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 .
This Office Action is responsive to the RCE filed on 01/09/2026. The claim(s) 1-20 is/are pending, of which the claim(s) 1, 10, & 19 is/are in independent form.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Response to Arguments
Applicant’s arguments, see Remarks page 11, filed 01/09/2026 with respect to the amended independent claims have been fully considered and are persuasive. Therefore, the 103 rejection has been withdrawn.
However, upon further consideration, a new ground(s) of rejection is made in view of discovery of new prior art US 20250273969 A1 to Reimer (see, para. 10166) and its combination with prior cited arts.
Claim Rejections - 35 USC § 103
Claim(s) 1, 3- 7, 9- 10, 12- 16, & 18- 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al. (US 20210206286 A1) in view of Ko (US 20180170207 A1), and further in view of Riemer et al. (US 20250273969 A1, Provisional Application filing date: 2022-06-24). Choi and Ko are references of the record. The combination of Choi, Ko, and Riemer is referred as CKR hereinafter.
Regarding claim 1, Choi teaches a computer-implemented method [steps performed by the “the electronic device 100 may be one or more servers that exist on the network”. Fig. 4 shows structural details of the device 100] for intelligent [“an efficient charging reservation is proposed to a user”] charging of shipping container power supplies [charging outlets of the respective electric vehicles 200], the computer-implemented method comprising: ([046], Abstract);
retrieving [“communication unit 110 can transmit and receive data to and from an external electronic device 200”, “the user may inform the electronic device 100 of the current location “A” and the destination “B” of the electric vehicle”], by one or more processors, container data [data of the devices 200 including data about “collect information in real time”] for a set of shipping containers [under BRI, the electric vehicles 200 (see fig. 1) can be called shipping containers because they can be used to ship people/loads from a position A to position B without requiring a tractor to pull them. The specification (pgpub) in 052 infers that vehicles that draw power and can be moved fall within the BRI of the claimed shipping containers. Please note that the claim covers every possible types of the containers], wherein the container data for each shipping container includes [“each electric vehicle may provide its identification (ID), destination, and information on the remaining charge status” indicates the reservation requesting vehicle(s) has/have a battery or a power supply] a power supply indication [battery/charger of the vehicle is a power supply for the vehicle. The EVs 200 of fig. 1 are vehicles with a power supply present] ([048, 056, 061, 097, 0104]);
identifying [receiving of the ID of the EVs requesting reservation means identifying of the vehicles that needs power and also include a power supply like a battery], by the one or more processors, one or more shipping containers from the set of shipping containers, wherein the power supply indication for each of the one or more shipping containers indicates that the one or more shipping containers include a power supply [e.g., a battery or charger of the EV] ([0140], fig. 1);
applying, by the one or more processors, a charging model [learning processor 140 which can be artificial intelligence like shown in figs. 2-3 and using it to the data of the vehicles] to the container data of the one or more shipping containers to determine, for each of the one or more shipping containers, (i) a charging prioritization [priority determination actions (see fig. 19) for the clusters of vehicles and vehicles within the clusters and performed by “the charging priority determination unit 440” of fig. 4, “determine the charging priority between the reservation-requested electric vehicles”], (
causing [“assign a charger to the electric vehicle on the basis of the priority determined by the charging priority”], by the one or more processors, each of the one or more shipping containers to receive the respective charge value based upon the charging prioritization for each of the one or more shipping containers ([0109, 0183]).
Choi teaches a computing device [server 100, fig. 1] determining charging priorities to the power requesting vehicles 200 and authorizing to charge them to collect the payments. Choi focuses on allowing to charge the vehicles 200 at optimum charging locations when the vehicles attempt to book the charging while travelling on a road having pluralities of the charging stations from a starting point to a destination (Figs. 6- 7, 11).
However, Choi fails to teach its server 100 to determine, or each of the one or more shipping containers, (ii) a respective charge value, the respective charge value indicating how much charge a power supply of each of the one or more shipping containers should receive (iii) a respective charging rate, and (iv) respective charge level, indicating a total charge capacity percentage a power supply of each of the one or more shipping containers should reach, and
the causing, by the one or more processors, each of the one or more shipping containers to receive the respective charge value in a sequential order based upon the charging prioritization for each of the one or more shipping containers step is-- at the respective charging rate to reach the respective charge level. In summary, Choi teaches all limitations except those shown above with strikethrough emphasis.
Ko teaches a power network server 300 (analogous to Choi’s item 100) managing charging (with the charging stations 200) of the pluralities of the electric vehicles 100 (analogous to Choi’s item 200) requesting electric power based on the retrieved vehicle data and controlling charging rate based on the sensed temperature (Abstract, Fig. 3, [070, 090]). More specifically, Ko teaches a computer-implemented method for intelligent charging of shipping container power supplies, the computer-implemented method comprising:
retrieving, by one or more processors, container data [“collect information about energy consumption of the battery and vehicle state from the vehicle 100, transmit the information to the power network server 300”] for a set of shipping containers ([071-074]),
applying, by the one or more processors, a charging model [learning logic used at the server 300] to the container data of the one or more shipping containers to determine, for each of the one or more shipping containers, (ii) a respective charge value [“power network server may be perform charging the battery according to the charging amount and”], the respective charge value indicating how much charge a power supply [battery of each of the vehicle] of each of the one or more shipping containers should receive, (iii) a respective charging rate [“the charging amount and based on a charging current rate of the following equation 1”], and (iv) a respective charge level [Fig. 11, “server 300 then sets a range of charging or discharging current based on the amount of charging or discharging energy and extra energy, in 440.”], server 300 may then charge the battery 150 according to the charging amount, and based on the charging current rate”] the respective charge value at the respective charging rate [“perform battery charging based on the charging current rate”] to reach the respective charge level for each of the one or more shipping containers ([007- 009, 2075, 083, 088, 0123, 0128]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Ko and Choi because they both related to a server managing charging of the pluralities of the vehicles based on the retrieved data for the vehicles using machine learning models and (2) modify the system/method of Choi’s one or more processors (of the server 100) also to determine a charge value, charging rate, and a respective charging rate to cause the vehicles (items 200 of Choi) to receive respective charge value at the respective charging rate to reach the respective charge level as in Ko. Doing so would minimize degradation of the respective power supplies/batteries of the respective vehicles 200 due to rise in temperature and also charging cost during peak hours for the vehicles 200 (Ko [083, 083]. Fig.1).
Choi in view of Ko still fails to teach to teach its determined (iv) respective charge value indicating a total charge capacity percentage a power supply of each of the one or more shipping containers should reach as claimed.
Riemer teaches a server system using one or more charging models to process received information of pluralities of shipping containers for intelligent thereof. Specifically, Riemer teaches computer-implemented method for intelligent
charging of shipping container power supplies, the computer-implemented method comprising:
applying, by the one or more processors, a charging model [“predictive model”] to the container data of the one or more shipping containers to determine, for each of the one or more shipping containers, (iii) a respective charging rate [“charging scheme 1280c (rather than the charging scheme1280a) may comprise adjusting the rate of charging the battery”] and (iv) a respective charge level, indicating a total charge capacity percentage [“charging scheme may be selected that attempts to charge currently selected device's battery to a predetermined percentage of capacity (e.g., 55%, 80%, 90%, 95%, etc.) within a predetermined period”] a power supply of each of the one or more shipping containers should reach ([0157, 0166, 0174]);
causing [Fig. 9, step 920], by the one or more processors, each of the one or more shipping containers to receive the respective charge value at the respective charging rate to reach the respective charge level for each of the one or more shipping containers ([0166]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Riemer and Choi in view of Ko because they both related to a server’s learning model monitoring battery usages of the battery power devices to determine optimum charging scheme and (2) modify the server of Choi in view of Ko to determine a respective charge level that indicates a total charge capacity percentage a power supply of each of the one or more shipping containers should reach as in Riemer. The motivation doing so would have been to achieve lower battery degradation as compared to conventional charging approaches (Riemer [0157]). Furthermore, doing so would allow users of the vehicles 200 (of Choi in view of Ko) being aware about up to what charge level the power supply (battery) of each shipping containers/vehicles are charged so that they can request to change the level if they need different level of charge (Riemer [0156]). Accordingly, the combination of Choi, Ko, and Riemer teaches each elements of the claim and renders invention of this claim obvious to PHOSITA.
Regarding claim 3, CKR further teaches the computer-implemented method of claim 1, wherein the charging model is a machine learning (ML) model configured to receive container data and output the charging prioritization, the respective charge value, and the respective charging rate, and the method further comprises: training, by the one or more processors, the ML model using (i) a plurality of training container data, (ii) a plurality of training charging prioritizations, (iii) a plurality of training charge values, and (iv) a plurality of training charging rates; and applying, by the one or more processors, the ML model to the container data in order to output, for each of the one or more shipping containers, (i) the charging prioritization, (ii) the respective charge value, and (iii) the respective charging rate for each of the one or more shipping containers (Choi [056, 076, 088, 091] & Ko [0075, 080] & Riemer [0166]).
Regarding claim 4, CKR further teaches the computer-implemented method of claim 1, further comprising: retrieving, by the one or more processors, container data for a set of shipping containers, wherein the container data for each shipping container includes (i) the power supply indication, (ii) a container weight value, (iii) a remaining travel distance value [“the destination” in relation to the starting location], (iv) an additional charging requirement indication, or (v) a delivery deadline value (Choi [0105, 0140, 0143]).
Regarding claim 5, CKR further teaches the computer-implemented method of claim 1, further comprising: calculating, by the one or more processors executing the charging model, the respective charge value based upon a remaining travel distance value corresponding to a respective shipping container of the one or more shipping containers, wherein at least one respective charge value is less than a maximum capacity of a respective power supply (Choi [105, 0175] & Ko [080]).
Regarding claim 6, CKR further teaches the computer-implemented method of claim 1, further comprising: retrieving, by the one or more processors, a set of travel data that includes (a) forecasted weather data, (b) real-time weather data, (c) forecasted traffic data, or (d) real-time traffic data [“receiving traffic information signals”]; and applying, by the one or more processors, the charging model to the container data of the one or more shipping containers and the set of travel data to determine (i) the charging prioritization for each of the one or more shipping containers, (ii) the respective charge value for each of the one or more shipping containers, and (iii) the respective charging rate for each of the one or more shipping containers (Choi [105, 0175], Figs. 11, 19 & Ko [0104]).
Regarding claim 7, CKR further teaches the computer-implemented method of claim 1, further comprising:
applying, by the one or more processors, the charging model to the container data of the one or more shipping containers to determine (i) the charging prioritization for each of the one or more shipping containers, (ii) the respective charge value for each of the one or more shipping containers, and (iii) the respective charging rate for each of the one or more shipping containers, wherein the respective charge value includes a buffer charge value [“extra energy R2”] configured to enable each of the one or more shipping containers to reach a respective destination without fully draining a respective power supply (Choi, Fig. 19 & Ko [009, 0123] & Reimer [0166]).
Regarding claim 9, CKR further teaches the computer-implemented method of claim 1, further comprising:
retrieving, by the one or more processors, a set of transportation vehicle data [feedback (yes/no) selected information by the user in figs. 13- 15 of Choi provide back to the server 100] based upon one or more transportation vehicles designated to transport shipping containers of the one or more shipping containers; and applying, by the one or more processors, the charging model to the container data of the one or more shipping containers and the set of transportation vehicle data to determine (i) the charging prioritization for each of the one or more shipping containers, (ii) the respective charge value for each of the one or more shipping containers, (iii) the respective charging rate for each of the one or more shipping containers, and (iv) a transportation configuration [e.g., best way shown in fig. 15 or other information shown in display 200a for each vehicle after user’s feedback] for each of the one or more shipping containers (Choi [056, 088, 0147-0149], fig. 13-16 & Ko [075]).
Regarding claims 10, 12- 16, & 18, CKR teaches inventions of these claims for the similar reasons as set forth above since they are system/apparatus claims for the method claims discussed above. The device 100 of Choi is mapped with claimed a computing device with a processor, a networking interface, and a computer-readable medium for intelligent charging of shipping container power supplies.
Regarding claims 19 -20, CKR teaches inventions of these computer readable claims for the similar reasons set forth above in the method claims 1 & 3 respectively.
Claim(s) 8 & 17 is/are rejected under 35 U.S.C. 103 as being unpatentable CKR (as in claims 1 & 10) and further in view of Masters [reference of record] (US 20120310765 A1).
Regarding claim 8, CKR further teaches the computer-implemented method of claim 1, further comprising:
retrieving, by the one or more processors, a set of regional charging data [information about the charging stations like temperature of Ko that are on the path between start and destination] for one or more charging regions that includeschanging the charging current rate according to temperature”], shipping containers, (iii) the respective charging rate for each of the one or more shipping containers, and (iv) an optimal route [Fig. 11 shows determination and providing of the optimal/best route/way] for each of the one or more shipping containers (Choi, Fig. 11, [0180-182] & Ko [0018-019]).
CKR fails to teach that the regional charging data to include (a) a power generation method indication for each region and (c) a charging cost for each region.
Masters teaches a server computer [item 110] executing a computer-implemented method comprising: retrieving, by the one or more processors, a set of regional charging data [“registered conditions 125 include …and cost, the type of power generation (coal, wind, hydro, etc.),”] for one or more charging regions [locations of the data centers 150] that includes (a) a power generation method indication for each region, (b) a charging rate for each region, and ( c) a charging cost for each region; and applying, by the one or more processors, appropriate control actions [“proactively adjust workload distribution”] to reduce the cost for the power ([011, 020-022]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Masters and CKR because they both related to a server computer trying to minimize cost for power and (2) modify the processor of the CKR to incorporate missing limitations (retrieve data about one or more charging regions that includes (a) a power generation method indication for each region and (c) a charging cost for each region) from Masters. Doing so would help to avoid charging of the vehicles of the CKR in charging stations of a region that are estimated to have a cost increase due to an impending occurrence of a condition (Masters [010]).
Regarding claim 17, CKR in view of Masters teaches invention of this claim for the similar reason as set forth above.
Allowable Subject Matter
Claims 2 & 11 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.
As to claims 2 & 11, prior art does not teach or suggest the inclusion of “wherein the one of the one or more shipping containers that includes the autonomous transportation module has a higher charging prioritization than the one or more shipping containers that do not have the autonomous transportation module” when viewed together with remaining limitations of the claim.
Conclusion
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115
1 As another example, a charging scheme may be selected that attempts to charge currently selected device's battery to a predetermined percentage of capacity (e.g., 55%, 80%, 90%, 95%, etc.) within a predetermined period (e.g., 30 minutes, one hour, four hours, etc.) and then charges the device at a predetermined charge rate, such as a low recommended charge rate, a trickle charge rate, etc.
2 “calculate charging or discharging amount of energy by reflecting the current ambient temperature based on the converted learned energy consumption, set a range of charging or discharging current based on the charging or discharging amount of energy and an extra amount of energy”