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 .
Notice to Applicant
The following is a Non-Final, first Office Action responsive to Applicant’s communication of 11/20/2024, in which applicant filed the application. Claims 1-20 are pending in the instant application and have been rejected below.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/20/24 is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities: the drawings such as FIG. 1 disclose “power consumption information, contract information”. Power related to “contract” or “contracted” is in pages 2, 12-14, and 18 and more.
However, it appears typographical errors have led to “contact” in a number of places – nine instances – page 3, line 10; page 3, lines 17; page 15, lines 17; page 6, line 2; page 12, line 18; page 12, line 22; page 14, line 7; page 17, line 14; page 18, line 11.
Appropriate correction is required, as it appears each “contact” or “contacted” is intended to be “contract” or “contracted.”
35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, requires the specification to be written in “full, clear, concise, and exact terms.” The specification is replete with terms which are not clear, concise and exact. The specification should be revised carefully in order to comply with 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112. Examples of some unclear, inexact or verbose terms used in the specification are: Equation [1] and the explanation on page 4; page 6; page 15; page 19. Each time, the description states there is a “arrival quantityn” ; but each equation is devoid of such a quantity, and instead has a “arrival quantityb”, with focus on the “b” in each equation. Examiner’s best guess is that the description is correct and the equations need to be corrected, where the denominator should be referring to “n” in the “arrival quantityn”. It appears the Certified Foreign copy supports such a change.
Claim Objections
Claims 2, 4, and 11 are objected to because of the following informalities:
Claims 2, 4, 12, and 14 recite “contacted”. It appears this is intended to be “contracted” consistent with other portions of the disclosure. Appropriate correction is required.
Claim 11 recites “rede-sharing.” This is incorrectly spelled and should recite “ride-sharing.”
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7 and 17 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. As explained in the Specification section above, it is unclear what the “arrival quantityb” in the denominator in claims 7, 17 is referring to. It appears the equation is written incorrectly, and “arrival quantityn” in the second-to-last line of claim 7, is intended to be in the denominator.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 4, 7, 10, 12, 14, 17, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 2 and 12, the claim recites “wherein the processor is configured to collect… travel demand of respective buildings, for power prediction.” Claims 2 and 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential steps, such omission amounting to a gap between the steps. See MPEP § 2172.01. The omitted steps are: it is unclear if someone is looking to travel away from a building, what the claim is predicting relative to that building. Perhaps the “power prediction” is for the other portions of claim 2? It is unclear what is intended here.
Regarding claims 4 and 14, the claim recites “wherein the processor is configured to estimate the time zone requiring discharging of each of the buildings.” Claims 4 and 14 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are: claim 1 stated that the time zone is for “additional power is required for each of the buildings.” It is unclear why the building is now discharging to the vehicle. FIG. 5-6 and [0137-0138] as published stating “the service manager 100 may estimate the time zone requiring discharging and the amount of electrical power. when the predicted amount of electrical power calculated from the building power prediction module 120 (see FIG. 2) is higher than the capacity of the power facility or the contracted amount of electrical power as in Building #5 and/or Building #14, the required power estimation module 130 (see FIG. 2) may estimate that the electrical power is required as much as predicted electrical power at that time zone-contracted/facility capacity.” For purposes of applying prior art only, Examiner interprets claims 4 and 14 as reciting “wherein the processor is configured to estimate the time zone requiring discharging of electrical power from the electric vehicle for each of the buildings.
Regarding claims 7 and 17, as explained in the Specification section above, it is unclear what the “arrival quantityb” in the denominator is referring to. There is also insufficient antecedent basis for “arrival quantityn” in the second-to-last line of claim 7, as that is not present in the equation. For purposes of applying prior art only, Examiner interprets claims 7 and 17 as reciting:
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Claims 10 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are: it is unclear how the processor is “guiding” a path to a customer terminal. In addition, there is insufficient antecedent basis for “predetermined path”, as claim 1 recites “travel path.” Examiner’s best guess based on FIG. 2 and [0116] is that claim 10 and 20 are intended to be reciting “wherein the processor is configured to notify a customer terminal of the travel path
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more.
Step One - First, pursuant to step 1 in MPEP 2106.03, is directed to an apparatus which is a statutory category, we proceed to:
Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites–
… linking ride-sharing service and vehicle-to-grid, comprising:
...
collect power data of buildings,
predict power consumption of the buildings based on the power data, respectively,
calculate a time zone in which an additional power is required for each of the buildings and a required amount of electrical power based on the collected power data and the predicted power consumption,
estimate a travel demand of a region where the buildings exist, and
set a travel path of a ride-sharing vehicle for each time zone within the region based on the time zone requiring the additional power, the required amount of electrical power and the travel demand.
As drafted, this is, under its broadest reasonable interpretation, directed to the Abstract idea groupings of 1) “mental processes” (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (See e.g. MPEP 2106.04(a)(2)(III)(A) - a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350); here we have limitations on collecting power data, predicting power consumption, calculating a time when additional power is required and a required amount; estimate a travel demand of a region, and setting a desired path of a single ride sharing vehicle within each time zone based on the additional power and the travel demand), 2) Mathematical relationships (predict an amount of power consumption; calculate a time and an amount of power based on predictions; estimate a travel demand in a region) and 3) “certain methods of organizing human activity” (commercial or legal interactions (sales activities) and managing personal behavior (following rules or instructions) as here we have a series of activities for optimizing sales (an amount of power; predict power consumption, additional power needed at a time), and setting a travel path as a plan to be followed to fulfill both travel demand and need for additional power). It only plans a path in the end, which is a recommendation that may or may not be followed.
Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional elements that are:
An electric vehicle charging and discharging apparatus for linking ride-sharing service and vehicle-to-grid, comprising:
a processor; and
a memory storing software, when executed by the processor, causing the processor to:
Here, the preamble has many portions which are not in the claim, so it is unclear how they may be used at this time. Accordingly, “electric vehicle charging and discharging apparatus for linking ride-sharing service and vehicle-to-grid” is considered “field of use” at this time (MPEP 2106.05h). There is no charging or discharging required; there is no “ride-sharing service”; there is no “grid” in the body of the claim. Rather, the body of the claim is just software and does not appear to state what the vehicle, discharging, or grid may be doing (if anything). The processor, and memory storing software, when executed by the processor is considered no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)) and individually or in combination is consideration “field of use” (MPEP 2106.05h). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55.
Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer system, are MPEP 2106.05(f) (Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and MPEP 2106.05h (field of use). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. The claim is not patent eligible. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent claim 11 is at step 1, is directed to a statutory category of a method. Claim 11 is rejected for the same reasons as claim 1 at step 2a, prong 2 and step 2b. In addition, unlike claim 1, at step 2a, prong 2 and step 2B no computer is even recited. The claim is broad enough to encompass a person with pen and paper performing each step – collecting power data, predicting power consumption, calculating a time zone for additional power, estimating a travel demand of a region, and setting a travel path based on additional power, the required amount of electric power, and the travel demand. As an initial step, Examiner suggests reciting the missing computer/processor executing the steps. The body of the claim 11 is also devoid of any steps related to the vehicle, charging, discharging, linking, or the grid as in claim 1. It only plans a path in the end, which is a recommendation that may or may not be followed. As further suggestions, Examiner suggests considering focusing on additional elements, such as “discharging electric power from the vehicle power to the building” in [0133] as published.
Claims 2 and 12 narrow the abstract idea by collecting a variety of named “location data” to be used in the estimation of past power usage data, power facility data, “contacted” power data, and travel demand. It is unclear how the “power facility data” is used, or the “contacted power data.” To the extent the processor is “collecting” or storing data, this is also considered “apply it [abstract idea] on a computer” (MPEP 2106.05f) and field of use (MPEP 2106.05h) as in claim 1.
Claims 3 and 13 narrow the abstract idea for the prediction to be for a “specific period.”
Claims 4 and 14 narrow the abstract idea by explaining more details on how the time is estimated.
Claims 5 and 15 narrow the abstract idea by explaining more details on how the travel demand is generated.
Claims 6 and 16 narrow the abstract idea by estimating “actual” travel demand by collecting data on different movements. This narrows the abstract idea in that it is looking at quantities/numbers of rides/travel to calculate “actual” travel demand. To extent computer is performing the operation, this is also considered “apply it [abstract idea] on a computer” (MPEP 2106.05f) and field of use (MPEP 2106.05h) as in claim 1.
Claims 7 and 17 narrow the abstract idea by explicitly having mathematical relationships for estimating travel demand.
Claims 8 and 18 narrow the abstract idea by explicitly having mathematical relationships for the “certain methods of organizing human activity” purpose of “maximizing profitability”.
Claims 9 and 19 narrow the abstract idea by stating that the “optimization model” uses different models (including “auction model” and “linear programming”) to set a travel path. There are no details at this time on what the models may be considering from the earlier limitations. “Reinforcement learning” is also utilized, but there are no details on what it is doing or considering from earlier limitations; at this time, it is considered “apply it [abstract idea] on a computer” (MPEP 2106.05f) and “field of use” (MPEP 2106.05h). This is similar to merely “using machine learning,” for a business purpose, similar to Example 47-48, and similar to the Recentive v. Fox, No. 2023-2437 (Fed. Cir. Apr. 18, 2025) decision as an example. As in Recentive v. Fox, “Here, the claims do not delineate steps through which the machine learning technology achieves an improvement.”
Claims 10 and 20 narrow the abstract idea by relaying the path “to a customer” and having a passenger make a reservation for a ridesharing vehicle according to the path the vehicle is planned on taking. Additional elements here are “customer terminal”, which is considered a display that the processor of claim 1 sends the recommended path to, is considered “apply it [abstract idea] on a computer” (MPEP 2106.05f) and “field of use” (MPEP 2106.05h) at step 2a, prong two and step 2B. At step 2B, it is also considered a conventional computer activity of “Receiving or transmitting data over a network, e.g., using the Internet to gather data,” Symantec, 838 F.3d at 1321” (See MPEP 2106.05d(II)), since the process or claim 1 sends/transmits the information to the terminal, and gets information regarding reservation from a passenger.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-4 and 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Harty (US 2020/0276910) and Daniel (US 2021/0221247).
Concerning claim 1, Harty discloses:
An electric vehicle charging and discharging apparatus for linking ride-sharing service and vehicle-to-grid (Harty – see par 39, FIG. 1 – Electric vehicle 22 uses edge network interface circuitry 44 to communicate with a vehicle-to-grid (V2G) service 24 and a mobility service; see par 41 - The EV 22 may also be part of a fleet of vehicles that provide on demand transportation services (e.g., UBER, LYFT) to individuals such as, for example, a potential passenger 42. Potential passenger 42 may use a client device (not shown) to issue a transport request to the EV 22 via the mobility service 26, where the transport request indicates a pick-up location that is different from the discharge location of the SE 40. The EV 22 may selectively grant the transportation request in exchange for compensation), comprising:
a processor (Harty – see par 63 – computing system 120 may be incorporated into an electric vehicle such as EV 22 (FIG. 1)); and
a memory storing software, when executed by the processor, causing the processor (Harty – see par 64 - The processor(s) 122 may execute instructions 136 retrieved from the system memory 126 and/or the mass storage 134 to perform one or more aspects of the method 46 (FIG. 2), the method 56 (FIG. 4A), the method 66 (FIG. 4B), the method 80 (FIG. 5) and/or the method 106 (FIG. 8); see par 65 - execution of the instructions 136 may cause the system 120 to detect a transport request and a V2G energy request, wherein the transport request and the V2G energy request are associated with overlapping service periods, automatically select one of the transport request or the V2G energy request as a granted request, and configure an EV to satisfy the request.) to:
collect power data of buildings (Harty – see par 40 - the EV 22 is part of a fleet of vehicles that provide unused energy stored in the battery 32 to facilities such as, for example, a building 34 (e.g., owned, operated and/or occupied by a business, governmental entity or other organization) that experiences a gap (e.g., shortage) between renewable energy collected by a source such as, for example, a solar panel array 36 and the energy demand of the building 34.)
Harty discloses “expected” gap (shortage) in renewable energy collected by a source (See par 40) and having “predictive” grid factors related to energy requests from buildings 34 (See par 75).
To any extent that Harty does not disclose, Daniel discloses:
predict power consumption of the buildings based on the power data, respectively (Daniel – see par 19 - Said data and usage analysis, may typically include measurement of energy use on a mains (Grid supply), on household or building circuits ; local data on generation outputs and demand data (building, EV chargers see par 52-53 - Predictions may make use of machine learning, pattern recognition and feature and event detection (e.g. of a high load, occupancy event, start of a charge cycle )… rises in consumption triggered by occupancy, e.g. return to work, holiday modes; see par 132 – home/buildings; forecast energy demand needs… includes risk scoring of occupancy or non-occupancy of building).
Harty and Daniel disclose:
calculate a time zone in which an additional power is required for each of the buildings and a required amount of electrical power based on the collected power data and the predicted power consumption (Harty –see par 46 - More particularly, a V2G data structure 54a (e.g., relational database, table, list) may contain a plurality of V2G energy requests, where the V2G data structure 54a might be maintained on a system such as the V2G service 24 (FIG. 1), already discussed. The V2G data structure 54a documents various request attributes such as, for example, request identifier (ID), timestamp (e.g., time when the request was issued or received), numerical value (e.g., kWh, price per kWh, credits, cryptocurrency, etc.), discharge location, charge location, start time, end time; see par 58 - an EV is dispatched to the vicinity at illustrated block 94 during the weather-related reduction of solar energy exposure. Dispatching the EV to the vicinity of the reduced solar energy exposure may enable the EV to more quickly service V2G energy requests from facilities experiencing a gap/shortage between collected renewable energy and energy demand; see par 77 – the receiving module 214 determines whether the transport timing and the charge timing at least partially overlap. In other examples, the overlap may be based on geography, amount of charge the EV 22 is required to have to satisfy the requests, etc.
see also Daniel – see par 186 - Within said optimisation and management system, the method may actively manage or recommend addition, of extra battery resource on a low voltage network, either as a central resource or as an aggregate of distributed resources, so as to aid management and balancing on the network, for example storing of excess local solar generation at peak solar hours, or discharging at peak domestic demand. can also apply to vehicle-to-grid, or vehicle-to-home applications, where said software system may help to co-ordinate the charging and discharging of such electric vehicle chargers to achieve different outcomes),
estimate a travel demand of a region where the buildings exist (Harty – see par 45 - Illustrated processing block 48 provides for detecting a transport request and a V2G energy request, wherein the transport request and the V2G energy request are associated with overlapping service periods (e.g., concurrent/simultaneous demands). see par 47 - a mobility data structure 54b (e.g., relational database, table, list) may contain a plurality of transport requests. The illustrated mobility data structure 54b documents various request attributes such as, for example, request ID, timestamp, numerical value (e.g., kWh, distance, price per mile, credits, cryptocurrency, etc.), start location, end location, start time, end time, and so forth. see par 73 - request may be made by the potential passenger 42 inputting information, such as logistical factors. The logistical factors may include at least a portion of the route, the origin, the destination, address, coordinates, point of interest, one or more roadway names, and a waypoint. The logistical factors also be an event, invitation, ticket, or other item associated with a time or location; see par 77 - In response to the receiving module 214 receiving multiple requests, such as a transport request and a V2G energy request, the receiving module 214 may determine the extent to which the requests overlap. For example, the receiving module 214 may calculate transport timing for transport based on the logistical factors associated with the transport request. In other examples, the overlap may be based on geography; see par 78 - transport module 216 uses the one or more logistical factors and/or the transport request to determine a first numerical value associated with remuneration for the transport; In particular, the first numerical value may be based on proximity to the origin in the transport request, the current cost of fuel and/or power, as well as potential passenger loyalty), and
set a travel path of a ride-sharing vehicle for each time zone within the region based on the time zone requiring the additional power, the required amount of electrical power and the travel demand (Harty – see par 83 - the grant module may dispatch the EV 22 to the origin associated with the transport request or the charging location associated with the V2G energy request. Accordingly, the grant module 220 may generation a path plan for the EV 22 that facilitates the EV 22 navigating to a location associated with the grated request. For example, the grant module 220 may access the vehicle sensors 238 or the vehicle systems 240 to determine the current location of the EV 22 and store, calculate, and/or provide route and/or destination information and facilitate features like turn-by-turn direction for the EV 22 based on the granted request. Accordingly, the hybrid vehicle-to-grid and mobility service request system 202 manages the requests received for an EV 22 to facilitate maximizing the benefit that can be conferred to the EV 22. Therefore, the operator can use an EV or fleet of EVs to the greatest effect;
see also Daniel see par 185 – optimization system to consider and measure and forecast different usage across phases… move demand onto alternate phases to help balance, e.g. through electric vehicle charge-points that may optionally select or draw power from different phases in response to a request; see par 186 - aid management and balancing on the network, for example storing of excess local solar generation at peak solar hours, or discharging at peak domestic demand. can also apply to vehicle-to-grid, or vehicle-to-home applications, where said software system may help to co-ordinate the charging and discharging of such electric vehicle chargers to achieve different outcomes; par 216 - ledger approaches may be used to help govern interactions for assets within a close community, building, site, community or low voltage network. Approach to use ledgers… at particular locations as well as within assets such as electronic vehicles).
Both Harty and Daniel are analogous art as they are directed to using vehicles for giving energy to respond to electricity demand (Harty Abstract par 39-40; Daniel Abstract, par 12, 172/186 (vehicle to grid)). Harty discloses “expected” gap (shortage) in renewable energy collected by a source (See par 40) and having “predictive” grid factors related to energy requests from buildings 34 (See par 75). Daniel improves upon Harty by disclosing predictions for usage and demand for homes/buildings on the electric grid based on scoring aspects such as occupancy or non-occupancy (See par 19, 52-53, 132). One of ordinary skill in the art would be motivated to further include predicting electric/energy demand based on factors such as occupancy to efficiently improve upon the “expected” gaps in energy collected and “predictive” energy requests from buildings in Harty.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the hybrid vehicle-to-grid and mobility service requests that maximizes benefit in Harty (see Abstract, FIG. 12, par 90) to further use predictions of electricity/energy demands based on building data such as occupancy, return to work as disclosed in Daniel (See par 19, 52-53, 132), since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning independent claim 11, Harty and Daniel disclose:
An electric vehicle charging and discharging method for linking ride-sharing service and vehicle-to-grid (Harty – see par 39, FIG. 1 – Electric vehicle 22 uses edge network interface circuitry 44 to communicate with a vehicle-to-grid (V2G) service 24 and a mobility service; see par 41 - The EV 22 may also be part of a fleet of vehicles that provide on demand transportation services (e.g., UBER, LYFT) to individuals such as, for example, a potential passenger 42. Potential passenger 42 may use a client device (not shown) to issue a transport request to the EV 22 via the mobility service 26, where the transport request indicates a pick-up location that is different from the discharge location of the SE 40. The EV 22 may selectively grant the transportation request in exchange for compensation).
The remaining limitations are similar to claim 1 and are rejected for the same reasons as above.
It would have been obvious to combine Harty and Daniel for the same reasons as claim 1 above.
Concerning claims 2 and 12, Harty and Daniel disclose:
The apparatus of claim 1, wherein the processor is configured to collect location data of respective buildings for estimation of past power usage data (Harty – see par 40 - the EV 22 may include a V2G subsystem 38 that enables unused energy stored in the battery 32 to be transferred (e.g., discharged) to the building 34 via source equipment (SE) 40 installed at the location of the building 34. In this regard, when gaps/shortages are encountered or expected, the building 34 (or associated system) may issue V2G energy requests to the EV 22 via the V2G service 24.), power facility data (Harty – see par 75 - The grid factors may include, but are not limited to, the available charge on the grid, the charge level of a particular charge point such as SE 40, traffic, geolocation, weather, etc. The grid factors may be historical, current, and/or predictive. For example, the charge level for the SE 40 may include a prediction of a weather-related reduction of the charge levels.)…
Harty does not disclose “contacted power data”, which is believed to be “contracted power” based on objections.
Daniel discloses the “contacted power data” along with the “power prediction” as in claim 1:
contacted power data… for power prediction (Daniel – see par 3 – resources include electric vehicles; see par 109 - example and embodiment, is where distributed ledger approaches are used to create and manage a smart contract between parties or form a shareable coin to mediate e.g. how KWh's of solar generation, Battery capacity, or local flexibility is shared on either a local ledger basis—where a trusted party is an asset such as a meter/charger/network node, within a location, that is included as a location-stamp within a hash of a time-stamp and transaction between parties. see par 129 - The flexibility 15 of resources may be traded via exchange means 5 such as data, contracts, marketplace platforms, with energy actors 46 such as aggregators, suppliers, local networks, grid, or peer-to-peer or communities 47, via contracts 49 and enable financial payments 48, or other benefits 50 such as carbon offsets).
Harty and Daniel disclose, as best understood in light of 112 rejections:
“and the travel demand of respective buildings,” for power prediction (Harty – see par 45 - Illustrated processing block 48 provides for detecting a transport request and a V2G energy request, wherein the transport request and the V2G energy request are associated with overlapping service periods (e.g., concurrent/simultaneous demands). see par 73 - request may be made by the potential passenger 42 inputting information, such as logistical factors. The logistical factors may include at least a portion of the route, the origin, the destination, address, coordinates, point of interest, one or more roadway names, and a waypoint. The logistical factors may also be …other item associated with a time or location; see par 77 - the receiving module 214 may determine the extent to which the requests overlap. … may calculate transport timing for transport based on the logistical factors associated with the transport request. In other examples, the overlap may be based on geography; see par 78 - transport module 216 uses the one or more logistical factors and/or the transport request to determine a first numerical value associated with remuneration for the transport; In particular, the first numerical value may be based on proximity to the origin in the transport request, the current cost of fuel and/or power, as well as potential passenger loyalty;
Daniel – see par 128 - Flexibility is the ability to provide resources that can increase or decrease demand, store or provide power to aid the energy network in managing variability and volatility and balance supply and demand on the network. the ability to manage and optimise the energy resources and their flexibility at end sites provides a range of advantages at all levels of the network, and becomes increasingly important as more variable energy supplies, such as … with the electrification of mobility and heat, that add increasing loads onto the network that vary with location, time and season. see par 130 - 2, mobile phone network masts with batteries 44, sites and buildings 45 with flexible demand side resources, and similarly an electric vehicle charger cluster 33 formed of individual Electric Vehicle charger apparatus 34 (that may also be co-located in a home or street), and an example electric vehicle 35).
It would have been obvious to combine Harty and Daniel for the same reasons as claim 1 above. In addition, Harty discloses considering available charge on the grid, looking at grid factors that are historical, current, and/or predictive (See par 75). Daniel improves upon Harty by including contracts agreeing to supply energy (See par 109, 129) and considering flexible demand resources including vehicle charger clusters co-located in a street (See par 130).
Considering claims 3 and 13, Harty and Daniel disclose:
The apparatus of claim 2, wherein the processor is configured to predict a power usage pattern during a specific period for each of the buildings by using the collected past power usage data (Daniel – see par 52 - Predictions may make use of machine learning, pattern recognition and feature and event detection (e.g. of a high load, occupancy event, start of a charge cycle), training of neural networks to aid recognition of patterns or classifying patterns that are unusual, use of modelling, convolution and comparison, forecasting and probabilistic modelling (e.g. of energy load profiles on event detection, solar profiles, EV charge patterns), See par 53 - detecting the start of a high-load appliance such as a cooker, air-conditioner or washing machine, by detecting substantial step change in energy use, and disaggregation and pattern recognition approaches, such as referring to past profiles and learnt behaviour. This has been found to be particularly advantageous for informing forward predictions for such high-loads, or standard electric vehicle charging events, as well as rises in consumption triggered by occupancy (e.g. detection of return to work, away—e.g. holiday modes, night time slow down), and various tools such as risk-profiles can lend weight to the stability of such forecasts and past reliability to inform energy management and how predictions are used for trading, battery charge plan adjustment).
It would have been obvious to combine Harty and Daniel for the same reasons as claim 1 above.
Concerning claims 4 and 14, Harty and Daniel disclose:
The apparatus of claim 2, wherein the processor is configured to estimate the time zone requiring discharging of each of the buildings and the amount of electrical power in consideration of the power facility data and the contacted power data with respect to each of the buildings (Daniel – see par 3 – resources include electric vehicles; See par 112 - distributed energy storage assets, EV charge apparatus or electric vehicles, where said vehicle contracts the management and optimisation system to perform … maximising income opportunities and contracted revenue or payments from such parties, such as may occur in Energy As A Service (EaaS) models or Transport As A Service (TaaS) models; see par 129 – as in claim 2 above – resources… use contracts … with energy actors; see par 145 - optimisation and decision logic, may also make use of linear programming techniques to focus an optimisation between maximising various properties (e.g. demand, PV supply, grid tariff price, weather) within a specific interval and time unit (TU), and establish a typical flow chart of measured or expected characteristics, and how, e.g. by varying a battery charge rate/discharge parameter in a household battery or electric vehicle charging plan, a local optimisation could occur for predicted time interval (see FIG. 8).; see par 157 - benefit, may include a method of generating a plan (for flexibility e.g. charging/discharging of an asset), based on sharing current and monitored data with a prediction engine and an economic model, wherein said economic model calculates an impact of the example plan subject to other data (e.g. battery, PV sizing, choices, tariffs) and with reference to a tariff model or store; and said prediction engine calculates a forward model of consumption and generation for applying such a plan, along with other factors and data (e.g. weather or other consumption predictions) and stores the prediction, to enable performance monitoring and feedback to the system or requests for new predictions).
It would have been obvious to combine Harty and Daniel for the same reasons as claim 1 above.
Claims 5-7 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Harty (US 2020/0276910) and Daniel (US 2021/0221247), as applied to claims 1-4 and 11-14 above, and further in view of Dytckov, “Integrate, not compete! On Potential Integration of Demand Responsive Transport Into Public Transport Network,” 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), pages 2056-2063.
Concerning claims 5 and 15, Harty discloses that it’s “vehicle” includes “rail transport” (See par 35) and that the EV might be a “bus” (See par 39). Harty discloses having transport requests with demands for different service periods (See par 45) and having data on request attributes (See par 47).
Dytckov discloses:
The apparatus of claim 1, wherein the processor is configured to collect public transportation demand data (Dytckov – see page 2056, col. 1, Introduction - Demand Responsive Transport (DRT) is an umbrella concept incorporating a big variety of transport services where travelers have the power to affect where vehicle ride, when vehicle ride, or where they stop; see page 2057, last paragraph – page 2058, Col. 1, 1st paragraph – demand generation method relies on generating trip characteristics form conditional probability distributions; page 2058, col. 1, 2nd paragraph – data used in demand generation shown in FIG. 3; data sources are “statistics Sweden (SCB), Open Street Maps (OSM), and travel survey in Scania Sweden (RVU)”; page 2058, col. 1, 3rd paragraph - RVU distinguish 17 trip purposes. To assign trip destinations to the appropriate locations, building types were extracted from SCB and supplemented by points of interest from OSM. Trip purposes are mapped onto appropriate building types so that, for example, the destination of a trip home is allocated to a residential building and trips to
pick up or drop off kids to schools or preschools; page 2060, Col. 2 - The important aspect is that DRT vehicles are connected to specific bus departures in a way that the whole trip is satisfactory for a traveler as explained in section
IV. The depot for DRT vehicles is assumed to be located at the main bus stop in Sjobo town. The fleet size is unlimited as the goal of the
service is to serve all the requested trips, but the operational costs force the routing algorithm to combine trips into the same vehicle for ride-sharing when possible.).
Harty, Daniel, and Dytckov disclose:
ride-sharing service participation record data according to regions, for each time zone, to generate travel demand information (as in claim 1 - Harty – see par 45 - Illustrated processing block 48 provides for detecting a transport request and a V2G energy request, wherein the transport request and the V2G energy request are associated with overlapping service periods (e.g., concurrent/simultaneous demands). see par 47 ; see par 73 - request may be made by the potential passenger 42 inputting information, such as logistical factors. The logistical factors may include at least a portion of the route, the origin, the destination, address, coordinates, point of interest, one or more roadway names, and a waypoint. logistical factors also be an event, invitation, ticket, or other item associated with a time or location; see par 77 - … a transport request and a V2G energy request, the receiving module 214 may determine the extent to which the requests overlap. … the overlap may be based on geography; see par 78 - transport module 216 uses the one or more logistical factors and/or the transport request to determine a first numerical value … In particular, the first numerical value may be based on proximity to the origin in the transport request, the current cost of fuel and/or power, as well as potential passenger loyalty
See also Dytckov- See page 2059, col. 1, 2nd paragraph - we define the area where the destinations of the secondary trips can be allocated. The destination of any trip is limited to this elliptical area. The limitation of the elliptical trip space aims to approximate the spacetime constraints for secondary trips (as, for example, in
[25]) by putting more potential destinations in between home and main activity; see page 2059, col. 2, 3rd paragraph - spatial distribution of the generated demand corresponds to the expectations: about half of the trips related to Sjobo happen within the municipality, while the largest long-distance demand is split between the neighbouring municipalities. Fig. 6 shows the comparison between the
number of commuters to or from Sjobo generated by the described
procedure and the number of commuters from SCB.)
Harty and Daniel are analogous art as they are directed to using vehicles for giving energy to respond to electricity demand (Harty Abstract par 39-40; Daniel Abstract, par 12, 172/186 (vehicle to grid)); Harty and Dytckov are analogous art as they are directed to handling transportation requests (See Harty Abstract, par 45; Dytckov Abstract). Harty discloses that it’s “vehicle” includes “rail transport” (See par 35) and that the EV might be a “bus” (See par 39). Harty discloses having transport requests with demands for different service periods (See par 45) and having data on request attributes (See par 47). Dytckov improves upon Harty and Daniel by disclosing having travel/transport data from public data (See age 2058; 2060). One of ordinary skill in the art would be motivated to further include public transport data from bus usage to efficiently improve upon the mention that an EV could be a “bus” or it could be “rail transport” in Harty.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the hybrid vehicle-to-grid and mobility service requests that maximizes benefit in Harty (see Abstract, FIG. 12, par 90) to further use predictions of electricity/energy demands based on building data such as occupancy, return to work as disclosed in Daniel (See par 19, 52-53, 132), and to further include transport data related to use of buses as disclosed in Dytckov, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claims 6 and 16, Harty and Daniel and Dytckov disclose:
The apparatus of claim 5, wherein the processor is configured to estimate an actual travel demand when the ride-sharing vehicle performs movement between building in consideration of the generated travel demand information (Harty – see par 83 - the grant module may dispatch the EV 22 to the origin associated with the transport request or the charging location associated with the V2G energy request. Accordingly, the grant module 220 may generation a path plan for the EV 22 that facilitates the EV 22 navigating to a location associated with the grated request. For example, the grant module 220 may access the vehicle sensors 238 or the vehicle systems 240 to determine the current location of the EV 22 and store, calculate, and/or provide route and/or destination information and facilitate features like turn-by-turn direction for the EV 22 based on the granted request;
see also Dytckov – See page 2057, Col. 2, Last section - We generate the demand on the micro level explicitly allocating the origin, the destination and the start time for each trip of every individual. Such a level of detail is required to compile a vehicle routing problem; See page 2060, Col. 1, last section - The type of vehicle
routing problem that is solved is the dial-a-ride problem with time windows. The problem consists of the set of trip requests described by their origin, destination, time interval for picking up, time interval for dropping off, and maximum in-vehicle time. The optimisation goal is to find vehicle routes that minimise operational costs. The cost model is based on [26] and summarised in table I. Additionally, a very high penalty for not-serving a traveller is set. The penalty forces the optimiser to route vehicles for all the travellers as a first priority and optimise the operational costs as a second).
It would have been obvious to combine Harty and Daniel and Dytckov for the same reasons as claim 5 above.
Concerning claims 7 and 17, Harty and Daniel and Dytckov disclose:
The apparatus of claim 6, wherein: the processor is configured to estimate the travel demand through Equation 1 below by using the number of persons to depart from a departure region and the number of persons to move to another region,
PNG
media_image2.png
122
486
media_image2.png
Greyscale
wherein A and B denote buildings, X means a travel demand quantity,
PNG
media_image3.png
50
80
media_image3.png
Greyscale
denotes a travel demand quantity when moving from A to B, and n denotes a set of all buildings, wherein an Arrival quantityB is the number of persons moving to a building B, an Arrival quantityn is the number of persons moving to all the buildings, and a Departure quantityA is the number of persons to depart from a building A (Dytckov – See page 2057, Col. 2, last section - generate the demand on the micro level explicitly allocating the origin, the destination and the start time for each trip of every individual. Such a level of detail is required to compile a vehicle routing problem; page 2058, col. 1, 1st paragraph – demand generator method relies on generating trip characteristics from conditional probability distributions; page 2058, col. 1, last paragraph - To assign trip destinations to the appropriate locations, building types were extracted from SCB (Statistics Sweden) and supplemented by points of interest from OSM (Open Street Maps). Trip purposes are mapped onto appropriate building types so that, for example, the destination of a trip home is allocated to a residential building and trips to pick up or drop off kids to schools or preschools. RVU is also used to estimate the distribution of the start time of trips given trip purpose distribution density of trip chains for each age group (P(trip chain|age group) in Fig. 3). A trip chain is a sequence of trips executed by a traveler within a day; see page 2059, paragraph 4) - define the area where the destinations of
the secondary trips can be allocated; approximate the spacetime
constraints for secondary trips; paragraph 7) buildings is randomly drawn as a destination; see page 2059, col. 2, 1st paragraph - described simple procedure cannot represent the accurate demand, but it captures the distribution of trip lengths and trip times. For instance, it leads to (what we believe) is an accurate situation that in the early morning a lot of short distance trips to drop off the kids at schools are generated).
It would have been obvious to combine Harty and Daniel and Dytckov for the same reasons as claim 5 above. Harty discloses that it’s “vehicle” includes “rail transport” (See par 35) and that the EV might be a “bus” (See par 39). Harty discloses having transport requests with demands for different service periods (See par 45) and having data on request attributes (See par 47). Dytckov improves upon Harty and Daniel by disclosing trip characteristics using probability distributions for different trips, where trips are between different buildings (home to schools) (See page 2058-2059). One of ordinary skill in the art would be motivated to further include probability distributions for different trips to efficiently improve upon the “transport requests” with demands for different service periods (See par 45) in Harty.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Harty (US 2020/0276910) and Daniel (US 2021/0221247), as applied to claims 1-4 and 11-14 above, and further in view of Zhang, “On the Values of Vehicle-to-Grid Electricity Selling in Electric Vehicle Sharing,” 2021, Manufacturing & Services Operations Management, Vol. 23, No. 2, pages 488-507.
Concerning claims 8 and 18, Harty and Daniel disclose:
The apparatus of claim 1, wherein the processor is configured to set a travel path maximizing profitability (Harty – see par 78 - first numerical value may include a cost benefit analysis associated with the remuneration for the transport; see par 83 - the hybrid vehicle-to-grid and mobility service request system 202 manages the requests received for an EV 22 to facilitate maximizing the benefit that can be conferred to the EV 22. Therefore, the operator can use an EV or fleet of EVs to the greatest effect.), and wherein the profitability is calculated through Equation 2 below, profitability = passenger boarding fee (Harty – see par 47 – mobility data structure has request attributes in numerical value including “price per mile, credits”; see par 78 - transport module - For example, the first numerical value may represent the amount that the potential passenger 42 is willing to pay for transport or the amount the potential passenger 42 is willing to pay per mile) + electric vehicle discharging fee (Harty – see par 46 – V2G request attributes include numerical value (e.g. price per kWh, credits), discharge location) + a service fee of building manager (Harty – see par 56 - determine a third numerical value (“Previous,” e.g., price per kWh) associated with a previous charge of an EV; see par 78 - the first numerical value may be or include the cost associated with the energy expenditure incurred by the EV 22 for satisfying the transport request)…
Harty discloses maximizing the benefits of choosing vehicle-to-grid and mobility service requests for a fleet of EVs (electric vehicles) (See par 83).
Zhang discloses considering the impact of Battery degradation costs relative to V2G; and both Harty and Zhang disclose the final two steps:
set a travel path maximizing profitability, and wherein the probability is calculated through Equation 2 below,
[Equation 2]
profitability = fee … “ - electric vehicle battery degradation cost (Zhang 2020 – See page 493, Col. 2, 1st paragraph- Each unit of SoC change incurs a unit degradation cost cdege , where e is the chosen battery capacity. Let rij be the revenue per period generated by one trip from zone i ∈ ( to zone j ∈ (; see page 494, Col. 1, 3rd paragraph “Selling arcs” - Flows on these arcs represent EVs selling electricity back to the grid in zone i from period t to t + 1 and the SoC decreasing from b to b – bS)) – electric vehicle charging cost (Zhang 2020 – see page 492, col. 1, 1st paragraph - consider service zone planning and fleet management in EV sharing. We particularly focus on the impact of integrating V2G by taking into account uncertainties of carsharing demand and electricity price. Specifically, we consider a reservation-based EV system for finite time periods. The service region is discretized into small zones with different costs of operating and acquiring parking facilities. The parking, charging, and V2G facilities have capacity limits, and both the costs and capacities may be different
from zone to zone) – electric vehicle operating cost (Harty – see par 78 - the remuneration may also account for the cost to the EV 22 for satisfying the transport request. For example, the first numerical value may be or include the cost associated with the energy expenditure incurred by the EV 22 for satisfying the transport request. see also Zhang – see page 489, col. 2, section 1.2, 3rd paragraph - scale both of the first-stage and second-stage cost parameters to daily cost and minimize a weighted sum of the total planning cost of building an integrated EV sharing system and the expected operational cost for operating and charging EVs and selling electricity; see page 494, col. 2, section 3.3 - After constructing a spatial-temporal-SoC network & for each realized demand and electricity price, we calculate recourse decisions ya ≥ 0, ∀a ∈ !, representing EV movements over the network &, including EV rentals, relocation, idling, charging, and returning electricity to the grid).
Harty, Daniel, and Zhang are analogous art as they are directed to using vehicles for giving energy to respond to electricity demand (Harty Abstract par 39-40; Daniel Abstract, par 12, 172/186 (vehicle to grid); Zhang Abstract). Harty discloses maximizing the benefits of choosing vehicle-to-grid and mobility service requests for a fleet of EVs (electric vehicles) (See par 83). Zhang improves upon Harty and Daniel by disclosing specific battery degradation costs and other costs in balancing vehicle-to-grid selling and vehicle rentals for maximizing profits (See page 500, 506 in Conclusions). One of ordinary skill in the art would be motivated to further include battery degradation costs and other operating costs to efficiently improve upon the maximizing of choosing vehicle-to-gird and mobility services requests for electric vehicles in Harty.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the hybrid vehicle-to-grid and mobility service requests that maximizes benefit in Harty (see Abstract, FIG. 12, par 90) to further use predictions of electricity/energy demands based on building data such as occupancy, return to work as disclosed in Daniel (See par 19, 52-53, 132), and to further include battery degradation costs and other operational costs for EV sharing and V2G operations as disclosed in Zhang, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Harty (US 2020/0276910) and Daniel (US 2021/0221247), as applied to claims 1-4 and 11-14 above, and further in view of Bertsekas, "New auction algorithms for path planning, network transport, and reinforcement learning," 2022, arXiv preprint arXiv:2207.09588, Arizona State University SCAI Report, pages 1-31.
Concerning claims 9 and 19, Harty discloses generating a path plan for an EV (See par 83), where the system includes “artificial intelligence” (See par 39), and programming autonomous navigation routes (See par 45), where transport requests and energy requests are compared and the request with the highest value (e.g. most Euros – par 53). Daniel discloses that “linear” programming techniques is a known technique for optimization between maximizing various properties (See par 145) and using linear programming for maximizing a goal in a time interval relative to an electric vehicle charging plan (See par 54).
Bertsekas discloses:
The apparatus of claim 1, wherein the processor is configured to use an optimization model comprising reinforcement learning, auction model, and linear programming, in order to set the travel path (Bertsekas see page 2, 1st paragraph - Our proposed methodology aims to improve the efficiency and flexibility of existing auction algorithms for linear single commodity network optimization, including shortest path planning, matching, assignment, and network
transportation problems. See page 9, Section 3 - We will now introduce a generalization of our path planning algorithm, which we call auction/weighted path construction (AWPC for short). The algorithm incorporates a length (or weight) aij for every arc (i, j), and aims to provide a path with near-minimum total length. Each length aij encodes a measure of desirability of including arc (i, j) into a path from the origin to the destination. see page 20, last paragraph – page 21, 1st paragraph “we should mention that the use of reinforcement learning (RL) methods in conjunction with our path construction algorithms is facilitated by the fact that the initial prices are unrestricted. This makes our algorithms well-suited for large-scale and time-varying environments, such as data mining and transportation, where requests for solution of path construction problems arise continuously over time; see page 21, 2nd paragraph - There are also possibilities for incorporation of the AWPC (auction/weighted path construction) algorithm within the RL (reinforcement) methodology. Indeed, several RL methods rely on the computation of (nearly) shortest paths. Notable examples include multistep lookahead minimization and tree search methods. see page 27, Section 5.3 “auction algorithms for the linearly weighted version of the problem”).
Harty and Daniel are analogous art as they are directed to using vehicles for giving energy to respond to electricity demand (Harty Abstract par 39-40; Daniel Abstract, par 12, 172/186 (vehicle to grid)); Harty and Bertsekas are analogous art as they are directed to generating path plans or routes (Harty par 45, 53, 45; Bertsekas Abstract). Harty discloses generating a path plan for an EV (See par 83), where the system includes “artificial intelligence” (See par 39), and programming autonomous navigation routes (See par 45), where transport requests and energy requests are compared and the request with the highest value (e.g. most Euros – par 53). Daniel discloses that “linear” programming techniques is a known technique for optimization between maximizing various properties (See par 145) and using linear programming for maximizing a goal in a time interval relative to an electric vehicle charging plan (See par 54). Bertsekas improves upon Harty and Daniel by disclosing use of reinforcement learning, auction model, and linear programming for transportation path planning. One of ordinary skill in the art would be motivated to further include use of reinforcement, auction, and linear for path planning to efficiently improve upon the path plan and choosing which request generates more money for electric vehicles in Harty and the use of linear programming for maximizing a goal in Daniel.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the hybrid vehicle-to-grid and mobility service requests that maximizes benefit in Harty (see Abstract, FIG. 12, par 90) to further use predictions of electricity/energy demands based on building data such as occupancy, return to work as disclosed in Daniel (See par 19, 52-53, 132), and to further include reinforcement learning, auction model, and linear programming for transportation path planning as disclosed in Zhang, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Harty (US 2020/0276910) and Daniel (US 2021/0221247), as applied to claims 1-4 and 11-14 above, and further in view of Zhu, "Joint transportation and charging scheduling in public vehicle systems—A game theoretic approach," 2018, IEEE Transactions on Intelligent Transportation Systems, Vol. 19, No. 8, pages 2407-2419.
Concerning claims 10 and 20, Harty and Daniel disclose:
The apparatus of claim 1, wherein the processor is configured …to a customer terminal, and perform reservation with respect to a passenger to use the ride-sharing vehicle according to the path predetermined for each time zone (Harty – see par 41 - The EV 22 may also be part of a fleet of vehicles that provide on demand transportation services (e.g., UBER, LYFT) to individuals such as, for example, a potential passenger 42. Accordingly, the potential passenger 42 may use a client device (not shown) to issue a transport request to the EV 22 via the mobility service 26, where the transport request indicates a pick-up location that is different from the discharge location of the SE 40. The EV 22 may selectively grant the transportation request in exchange for compensation; see par 72-73 – potential passenger 42 may input one or more requests into portable device 224 using input device such as touch screen; portable device 224 may run application that allows potential passenger to interface with request generation module 234; request by passenger inputting information includes logistical factors such as route, origin, destination, address; time of arrival; time of departure).
Zhu discloses the limitations as best understood in light of the 112b rejections:
The apparatus of claim 1, wherein the processor is configured “to guide the predetermined path to a customer terminal,” and perform reservation with respect to a passenger to use the ride-sharing vehicle according to the path predetermined for each time zone (Zhu – see page 2407, Col. 1 – public vehicle systems provide ride-sharing services; PVs typically are electric vehicles with large capacities just like buses and are connected to smart grids for self-charging; page 2407, Col. 2, 1st paragraph - If a passenger/user needs a trip service, he/she sends a request to the cloud via a smart phone, including an earliest start time, a pickup position
(origin) and a dropoff position (destination), etc. Then the cloud computes the ride matches between PVs and passengers, and calculates paths for PVs, and finally schedules a suitable PV to drive him/her from the origin to the destination, wherein the paths may be shared with others; See FIG. 1 – passengers have smartphones which are “customer terminals”; see page 2410, col. 1, “PVG Utility Model,” The utility model for each PVG should reflect its transportation
and charging willingness considering the trip demands of passengers, electricity prices, and its own energy states).
Harty and Daniel are analogous art as they are directed to using vehicles for giving energy to respond to electricity demand (Harty Abstract par 39-40; Daniel Abstract, par 12, 172/186 (vehicle to grid)); Harty and Zhu are analogous art as they are directed to handling transportation requests (See Harty Abstract, par 45; Zhu Abstract). Harty discloses that passengers have a client device where they indicate a pick-up location and can input route, origin, destination, time of arrival/departures (See par 41, 72-73). Zhu improves upon Harty and Daniel by disclosing use of that paths for vehicles may be shared with others where passengers have smartphones. One of ordinary skill in the art would be motivated to further include having paths for vehicles shared to efficiently improve upon the path plan and choosing which request generates more money for electric vehicles in Harty.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the hybrid vehicle-to-grid and mobility service requests that maximizes benefit in Harty (see Abstract, FIG. 12, par 90) to further use predictions of electricity/energy demands based on building data such as occupancy, return to work as disclosed in Daniel (See par 19, 52-53, 132), and to further include share paths for vehicles servicing passengers as disclosed in Zhu, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Conclusion
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619