
[0070] POS and SCO terminals are both optional as noted by may include “As shown in FIG. 2 , a system for service location optimization may be employed in retail service location 100 as depicted in FIG. 1 . Specifically, as shown in FIG. 1 , retail service location 100 may include one or more manned POS terminals 110, also referred to a “full service” checkouts, one or more unmanned POS terminals 150, also referred to a “self” checkouts”].
Regarding claim 15, modified Pachigar teaches the method of claim 12, .
Modified Pachigar teaches store analysis but does not explicitly teach, however in the similar field of store analysis Laserson discloses
wherein determining further comprises identifying the relationship as a situation in which customers are likely turning to use the SCO terminals due to understaffed cashiers at the POS terminals [see at least Fig. 1A and [0044] “A fourth state identified from the preference model of FIG. 1A (top right quadrant) represents a situation when the customer has limited options because both the POS terminals 140 and the SST 130 have long queues and the customer's wait times are long. This state when combined with the opened POS terminals 140 and opened SSTs 130 may also be used to identify that more lanes (SCO and cashier-assisted) need or needed to be opened for customer during the current interval or time or during the interval of time being reported.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Pachigar with Laserson to include the limitation(s) above as disclosed by Laserson. Doing so would improve modified Pachigar’s (Pachigar [0072]) store analysis via additional methods of analysis to determine proper staffing [see at least Laserson [0007, 0037, 0061] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Pachigar and b) Laserson and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 20, modified Pachigar teaches the system of claim 19,
and Pachigar teaches wherein the operations further comprise: determining a particular hour to be “busy” when usage at both the SCO terminals and the POS terminals are above one or more thresholds [see at least [0105] “An operational level of POS terminals 110 and 150 may be determined based on collected current or historical data, or may be specified by a user. … If the specified number or operational percentage of POS terminals 110 and 150 is such that an expected wait time is above a predetermined threshold, the system may limit the number or operational percentage of POS terminals 110 and 150 to a range in which the expected wait time is below the predetermined threshold.”].
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pachigar in view of Laserson as applied to claim(s) 12 above and further in view of Askarov published July 15, 2024 (reference U on the Notice of References Cited).
Regarding claim 14, modified Pachigar teaches the method of claim 12,
and Pachigar teaches (original vs citation) wherein calculating further comprises determining a percentage of time that particular cashiers operating the POS terminals are not processing transactions out of total sign-on durations for the particular cashiers [see at least [0037, 0061] calculate idle time “Transaction logs 134 and 144 are analyzed by preference analytic manager 113 in view of the 2×2 preference model (FIG. 2A) and in view of the basket sizes (total number of purchased items) of each transaction. Low and high occupancy for the SSTs 130 and POS terminals 140 is calculated by preference analytical manager 113 as a percentage of idle time (no transaction activity being reported) at each of the terminals 130 and 140 in any given configured interval or time. …
In an embodiment of 211 and 230, at 231, the self-checkout analytic service calculates the first occupancy rate based on a first idle time during which the POS terminals 140 are inactive/idle for the interval. The self-checkout analytic service also calculates a second occupancy rate based on a second idle time during which the SSTs 130 are inactive/idle for the interval.”].
Modified Pachigar teaches store analysis but does not explicitly teach, however in the similar field of store analysis Askarov discloses
wherein calculating further comprises determining a percentage of time that particular cashiers operating the POS terminals are not processing transactions out of total sign-on durations for the particular cashiers [see at least [pg 3-4] calculate idle time “There are three different types of idle time:
Worker’s idle time percentage: This number reflects the frequency with which employees engage in activities unrelated to work, highlighting instances of inactivity within the workplace.
…
Computer idle time percentage: This type of idle percentage shows when employees are not actively utilizing their computer systems during designated work hours. Also, when computers remain inactive.
…
Percentage of Idle Time Formula
The percent idle time formula is very simple and is as follows:
Idle Time Percentage= (Total Time/Idle Time) × 100
Suppose an employee works for 8 hours in a day but is actively engaged in work-related tasks for only 6 hours. The idle time is the difference between the total and active time, which is 8 – 6 hours = 2 hours. Using the formula:
Idle Time Percentage= (8hours/2hours) × 100=25%
The idle time percentage is 25%, indicating that 25% of the total work hours were spent in idle or non-productive activities.”;
[pg 9] sign-on durations used in equations above as employee works for set hours in a day “2. Time Tracking Software: Tools like Monitask use automated data entry, where employees log in and out digitally to track mouse clicks, keyboard activity, and application usage, providing a comprehensive picture of each employee’s time allocation.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Pachigar with Askarov to include the limitation(s) above as disclosed by Askarov. Doing so would improve modified Pachigar’s (Pachigar [0072]) store analysis via additional methods of analysis to determine proper staffing [see at least Askarov [pg 1-2] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Pachigar and b) Askarov and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pachigar in view of Laserson as applied to claim(s) 12 above and further in view of Bai et al. (US 2019/0147350 A1).
Regarding claim 16, modified Pachigar teaches the method of claim 12, .
Modified Pachigar does not explicitly teach, however Bai discloses
wherein training further comprises implementing the MLM as a decision tree with a Gini index for feature selection [see at least [0039] “In particular, the present invention may approximate the model (for example, the model trained via the machine learning, such as a linear regression (LR) model, a deep neural network (DNN) model, a factorization machine (FM) model, a virtual vector machine (SVM) model) that is difficult to be understood as a decision tree model and present the approximated decision tree model, so that the user may better understand the model based on the presented decision tree model. Taking the machine learning field as an example, in a system such as a modeling platform, a business decision software, or other system that needs to explain the model prediction mechanism to the user, the present invention may approximate the machine learning model as a decision tree model and present the approximated the decision tree model to the user (for example, present in a graphical or graphical form), thereby helping the user well understand the machine learning model that is previously difficult to be understood.”;
[0081-0083] “Referring again to FIGS. 2 and 3, based on the decision tree training samples acquired in step S120, step S130 may be performed, for example, the decision tree model training module 230 may train the decision tree model using the at least one decision tree training sample. Here, the decision tree model training module 230 may receive the decision tree training samples acquired by the decision tree training sample acquisition module 220, and fit the decision tree model approximate to the prediction model using a decision tree algorithm based on the obtained decision tree training samples.
The decision tree is a tree structure in which each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category. Here, the decision tree model may be established in various ways.
Specifically, in the process of establishing the decision tree model, certain criteria may be applied to select the best attribute for splitting, so that the category of a subtree obtained according to a judgment of a node is as pure as possible, that is, the attribute with the strongest differentiating ability. Common measuring criterions of the attribute information include information gain, gain ratio, Gini index, etc., the details are as follows:”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Pachigar with Bai to include the limitation(s) above as disclosed by Bai. Doing so would improve modified Pachigar’s (Pachigar [0072]) machine learning via an easy to understand machine learning [see at least Bai [0002-0021] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Pachigar and b) Bai and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pachigar in view of Laserson as applied to claim(s) 12 above and further in view of Graph Maker published September 20, 2024 (reference V on the Notice of References Cited) and Marom et al. (US 2024/0330823 A1).
Regarding claim 18, modified Pachigar teaches the method of claim 12, as well as POS terminal idle time (POS terminals based on the POS idle times).
Pachigar teaches (original vs citation vs clarification: strikethrough of italicized original followed by plain lettering clarification in bold) further comprising: generating a visualization of staffing levels for a predefined interval over time to graphically depict a distribution of data; and
providing the visualization to enable staffing level decisions at the store [see at least [0075, 0072] machine learning used to predict metrics of a store, where metrics [0072] are not limited “Prediction modeling module 230 may use the measurements and metrics produced by service location metric calculator 210 to model and then predict one or more metrics of retail service location 100, such as those discussed above, based on actual measured past metrics of retail service location 100, or of other of retail service locations 100, such as may be stored in database 220, and user-supplied settings for one or more operational characteristics of retail service location 100.”;
[0113, 0105] ([0113]) metrics such as staffing level as manned POS require staff “At operation 615, the method may calculate total POS terminals 110 and 150” such as ([0105]) current operations of terminals such as staffing “An operational level of POS terminals 110 and 150 may be determined based on collected current or historical data, or may be specified by a user.”;
[0042, 0044, 0047] further define machine learning “Such analysis may not only indicate potential performance gains for a particular retail service location 100, but may proactively identify retail service locations 100 that may benefit from such an analysis. …
Queue theory modelling may be used to understand and design workflows for multiple retail service locations 100. … Such analysis may lead to a better understanding of the operation of a retail service locations 100, and may be used to suggest improved configurations and operations of retail service locations 100.”;
[0042, 0044, 0047] multiple locations “Such analysis may not only indicate potential performance gains for a particular retail service location 100, but may proactively identify retail service locations 100 that may benefit from such an analysis.”;
[0082] report based on output of machine learning “Service location optimizer 260 may include functions for retrieving data related to the operation of one or more retail service locations 100, analyzing such data, e.g., to model and then predict one or more metrics of retail service location 100 or to generate recommended operational characteristics of retail service location 100 to optimize one or more metrics of retail service location 100 based on the output of the machine-learning model, and/or generate one or more reports or user interfaces to display the generated models, predictions, or recommendations, e.g., as generated based on the machine-learning model.”;
[0111, 0113-0114] further define report such as staffing level decision “FIG. 6 depicts a flowchart of calculating an optimized customer wait time in a method of service location optimization, according to one or more embodiments.” … step 615 current staffing is used to determine step 630 “At operation 625, the method may calculate a predicted service rate of the operational POS terminal 110 or 150. At operation 630, the method may calculate an average waiting time in a queue of the operational POS terminal 110 or 150.” … step 630 is used to determine step 640-650 which determines more or less staff are needed “Otherwise, at operation 645, the method may select the total operational POS terminals 110 and 150 from the second list as an optimal total POS terminals 110 and 150 for the specified conditions.” where “a second list, or data frame D2, of operational POS terminals 110 and 150 having an average wait time greater than the target average waiting time per customer”].
Modified Pachigar does not explicitly teach, however Graph Maker discloses
(original vs citation vs clarification: strikethrough of italicized original followed by plain lettering clarification in bold) generating a visualization of staffing levels for a predefined interval over time to graphically depict item 1 versus item 2 [see at least [pg 1-2] a spreadsheet of items 1 and 2 and a graph based on the items].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Pachigar with Graph Maker to include the limitation(s) above as disclosed by Graph Maker. Doing so would improve modified Pachigar’s (Pachigar [0072]) store analysis via clarified improvements of reports [see at least Graph Maker [pg 1-2] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Pachigar and b) Graph Maker and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Modified Pachigar in view of Graph Maker does not explicitly teach, however Marom discloses
(original vs citation vs clarification: strikethrough of italicized original followed by plain lettering clarification in bold) generating a visualization of staffing data for a predefined interval over time to graphically depict a distribution of items per sign-on duration for the POS terminals versus POS terminal idle time [see at least [0014-0015] “Future predicted metrics and events are derived using the store's sales forecasting system over a given period of time, such as over a day, a week, a few weeks, a month, etc. … The metrics, events, certain customer data, and certain employee data are organized by transaction into data sets using a transaction identifier, a terminal identifier, a cashier identifier, a customer identifier, and/or time stamps associated with a given transaction.”;
[0004] “The MLM is trained to produce the scores and the prescriptive action identifiers at predefined intervals over a given period of time that extends from a current time to a future time.”;
[0043] “In an embodiment, the MLM 116 produces sets of scores for business hours of a store over a period of time of a week into the future in predefined intervals of time. For example, for a given business day in which a store opens at 8:00 am and closes at 10:00 pm, the MLM 116 is provided with forecasted data from the forecasting system 126 that covers a configurable interval of time (e.g., a week into the future) as well as actual store data over a historical time period (e.g., a prior week). The MLM 116 provides, as output, scores for the features and the combination of features along with the predicted action identifiers at each interval of time projected out from a current interval of time to a week into the future, such as for each half hour or each hour of operation of the store beginning at 8:00 am and ending at 10:00 pm for a week's worth of business days. Each interval maps to a corresponding portion of a given business day and each interval includes its own set of scores for the features and combination of features and corresponding action identifiers. At each current interval of time, updated sets of scores for the remaining business days and hours of the store included in the overall period are provided by the MLM 116. So, the MLM 116 provides a time series for a preset period of time and continuously updates predictions for the period of time at predetermined intervals within the period based on current data being received from the store.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Pachigar in view of Graph Maker with Marom to include the limitation(s) above as disclosed by Marom. Doing so would improve modified Pachigar in view of Graph Maker’s (Pachigar [0072]) store analysis via clarified improvements of reports based on more specific inputs [see at least Marom [0014-0015, 0004, 0043] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Pachigar in view of Graph Maker and b) Marom and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pachigar et al. – EP 4300392 A1 (relevant because it teaches same as US 2024/0403741 A1)
Mohamad et al. – Application of Discrete Event Simulation (DES) for Queuing System Improvement at Hypermarket (relevant because it teaches simulating queuing)
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/JAMES WEBB/Examiner, Art Unit 3624
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