DETAILED ACTION
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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of Claims
Claims 1-20 are currently pending and are being hereby examined herein. Claims 1, 4, 6-10, 12, and 15-20 are amended.
Joint Inventors
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Response to Amendment / Remarks
Any reference to the prior office action refers to the Non-Final Rejection dated 5 June 2026.
All objections to the claims from the prior office action are withdrawn in view of the amended claims.
All rejections under 35 U.S.C. 101 are withdrawn because the amended independent claims now incorporate a positive and physical control step in the real-world environment, and therefore, now recite a practical application.
Applicant’s arguments, with respect to the prior art of record from the prior office action, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
The claims are objected to because of the following informalities:
Claims 1 and 12: “at least one of a presence of an obstacle, a closed path in the facility environment, and a charging state of the at least one autonomous mobile robot” should be “at least one of a presence of an obstacle, a closed path in the facility environment, [[and]]or a charging state of the at least one autonomous mobile robot”.
Appropriate corrections are required.
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 U.S. Pub. No. 2021/0311498 (Okazaki et al., hereinafter, Okazaki) in view of EP 4 310 746 A1 (Wisetjindawat and Harada, hereinafter, Wisetjindawat) in further view of U.S. Pub. No. 2024/0320582 (Taub et al., hereinafter, Taub).
Regarding Claim 1, Okazaki discloses A processor-implemented method (see at least [0053]), the method comprising:
acquiring worker traffic data and environment sensing data of a facility environment that includes a plurality of autonomous mobile robots (see at least FIG. 1 and FIG. 2: travel information 82, passenger information 84, and waiting passenger information 86 are acquired by operation management device 12);
generating a dynamic operation profile for the plurality of autonomous mobile robots, based on the worker traffic data and the environment sensing data (see at least [0057], [0100]-[0113], and FIG. 17: “The operation monitoring unit 18 obtains the operation states of the vehicles 52 in accordance with the travel information 82 transmitted from each vehicle 52. The travel information 82 includes the current position of the vehicles 52, as described above. The operation monitoring unit 18 compares the position of each vehicle 52 with the travel plan 80 and calculates a delay amount DL of the vehicle 52 with respect to the travel plan 80”; “The plan generation unit 14 estimates the boarding and exiting time of the largest delay vehicle 52 in accordance with at least one of the passenger information 84 and the waiting passenger information 86 (S34)”; “On the other hand, if the risk of increase in delay R exceeds the reference risk R (No in S38), the plan generation unit 14 prioritizes the recovery of the delay and thus the solving of the interval error. Therefore, in this case, the plan generation unit 14 generates the travel plan 80 according to the second solving policy of decelerating some vehicles 52 on the travel plan 80”);
transmitting a control command to the plurality of autonomous mobile robots in accordance with the dynamic operation profile, wherein the control command causes at least one autonomous mobile robot of the plurality of autonomous mobile robots to change at least one of a path, a speed, and a driving interval of the at least one autonomous mobile robot… (see at least [0047] and [0056]: “The communication device 16 transmits the travel plan 80 generated and regenerated by the plan generation unit 14 to the vehicles 52”; “The vehicles 52 travel autonomously in accordance with a travel plan 80 provided by the operation management device 12. The travel plan 80 defines a travel schedule for each vehicle 52”; “The vehicles 52 travel autonomously to ensure departure at specified departure timing determined in the travel plan 80”); and
updating the dynamic operation profile by adjusting operation parameters of at least some of the plurality of autonomous mobile robots in the facility environment based on the worker traffic data, the environment sensing data… (see at least [0056]-[0057], [0100]-[0113], and FIG. 17: “After generating the travel plan 80, the plan generation unit 14 stands by for a certain amount of time (S44), returns to step S30 again, and then repeats the same process”; “When the risk of increase in delay R is calculated, the plan generation unit 14 compares the risk of increase in delay R with a predetermined reference risk Rdef (S38). As a result of the comparison, if the risk of increase in delay R is small (Yes in S38), avoiding the increase in the travel time and the waiting time is prioritized over recovering the delay (and thus eliminating the interval error). Therefore, in this case, the plan generation unit 14 generates the travel plan 80 according to the first solving policy that does not decelerate any vehicle 52 (S40)”; “On the other hand, if the risk of increase in delay R exceeds the reference risk R (No in S38), the plan generation unit 14 prioritizes the recovery of the delay and thus the solving of the interval error. Therefore, in this case, the plan generation unit 14 generates the travel plan 80 according to the second solving policy of decelerating some vehicles 52 on the travel plan 80”).
Furthermore, one of ordinary skill in the art would understand there are monetary costs associated with deploying additional vehicles that need to be balanced with the longer wait times riders experience when there are fewer vehicles / Okazaki suggests updating the dynamic operation profile by adjusting operation parameters of at least some of the plurality of autonomous mobile robots in the facility environment based on…cost data associated with operating the plurality of autonomous mobile robots (see at least [0054]: “the plan generation unit 14 determines whether to add new a vehicle 52 to the fleet and whether to reduce the number of the vehicles 52 of the fleet according to a transportation demand and other factors”; one of ordinary skill in the art would understand other factors to include cost data to some extent, otherwise the number of vehicles would always be high; Okazaki does not explicitly state there is a monetary cost to the additional vehicles, but as one of ordinary skill in the art would understand, if the only goal was to minimize wait time, the number of vehicles would almost always increase).
However, Okazaki does not appear to explicitly disclose wherein the control command causes at least one autonomous mobile robot of the plurality of autonomous mobile robots to change at least one of a path, a speed, and a driving interval of the at least one autonomous mobile robot, in response to the environment sensing data indicating at least one of a presence of an obstacle, a closed path in the facility environment, and a charging state of the at least one autonomous mobile robot and updating the dynamic operation profile by adjusting operation parameters of at least some of the plurality of autonomous mobile robots in the facility environment based on…cost data associated with operating the plurality of autonomous mobile robots.
Wisetjindawat, in the same field of transportation networks, and therefore analogous art, explicitly teaches wherein the control command causes at least one autonomous mobile robot of the plurality of autonomous mobile robots to change at least one of a path, a speed, and a driving interval of the at least one autonomous mobile robot, in response to the environment sensing data indicating at least one of a presence of an obstacle, a closed path in the facility environment, and a charging state of the at least one autonomous mobile robot (see at least [0002], [0008], [0014], [0016], [0104], and [0106]: network responds to real-time information, drivable range is considered for bus scheduling, remaining charge level is evaluated to determined drivable range, buses may be self-driving).
Combining the teachings of Wisetjindawat (confirming charge level / drivable range) with the teachings of Okazaki (directed to maintaining spacing on an already determined fixed route, including by adding additional vehicles) would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, with the motivation of using the known technique of Wisetjindawat of ensuring the vehicle being added has enough energy to travel the required distances to improve the similar invention of Okazaki in the same way (i.e., the combination is at least ensuring the vehicles that may be added to Okazaki have adequate charge).
Taub, in the same field of transportation planning, and therefore analogous art, explicitly teaches that reducing the number of vehicles reduces operation costs (see at least [0007]: “reduce the number of vehicles needed and the operational costs”). Therefore, Taub teaches updating the dynamic operation profile by adjusting operation parameters of at least some of the plurality of autonomous mobile robots in the facility environment based on…cost data associated with operating the plurality of autonomous mobile robots ([0007]: “reduce the number of vehicles needed and the operational costs”).
Combining teachings of Taub (optimization of time tables for planning a public transit system based on historical ridership data) with the teachings of Okazaki (primarily directed to maintaining spacing on an already determined fixed route) / the Okazaki and Wisetjindawat combination (directed to also confirming charge level in real time before adding a vehicle) would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, with the motivation of developing a baseline transportation plan with reduced cost and better service to riders (see at least Taub [0007]-[0008] and [0010]).
Regarding Claim 2, the Okazaki, Wisetjindawat, and Taub combination teaches Claim 1. Furthermore, Okazaki further discloses wherein the worker traffic data comprises a movement position and a count of workers entering the facility environment (see at least [0019], [0050]-[0052], and [0102]-[0106]: “The communication device may receive at least one of two kinds of information: passenger information sent from the vehicle and concerning passengers of the vehicle, and waiting passenger information sent from a station terminal at a station on the travel route and concerning people waiting for the vehicle at the station, and the plan generation unit may select one of the two or more solving policies in accordance with the number of vehicles and at least one of the passenger information and the waiting passenger information”; “The in-station sensor 72 detects a state of the station 54, in particular, the number and attributes of persons waiting for the vehicle 52 at the station 54. The in-station sensor 72 is, for example, a camera that captures images of the station 54, a weight sensor that detects the total weight of the waiting people, or the like. Information detected by the in-station sensor 72 is transmitted to the operation management device 12 as waiting passenger information 86”; “The in-vehicle sensor 64 detects conditions inside the vehicle 52, in particular, the number and attributes of the passengers. Attributes are characteristics that affect the boarding/exiting time of passengers, and may include, for example, at least one of the following: the use of wheelchairs, the use of white canes, the use of strollers, the use of braces, and age groups. Such an in-vehicle sensor 64 is, for example, a camera that captures images of the interior of the vehicle, a weight sensor that detects the total weight of the passengers, and the like. Information detected by the in-vehicle sensor 64 is transmitted to the operation management device 12 as passenger information 84”).
Regarding Claim 3, the Okazaki, Wisetjindawat, and Taub combination teaches Claim 1. Furthermore, Okazaki further discloses wherein the environment sensing data comprises robot state information of the plurality of autonomous mobile robots and path state information of the facility environment acquired from at least some of the plurality of autonomous mobile robots and sensors within the facility environment (see at least [0049]-[0052], [0057], [0102]-[0106], [0113], and FIG. 2: “The vehicle 52 further includes an in-vehicle sensor 64 and a communication device 68. The in-vehicle sensor 64 detects conditions inside the vehicle 52, in particular, the number and attributes of the passengers. Attributes are characteristics that affect the boarding/exiting time of passengers, and may include, for example, at least one of the following: the use of wheelchairs, the use of white canes, the use of strollers, the use of braces, and age groups. Such an in-vehicle sensor 64 is, for example, a camera that captures images of the interior of the vehicle, a weight sensor that detects the total weight of the passengers, and the like. Information detected by the in-vehicle sensor 64 is transmitted to the operation management device 12 as passenger information 84”; “A station terminal 70 is provided at each station 54. The station terminal 70 has a communication device 74 and an in-station sensor 72. The in-station sensor 72 detects a state of the station 54, in particular, the number and attributes of persons waiting for the vehicle 52 at the station 54. The in-station sensor 72 is, for example, a camera that captures images of the station 54, a weight sensor that detects the total weight of the waiting people, or the like. Information detected by the in-station sensor 72 is transmitted to the operation management device 12 as waiting passenger information 86. A communication device 16 is provided to enable the transmission of the waiting passenger information 86”; “The operation monitoring unit 18 obtains the operation states of the vehicles 52 in accordance with the travel information 82 transmitted from each vehicle 52. The travel information 82 includes the current position of the vehicles 52, as described above”; “the day of the week and time of the day, information on events in the vicinity of the stations, information on traffic congestion in the travel route 50, and the reservation status of the vehicles 52, if available, may be used to select the solving policy”).
Regarding Claim 4, the Okazaki, Wisetjindawat, and Taub combination teaches Claim 1. Furthermore, Okazaki further discloses wherein the generating of the dynamic operation profile comprises:
determining a commercial speed for the plurality of autonomous mobile robots in the facility environment, based on the worker traffic data, the environment sensing data, and performance data of an autonomous mobile robot (see at least [0113], FIG. 13, FIG. 14, and FIG. 17: “Although the travel plan 80 described heretofore specifies only the times of departure at the stations 54, the travel plan 80 may be in other forms. For example, the travel plan 80 may provide arrival times at the stations 54, an average travel speed VA of each vehicle 52, and the like, instead of or in addition to the departure times at the stations 54”);
determining a round-trip drive time for the plurality of autonomous mobile robots based on the commercial speed (see at least [0057]-[0063], [0077], [0113], FIG. 5, FIG. 13, and FIG. 14: lap time TC is a known calculation, standard scheduled speed VS in a known calculation, schedules are generated which show arrival times to each stop in the loop based on the traveling speeds; furthermore, “The solving policy may be selected by considering any factors other than those listed above if it is selected in accordance with at least the number of the vehicles 52, N. For example, the day of the week and time of the day, information on events in the vicinity of the stations, information on traffic congestion in the travel route 50, and the reservation status of the vehicles 52, if available, may be used to select the solving policy. The number of and the size of intervals between the stations 54 and the vehicles 52 may be changed as appropriate”);
determining a number of service autonomous mobile robots in the facility environment based on the worker traffic data, the environment sensing data, the performance data, and the round-trip drive time (see at least [0054], [0113], FIG. 13, and FIG. 14: “the plan generation unit 14 determines whether to add new a vehicle 52 to the fleet and whether to reduce the number of the vehicles 52 of the fleet according to a transportation demand and other factors”); and
generating the dynamic operation profile for at least some of the plurality of autonomous mobile robots corresponding to the determined number of service autonomous mobile robots (see at least [0054]: “If it is determined that an increase or reduction of vehicles is necessary, the plan generation unit 14 generates another travel plan 80”).
Regarding Claim 5, the Okazaki, Wisetjindawat, and Taub combination teaches Claim 4. Furthermore, Okazaki further discloses wherein the generating of the dynamic operation profile further comprises:
generating the dynamic operation profile by adjusting any one or any combination of any two or more of a path, a speed, and a driving interval of one or more autonomous mobile robots in an initial dynamic operation profile of the plurality of autonomous mobile robots, the adjustment being based on the worker traffic data and the environment sensing data, which are acquired in real time (see at least [0057], [0059], [0103]-[0110], [0113], and FIG. 17: “The operation monitoring unit 18 compares the position of each vehicle 52 with the travel plan 80 and calculates a delay amount DL of the vehicle 52 with respect to the travel plan 80. The delay amount DL may be a difference in distance between the target position and the actual position of the vehicles 52, or may be a difference in time between the target time to reach a specific point and the actual arrival time. The delay amount DL may be obtained at regular time intervals (e.g., every minute) or at the time when a specific event occurs”; “In this case, the operation management device 12 generates the travel plan 80 so that the departure interval of the vehicles 52 at each station 54 can be 20/4=5 minutes, which is the time calculated by dividing the lap time TC by the number of the vehicles 52, N”; “As illustrated in FIG. 17, when a delay exceeding a certain level occurs (Yes in S30), the plan generation unit 14 estimates the boarding and exiting time of the largest delay vehicle 52 in accordance with at least one of the passenger information 84 and the waiting passenger information 86 (S34)”; “On the other hand, if the risk of increase in delay R exceeds the reference risk R (No in S38), the plan generation unit 14 prioritizes the recovery of the delay and thus the solving of the interval error. Therefore, in this case, the plan generation unit 14 generates the travel plan 80 according to the second solving policy of decelerating some vehicles 52 on the travel plan 80”; “The number of and the size of intervals between the stations 54 and the vehicles 52 may be changed as appropriate”; “Although the travel plan 80 described heretofore specifies only the times of departure at the stations 54, the travel plan 80 may be in other forms. For example, the travel plan 80 may provide arrival times at the stations 54, an average travel speed VA of each vehicle 52, and the like, instead of or in addition to the departure times at the stations 54”).
Regarding Claim 6, the Okazaki, Wisetjindawat, and Taub combination teaches Claim 5. Furthermore, Okazaki further discloses transmitting a control command to at least some of the plurality of autonomous mobile robots in accordance with the dynamic operation profile by adjusting any one or any combination of any two or more of the path, the speed, and the driving interval of one or more of the plurality of autonomous mobile robots in the initial dynamic operation profile (see at least [0047], [0056], and [0113]: “The communication device 16 transmits the travel plan 80 generated and regenerated by the plan generation unit 14 to the vehicles 52”; “The vehicles 52 travel autonomously in accordance with a travel plan 80 provided by the operation management device 12”; “Although the travel plan 80 described heretofore specifies only the times of departure at the stations 54, the travel plan 80 may be in other forms. For example, the travel plan 80 may provide arrival times at the stations 54, an average travel speed VA of each vehicle 52, and the like, instead of or in addition to the departure times at the stations 54”).
Regarding Claim 7, the Okazaki, Wisetjindawat, and Taub combination teaches Claim 1. Furthermore, Okazaki further discloses “the plan generation unit 14 determines whether to add new a vehicle 52 to the fleet and whether to reduce the number of the vehicles 52 of the fleet according to a transportation demand and other factors” (see at least [0054]) and wherein the updating of the dynamic operation profile comprises…updating the dynamic operation profile by adjusting the operation parameters of at least some of the plurality of autonomous mobile robots based on the target number of the service autonomous mobile robots (see at least [0054]: “If it is determined that an increase or reduction of vehicles is necessary, the plan generation unit 14 generates another travel plan 80”).
As previously discussed, one of ordinary skill in the art would understand there are monetary costs associated with deploying additional vehicles that need to be balanced with the longer wait times riders experience when there are fewer vehicles (Okazaki does not explicitly state there is a monetary cost to the additional vehicles, but as one of ordinary skill in the art would understand, if the only goal was to minimize wait time, the number of vehicles would increase).
Taub (as part of the same combination as Claim 1 / with the same motivation to combine as Claim 1) teaches determining a total profit cost from operating the plurality of autonomous mobile robots in the facility environment, based on the worker traffic data and the cost data; determining a target number of service autonomous mobile robots in the facility environment to maximize the total profit cost (see at least [0007], [0079], [0081], and [0087]-[0096]: “Advantage of the invention can include more accurate rideshare planning, an ability to reduce the number of vehicles needed and the operational costs, improve vehicle utilization, improve rider travel duration in fixed route transit systems, reduction in rider wait times at transportation hubs, or any combination thereof”; “In some embodiments, an overall cost can be determined. The overall cost can be based on operational cost (e.g., the cost of operating the fixed route service and/or the on-demand-service) and rider's time cost”; “The method can involve determining, via the computing device, whether one or more of a subset of the fixed route lines or trips are to be removed from the received set of fixed route lines and trips, and whether the remaining fixed route trips are to be adjusted in time”; “In some embodiments, an optimal proposal for each ride can be determined. The optimal proposal can be a set of proposals, one for each ride, that minimize the overall cost”; “In some embodiments, where on-demand vehicles are used, the proposals for each ride request can be used to determine a vehicle fleet size necessary to service the plurality of ride requests”; “As long as no rides require the operation of a specific fixed route trip, the particular fixed route trip can be deactivated, and its operational cost avoided”).
Regarding Claim 8, the Okazaki, Wisetjindawat, and Taub combination teaches the limitations of Claim 7. Furthermore, Taub further teaches (as part of the same combination as Claim 1 / with the same motivation to combine as Claim 1) wherein the determining of the total profit cost comprises: determining a profit cost from operating the plurality of autonomous mobile robots in the facility environment, based on the worker traffic data; and determining the total profit cost by deducting a loss cost incurred by the plurality of autonomous mobile robots in the facility environment from the profit cost, the deduction being based on the environment sensing data and the cost data (see at least [0007]-[0008] and [0086]-[0087]: equation 1 is based on operational costs and the value of the riders’ time).
Regarding Claim 9, the Okazaki, Wisetjindawat, and Taub combination teaches the limitations of Claim 8. Furthermore, Taub further teaches (as part of the same combination as Claim 1 / with the same motivation to combine as Claim 1) wherein the loss cost incurred by the plurality of autonomous mobile robots in the facility environment comprises a fixed cost and a variable cost that are associated with operating an autonomous mobile robot in the facility environment (see at least [0087]: equation 1 is based on overhead (i.e., fixed cost) and based on operational time (i.e., variable cost)).
Regarding Claim 10, the Okazaki, Wisetjindawat, and Taub combination teaches Claim 1. Furthermore, Okazaki further discloses transmitting a control command to the plurality of autonomous mobile robots in the facility environment in accordance with the updated dynamic operation profile (see at least [0056]: “The communication device 16 transmits the travel plan 80 generated and regenerated by the plan generation unit 14 to the vehicles 52”).
Regarding Claim 11, the Okazaki, Wisetjindawat, and Taub combination teaches Claim 1. Furthermore, Okazaki further discloses A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors of an electronic device individually or collectively, cause the electronic device to perform the method of claim 1 (see at least [0053]).
Regarding Claim 12, most limitations are similar to Claim 1; therefore, Claim 12 is rejected for the same reasons as Claim 1. Furthermore, Okazaki discloses An electronic device comprising: one or more processors respectively comprising processing circuitry; and memory storing instructions (see at least [0053] and FIG. 2).
Regarding Claim 13, Claim 13 is substantially similar to Claim 2 and rejected for the same reasons as Claim 2.
Regarding Claim 14, Claim 14 is substantially similar to Claim 3 and rejected for the same reasons as Claim 3.
Regarding Claim 15, Claim 15 is substantially similar to Claim 4 and rejected for the same reasons as Claim 4.
Regarding Claim 16, Claim 16 is substantially similar to Claim 5 and rejected for the same reasons as Claim 5.
Regarding Claim 17, Claim 17 is substantially similar to Claim 6 and rejected for the same reasons as Claim 6.
Regarding Claim 18, Claim 18 is substantially similar to Claim 7 and rejected for the same reasons as Claim 7.
Regarding Claim 19, Claim 19 is substantially similar to Claim 8 and rejected for the same reasons as Claim 8.
Regarding Claim 20, Claim 20 is substantially similar to Claim 9 and rejected for the same reasons as Claim 9.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDRA ROBYN MORFORD whose telephone number is (571)272-6109. The examiner can normally be reached Monday - Friday 8:00 AM - 4:00 PM ET.
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/A.R.M./Examiner, Art Unit 3658
/THOMAS E WORDEN/Supervisory Patent Examiner, Art Unit 3658
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