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 .
Claims 1-11 are pending.
Response to Amendment
Applicant’s amendments to the claims 1-6 and 9-11 have overcome 101 rejections directed to non-statutory subject matter. The 101 rejections of the claims 1-6 and 9-11, directed to non-statutory subject matter, have been withdrawn.
Response to Arguments
Applicant’s arguments with respect to the 101 rejections of the claims (see Amendment Pages 6-7) are directed to that “the processor is configured … to transmit the determined information to a management terminal, thereby causing installation of the determined batteries into the rechargeable-battery compartment of a refrigerator mounted on the portable cold-energy equipment prior to transportation” “clearly integrate such an idea into a practical application.”
Examiner respectfully disagrees and submits that the transmitting data is an insignificant extra-solution activity under MPEP 2106.05(g), without imposing meaningful limits. The limitation amounts to necessary data output. (i.e., all uses of the recited judicial exception require such data output). The claim is missing how the transmitting the determined information causes installation of the determined batteries, to integrate the determined information into a practical application. Accordingly, the 101 rejections of the claims are maintained. Examiner encourages Applicant to conduct an interview with Examiner for a guidance to overcome the 101 rejections.
Applicant’s arguments with respect to the 102 rejections of the claims 1-11 have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
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.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 2A, Prong One) Independent claim 1 recites, “estimate, based on the received data correlated with the power consumption and the received data related to the set temperature, an amount of power consumption expected during transportation of the portable cold-energy equipment to a predetermined destination; and determine, based on the estimated amount of power consumption, a type and a quantity of rechargeable batteries for storage in a rechargeable-battery compartment of a refrigerator to be mounted on the portable cold-energy equipment.”
Under their broadest reasonable interpretation and based on the description provided in the published Specification, such as paragraphs [0115]-[0120], for instance, the limitation of the estimation and determination, as claimed, are processes that entails purely mathematical relationships, mathematical formulas or equations, and mathematical calculations.
Accordingly, the claim recites an abstract idea.
(Step 2A, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim recites the additional limitations of, “a network interface; and a processor configured to: receive data correlated with power consumption of portable cold-energy equipment and data related to a set temperature of the portable cold-energy equipment via the network interface; transmit, via the network interface, the determined type and the determined quantity to a management terminal to cause installation of the determined type and the determined quantity of rechargeable batteries in the rechargeable-battery compartment prior to the transportation; wherein the data correlated with the power consumption includes at least one item selected from a group consisting of: internal humidity data of a container including the portable cold-energy equipment, internal ventilation amount data of the container, and outside air humidity data.”
The additional limitations “a network interface” and “a processor” as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used to identify and group as recited in the claim, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic or computer components. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not indicative of integration into a practical application- see MPEP 2106.05(f)
The additional limitation of “receive data correlated with power consumption of portable cold-energy equipment and data related to a set temperature of the portable cold-energy equipment via the network interface; … wherein the data correlated with the power consumption includes at least one item selected from a group consisting of: internal humidity data of a container including the portable cold-energy equipment, internal ventilation amount data of the container, and outside air humidity data” and “transmit, via the network interface, the determined type and the determined quantity to a management terminal to cause installation of the determined type and the determined quantity of rechargeable batteries in the rechargeable-battery compartment prior to the transportation” are insignificant extra-solution activities (receiving and transmitting data) under MPEP 2106.05(g), without imposing meaningful limits. The limitation amounts to necessary data gathering and data output. (i.e., all uses of the recited judicial exception require such data gathering or data output). The claim does not recite an improvement in a technology as set forth in MPEP 2106.04(d) and MPEP 2106.05(a). Accordingly, the additional limitations recited in the claim do not integrate the abstract idea into a practical application.
(Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The additional features including “a network interface” and “a processor” as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used to estimate and determine as recited in the claim, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic or computer components. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more. See Elec. Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (“Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information.”)
The feature “receive data …” represents a function that is recognized as well-understood, routine, and conventional, for instance, as demonstrated in Lowe et al. (US 2023/0174008 A1) [0057] (“The inputs to the model on which the prediction of the energy requirements is made include one or more of the parameters: desired temperature set point; expected weather conditions during the journey; start time of journey; duration of journey; number, times and/or times duration of delivery drops; payload mass and/or type; and the determination takes into account the further input of the initial state of charge of the batteries. Typically, the software may connect to a logistics software program used by the operator to extract inputs such as the temperature set point, route of journey, drops, etc. Weather conditions include factors that influence the performance of the system in cooling the interior to the desired setpoint, such as expected hours of sunlight, which affects solar generation, and ambient temperature, which affects how much cooling is required to maintain the set point temperature. Weather conditions may be obtained from a third-party system. These can be correlated with the time and duration and route of the journey to find the conditions pertaining to a particular journey. The model finds the expected energy requirement to meet the set point temperature given the conditions and payload, and based on this energy requirement calculates the amount of additional energy needed to be stored in the batteries given the initial charge state of the batteries. This can be satisfied by swapping in additional batteries and/or charging the batteries in situ or in a swapping station.”), and BRISCOE et al. (US 2022/0088997 A1) paragraph [0026] (“The transport climate control system 100 includes a climate control unit (CCU) 110 that provides environmental control (e.g. temperature, humidity, air quality, etc.) within a climate controlled space 106 of the transport unit 105. The transport climate control system 100 also includes a programmable climate controller 107 and one or more sensors (not shown) that are configured to measure one or more parameters of the transport climate control system 100 (e.g., an ambient temperature outside of the transport unit 105, a space temperature within the climate controlled space 106, an ambient humidity outside of the transport unit 105, a space humidity within the climate controlled space 106, etc.) and communicate parameter data to the climate controller 107.”).
The feature “transmit” data represents a function that is recognized as well-understood, routine, and conventional, for instance, as demonstrated in Lowe et al. (US 2023/0174008 A1) Paragraph [0062] “According to a further aspect of the disclosure, there is provided a system for charging rechargeable batteries for powering mobile refrigeration units, the refrigeration units being in use attached to a trailer or vehicle to cool an interior space thereof during a journey, the system comprising: a. a swapping station comprising charging bays arranged to receive plural respective rechargeable batteries removed from refrigeration units for charging; b. a mains electricity connector for receiving and optionally exporting power to the national electricity grid; c. charging control circuitry for selectively charging connected rechargeable batteries from mains electricity; d. a processor for executing the computer program of any of claims 10 to 15, to determine a number of batteries and/or battery charge level for each battery to supply that energy, wherein the computer program is executed either locally or remotely to the charging system; and e. in accordance with the determination, to display to an operator which batteries are to be swapped into the refrigeration unit and/or display a schedule of when to swap the batteries and/or to activate the charging control circuitry to charge the battery level to the required level.”), and BRISCOE et al. (US 2022/0088997 A1) Paragraph [0009] (“In an embodiment, the method further includes obtaining an in-route energy level of the energy storage source while a transport unit including the transport climate control system is in transit during the route, determining, using a controller, an expected energy level of the energy storage source for current progress of the transport unit along the route, comparing the expected energy level of the energy storage source to the in-route energy level of the energy storage source, and displaying a notification via the human-machine interface when the expected energy level of the energy storage source exceeds the in-route energy level of the energy storage source. In an embodiment, determining the expected energy level of the energy storage source comprises determining an energy cost of the current progress of the transport unit along the route, and subtracting the energy cost from the pre-route energy level of the energy storage source. In an embodiment, determining the expected energy level of the energy storage source comprises obtaining an elapsed time during which the transport unit has been traveling along the route. In an embodiment, determining the expected energy level of the energy storage source further comprises obtaining a number of door openings performed during the route.”)
Therefore, the additional claimed features do not amount to significantly more and the claim is not patent eligible.
Independent claims 7 and 8 are not patent eligible for similar reasons, as explained above, for independent claim 1.
Dependent claims 2-6 and 9-11 simply add more detail to or are cumulative to the abstract idea of independent claim 1.
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.
Claims 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Lowe et al. (US 2023/0174008 A1) (“Lowe”), in view of OKUDA (US 2022/0357162 A1) (“Okuda”).
Regarding independent claim 1, Lowe teaches:
A selection apparatus comprising: a network interface; and a processor configured to: (Lowe: Abstract “Refrigeration units for cooling the interior of a trailer or vehicle, related software, systems and methods for deploying and managing such units. The refrigeration unit comprises a refrigeration system configured to mount to the trailer or vehicle. The refrigeration unit further comprises a battery rack configured to receive at least one of a plurality of rechargeable batteries so as to allow it to be swapped into and out of the rack to provide adaptive battery capacity. A power management system is configured to receive DC power from the plural batteries and deliver power to a compressor of the refrigeration system. A controller is configured to control the refrigeration system to cool the interior to a predetermined temperature.”) (Lowe: [0054] “In another aspect, the present disclosure relates to a computer program for manging power requirements of mobile refrigeration units, the refrigeration units being in use attached to a trailer or vehicle to cool an interior space thereof during a journey and being powered by, at least in part, one or more rechargeable batteries and optionally one or more solar panels, the computer program comprising processor readable instructions, which when executed by the processer cause the computer to: …”) (Lowe: [0107] “A system controller 75 is provided with communication links to the various parts of the TRU 10 to control and monitor the refrigeration process, i.e. to pull down and maintain a set point temperature, and to manage and monitor the various energy sources. Thus, the controller 75 communicates with the power converters 70, 64, 66 and the BMS/contactors of all the battery packs, to control the fans 301,34a, the compressor, the power provided by the PV panels and from the connector, the sensors 44 and voltage sensors, and any other elements of the TRU 10 in order to exchange data and send control signals.”) (Lowe: [0117] “The system controller 75 can also be controlled directly from the cloud by the software 120, so settings can also be adjusted remotely.”)
receive data correlated with power consumption of portable cold-energy equipment and data related to a set temperature of the portable cold-energy equipment via the network interface; estimate, based on the received data correlated with the power consumption and the received data related to the set temperature, an amount of power consumption expected during transportation of the portable cold-energy equipment to a predetermined destination; (Lowe: [0057] “The inputs to the model on which the prediction of the energy requirements is made include one or more of the parameters: desired temperature set point; expected weather conditions during the journey; start time of journey; duration of journey; number, times and/or times duration of delivery drops; payload mass and/or type; and the determination takes into account the further input of the initial state of charge of the batteries. Typically, the software may connect to a logistics software program used by the operator to extract inputs such as the temperature set point, route of journey, drops, etc. Weather conditions include factors that influence the performance of the system in cooling the interior to the desired setpoint, such as expected hours of sunlight, which affects solar generation, and ambient temperature, which affects how much cooling is required to maintain the set point temperature. Weather conditions may be obtained from a third-party system. These can be correlated with the time and duration and route of the journey to find the conditions pertaining to a particular journey. The model finds the expected energy requirement to meet the set point temperature given the conditions and payload, and based on this energy requirement calculates the amount of additional energy needed to be stored in the batteries given the initial charge state of the batteries. This can be satisfied by swapping in additional batteries and/or charging the batteries in situ or in a swapping station.”) (Lowe: [0081] “FIG. 1 shows a perspective view of an example of a mobile refrigeration unit, more specifically in this example a Transport Refrigeration Unit 10, attached to a semi-trailer 12 of the sort that can be attached to and pulled by a tractor unit (not shown) to transport goods loaded to the interior of the trailer, where the TRU 10 implements a system for refrigerating the interior of the trailer. It will be appreciated that the TRU may equally be attached to other vehicles types, such as rigid body trucks, vans and lorries. As will be described further below, the TRU forms part of an overall system 5 for providing and managing a fleet of TRUs for respective trailers or other vehicles.”) [The transport refrigeration unit (TRU) 10 or the mobile refrigeration unit reads on “portable cold-energy equipment”. Obtaining the weather conditions or other inputs that are used in the prediction of the energy requirement reads on “receive data correlated with power consumption …”, and extracting the temperature setpoint that is used in the prediction of the energy requirement reads on “receive … data related to a set temperature …”. Predicting the energy requirement reads on “estimate … an amount of power consumption expected …”. The predicted energy requirement for the journey to the drops reads on “an amount of power consumption expected during transportation … to a predetermined destination”. ]
determine, based on the estimated amount of power consumption, a type and a quantity of rechargeable batteries for storage in a rechargeable-battery compartment of a refrigerator to be mounted on the portable cold-energy equipment; and (Lowe: [0092] “An electrical system 45 of power electronics is provided, the primary purpose of which is to supply electric power to drive the compressor and the fans. The fixed batteries 50 and the swappable batteries 22 are connected to a bus 52. In the present example, the bus is provided within a DC power distribution unit in the TRU main body, which may further comprise fuses, contactors, and CAN I/O module for communications with the controller. In the present example, the batteries are 48 VDC 10 kWh capacity and the TRU may have 4 fixed battery modules and the battery racking system 20 may accommodate up to 6 batteries. However, it will be appreciated that different voltages, capacities or numbers of fixed and/or swappable batteries and or their positioning may be adopted. In some examples, the batteries may be fixed or all batteries may be swappable. The number of batteries can be adjusted on a per journey basis, as energy demand can change between customers, season and application.”) (Lowe: [0122] “The software 120 then looks at the TRUs available and selects the one which best matches the requirements for the journey. The software predicts the battery capacity, i.e. energy, required to complete the journey for the best match according to the input parameters, in particular the logistics schedule and weather forecast. It will be appreciated that weather conditions and expected hours of daylight during the journey will influence how much energy is generated via solar during the journey. Ambient air temperature will affect the cooling required. The number of stops for unloading affects loss of cooling, which requires additional energy from the system to compensate for.”) (Lowe: [0123] “Once the energy required is predicted, in step 720, the software determines how many batteries are required for the journey and how much they need to be charged, taking into account the initial charge of the batteries and expected charging until the trailer must leave. Based on this, the closest match will then be charged according to the predicted energy requirement and/or the operator is instructed to swap, add or remove batteries to adapt the number of batteries if required and allow the operator to vary the on-vehicle battery capacity.”) [The batteries of different voltages, capacities, or mounting provisions (fixed or swappable) read on “a type … of rechargeable batteries”. Determining how many batteries are required and how much they need to be charged according to the predicted energy requirement for the journey reads on “determine, based on the estimated amount of power consumption, a type and a quantity of rechargeable batteries …”.]
transmit, via the network interface, the determined type and the determined quantity to a management terminal to cause installation of the determined type and the determined quantity of rechargeable batteries in the rechargeable-battery compartment prior to the transportation; (Lowe: [0062] “According to a further aspect of the disclosure, there is provided a system for charging rechargeable batteries for powering mobile refrigeration units, the refrigeration units being in use attached to a trailer or vehicle to cool an interior space thereof during a journey, the system comprising: a. a swapping station comprising charging bays arranged to receive plural respective rechargeable batteries removed from refrigeration units for charging; b. a mains electricity connector for receiving and optionally exporting power to the national electricity grid; c. charging control circuitry for selectively charging connected rechargeable batteries from mains electricity; d. a processor for executing the computer program of any of claims 10 to 15, to determine a number of batteries and/or battery charge level for each battery to supply that energy, wherein the computer program is executed either locally or remotely to the charging system; and e. in accordance with the determination, to display to an operator which batteries are to be swapped into the refrigeration unit and/or display a schedule of when to swap the batteries and/or to activate the charging control circuitry to charge the battery level to the required level.”) (Lowe: [0109] “A Human Machine Interface (HMI) is provided comprising a display and input means, e.g. a touch screen 80, connected with the system controller 75, e.g. by WiFi, by which an operator can locally see the status of the TRU and provide input/control.”) [The HMI reads on “a management terminal”. The HMI displaying the system controller’s instruction to the operator regarding the batteries reads on “transmit … to a management terminal to cause installation …”.]
Lowe does not expressly teach: wherein the data correlated with the power consumption includes at least one item selected from a group consisting of: internal humidity data of a container including the portable cold-energy equipment, internal ventilation amount data of the container, and outside air humidity data.
Okuda teaches:
wherein the data correlated with the power consumption includes at least one item selected from a group consisting of: internal humidity data of a container including the portable cold-energy equipment, internal ventilation amount data of the container, and outside air humidity data. (Okuda: [0095] “The control unit 11 selects the divided sections in the travel order (step S124), and calculates the power consumption prediction amount in the selected section based on traffic information (average travel time) in the section, and information such as the outside temperature in the section and the humidity in the section (step S125).”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Lowe and Okuda before them, to modify the weather information that is used to predict of energy requirement for the journey, to incorporate outside humidity information of the journey.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for considering the humidify factor that affect the power consumption or the energy requirement for an air-conditioned vehicle. (Okuda: [0045] “The time required for movement varies depending on traffic information such as occurrence of an accident and unnavigability, and traffic congestion or a congestion situation, and thus the predicted power consumption amount can vary. Depending on the temperature or humidity, the power consumption amount of the energy storage device or the air conditioning installation in the cab or the cabin varies. The time required for the movement and the power required for the movement vary depending on the wind speed or the wave height, so that the predicted power consumption amount can be changed. The movable distance is accurately calculated using these pieces of information.”)
Regarding claim 2, Lowe and Okuda teach all the claimed features of claim 1. Lowe further teaches:
wherein the processor is configured to use a trained result acquired based on machine learning of a relationship between the received data correlated with the power consumption, the received data related to the set temperature, and a ground-truth amount of power consumption to calculate the amount of power consumption. (Lowe: [0046] “The unit may be arranged to monitor its usage, and comprising communication means to stream the usage data to a remote software platform, the usage data comprising one or more of: start time of journey; duration of journey; temperature set point; weather during journey; number, times and/or duration of delivery drops; payload mass and/or type; location data; energy usage; and actual temperature profile. The data may be used to model the performance of the refrigeration unit, i.e. the energy needed to achieve the temperature profile, given the other input parameters, so that predictions of energy use for future journeys can be made by inputting the appropriate input variables for the future journey. The data can also give real time feedback to operators of the system or the driver of the vehicle, and/or directly make adjustments to the refrigeration cycle. Energy usage may be any one or more of production, consumption and/or storage of electrical energy in the unit, i.e. solar energy, battery energy flows, energy consumed by the refrigeration unit, etc. Weather conditions may include ambient temperature and/or expected solar. These can be correlated with the times of the journey to estimate the amount of solar energy that will be generated and amount of energy to maintain the set point temperature. Payload mass and/or type affects the amount of cooling required to reach the set point.”) (Lowe: [0130] “This streamed usage data creates historical data 620 of the performance of a TRU 10, i.e. the “response” of the system in terms of energy usage and temperature profile achieved, based on the “stimuli” to the system, i.e. the input data to the system as described above, i.e. the start time and duration of the trip, the desired temperature set-point, the prevailing weather conditions, the number, times, and durations of delivery drops, the payload capacity/utilisation, the location during the journey. This historical data is used to train the Digital Twin model via machine learning algorithms 640. The trained digital twin represents the digital behaviour blueprint of an individual TRU capturing the real-life response caused by indefinite combinations of stimuli at any given point in time.”) (Lowe: [0131] “FIG. 6c shows in more detail the digital twin models 610 being used to predict energy usage for a TRU 10 and across the fleet 10a . . . 10n. The software 120 continuously monitors the present state 650 of the individual TRUs 10. This includes GPS location and battery state of charge. The present state provides a first input to the model 610. Future stimuli 655 are also input into the model 610, including the logistics schedule, i.e. when the TRUs must depart, number of deliveries, set-point temperature, time and duration of trip; and the weather forecast, i.e. to estimate ambient temperatures and solar energy available for the PV panels. Based on these input parameters 650,655 to the digital twin model 610 in the IoT software platform 120, the software can predict the energy required for a particular TRU to make the scheduled journey. Thus the data that is used to create the digital twin 610 using machine learning 640 which is then used to make predictions 660 for a specific TRU/journey and to manage the batteries across the fleet.”)
Regarding claim 3, Lowe and Okuda teach all the claimed features of claim 1. Lowe further teaches:
wherein the processor is configured to calculate, based on time expected for the transportation to the destination, the amount of power consumption. (Lowe: [0130] “This streamed usage data creates historical data 620 of the performance of a TRU 10, i.e. the “response” of the system in terms of energy usage and temperature profile achieved, based on the “stimuli” to the system, i.e. the input data to the system as described above, i.e. the start time and duration of the trip, the desired temperature set-point, the prevailing weather conditions, the number, times, and durations of delivery drops, the payload capacity/utilisation, the location during the journey. This historical data is used to train the Digital Twin model via machine learning algorithms 640. The trained digital twin represents the digital behaviour blueprint of an individual TRU capturing the real-life response caused by indefinite combinations of stimuli at any given point in time.”)
Regarding claim 4, Lowe and Okuda teach all the claimed features of claim 1. Lowe further teaches:
wherein the processor is configured to use table data representing a relationship between the received data correlated with the power consumption, the received data related to the set temperature, and a ground-truth amount of power consumption to calculate the amount of power consumption. (Lowe: [0130] “This streamed usage data creates historical data 620 of the performance of a TRU 10, i.e. the “response” of the system in terms of energy usage and temperature profile achieved, based on the “stimuli” to the system, i.e. the input data to the system as described above, i.e. the start time and duration of the trip, the desired temperature set-point, the prevailing weather conditions, the number, times, and durations of delivery drops, the payload capacity/utilisation, the location during the journey. This historical data is used to train the Digital Twin model via machine learning algorithms 640. The trained digital twin represents the digital behaviour blueprint of an individual TRU capturing the real-life response caused by indefinite combinations of stimuli at any given point in time.”) (Lowe: [0131] “FIG. 6c shows in more detail the digital twin models 610 being used to predict energy usage for a TRU 10 and across the fleet 10a . . . 10n. The software 120 continuously monitors the present state 650 of the individual TRUs 10. This includes GPS location and battery state of charge. The present state provides a first input to the model 610. Future stimuli 655 are also input into the model 610, including the logistics schedule, i.e. when the TRUs must depart, number of deliveries, set-point temperature, time and duration of trip; and the weather forecast, i.e. to estimate ambient temperatures and solar energy available for the PV panels. Based on these input parameters 650,655 to the digital twin model 610 in the IoT software platform 120, the software can predict the energy required for a particular TRU to make the scheduled journey. Thus the data that is used to create the digital twin 610 using machine learning 640 which is then used to make predictions 660 for a specific TRU/journey and to manage the batteries across the fleet.”)
Regarding claim 5, Lowe and Okuda teach all the claimed features of claim 1. Lowe further teaches:
wherein the processor is configured to select, from a predetermined plurality of rechargeable batteries, a rechargeable battery that has a power storage capacity exceeding the amount of power consumption. (Lowe: [0068] “In another aspect the disclosure relates to a mobile refrigeration unit powered by rechargeable batteries, wherein the unit has adaptive battery capacity and has no other power source, except possibly solar energy. Predictive software may be used to model the refrigeration unit and predict the amount of energy needed for a scheduled journey, and provides outputs to cause the battery capacity to be adapted to provide that amount of energy, where that amount of energy may be less than the total possible battery capacity of the unit.”)
Regarding claim 6, Lowe and Okuda teach all the claimed features of claim 1. Lowe further teaches:
wherein the received data correlated with the power consumption includes either internal temperature data of the container, or outside air temperature data, or both the internal temperature data of the container and the outside air temperature data. (Lowe: [0046] “The unit may be arranged to monitor its usage, and comprising communication means to stream the usage data to a remote software platform, the usage data comprising one or more of: start time of journey; duration of journey; temperature set point; weather during journey; number, times and/or duration of delivery drops; payload mass and/or type; location data; energy usage; and actual temperature profile. The data may be used to model the performance of the refrigeration unit, i.e. the energy needed to achieve the temperature profile, given the other input parameters, so that predictions of energy use for future journeys can be made by inputting the appropriate input variables for the future journey. The data can also give real time feedback to operators of the system or the driver of the vehicle, and/or directly make adjustments to the refrigeration cycle. Energy usage may be any one or more of production, consumption and/or storage of electrical energy in the unit, i.e. solar energy, battery energy flows, energy consumed by the refrigeration unit, etc. Weather conditions may include ambient temperature and/or expected solar. These can be correlated with the times of the journey to estimate the amount of solar energy that will be generated and amount of energy to maintain the set point temperature. Payload mass and/or type affects the amount of cooling required to reach the set point.”) (Lowe: [0059] “In an embodiment the computer program is arranged to receive usage data from at least one refrigeration unit during a journey, the data including said one or more parameters and data indicating the actual temperature achieved by the refrigeration unit and energy consumption of the refrigeration unit, which data is used to model performance of an individual refrigeration unit.”)
Regarding independent claim 7:
The claim recites similar limitations as corresponding claim 1 and is rejected using the same teachings and rationale.
Regarding independent claim 8:
The claim recites similar limitations as corresponding claim 1 and is rejected using the same teachings and rationale.
Regarding claim 9, Lowe and Okuda teach all the claimed features of claims 1-2. Lowe further teaches:
wherein the processor is configured to calculate, based on time expected for the transportation to the destination, the amount of power consumption. (Lowe: [0130] “This streamed usage data creates historical data 620 of the performance of a TRU 10, i.e. the “response” of the system in terms of energy usage and temperature profile achieved, based on the “stimuli” to the system, i.e. the input data to the system as described above, i.e. the start time and duration of the trip, the desired temperature set-point, the prevailing weather conditions, the number, times, and durations of delivery drops, the payload capacity/utilisation, the location during the journey. This historical data is used to train the Digital Twin model via machine learning algorithms 640. The trained digital twin represents the digital behaviour blueprint of an individual TRU capturing the real-life response caused by indefinite combinations of stimuli at any given point in time.”)
Regarding claim 10, Lowe and Okuda teach all the claimed features of claims 1-2. Lowe further teaches:
wherein the processor is configured to select, from a predetermined plurality of rechargeable batteries, a rechargeable battery that has a power storage capacity exceeding the amount of power consumption. (Lowe: [0068] “In another aspect the disclosure relates to a mobile refrigeration unit powered by rechargeable batteries, wherein the unit has adaptive battery capacity and has no other power source, except possibly solar energy. Predictive software may be used to model the refrigeration unit and predict the amount of energy needed for a scheduled journey, and provides outputs to cause the battery capacity to be adapted to provide that amount of energy, where that amount of energy may be less than the total possible battery capacity of the unit.”)
Regarding claim 11, Lowe and Okuda teach all the claimed features of claims 1-2. Lowe further teaches:
wherein the received data correlated with the power consumption includes either internal temperature data of the container, or outside air temperature data, or both of the internal temperature data of the container and the outside air temperature data. (Lowe: [0046] “The unit may be arranged to monitor its usage, and comprising communication means to stream the usage data to a remote software platform, the usage data comprising one or more of: start time of journey; duration of journey; temperature set point; weather during journey; number, times and/or duration of delivery drops; payload mass and/or type; location data; energy usage; and actual temperature profile. The data may be used to model the performance of the refrigeration unit, i.e. the energy needed to achieve the temperature profile, given the other input parameters, so that predictions of energy use for future journeys can be made by inputting the appropriate input variables for the future journey. The data can also give real time feedback to operators of the system or the driver of the vehicle, and/or directly make adjustments to the refrigeration cycle. Energy usage may be any one or more of production, consumption and/or storage of electrical energy in the unit, i.e. solar energy, battery energy flows, energy consumed by the refrigeration unit, etc. Weather conditions may include ambient temperature and/or expected solar. These can be correlated with the times of the journey to estimate the amount of solar energy that will be generated and amount of energy to maintain the set point temperature. Payload mass and/or type affects the amount of cooling required to reach the set point.”) (Lowe: [0059] “In an embodiment the computer program is arranged to receive usage data from at least one refrigeration unit during a journey, the data including said one or more parameters and data indicating the actual temperature achieved by the refrigeration unit and energy consumption of the refrigeration unit, which data is used to model performance of an individual refrigeration unit.”)
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.
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/MICHAEL W CHOI/Primary Examiner, Art Unit 2116