The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claims 1-24 are presented for examination
Response to Arguments
Applicant’s arguments, see pages 8-10, filed February 20, 2026, with respect to the rejection(s) of claim(s) 1, 9, 17, and 23 under U.S.C. 102 and 103 have been fully considered and are not persuasive. Therefore, the rejection has been maintained.
Applicant argue that the prior arts teach different field of invention, that is not the case. By looking at the claims and the specification, applicant simply claims that a sensor that collects data and process the data to perform energy management using machine learning. The prior art teaches the same thing. Applicant needs to be more specific in the claims by providing more information about what the invention does in the claims to different from prior art.
Anyway, to help prosecution of this application, an additional rejection is given for independent claims 1, 17, and 23.
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 1-8, 17-22, and 23-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Independent claim 1 is directed to “steps [that] can be performed in the human mind, or by a human using pen and paper” found by the courts to be patent ineligible for abstract idea. For instance, in CyberSource, the concept of obtaining and comparing intangible data were found to be directed to an abstract idea since the claimed steps “can all be performed in the human mind.”
The claim recites collecting data, evaluating the collecting data by performing manipulation of the collecting data and make adjustment based on the collecting data without additional elements.
The limitations of collecting sensor data resulting from a detection by the at least one sensor, the sensor data comprising sensor values specific to a characteristic to be detected by the at least one sensor;
evaluating the collected sensor data, the evaluating comprising generating an operating specification for the at least one sensor based on a detection of at least one deviation pattern in the sensor values of the collected sensor data that indicates a variation of the characteristic to be detected, the operating specification defining a future activity pattern for the detection by the at least one sensor;
initiating at least one change of an operational setting for the detection by the at least one sensor, the change being initiated based on the generated operating specification.
Are directed to an abstract idea. That is, the method simply performs mathematical operations that which can be “performed in the human mind” or by “pen and paper.”
In addition, Flook and Benson have long held that patent claims directed mathematical formula or “algorithm” are not patent eligible for abstract idea as well.
The limitation initiating at least one change of an operational setting for the detection by the at least one sensor, the change being initiated based on the generated operating specification. It not considered to be an additional element of the claim because it simply shows that an initiating of a change which can be done by human mind and pen and paper.
The additional elements, imitate a change in operational is well-known and conventional/generic operating that can be done on paper. Therefore, the claimed invention as a whole does not amount to significantly more than the abstract idea.
Accordingly, for the reasons provided above, dependent claim 2 is also directed to an abstract idea, hence, not patent eligible under 35 USC 101. In claim 2, the sensor values being measured values that represent a quantification of the characteristic to be detected, the characteristic being a measurand. This simply show the characteristic of the data which is not considered significantly more. By looking at the data, visually and by human mind, pen, and paper, the characteristic of the data can be evaluating mathematically. Claims 3-5 carry out the same deficiency.
Applicant is advised to add include limitation of claim 6 to independent claims 1, 17, and 23 to show that fix the deficiency as well as the reason the sensor data are collected and evaluated which shows in claim 9. The difference between independent claim 9 and the other independent claims is that claim 9 is more practical and directed toward energy management which the applicant claims, while the other independent claims are more abstract.
Claim Rejections - 35 USC § 102
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)(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.
Claims 1, 17, and 23 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Brown (US Patent 10474213).
As per claim 1, Brown teaches a computer-implemented method [800, fig. 8] for managing energy of at least one sensor [160, fig. 3], the method comprising:
collecting sensor data resulting from a detection by the at least one sensor, the sensor data comprising sensor values specific to a characteristic to be detected by the at least one sensor [col. 5 lines 5-10, col. 10 lines 42-49 as pointed out, the sensor may measure a characteristic, such as temperature, humidity, pressure, vibration, light, motion, sound, proximity, flow rate, electrical voltage, and electrical current. In this case, the sensor detect specific signal and make measurement of the signal by conversion].
evaluating the collected sensor data, the evaluating comprising generating an operating specification for the at least one sensor based on a detection of at least one deviation pattern in the sensor values of the collected sensor data that indicates a variation of the characteristic to be detected, the operating specification defining a future activity pattern for the detection by the at least one sensor [col. 6 lines 45-54, col. 12 lines 45-55, as pointed out each sensor node has power budget where the measured data can be analyzed and prediction can be made as a result. For example, each of the sensor nodes 102a-102n may have a power budget based on predicted energy input and/or energy output needs. For example, an energy input need may be a function of past patterns and/or cycles as well as predictable future opportunities to harvest energy from the environment (e.g., weather and/or day/night/seasonal patterns on a photovoltaic device, etc.), at least up to the storage capacity. The predicted energy expenditures may also be based on the predicted power consumed given known upcoming calendar and/or event data as well as other inputs. Where the sensor can implement a learning process to make future prediction based on the collecting data. For example, the sensor 102 may implement a learning process and/or predictive modeling on the CPU 202a (to be described in more detail in connection with FIGS. 12-14). New rules may be stored in the memory 204a. In another example, the machine learning process(es) may be run on the mobile computing device 170 and/or the network computing services block 174. The processing power and energy to implement the learning process(es) may use more aggregated data and/or processing power than the sensor 102 can process efficiently while running on the energy storage unit 214a. By offloading the processing, the overall energy used by the sensors 102a-102n may be reduced].
initiating at least one change of an operational setting for the detection by the at least one sensor, the change being initiated based on the generated operating specification [col. 11 lines 27-35, col. 15 lines 52-60, as pointed out, based on the data analysis, sub of the sub systems can be shut down or partially shutdown in order to manage power consumption. For example, initiating a series of prioritized shutdowns of partial or entire subsystems when the available power from the collector 210 and/or battery 214 becomes insufficient to keep the SPD 200 fully powered. As well as, when the remaining energy in the battery 214 (FIG. 4) drops to the lowest threshold, below which proper operation of the SPD may no longer be guaranteed, in some embodiments, the energy collection procedures 236 (FIG. 4) and/or the power management procedures 232 (FIG. 4) initiate a series of controlled shutdowns of any subsystems that remain on, including the majority of the SPD itself. Once this is accomplished, the SPD may be held in a reset condition, through the use of an automatic circuit until sufficient energy is delivered to the battery 214 (FIG. 4) to allow initiation of SPD startup].
As per claims 17 and 23, they do not teach or further define over the limitations recited in the rejected claims above. Therefore, claims 17 and 23 are also anticipated by Brown for the same reasons set forth in the rejected claims above.
To help with the prosecution of this application, additional rejection is given below
Claims 1, 17, and 23 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cella (US Patent Application 20210356945).
As per claim 1, Cella teaches a computer-implemented method [700, fig. 7] for managing energy of at least one sensor [102, fig. 2A], the method comprising:
collecting sensor data resulting from a detection by the at least one sensor, the sensor data comprising sensor values specific to a characteristic to be detected by the at least one sensor [0188, 0196, as pointed out the sensor can collect data where from the data specific type of data can be identified. For example, an IoT sensor 102 is a sensor device that is configured to collect sensor data and to communicate sensor data to another device using at least one communication protocol. Where, the type of sensor data provided by the sensor 102 (e.g., vibration sensor data, temperature data, humidity data, etc.), models used to analyze sensor data from the sensor 102 (e.g., a model identifier), alarm limits associated with the sensor 102, and the like].
evaluating the collected sensor data, the evaluating comprising generating an operating specification for the at least one sensor based on a detection of at least one deviation pattern in the sensor values of the collected sensor data that indicates a variation of the characteristic to be detected, the operating specification defining a future activity pattern for the detection by the at least one sensor [00192, from the collected data, the data can be analyzed and classified where a prediction can be made. For example, performing analytics tasks on the sensor data, providing the results of the analytics and/or visualizations of the sensor data to a user via a portal and/or a dashboard, training one or more machine-learned models using the sensor data, determining predictions and/or classifications relating to the operation of the industrial setting 120 and/or industrial devices of the industrial setting 120 based on the sensor data, controlling an aspect and/or an industrial device of the industrial setting 120 based on the predictions and/or classifications, issuing notifications to the user via the portal and/or the dashboard based on the predictions and/or classifications, and the like].
initiating at least one change of an operational setting for the detection by the at least one sensor, the change being initiated based on the generated operating specification [0213, 0223-0224, 0241, as pointed out the collected data is analyzed where changes in the measurements can be identified. For example, if the measure show that the data is above a threshold, an action can be undertaken to show the changes].
As per claims 17 and 23, they do not teach or further define over the limitations recited in the rejected claims above. Therefore, claims 17 and 23 are also anticipated by Cella for the same reasons set forth in the rejected claims above.
Claims 1-8 and 11-24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lueken (US Patent Application 20240264234).
As per claim 1, Lueken teaches a computer-implemented method [600, fig. 6] for managing energy of at least one sensor, the method comprising:
collecting sensor data resulting from a detection by the at least one sensor, the sensor data comprising sensor values specific to a characteristic to be detected by the at least one sensor [0060, 0067 fig. 6, as pointed out and shown in figure 6 step 610, sensor data is received related to the usage of the battery where the sensor data may include characteristic of the battery the data is being detected from or for].
evaluating the collected sensor data, the evaluating comprising generating an operating specification for the at least one sensor based on a detection of at least one deviation pattern in the sensor values of the collected sensor data that indicates a variation of the characteristic to be detected, the operating specification defining a future activity pattern for the detection by the at least one sensor [0060-0062, fig. 6 as pointed out form figure 6 step 214, the collected data can be inputted to a machine learning where the data can be processed to generate a predicted temperature of the battery. During the data collection, data pattern of the battery can be identified related to time such as voltage pattern, energy dispatch pattern and so forth].
initiating at least one change of an operational setting for the detection by the at least one sensor, the change being initiated based on the generated operating specification [0065-0066, figure 6, steps 620 and 600, based on the analysis, the adjustment can be made on the usage of the battery as shown. In step 620, the computing device may, based on the determined difference between the predicted temperature and the measured temperature, send an indication of a state of the battery. For example, the computing device may, based on the difference satisfying a threshold, send an indication that an anomaly of the battery is detected. In step 620, the computing device may, based on the determined difference between the predicted temperature and the measured temperature, send an indication of a state of the battery. For example, the computing device may, based on the difference satisfying a threshold, send an indication that an anomaly of the battery is detected].
As per claim 2, Lueken teaches the generating is based on the detection of the at least one deviation pattern in the sensor values of the collected sensor data, the sensor values being measured values that represent a quantification of the characteristic to be detected, the characteristic being a measurand [0044, 0051, 0066, for example quantity charge and discharge cycles of the battery are being collected, capacity of the battery, and so forth].
As per claim 3, Lueken teaches the generating of the operating specification for the at least one sensor is further based on: a detection time indicating a time of detection or transmission of the sensor values by the least one sensor, a change time indicating a time of change of the sensor values, and/or a deviation indicator that is based on a standard deviation of a series of the sensor values [0066, 0069 data specify state of change of the battery, such as normal and abnormal state and usage state. In this case, the machine learning can be used to determine the difference between the measured value of the sensor and predicted value. Where each value or segment of measurement related to specific time segment].
As per claim 4, Lueken teaches the evaluating comprises applying at least one rule based on the sensor values of the collected sensor data using a rule-based engine [0066-0067, detecting and modifying usage pattern of the battery where machine learning language can be used to make prediction and decision accordingly].
wherein the initiating of the change of the operational setting is based on the applied at least one rule [0066, where adjustment of the usage of the battery can be done as a result].
As per claim 5, Lueken teaches the at least one rule specifies applying a procedure to derive a standard deviation to detect the at least one deviation pattern in the collected sensor data of the at least one sensor and deciding on the operating specification based on the procedure to derive the standard deviation [0066-0067, collecting sensor data as described above and make decision based on the change of the data].
applying at least one external environmental monitoring system to predict external changes of an environment of the at least one sensor and deciding on the operating specification based on an output of the at least one external environmental monitoring system [0065-0067, 0069 using machine learning to make decision and adjustment. For example, using prediction to compare the measured and historical data].
As per claim 6, Lueken teaches receiving a flag for the at least one sensor, the flag being specific to a sensor type of the at least one sensor [0049, 0060, data specifies anomaly detection of the battery. For example, the receiving data maybe related to abnormal detection of the battery when the battery is being used].
carrying out the initiating of the at least one change of the operational setting based on the received flag, to enable the energy management and/or the application of the rules based on the received flag [0065-0066, perform animally detection and adjust the usage of the battery. This process is being carried out by the machine learning analysis model which enables the energy management of the battery].
As per claim 7, Lueken teaches a continuous learning and optimization of the generating of the operating specification, including the detection of the at least one deviation pattern, is carried out, the continuous learning and optimization comprising [0044, as pointed out the machine learning can perform pattern optimization in the change and discharge cycles of the battery]:
periodically updating the generated operating specification based on further collected sensor data of the at least one sensor [0046, fig. 4, where at step 429 calculation can be performed which is viewed as update].
carrying out the evaluating of the further collected sensor data to amend or replace the operating specification based on the sensor values of the further collected sensor data [0054-0055, where at 432 adjustments can be made accordingly].
As per claim 8, Lueken teaches the operating specification specifies a pattern of the at least one change of the operational setting, the pattern comprising a sequence of changes including different operational settings, and/or states of the operation, of the at least one sensor over time [0056, energy usage pattern within specific period of time].
As per claim 11, Lueken teaches calculating a mean and a standard deviation of the collected sensor data over a defined time period [0057, calculate pattern based on estimating value].
generating the operating specification for the at least one sensor based on the calculated mean and standard deviation of the collected sensor data [0057-0058, perform engorge dispatch generation based on the estimation].
As per claim 12, Lueken teaches the generating of the operating specification is further based on an external model responsive to external environment monitoring, and/or monitoring of external user operations impacting the at least one sensor [0033, 0049, external data and state variable to allow change and adjustment].
As per claim 13, Lueken teaches monitoring a battery of the at least one sensor to determine whether the battery has a low battery status, wherein the generating of the operating specification is based on whether the battery has a low battery status [0065, detecting abnormality or any other status of the battery such as capacity and so].
As per claim 14, Lueken teaches the low battery status is alerted to a user [0065, status related to abnormal state of the battery].
As per claim 15, Lueken teaches changing the operational setting for the detection of the at least one sensor based on at least one trigger, the at least one trigger comprising at least a signal indicating an external event [0066-0067, change the configuration state of the battery or make adjustment to the usage of the battery].
As per claim 16, Lueken teaches the external event is a user input, a time event, a defined deviation pattern, and/or a defined timepoint of a low battery status of a battery of the at least one sensor [0062, input usage data where the input may trigger from an event].
As per claims 17-24, they do not teach or further define over the limitations recited in the rejected claims above. Therefore, claims 17-24 are also anticipated by Lueken for the same reasons set forth in the rejected claims above.
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 of this title, 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 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Lueken (US Patent Application 20240264234). in the view of Goldfarb (US Patent Application 11130422).
As per claim 9, Lueken does not teach the operational settings include at least one of the following: a sleep state in which receiving of commands of the at least one sensor is enabled and a transmission of the sensor data from the at least one sensor is disabled, the initiating of the at least one change of the operational setting being carried out by sending at least one of the commands to the at least one sensor, an awake state in which the receiving of commands and the transmission of the sensor data from the at least one sensor is enabled, and a rate at which the detection by the at least one sensor takes place, and the at least one sensor is initially in the awake state for a defined timeframe to provide the collected sensor data for the evaluating.
a sleep state in which receiving of commands of the at least one sensor is enabled and a transmission of the sensor data from the at least one sensor is disabled, the initiating of the at least one change of the operational setting being carried out by sending at least one of the commands to the at least one sensor, an awake state in which the receiving of commands and the transmission of the sensor data from the at least one sensor is enabled, and a rate at which the detection by the at least one sensor takes place, and the at least one sensor is initially in the awake state for a defined timeframe to provide the collected sensor data for the evaluating [as pointed out power can be conserved where user can set wakeup and sleeping time].
(55) The process 300 may include predicting a set of power consumption values based on a set of records indicating previous power consumption values by the vehicle, as indicated by block 354. Some embodiments may predict a rate that energy will be consumed from a battery based on a user-entered amount that are then stored in a set of records indicating previous power consumption values by components of the vehicle. For example, some embodiments may cause the display of a UI having UI elements that permit a user to configure user-related lifestyle attributes or other attributes usable to predict electricity consumption. A web application or native application may be coupled, via the Internet and a webserver, to a network interface of a BMS of a vehicle. A user may then configure one or more attributes by manipulating one or more UI elements of the web application or native application to indicate values such as a preferred clock time of waking up, clock time for sleeping, preferred temperature ranges, preferred humidity values, preferred windows of time to use specific electricity-consuming devices such as a water pump, air conditioning device, heating device such as a stove or a microwave, or the like. Some embodiments may then adjust a predicted power consumption based on these attributes or the set of durations indicated by these attributes. For example, some embodiments may increase an power consumption for the set of clock times between 2 PM to 3 PM of a time interval set as being the time between 1 PM to 5 PM, where the set of clock times from 2 PM to 3 PM are indicated by a user as being moments of increased activity.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the design of Lueken to include the method of Goldfarb to set a time for the device to wake and sleep so data can be collected in order to manage power consumption.
As per claim 10, Lueken does not teach the rate is the rate at which sensor values are collected.
However, Goldfarb teaches the rate is the rate at which sensor values are collected [from the paragraph above, it shows rate energy will be consumed on the battery].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the design of Lueken to include the method of Goldfarb to set a time for the device to wake and sleep so data can be collected in order to manage power consumption.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/VOLVICK DEROSE/Primary Examiner, Art Unit 2176