DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/07/2026 has been entered.
Claim status
This action is in response to applicant filed on 03/16/2026
Claim 24 has been amended.
Claims 24-43 are pending for examination.
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.
Claim(s) 24-43 is/are rejected under 35 U.S.C. 103 as being unpatentable over The combination of Graefe et al. (US 2019/0222652) in view of De Magalhaes et al. (US 2020/0250508)
Regarding claim 24: Graefe disclose a network system comprising:
- a plurality of edge devices (fig. 2,3: items 61a, 61b, 62a, 62b), e.g. comprising luminaires (¶0042:"For instance, the loT devices may be [...] light emitting devices |[...]"), said plurality of edge devices being arranged at a plurality of locations (figs. 2-3), the plurality of edge devices comprising:
- at least a sensor (figs. 2, 3: item 262), said sensor being configured for obtaining environmental data related to an event in the vicinity of an edge device of the plurality of edge devices, said sensor being set up according to at least one configuration parameter (¶0030), and
– a processing means (figure 3: item 302) configured to process input data in accordance with a model to derive the at least one configuration parameter of the sensor (¶0050);
- wherein the network system is configured to determine an updated model over time and to reset the processing means so as to process input data in accordance with the updated model (¶0064: "In various embodiments, the fog/edge infrastructure-aided dynamic mapping may be used for the (re)configuration of sensors 262 arrangements based on various trigger events or conditions.").
The combination of Graefe and De Magalhaes does not explicitly disclose the network system tom be self-learning over time.
In analogous art regarding networks, De Magalhaes disclose a network system that is self-learning over time and update a model over time (¶0041).
Before the effective filing date, it would have been obvious to the one of the ordinary skill in the art to include the feature of the network system tom be self-learning over time, as disclose by De Magalhaes, to the network of The combination of Graefe and De Magalhaes . The motivation is to minimize downtime and greater operational efficiency.
Regarding claim 25: The combination of The combination of Graefe and De Magalhaes disclose the network system of claim 24, further comprising at least one remote device configured to determine the updated model (Graefe: ¶0038: cloud 258 and 260).
Regarding claim 26: The combination of The combination of Graefe and De Magalhaes and De Magalhaes disclose the network system of claim 25, further comprising a central control system in communication with said plurality of edge devices, wherein the at least one remote device comprises the central control system (Graefe: ¶0038).
Regarding claim 27: The combination of The combination of Graefe and De Magalhaes and De Magalhaes disclose the network system of claim 25, further comprising a fog device associated with a subset of said plurality of edge devices, wherein the at least one remote device comprises the fog device.( Graefe: ¶0043-0046)
Regarding claim 28: The combination of The combination of Graefe and De Magalhaes and De Magalhaes disclose the network system of claim 27, wherein the fog device is configured to: receive environmental data from said subset, process data received from said subset, and update the model based on the processed data. (Graefe: ¶0043-0046)
Regarding claim 29: The combination of Graefe and De Magalhaes disclose the network system of claim 24, wherein the input data comprises any one or more of the following: environmental data measured by the sensor, edge processed data based on the environmental data, central control system processed data, fog processed data, or data from external data sources. (Graefe: ¶0020)
Regarding claim 30: The combination of Graefe and De Magalhaes disclose the network system of claim 24, wherein the processing means and the sensor are included in a first edge device of a plurality of edge devices, and wherein the input data further comprises data received from a second edge device of the plurality of edge devices. (Graefe: Fig. 3, ¶0048)
Regarding claim 31: The combination of Graefe and De Magalhaes disclose the network system of claim 24, wherein the at least one configuration parameter comprises one or more of the following:
an operating parameter for the sensor, an operational status, a sensing range, a sensing protocol, or an encryption key (Graefe: ¶0055).
Regarding claim 32: The combination of Graefe and De Magalhaes disclose the network system of claim 24, wherein the at least one model is based on a neural network comprising a plurality of layers, each layer comprising a plurality of neurons and each neuron being associated with a bias, an activation function, and at least one weight associated with at least one neuron of a lower layer, and wherein the network system is configured to update the model by modifying at least one of the number of layers, the number of neurons, a weight, a bias, or an activation function (Graefe: ¶0076).
Regarding claim 33: The combination of Graefe and De Magalhaes disclose the network system of claim 32, but does not explicitly disclose wherein the network system is configured to update the model by retraining the last layer. However, the claim merely disclose a particular layer to update the system. The system update the system to be multilayer and updating said layers (Graefe: ¶0075-0076). Therefore, before the effective filing date, it would have been obvious to the one of the ordinary skill in the art to include the feature of wherein the network system is configured to update the model by retraining the last layer, since having a limited universe of potential options (layers to train), the selection of any particular option (last layer) would have been obvious to one of ordinary skill in the art. In re Jones, 412 F.2d 241, 162 USPO 224 (COPA 1969). Since either option would provide the same predictable result of (training the system with previous data), either option would have been obvious to one of ordinary skill.
Regarding claim 34: The combination of Graefe and De Magalhaes disclose the network system of claim 24, wherein the at least one model is based on a decision tree comprising a plurality of branches, each branch comprising a threshold, and wherein the network system is configured to update the model by modifying at least one of the number of branches and a threshold.( Graefe: Fig. 4, ¶0086: system received information from plurality of sensor (branches) and based on trigger event and conditions (thresholds) sensors threshold are adjusted and optimization of the algorithm is applied)
Regarding claim 35: The combination of Graefe and De Magalhaes disclose the network system of claim 24, wherein the event comprises one of an event related to an object in the one or more edge devices or in the vicinity of the one or more edge devices, an event related to a state of an object in the one or more edge devices or in the vicinity of the one or more edge devices, an event related to the area in the vicinity of the one or more edge devices, or an event related to a state of a component of the edge device, and/or
wherein the sensor is selected from: an optical sensor, a sound sensor, a radar, a LIDAR, a humidity sensor, an air quality sensor, a temperature sensor, a motion sensor, an antenna, an RF sensor, a metering device, a vibration sensor, a malfunctioning sensor, a measurement device for measuring a maintenance related parameter of a component of the edge device, or an alarm device, and/or
wherein the plurality of edge devices comprises any one or more of the following: a luminaire, a bin, a sensor device, a street furniture, a charging station, a payment terminal, a parking terminal, a street sign, a traffic light, a telecommunication cabinet, a traffic surveillance terminal, a safety surveillance terminal, a water management terminal, a weather station, an energy metering terminal, or an access lid in a pavement (Graefe: ¶0042:"For instance, the loT devices may be [...] light emitting devices |[...]").
Regarding claim 36: The combination of Graefe and De Magalhaes disclose the network system of claim 24, wherein the plurality of edge devices comprises an edge device with multiple sensors and wherein the processing means is configured to process input data in accordance with the model to derive at least one configuration parameter of each sensor of the multiple sensors. (Graefe: ¶0030, ¶0053)
Regarding claim 37: The combination of Graefe and De Magalhaes disclose the network system of claim 24, wherein the plurality of edge devices comprises an edge device with at least two sensors configured for obtaining at least two sets of environmental data related to an event in the vicinity of the edge device. (Graefe: ¶0030, ¶0053)
Regarding claim 38: The combination of Graefe and De Magalhaes disclose the network system of claim 24, wherein a classification module is configured to determine classification data (different categories) of the event based on the obtained environmental data.( Graefe: ¶0084)
Regarding claim 39: The combination of Graefe and De Magalhaes disclose the network system of claim 38, wherein the classification data is used by the at least one remote device to determine the updated model. (Graefe: ¶0038 and ¶0100)
Regarding claim 40: The combination of Graefe and De Magalhaes disclose the network system of claim 38, wherein the classification data is used as a portion of the input data. (Graefe: ¶0038 and ¶0100)
Regarding claim 41: The combination of Graefe and De Magalhaes disclose the network system of claim 24, wherein the network is configured to determine the updated model based on one or more of the following: environmental data measured by the sensor, edge processed data based on the environmental data, central control system processed data, fog processed data, or data from external data sources. (Graefe: ¶0038 and ¶0100)
Regarding claim 42: The combination of Graefe and De Magalhaes disclose the network system of claim 24, wherein the network system is configured to have self-learning capabilities to derive an updated model over time, and/or wherein the network is configured to determine the updated model based on one or more of the following: a quality criterion or a reliability index of a sensor. (Graefe: ¶0051)
Regarding claim 43: Claim 43 further limit a feature that is an alternative of the claim is dependent upon (claim 42: reliability index of a sensor). Since the prior art met the claim of the other alternative of claim 42, then also meet the limitations of claim 43.
Response to Arguments
Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record cited in the PTO-892 and not relied upon is considered pertinent to applicant's disclosure.
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/OMAR CASILLASHERNANDEZ/ Primary Examiner, Art Unit 2689
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