DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 7-10, 14-17, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (US20210284043, referred to as Wang).
Regarding claim 1: Wang discloses: A computer-implemented method comprising: receiving input data indicating a current state of a charging network that includes a set of charging stations, a set of rechargeable entities and a set of objects with assigned destinations; executing, iteratively for each object among the set of objects, ([0096] described herein are scalable peer-to-peer vehicle charging solutions that are both low cost and easily to implement with minimal changes to the EVs. According to some embodiments, vehicles will share charge and sustain each other to reach their respective destinations. In some embodiments, a set of cloud-based schedulers may be used to automatically and dynamically monitor participants (e.g., EVs, etc.), decide which participants will be charge providers and receivers (or on standby), and/or control charging locally, regionally, or at a system level.) a decision making process to model decision making by a reinforcement learning (RL) agent, wherein the decision making includes application of a sequence of actions on the charging network, and an application of each action among the sequence of actions changes a state of the charging network; determining a sequence of states of the charging network based on results from application of the sequence of actions, wherein the sequence of states represent transitions of the set of rechargeable entities and the set of objects to complete delivery of the set of objects to the assigned destinations; and generating routing data to direct the set of rechargeable entities to navigate among the set of charging stations and to be coupled with the set of objects according to the sequence of states. ([0155] the Algorithm 3000 is capable of, with a periodicity of HI_I, performing a history information analysis 3024 based at least on extracted historic information 3026 that is compiled into history information 3028. In some embodiments, an artificial intelligence program uses Algorithm 3000 to make certain predictions based at least upon the history information 3028, including, but is not limited to, predicting congestion 3030, predicting the future charge distribution map 3032, and predicting future charge transaction possibilities 3034. In some embodiments, a status of all charger entities 3036 is then processed and stored in the Entity Information Database 3004. If the computing device decides to set up a relay charge sharing scenario between two localities or zones of the network, the relay setup, the computing device deploys the charging entities and optimizes the route and scheduled charge transactions therefore. To do so, the computing device uses an extracted charge map 3038 to generate a charge map 3040 of the current status and location of charging entities. To boost the overall charge in the network, the computing device uses artificial intelligence and Algorithm 3000 to compute a charge relay path 3042 from a charge rich region to a charge depleted region based at least upon long distance charging scenarios to optimize charge usage and minimize trip delays 3044. The computing device then determines the best speed and at least partial path match 3046 for the charging entity such that the relay connection is maintained. In some embodiments, some or all the information needed by the artificial intelligence, produced by the artificial intelligence, needed by Algorithm 3000, and/or produced by Algorithm 3000 may be stored in the Entity Information Database 3004. In some embodiments, with a periodicity of RC_I, routing and charge scheduling 3008 is performed for the network based at least upon the pre-determined optimization goals 3010.)
Regarding claim 2: Wang discloses: The computer-implemented method of claim 1,
Wang further discloses: wherein: the set of rechargeable entities include at least one of a non-autonomous electric tractor and an autonomous electric tractor; and the set of objects include semi-trailers. ([0099] The plurality of battery-powered vehicles can comprise at least one of one or more battery-powered terrestrial vehicles, one or more battery-powered aerial vehicles, one or more battery-powered aquatic vehicles, and/or one or more charging vehicles. In some embodiments, battery-powered terrestrial vehicles can comprise but are not limited to automobiles, passenger trucks, cargo vans, transport trucks, eighteen-wheelers, lulls, dump trucks, tractors, motorcycles, snowmobiles, trains, buses, lorries, tanks, trailers, trolleys, scooters, electric bicycles, electric scooters, trams, all-terrain vehicles, recreational vehicles, electric unicycles, electric tricycle, cultivator, harvester, mower, wagon, bulldozer, grader, loader, forklift, crane, paver, loader, street sweeper, garbage truck, front-end loader, feller buncher, backhoe, excavator, any other suitable terrestrial vehicles, equipment, or apparatuses, and any variants or combinations thereof.)
Regarding claim 3: Wang discloses: The computer-implemented method of claim 1,
Wang further discloses: further comprising distributing the routing data to a plurality of processors of the set of rechargeable entities. ([0121] FIG. 1 illustrates a system 1 for peer-to-peer charging between homogeneous and/or heterogeneous networks of electric vehicles. As illustrated, a computing device 10 (e.g., a cloud computing device) can comprise a routing algorithm and/or a charge transaction scheduling algorithm. In some embodiments, the computing device 10 can employ or comprise an artificial intelligence program configured to employ an algorithm to predict or calculate routing for electric vehicles in the network and schedule charge transfer events. In some embodiments, the system 1 can further comprise a first homogeneous vehicle network 12, a second homogeneous vehicle network 14, a homogeneous drone network 16, and/or a heterogeneous network 18. In some embodiments, battery powered entities within one of the networks 12, 14, 16, 18 may communicate with the computing device 10. In some embodiments, a battery powered entity (e.g., a vehicle) may communicate a current location, a current speed, a destination, a planned route, a battery state, and the like information to the computing device 10. In some embodiments, the computing device 10 can then carry out an algorithm, e.g., using the artificial intelligence program hosted at the computing device 10, to prepare optimized routing instructions for a plurality of vehicles or other battery powered entities in the system 1 and to prepare and provide charge transfer instructions.)
Regarding claim 7: Wang discloses: The computer-implemented method of claim 1,
Wang further discloses: further comprising: determining a cost associated with the sequence of actions, wherein the cost is based on: a distance traveled by the set of rechargeable entities under the sequence of states; a penalty that represents stagnation of the set of rechargeable entities; and a reward that encourages selection of less-utilized rechargeable entities and minimize time for delivery of the set of objects; and training the RL agent using the determined cost. ([0155] the Algorithm 3000 is capable of, with a periodicity of HI_I, performing a history information analysis 3024 based at least on extracted historic information 3026 that is compiled into history information 3028. In some embodiments, an artificial intelligence program uses Algorithm 3000 to make certain predictions based at least upon the history information 3028, including, but is not limited to, predicting congestion 3030, predicting the future charge distribution map 3032, and predicting future charge transaction possibilities 3034. In some embodiments, a status of all charger entities 3036 is then processed and stored in the Entity Information Database 3004. If the computing device decides to set up a relay charge sharing scenario between two localities or zones of the network, the relay setup, the computing device deploys the charging entities and optimizes the route and scheduled charge transactions therefore. To do so, the computing device uses an extracted charge map 3038 to generate a charge map 3040 of the current status and location of charging entities. To boost the overall charge in the network, the computing device uses artificial intelligence and Algorithm 3000 to compute a charge relay path 3042 from a charge rich region to a charge depleted region based at least upon long distance charging scenarios to optimize charge usage and minimize trip delays 3044. The computing device then determines the best speed and at least partial path match 3046 for the charging entity such that the relay connection is maintained. In some embodiments, some or all the information needed by the artificial intelligence, produced by the artificial intelligence, needed by Algorithm 3000, and/or produced by Algorithm 3000 may be stored in the Entity Information Database 3004. In some embodiments, with a periodicity of RC_I, routing and charge scheduling 3008 is performed for the network based at least upon the pre-determined optimization goals 3010.)
Regarding claim 8: Rejected using the same rationale as claim 1.
Regarding claim 9: Rejected using the same rationale as claim 2.
Regarding claim 10: Rejected using the same rationale as claims 2 and 9.
Regarding claim 14: Rejected using the same rationale as claim 7.
Regarding claim 15: Rejected using the same rationale as claims 1 and 8.
Regarding claim 16: Rejected using the same rationale as claims 2, 9, and 10.
Regarding claim 17: Rejected using the same rationale as claims 2, 9, 10, and 16.
Regarding claim 20: Rejected using the same rationale as claims 2, 9, and 10
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US20210284043, referred to as Wang) in view of Zhou et al. (Cn114234995A, referred to as Zhou).
Regarding claim 4: Wang discloses: The computer-implemented method of claim 1,
Wang does not explicitly disclose the following limitations, however Zhou, from an analogous field of endeavor, teaches: wherein the RL agent is an attention based deep neural network. ([pg. 8, lines 41-46]
the heterogeneous graph attention network model can be established by deep learning based on a graph neural network model, wherein the graph attention network is a new type of convolutional graph neural network, which only deals with one type of node or connection. Heterogeneous graph; heterogeneous graph attention network can usually handle multiple types of nodes, different types of nodes have different characteristics, and their characteristics may fall in different feature spaces, and nodes pass through various types of meta-paths To form a connection relationship, the relationship between nodes in a heterogeneous graph can have different semantics according to different meta-paths.)
Wang and Zhou are analogous art to the claimed invention since they are from the similar field of data processing optimization for navigation/routing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, with a reasonable expectation for success, to modify the vehicle charging use case disclosed in Wang to enable the attention based neural network taught in Zhou.
The motivation for modification would have been to provide a common reinforcement learning method for routing based optimization to the routing optimization process disclosed in Wang.
Regarding claim 11: Rejected using the same rationale as claim 4.
Regarding claim 18: Rejected using the same rationale as claims 4 and 11.
Claims 5-6, 12-13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US20210284043, referred to as Wang) in view of Mouradian et al. (WO2024062273, referred to as Mouradian).
Regarding claim 5: Wang discloses: The computer-implemented method of claim 1,
Wang does not explicitly disclose the following limitations, however Mouradian, from an analogous field of endeavor, further teaches: further comprising: encoding the current state of the charging network to generate a set of node embeddings that are vector representations of the set of charging stations in the charging network, wherein executing, iteratively for each object among the set of objects, the decision making process comprises: decoding a selection of a specific object using the set of node embeddings; decoding a selection of a specific rechargeable entity for the specific object based on the set of node embeddings and states of the set of rechargeable entities; decoding a selection of a specific charging station to be visited by the specific rechargeable entity based on states of the set of rechargeable entities and states of the set of objects; and applying a specific action that updates the state of the charging network, wherein the specific action is formed based on the specific object, the specific rechargeable entity and the specific charging station. ([0031] Machine-learning (ML) based VNF resource allocation: One may estimate VNFs’ needs in terms of central/graphics processing unit (CPU/GPU) as a function of the traffic the VNFs will process using the Support Vector Regression (SVR) based approach. Alternatively, the future traffic demand of VNFs may be predicted using ML, and the VNF resources may then be scaled proactively and dynamically. Additionally, one may consider a service function chain (SFC) when predicting VNF resource allocation using ML, where at each VNF, the resource information of all other VNFs in the SFC is also collected. [0044] the set of MDPs may also be referred to as stochastic games or Markov Games, where each agent has its own set of actions. The embodiments of the invention cover the stochastic games or Markov Games as well. For example, the set of MDPs may be one MDP modeled by one agent, and its set of actions captures the resource allocation for the input. [0045] An MDP is defined by a tuple (S, A, p, r) where S is a finite set of states, A is a finite set of actions, p is a transition probability from state .s' to state .s ’ after action a is executed, and r is the immediate reward obtained after action a is performed. We denote n as a “policy” which is a mapping from a state to action. The goal of an MDP is to find an optimal policy by training agents (e.g., reinforcement agents discussed herein) to observe the state of the environment and take actions to ultimately maximize the reward function. Of the tuple (S, A, p, r), the transition probability p may be referred to probability space represented by all the probability transitions within the MDP. The probability space depends on the state and action spaces, while the state space, the action space, and the reward function may be defined as follows, using VNFs as examples … [00134] Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door/window sensor, a flood/moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot.)
Wang and Mouradian are analogous art to the claimed invention since they are from the similar field of data processing optimization. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, with a reasonable expectation for success, to modify the vehicle charging use case disclosed in Wang to enable the VNF network routing taught in Mouradian.
The motivation for modification would have been to provide a common method for local network routing taught in Mouradian to perform the routing optimization process disclosed in Wang.
Regarding claim 6: Wang discloses: The computer-implemented method of claim 1,
Wang further discloses: wherein each action among the sequence of actions [is a 5-tuples] representing a starting charging station, an ending charging station, a specific rechargeable entity, a specific object, and a decoding step of the iterative execution of the decision making process. ([0155] the Algorithm 3000 is capable of, with a periodicity of HI_I, performing a history information analysis 3024 based at least on extracted historic information 3026 that is compiled into history information 3028. In some embodiments, an artificial intelligence program uses Algorithm 3000 to make certain predictions based at least upon the history information 3028, including, but is not limited to, predicting congestion 3030, predicting the future charge distribution map 3032, and predicting future charge transaction possibilities 3034. In some embodiments, a status of all charger entities 3036 is then processed and stored in the Entity Information Database 3004. If the computing device decides to set up a relay charge sharing scenario between two localities or zones of the network, the relay setup, the computing device deploys the charging entities and optimizes the route and scheduled charge transactions therefore. To do so, the computing device uses an extracted charge map 3038 to generate a charge map 3040 of the current status and location of charging entities. To boost the overall charge in the network, the computing device uses artificial intelligence and Algorithm 3000 to compute a charge relay path 3042 from a charge rich region to a charge depleted region based at least upon long distance charging scenarios to optimize charge usage and minimize trip delays 3044. The computing device then determines the best speed and at least partial path match 3046 for the charging entity such that the relay connection is maintained. In some embodiments, some or all the information needed by the artificial intelligence, produced by the artificial intelligence, needed by Algorithm 3000, and/or produced by Algorithm 3000 may be stored in the Entity Information Database 3004. In some embodiments, with a periodicity of RC_I, routing and charge scheduling 3008 is performed for the network based at least upon the pre-determined optimization goals 3010.)
Wang does not explicitly disclose the following limitations, however Mouradian, from an analogous field of endeavor, further teaches: sequence of actions is a 5-tuples ([0026] The physical functions interact with the VNF-FG through an entrance physical network logical interface 112 and an exit physical network logical interface 114. The VNF-FG includes seven VNFs (VNF 1 - VNF 7), and they are implemented in four physical nodes (PN 1 to PN 4). [0027] Packets are forwarded through traffic flows, which are processed through the VNFs. A traffic flow may be identified by a set of attributes embedded to one or more packets of the traffic flow. An exemplary set of attributes includes a 5-tuple (source and destination IP addresses, a protocol type, source and destination TCP/UDP ports). Traffic flows 1 to 3 are transmitted through the VNF-FG via logical links between VNFs. A logical link may be between two VNFs implemented within a single physical node, e.g., the logical link between VNF 1 and VNF 2. These VNFs may share the same resources of the underlying physical node and the performance of one may affect the other.)
As previously stated, Wang and Mouradian are analogous art to the claimed invention since they are from the similar field of data processing optimization. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, with a reasonable expectation for success, to modify the vehicle charging use case disclosed in Wang to enable the VNF network routing taught in Mouradian.
The motivation for modification would have been to provide a common method for local network routing taught in Mouradian to perform the routing optimization process disclosed in Wang.
Regarding claim 12: Rejected using the same rationale as claim 5.
Regarding claim 13: Rejected using the same rationale as claim 6.
Regarding claim 19: Rejected using the same rationale as claims 5 and 12.
Conclusion
The prior art made of record, and not relied upon, considered pertinent to applicant' s disclosure or directed to the state of art is listed on the enclosed PTO-892.
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/ATTICUS A CAMERON/ /JASON HOLLOWAY/ Primary Examiner, Art Unit 3658 Examiner, Art Unit 3658A