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 .
Status of Claims
Claims 1-6, 8-9, and 11-12 are currently pending in this application. Claims 7, 10, and 13-20 are canceled.
Claim Objections
Claim 5 is objected to because of the following informality: The acronym “RCE” (at line 11) is misspelled. The correct acronym is “RCS”, which stands for the refrigeration control system.
Appropriate correction is required.
Specification Objections
The disclosure is objected to because of the following informalities: The acronym “RCE” (See Specification at line 7 of [0008]) is misspelled. The correct acronym is “RCS”, which stands for the refrigeration control system.
Appropriate correction is 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 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.
Claims 1-3 are rejected under 35 U.S.C. 103 as being unpatentable over Wolf et al. (US 2020/0334600-A1) in view of Manoharan et al. ("Learn to chill - Intelligent Chiller Scheduling using Meta-learning and Deep Reinforcement Learning", ACM DL, November 2021, p.21-30).
With respect to claim 1, Wolf teaches a system for controlling a blast cell based on a blast freezing optimization schedule (cold storage facilities, such as refrigerated warehouses, are used to store temperature-controlled items and maintain the items at a reduced temperature to prevent them from decaying…Cold storage facilities range across a wide array of sizes, from small walk-in coolers to large freezer warehouses. Several types of cold storages are available, such as…blast freezers and chillers, [0003] and figs.1, 6-9, and 12) the system comprising:
a blast cell (warehouse 112 of facility 110 with enclosed space 114 {such as…blast freezers and chillers, [0003]}, fig.1) configured to receive items to be cooled (warehouse 112 with enclosed space 114 to keep frozen foods below a predetermined temperature limit, fig.1 and [0040]);
a refrigeration control system comprising a controller (RCS) (controller 132, fig.1), the controller being configured to control operation of the blast cell to cool the items received in the blast cell (The controller 132 is configured to activate the refrigeration system 130 based on feedback from the sensors 134 to keep the enclosed space 114 at a temperature below a predetermined temperature limit, [0040]); and
a computer system (scheduler 140, a management server computer, fig.1 and [0045]) in communication with the RCS (scheduler 140, a management server computer, in communication with the controller 132, fig.1 and [0045]), wherein the computer system is configured to perform operations (scheduler 140 of the management server computer, fig.1 and [0045]) comprising:
receiving storage facility data (controller 132 collects measurements from the sensors 134 and time stamps based on a chronometer 136 (e.g., clock, timer) and provides that information to the scheduler 140 {scheduler 140 receive the measurement data}, [0045]);
determining a blast schedule for the blast cell based on applying a simulation model to the storage facility data (The scheduler 140 uses such information to determine a thermal model of the warehouse 112, [0045]), wherein the simulation model is to generate the blast schedule based on determining that a blast cell cost per pallet maximizes a blast profit margin for the storage facility by at least a threshold amount (the scheduler 140 can receive an energy cost schedule 162…to determine an energy cost model 147 of the refrigeration system 110, [0052]; a pallet of ice cream in plastic pails may absorb and release heat energy in different amounts and at different rates than a pallet of cases of onion rings packaged in plastic bags within corrugated cardboard boxes…the scheduler 140 can use information about the thermal properties the inventory 120 or changes in the inventory 120 to modify the thermal model and modify the operational schedules 142 to account for changes to the thermal model, [0058]; The scheduler 140 may modify the operational schedules 142 to offset the effect cooling the seafood from the incoming 10° F. to the warehouse's setpoint of 5° F. while also anticipating and offsetting the effects of variable energy pricing by prescribing a longer and/or colder period of pre-cooling [0059]; power consumption happens when power is relatively less expensive (e.g., the height of the power cost curve 456 is comparatively lower than the example power cost curve 356). During the discharge period 462, the air temperature 418 is allowed to relax back toward the −1° F. threshold, rather than consume power that is more expensive during the peak 455 of the power cost curve 454. By scheduling the charge period 460 (e.g., extra precooling during low-cost power times) and the discharge period 462 (e.g., allowing temperatures to partly relax during high-cost power times), the total energy cost associated with the power cost curve 456 can be less than the total energy cost associated with unscheduled operations such as those represented by the sum of the power cost curves 356, fig.4A and [0077]; the scheduler 140 can analyze the energy cost schedule 162 to identify a period of time in which the per-unit cost of power (e.g., dollars per kilowatt hour for electricity) is relatively high, and then identify another period of time in which the per-unit cost of power is relatively lower and precedes the high-cost period (e.g., identify a low price period that occurs before a peak price period). The scheduler 140 can then determine that at least a portion of the low-price period is to be used for chilling the enclosed space 114 an additional amount below the nominal temperature setpoint. The scheduler 140 can also determine that the refrigeration system 130 should not be operated any more than necessary to maintain the maximum temperature setpoint of the inventory 120…the schedule can cause the controller 132 to provide the enclosed space 114 with an extra thermal charge of cooling using cheap power so the inventory can stay below the maximum temperature for at least a while without consuming expensive power, [0113]; the optimal operational schedule 138 is configured to cause the refrigeration system 130 to cool the enclosed space 114 by an additional amount below the nominal temperature setpoint during a period of time during which the utility provider 160 charges a relatively lesser price for power, and stops the additional cooling and allows the enclosed space 114 to warm back toward the predetermined nominal temperature threshold during a period of time during which the utility provider 160 charges a relatively greater price for power, [0117]), wherein the blast profit margin is a numeric value that indicates blast freeze operation efficiencies for a facility (the scheduler 140 can analyze the energy cost schedule 162 to identify a period of time in which the per-unit cost of power (e.g., dollars per kilowatt hour for electricity) is relatively high, and then identify another period of time in which the per-unit cost of power is relatively lower and precedes the high-cost period (e.g., identify a low price period that occurs before a peak price period). The scheduler 140 can then determine that at least a portion of the low-price period is to be used for chilling the enclosed space 114 an additional amount below the nominal temperature setpoint. The scheduler 140 can also determine that the refrigeration system 130 should not be operated any more than necessary to maintain the maximum temperature setpoint of the inventory 120, [0113]); and
returning the blast schedule for execution by the controller of the RCS to cause the controller to control components associated with the blast cell according to the blast schedule (the schedule can cause the controller 132 to provide the enclosed space 114 with an extra thermal charge of cooling using cheap power so the inventory can stay below the maximum temperature for at least a while without consuming expensive power, [0113]; the controller 132 can turn the refrigeration system 130 on and keep it on until a predetermined condition is set, such as by setting the temperature setpoint to a temperature below what the enclosed space 114 will reach in a practical amount of time (e.g., −20° F.) to cause the refrigeration system 130 to run substantially constantly for a predetermined amount of time. In another example, the controller 132 can run the refrigeration system 130 until a predetermined temperature (e.g., −6° F.) has been reached and/or stabilized. The controller 132 can then shut the refrigeration system 130 off (e.g., or reduce power usage) and start recording the temperatures sensed by the sensors 134 to over time as the enclosed space 114 is allowed to warm. The controller 132 and/or the scheduler 140 can process the timed temperature measurements to determine the thermal model 500, [0114]).
With respect to claim 1, Wolf does not appear to teach that the simulation model is trained to generate the schedule. However, it is known by Manoharan to teach of an intelligent chiller scheduling using meta-learning and deep reinforcement learning (Manoharan: title, abstract and figs.1-2) including a method to train a learning model (Manoharan: fig.1 and page 24) to generate a schedule (Manoharan: schedule cooling load of chillers, page 26-27). Because Manoharan’s teaching is also directed to scheduled thermal control system (Manoharan: title and abstract; Wolf: fig.1), it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teaching of a learning model trained to generate a schedule to control the cooling load of the chillers as taught by Manoharan with the scheduled thermal control system as taught by Wolf for the purpose of saving energy (Manoharan: page 29).
With respect to claim 2, Wolf teaches further wherein the storage facility data includes energy rates, labor costs, pallet movement data, refrigeration data, and blast cell data (the scheduler 140 can receive an energy cost schedule 162 from, for example, a utility provider 160 that provides power to the refrigeration facility 110, to determine an energy cost model 147 of the refrigeration system 110. The energy cost schedule 162 includes information about the cost of energy at different times and/or different days. The cost of energy can include one or more types of usage rates, such as fixed rates, step rates, time-of-use rates, demand rates, etc, [0052]).
With respect to claim 3, Wolf teaches further wherein the operations further comprise: generating activation instructions for the blast cell based at least in part on the storage facility data; and transmitting the activation instructions to the RCS, wherein the RCS is configured to automatically execute the activation instructions to control the blast cell according to the activation instructions (generating a plurality of candidate schedules for controlling the refrigeration system for the predetermined period of future time, [0132] and claim 21; operational schedules are executed in controlling the refrigeration system for the refrigeration facility. Each of the costs can indicate a combination of an energy cost and an energy consumption according to each of the candidate operational schedules. For example, a cost of a candidate operational schedule can be indicative of a summation of an energy cost and an energy consumption that incur when the candidate operational schedule is implemented to control the refrigeration facility, [0133]).
Allowable Subject Matter
Claims 4-6, 8-9, and 11-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The prior art of record, taken alone or in combination, fails to disclose or render obvious, which makes the following claims allowable over the prior art:
With respect to claim 4, wherein the operations further comprise: generating instructions to deactivate the blast cell based on a determination, by the simulation model, that the blast cell cost per pallet maximizes the blast profit margin for the storage facility by less than the threshold amount; and transmitting the instructions to a user device for presentation in a graphical user interface (GUI) display, wherein the user device is configured to: present the instructions with selectable options to (i) perform the instructions and (ii) reject the instructions; receive user input indicating selection of one of the selectable options; and transmit the user input to the computer system for automatic execution.
With respect to claim 5, wherein returning the blast schedule comprises transmitting the blast schedule to a computing device of a worker in the storage facility, wherein the computing device is configured to: output the blast schedule in a GUI display at the computing device; receive user input indicating selection of the blast schedule; and transmit a notification to the computer system indicating the user selection of the blast schedule, wherein the computer system performs operations further comprising: generating instructions to control the blast cell according to the user selection of the blast schedule; and returning the instructions to the controller of the RCE to cause the controller to automatically control operations of the blast cell based on the user selection of the blast schedule.
With respect to claim 6, wherein the operations further comprise: determining the blast cell cost per pallet based on: receiving time series data that includes, for a past period of time, pallet movement data, changes in temperature data, labor usage data, and energy consumption data; correlating the time series data; determining a cost per pallet over the past period of time based on applying a cost model to the correlated data; and determining the blast cell cost per pallet over a future period of time based on applying a cost projection model to the determined cost per pallet over the past period of time.
With respect to claim 8, wherein returning the blast schedule further comprises presenting the blast schedule in a GUI display at a user device based on presenting a module designating the blast cell that includes: (i) a countdown timer indicating an amount of time left in a current blast cycle for the blast cell, (ii) an action timer indicating an amount of time that the blast cell is paused or an action needs to be taken,(iii) a state of the blast cell, the state including at least one of on, off, or defrost,(iv) a product type in the blast cell,(vi) a supply temperature to the blast cell, and(vii) a capacity of the blast cell.
With respect to claim 9/8, wherein the module presents information that further includes a graphical element indicating a warning signal when action is required for the blast cell, wherein the warning signal corresponds to instructions, that when executed by the controller of the RCS, cause the controller to (i) control the components to turn off the blast cell or (ii) instruct automated machines in the facility to unload the items from the blast cell.
With respect to claim 11/8, wherein the module presents information that further includes a capacity of the blast cell, wherein the operations further comprise: receiving, from scanning devices, product information about the items that are loaded into the blast cell; identifying, based on the product information, a quantity of the items loaded into the blast cell; determining whether the quantity of the items (i) satisfies a minimum threshold capacity for operating the blast cell and (ii) is within a maximum threshold capacity for operating the blast cell; generating instructions for execution by the controller of the RCS to cause the blast cell to begin operating based on a determination that the quantity of the items (i) satisfies the minimum threshold capacity and (ii) is within the maximum threshold capacity; starting the countdown timer based on an indication that the instructions are executed, by the controller of the RCS; and updating, in the GUI display at the user device and in real-time, the countdown timer once the countdown timer starts.
With respect to claim 12/8, wherein: the blast cell comprises a plurality of blast cells, and the user device is configured to present, in another GUI display, a plurality of the modules, wherein each of the plurality of modules corresponds to a respective blast cell amongst the plurality of blast cells and is configured to present blast cycle operational information corresponding to the respective blast cell.
Conclusion
The additional prior arts made of record and have not been relied upon are considered pertinent to applicant's disclosure as follows: Nishitsuji (US-2022/0196278-A1), US-20190078833, US-20100070089, US-7706923, and US-20100179708.
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/HIEN D KHUU/Primary Examiner, Art Unit 2116 February 17, 2026