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 .
This action is in response to amendments filed February 11th, 2026. The status of the claims is as follows. Claims 1, 9, 12, 18 and 20 are amended. Claims 1-20 are currently pending.
Claim Objection
There is a typographical error present in the original Claim 17, where the present Claim 17 omits the original claim’s mathematical hyperbolic function as well as the subscript w in the hyperbolic function term “Cw(t)”. “The predictive sensing method according to Claim 16, wherein the hyperbolic function is represented by wherein, C(t) is the hyperbolic function, CL(t) is a characteristic function of the first trained model, CNL(t) is a characteristic function of the second model, and a and To are weighting parameters included during the aggregating” is instead interpreted as
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Appropriate correction is required.
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 1-5, 12-15, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sakaino et al. (JP2016126718A, hereinafter “Sakaino”) in view of Long et al. (US20210366255A1, hereinafter “Long”) further in view of Iino et al. (JPH05173602A, hereinafter “Iino”) further in view of Adhikari et al. (“Combining Multiple Time Series Models Through a Robust Weighted Mechanism” [2012], hereinafter “Adhikari”).
Regarding Claim 1,
Sakaino discloses A predictive sensor system comprising: a memory that has stored therein time series data that is collected from a sensor that detects a parameter …, and computer readable instructions; (Sakaino [Page 2 Final Paragraph, Line 2]; “The time-series data prediction apparatus 200 extracts an observation data input unit 201 that captures time-series data, a data storage unit 202 that accumulates the captured time-series data, and a statistical property of the accumulated time-series data for each time interval” wherein the storage unit storing the time-series data collected from the observation data input unit alongside an associated statistical property reads on a memory storing time series data collected from a sensor that detects a parameter)
and circuitry, which upon execution of the computer readable instructions, is configured to apply the time series data to a trained predictive sensor model, run the trained predictive sensor model on the time series data to generate predicted sensor measurement data for a future segment of time; (Sakaino [Page 2 Paragraph 8 Line 1]; “In order to achieve such an object, a first embodiment of the present invention is a time-series data predicting apparatus for predicting time-series data by support vector regression, wherein the statistical properties of the captured time-series data A statistical analysis unit that extracts each time interval, and a kernel selection that selects, for each time interval, a kernel function that matches the statistical properties of the time-series data extracted for each time interval by the statistical analysis unit And a support vector regression model using the kernel function selected for each time interval in the kernel selection unit, and calculates temporal changes in time series data after the time interval of the captured time series data And a data prediction unit for predicting the time series data” wherein the data prediction unit which applies the time-series data (circuitry configured to apply the time series data) through a support-vector regression model (trained predictive sensor model) to generate predicted time series sensor measurement data is performed)
and forecast an additional portion of the predicted data in another segment of time that occurs in between the first forecasting segment of time and the second forecasting segment of time based on the time series data and the trained predictive sensor model … (Sakaino [Page 2 Paragraph 7]; “Therefore, in the present invention, focusing on the statistical properties of time-series data, a time-series data prediction apparatus using adaptive kernel type prediction that predicts time-series data by selecting a kernel function in support vector regression for each time interval, and The method is provided.
In order to achieve such an object, a first embodiment of the present invention is a time-series data predicting apparatus for predicting time-series data by support vector regression, wherein the statistical properties of the captured time-series data A statistical analysis unit that extracts each time interval, and a kernel selection that selects, for each time interval, a kernel function that matches the statistical properties of the time-series data extracted for each time interval by the statistical analysis unit And a support vector regression model using the kernel function selected for each time interval in the kernel selection unit, and calculates temporal changes in time series data after the time interval of the captured time series data And a data prediction unit for predicting the time series data” wherein kernel functions in the support vector regression model selected for each time interval to predict the change over time of the time series data thus read on forecasts wherein changed data of the time series data is predicted for each prediction interval including the first and second forecasting segments of time and intervals in between disclosed segments)
Sakaino fails to explicitly disclose but Long discloses A parameter of an atmosphere in an inhabitable space; (Long [0039]; “A time series classifier (i.e., model and/or algorithm) can be trained to map the time-varying thermal profile measured from an IR thermal sensor 20 to risk of ignition categories for flashover, i.e., thermal data is analyzed over a predefined time window” wherein the sensor collected data parameters are of an atmosphere in an inhabitable space (environment with risk of ignition interpretable as atmosphere in an inhabitable space))
and generate a signal to cause an electronic device to generate a sensory output to alert an occupant in the inhabitable space of a level of the parameter of the atmosphere that corresponds with the predicted sensor measurement data (Long [0004]; “This thermal data is analyzed using a machine learning model that, for example, dynamically predicts the risk of a flashover event or the time to flashover based on the thermal data. The predicted risk of the flashover may then be indicated to the user to alert the user of the current and/or future risk level of flashover”)
It would have been obvious to modify Sakaino’s system comprising collecting time series data from sensors for application in a trained predictive sensor model to generate predicted sensor measurements to incorporate Long’s method of generating a signal according to the generated predicted sensor measurements. One would have been motivated to do so in order to “analyze the evolution of these aggregated risk categories over time to influence decision making, as a progression from lower to higher risk conditions may be considered in generating such a risk mitigation policy” (Long [0043])
Sakaino/Long fails to explicitly disclose but Iino discloses wherein the trained predictive sensor model includes a first trained model and a second trained model, the first trained model is trained to more closely match a portion of the predicted data in a first forecasting segment of time than the second trained model, the second trained model is trained to more closely match another portion of the predicted sensor measurement data in a second forecasting segment of time than the first trained model, the second forecasting segment of time occurring later in time than the first forecasting segment of time (Iino [0010]; “For example, the minimum sampling period is τ [sec] And From the data sampled in this cycle, the effective frequency characteristic estimation range of the time series model identified by the ordinary least squares method is 1 / 100τ to 1 / 5τ as described above.It is around [Hz]. Similarly, the effective prediction range by this model is, for example, τ to 100τ [sec]. Therefore, the effective frequency characteristic estimation range of the time series model identified by the least square method from the data sampled at another sampling period of 10 τ [sec] is 1/1000 τ to 1/ 50τ [Hz]. Similarly, the effective prediction range by this model is 10τ to 1000τ [sec].Therefore, if combined with the former model, 1 / 1000τ ~Frequency characteristic of 1 / 5τ [Hz] and τ to 1000τThe prediction range of can be accurately estimated. In this way, by identifying a plurality of time series models with different sampling periods by a plurality of identifying means and connecting the obtained time series models, a highly accurate frequency characteristic over a wide range, or,The prediction range can be estimated.” wherein the model having τ to 100 τ (first model) being combined with the former model having τ-1000τ in order to accurately estimate a near future prediction value and a far future prediction value is disclosed; wherein the first model is trained with a select forecasting segment of time different from the second trained model; wherein the second trained model is trained with a differing forecasting segment of time later than the first forecasting segment of time)
It would have been obvious to modify Sakaino/Long’s system comprising collecting time series data from sensors for application in a trained predictive sensor model to generate predicted sensor measurements and associated alerts to incorporate Iino’s plurality of models trained to closely match different portions of the time series data instead of a singular trained model. One would have been motivated to so because “It can be seen that if these are connected together, the step response or the predicted response can be estimated with much higher accuracy than the conventional estimation using a single model” (Iino [0011])
Sakaino/Long/Iino fails to explicitly disclose but Adhikari discloses to forecast an additional portion of the predicted data in another segment of time that occurs … by applying a joining function that includes a weighted parameter, and the circuitry is further configured to adjust the weighting parameter based on the time series data to transition between the first trained model and the second trained model (Adhikari [Section II];
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wherein the optimization of weights between each of the models and their individual time series forecasts thus reads on a dynamically forecasted portion of the time series data obtained through a joining function (linear forecast combination) including a weighted parameter (wi) which is adjusted based on the time series data to transition between the first trained model and the second trained model (wherein the optimization of the weights associated with each of the models thus reads on adjusting of the weighted parameter based on the time series data; wherein the adjustment of the weight itself implicitly reads on a transition of the combined forecast between the first and second models of the plurality of models since increasing a weight for one model is interpreted as transitioning the combined model towards that model)).
It would have been obvious to modify Sakaino/Long/Iino’s system comprising a plurality of models trained to closely match different portions of the time series data to use Adhikari’s dynamic weighting to transition between trained models. One would have been motivated to so because “combining multiple forecasts reduces the errors arising from faulty assumptions, bias, or mistakes in the data to a great extent” (Adhikari [Introduction]).
Regarding Claim 2,
The combination of Sakaino/Long/Iino/Adhikari teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination already discloses wherein the predictive sensor model is a weighted aggregation of the first model and the second model (Iino [0022]; “Next, in the control operation unit 4, among the step responses calculated from the Np time-series models identified by the Np identification units and the control amount prediction values, first, the step response synthesis unit 11 Individual step response (4)Are combined with the time scales, and one step response of the minimum sampling period g (t), g (t + τ), g (t + 2τ), g (t + 3τ), ..., g (t + 2 Np-1 τ) (7 ). Here, i = 1, ..., Np, t is the current time.
As a concrete synthesizing method, an average value of overlapping portions in the equation (4) is adopted, and the lacking data is obtained by interpolation calculation from two points on both sides. This state will be described with reference to FIGS. 6 (a) to 6 (c). For example, it is assumed that the step responses of the four models estimated from the four types of sampling periods (when Np = 4) are as shown by g 1 (t) to g 4 (t) in FIG. 6A. .. At this time, the partial linear interpolation g (t) = αg i ( t) + (1-α) g i + 1 (t) (α overlapping, 1 at the left of the g i + 1 (t), g i ( It is 0 at the right end of t) and is combined with each other to obtain the combined step response g (t) shown in FIG. 6 (c). The value of the weight α of each response g i (t) at this time is as shown in FIG. 6B.” wherein the synthesized model is computed through weights associated with the first and second models)
Regarding Claim 3,
The combination of Sakaino/Long/Iino/Adhikari teaches the method of Claim 2 (and thus the rejection of Claim 2 is incorporated). The combination already discloses wherein the predictive sensor model includes an adjusted weighting parameter that relates to the time series data (Iino [0022]; “Next, in the control operation unit 4, among the step responses calculated from the Np time-series models identified by the Np identification units and the control amount prediction values, first, the step response synthesis unit 11 Individual step response (4)Are combined with the time scales, and one step response of the minimum sampling period g (t), g (t + τ), g (t + 2τ), g (t + 3τ), ..., g (t + 2 Np-1 τ) (7 ). Here, i = 1, ..., Np, t is the current time.
As a concrete synthesizing method, an average value of overlapping portions in the equation (4) is adopted, and the lacking data is obtained by interpolation calculation from two points on both sides. This state will be described with reference to FIGS. 6 (a) to 6 (c). For example, it is assumed that the step responses of the four models estimated from the four types of sampling periods (when Np = 4) are as shown by g 1 (t) to g 4 (t) in FIG. 6A. .. At this time, the partial linear interpolation g (t) = αg i ( t) + (1-α) g i + 1 (t) (α overlapping, 1 at the left of the g i + 1 (t), g i ( It is 0 at the right end of t) and is combined with each other to obtain the combined step response g (t) shown in FIG. 6 (c). The value of the weight α of each response g i (t) at this time is as shown in FIG. 6B.”)
Regarding Claim 4,
The combination of Sakaino/Long/Iino/Adhikari teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination already discloses wherein: the memory has stored therein the trained predictive sensor model in addition to the time series data and the computer readable instructions that are executed by the circuitry to implement the trained predictive sensor model (Sakaino [Page 2 Paragraph 8 Line 2]; “A statistical analysis unit that extracts each time interval, and a kernel selection that selects, for each time interval, a kernel function that matches the statistical properties of the time-series data extracted for each time interval by the statistical analysis unit And a support vector regression model using the kernel function selected for each time interval in the kernel selection unit, and calculates temporal changes in time series data after the time interval of the captured time series data And a data prediction unit for predicting the time series data” wherein data prediction unit using the stored trained predictive sensor model reads on the memory storing the trained predictive sensor model and computer readable instructions to implement the trained predictive sensor model)
Regarding Claim 5,
The combination of Sakaino/Long/Iino/Adhikari teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination already discloses the sensor that detects the parameter of the atmosphere in the inhabitable space (Long [0039]; “A time series classifier (i.e., model and/or algorithm) can be trained to map the time-varying thermal profile measured from an IR thermal sensor 20 to risk of ignition categories for flashover, i.e., thermal data is analyzed over a predefined time window”)
Regarding Claims 12-14,
Claims 12-14 recite the method performed by the system of Claims 1-3. Thus, Claims 12-14 are rejected for reasons set forth in the rejection of Claims 1-3.
Regarding Claim 15,
The combination of Sakaino/Long/Iino/Adhikari teaches the method of Claim 14 (and thus the rejection of Claim 14 is incorporated). The combination already discloses wherein the running includes adjusting a first parameter related to the weighting parameter, the first parameter is included in the first trained model and is based on the time series data, and adjusting a second parameter related to the weighting parameter, the second parameter is included in the second trained model and is based on the time series data (Iino [0022]; “Next, in the control operation unit 4, among the step responses calculated from the Np time-series models identified by the Np identification units and the control amount prediction values, first, the step response synthesis unit 11 Individual step response (4)Are combined with the time scales, and one step response of the minimum sampling period g (t), g (t + τ), g (t + 2τ), g (t + 3τ), ..., g (t + 2 Np-1 τ) (7 ). Here, i = 1, ..., Np, t is the current time.
As a concrete synthesizing method, an average value of overlapping portions in the equation (4) is adopted, and the lacking data is obtained by interpolation calculation from two points on both sides. This state will be described with reference to FIGS. 6 (a) to 6 (c). For example, it is assumed that the step responses of the four models estimated from the four types of sampling periods (when Np = 4) are as shown by g 1 (t) to g 4 (t) in FIG. 6A. .. At this time, the partial linear interpolation g (t) = αg i ( t) + (1-α) g i + 1 (t) (α overlapping, 1 at the left of the g i + 1 (t), g i ( It is 0 at the right end of t) and is combined with each other to obtain the combined step response g (t) shown in FIG. 6 (c). The value of the weight α of each response g i (t) at this time is as shown in FIG. 6B.” wherein the synthesized model computed through gi(t) and their associated variable weight α for each of at least a first and second model reads on running including adjusting of first and second parameters of the trained models based on their time series data forecasting segments)
Regarding Claim 18,
The combination of Sakaino/Long/Iino/Adhikari teaches the method of Claim 12 (and thus the rejection of Claim 12 is incorporated). The combination already discloses wherein the trained predictive sensor model further includes a third trained model, and the third trained model is trained to more closely match an addition portion of the predicted data in a third forecasting segment of time than either the first trained model or the second trained module in a third forecasting segment of time that occurs in between the first forecasting segment of time and the second forecasting segment of time (Iino [0022]; “Next, in the control operation unit 4, among the step responses calculated from the Np time-series models identified by the Np identification units and the control amount prediction values, first, the step response synthesis unit 11 Individual step response (4)Are combined with the time scales, and one step response of the minimum sampling period g (t), g (t + τ), g (t + 2τ), g (t + 3τ), ..., g (t + 2 Np-1 τ) (7 ). Here, i = 1, ..., Np, t is the current time.
As a concrete synthesizing method, an average value of overlapping portions in the equation (4) is adopted, and the lacking data is obtained by interpolation calculation from two points on both sides. This state will be described with reference to FIGS. 6 (a) to 6 (c). For example, it is assumed that the step responses of the four models estimated from the four types of sampling periods (when Np = 4) are as shown by g 1 (t) to g 4 (t) in FIG. 6A. .. At this time, the partial linear interpolation g (t) = αg i ( t) + (1-α) g i + 1 (t) (α overlapping, 1 at the left of the g i + 1 (t), g i ( It is 0 at the right end of t) and is combined with each other to obtain the combined step response g (t) shown in FIG. 6 (c). The value of the weight α of each response g i (t) at this time is as shown in FIG. 6B.” wherein the synthesized model including four types of sampling periods and their respective four models reads on the trained predictive sensor model further including at least a third trained model matching an additional portion of the predicted data in a third forecasting segment of time (different sampling period between the first and second forecasting segments of time if g1(t) and g3(t) are of sampling periods before and after g2(t) respectively)
Regarding Claim 19,
The combination of Sakaino/Long/Iino/Adhikari teaches the method of Claim 12 (and thus the rejection of Claim 12 is incorporated). The combination already discloses wherein the first trained model characterizes a change in a first portion of the time series data over time in a linear form, and the second trained model characterizes another change in a second portion of the time series data over time in a nonlinear form (Iino [0002]; “By including the physical laws and nonlinear characteristics of the plant in the prediction model, which does not require an accurate dynamic characteristic model of the controlled object that can realize decoupling control and can easily configure the control system from the step response, for example, There is a feature that you can expect fine control of “
Iino [0023]; “ As a concrete synthesizing method, an average value of overlapping portions in the equation (4) is adopted, and the lacking data is obtained by interpolation calculation from two points on both sides. This state will be described with reference to FIGS. 6 (a) to 6 (c). For example, it is assumed that the step responses of the four models estimated from the four types of sampling periods (when Np = 4) are as shown by g 1 (t) to g 4 (t) in FIG. 6A. .. At this time, the partial linear interpolation g (t) = αg i ( t) + (1-α) g i + 1 (t) (α overlapping, 1 at the left of the g i + 1 (t), g i ( It is 0 at the right end of t) and is combined with each other to obtain the combined step response g (t) shown in FIG. 6 (c). The value of the weight α of each response g i (t) at this time is as shown in FIG. 6B.” wherein the partial linear interpolation of the multiple models reads on the first trained model (linear form characterized model) incorporating the second trained model (nonlinear form characteristic model) combined for determination of the synthesized model)
Regarding Claim 20,
Claim 20 recites a non-transitory computer program product that has computer readable instructions stored in memory to be executed by processing circuitry to perform the method executed by the system of Claim 1. Thus, Claim 20 is rejected for reasons set forth in the rejection of Claim 1.
Claims 6, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Sakaino et al. (JP2016126718A, hereinafter “Sakaino”) in view of Long et al. (US20210366255A1, hereinafter “Long”) further in view of Iino et al. (JPH05173602A, hereinafter “Iino”) further in view of Adhikari et al. (“Combining Multiple Time Series Models Through a Robust Weighted Mechanism” [2013], hereinafter “Adhikari”) further in view of Gluck et al. (US20150323200A1, hereinafter “Gluck”)
Regarding Claim 6,
The combination of Sakaino/Long/Iino/Adhikari teaches the method of Claim 5 (and thus the rejection of Claim 5 is incorporated). The combination of Sakaino/Long/Iino/Adhikari fails to explicitly disclose but Gluck discloses wherein the sensor is a carbon dioxide (CO2) sensor and the parameter is CO2 concentration of the atmosphere in the inhabitable space; (Gluck [0046]; “As shown in FIG. 3A, the room temperature sensor 11 may optionally include a carbon dioxide sensor 305, a photodiode 307, or both to sense the presence or approximate number of persons in the room. The room temperature sensor 11 may transmit carbon dioxide sensor readings and sensed light readings to the cloud server 13 so that the cloud server 13 can control the heat output from the radiators 19 based on the carbon dioxide sensor readings, the sensed light readings, or both”)
It would have been obvious to replace the solely thermal-based sensor of Sakaino/Long/Iino/Adhikari to include the carbon dioxide sensor of Gluck. One would have been motivated to do so because “the carbon dioxide readings may indicate a large number of persons in a room” (Gluck [0046]) and thus the atmospheric habitability evaluation can consider aspects of the atmosphere such as heat output to accommodate for larger populations.
Regarding Claim 10,
The combination of Sakaino/Long/Iino/Adhikari teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination of Sakaino/Long/Iino/Adhikari fails to explicitly disclose but Gluck discloses the sensor, the being at least one of a CO2 sensor, a humidity sensor, a light level sensor, or a thermometer; (Gluck [0046]; “As shown in FIG. 3A, the room temperature sensor 11 may optionally include a carbon dioxide sensor 305, a photodiode 307, or both to sense the presence or approximate number of persons in the room. The room temperature sensor 11 may transmit carbon dioxide sensor readings and sensed light readings to the cloud server 13 so that the cloud server 13 can control the heat output from the radiators 19 based on the carbon dioxide sensor readings, the sensed light readings, or both”)
It would have been obvious to replace the solely thermal-based sensor of Sakaino/Long/Iino/Adhikari to include the carbon dioxide sensor of Gluck. One would have been motivated to do so because “the carbon dioxide readings may indicate a large number of persons in a room” (Gluck [0046]) and thus the atmospheric habitability evaluation can consider aspects of the atmosphere such as heat output to accommodate for larger populations.
Regarding Claim 11,
The combination of Sakaino/Long/Iino/Adhikari/Gluck teaches the method of Claim 10 (and thus the rejection of Claim 10 is incorporated). The combination does not explicitly disclose but Gluck further discloses the electronic device that is at least one of a motor that is controllably actuated by the signal to control the motor to open at least one of a window, a door, or a vent, or a switch that controllably operates a light, or a fan; (Gluck [0046]; “the cloud server 13 would send a control signal to a thermal distribution device controller 14 to close the electro-mechanical air vent so that the heat output from the radiator 19 is reduced”
Gluck [0034]; “In the case of steam heating, such as a two-pipe system, the fluid flow control device may be an electro-mechanical air vent including valve and an actuator, such as an electric motor or a solenoid, for actuating the valve, which may be a latching solenoid valve or a ball valve. The controller may drive a motor or solenoid to actuate a valve to open and close”)
It would have been obvious modify the carbon sensor-based time series sensor measurement prediction system of Sakaino/Long/Iino/Adhikari/Gluck to include Gluck’s control of a motor-powered vent to open and close a valve to provide steam. One would have been motivated to do so in order “to provide a desired amount of steam to a steam radiator to reach a desired temperature setpoint” (Gluck [0034]).
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Sakaino et al. (JP2016126718A, hereinafter “Sakaino”) in view of Long et al. (US20210366255A1, hereinafter “Long”) further in view of Iino et al. (JPH05173602A, hereinafter “Iino”) further in view of Adhikari et al. (“Combining Multiple Time Series Models Through a Robust Weighted Mechanism” [2013], hereinafter “Adhikari”) further in view of Yang et al. (CN112509292A, hereinafter “Yang”)
Regarding Claim 7,
The combination of Sakaino/Long/Iino/Adhikari teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination of Sakaino/Long/Iino/Adhikari fails to explicitly disclose but Yang discloses output a forecasted time-to-reach at which an item of the predicted sensor measurement data is forecasted to reach a threshold value; (Yang [Abstract]; “for each sensor, predicting a first predicted value of the sensor at a first predicted time after the current time according to the detection value, a weight value preset by the sensor and an exponential smoothing prediction method; and for each sensor, if the first predicted value of the sensor is greater than a preset first alarm threshold value, generating first prompt information for prompting that the machine is about to fail after the first predicted time, and outputting the first prompt information.” wherein the first predicted time for when the predicted value is greater than the first alarm threshold reads on an output forecasted time-to-reach)
It would have been obvious to modify the sensor measurement prediction method of Sakaino/Long/Iino/Adhikari to determine Yang’s plurality of forecasted time-to-reach at threshold prediction values. One would have been motivated to do so because “first prompt information is output, the generated first prediction value is accurate, the failure prediction accuracy is improved, prompt can be performed before the machine breaks down, a user can intervene” (Yang [Page 3 Line 31]) wherein user intervention according to generated prompts reflective of failure urgencies at a critical forecasted time-to-reach can be performed to prevent further harm.
Regarding Claim 8,
The combination of Sakaino/Long/Iino/Adhikari teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination of Sakaino/Long/Iino/Adhikari fails to explicitly disclose but Yang discloses to output a forecasted time-period-to-reach, which is a period of time that extends from a forecasting time point at which the predicted sensor measurement data are begun to be forecasted until a forecasted time-to-reach at which an item of the predicted sensor measurement data is forecasted to reach a threshold value; (Yang [Abstract]; “for each sensor, predicting a first predicted value of the sensor at a first predicted time after the current time according to the detection value, a weight value preset by the sensor and an exponential smoothing prediction method; and for each sensor, if the first predicted value of the sensor is greater than a preset first alarm threshold value, generating first prompt information for prompting that the machine is about to fail after the first predicted time, and outputting the first prompt information.” wherein the first predicted time for when the predicted value is greater than the first alarm threshold reads on an output forecasted time-to-reach
Yang [Page 7 Line 29]; “Specifically, if the time segment of the calculation time used when predicting the first predicted value is 1h, the time segment of the calculation time used when predicting the third predicted value may be one detection period, so that the obtained third predicted value is more accurate” wherein the forecasted time-to-reach first predicted time associated with a 1h period reads on an output time-period-to-reach)
It would have been obvious to modify the sensor measurement prediction method of Sakaino/Long/Iino/Adhikari to determine Yang’s plurality of forecasted time-period-to-reach at threshold prediction values. One would have been motivated to do so because “first prompt information is output, the generated first prediction value is accurate, the failure prediction accuracy is improved, prompt can be performed before the machine breaks down, a user can intervene” (Yang [Page 3 Line 31]) wherein user intervention according to generated prompts reflective of failure urgencies at a critical forecasted time-periods-to-reach can be performed to prevent further harm.
Regarding Claim 9,
The combination of Sakaino/Long/Iino/Adhikari/Yang teaches the method of Claim 8 (and thus the rejection of Claim 8 is incorporated). The combination of Sakaino/Long/Iino/Adhikari/Yang already discloses to calculate a first forecasted time-to-reach at a first forecasting time point, the first forecasted time-to- reach being a time at which an item of the predicted sensor measurement data is forecasted to reach the threshold value, calculate a second forecasted time-to-reach at a second forecasting time point, the second forecasted time- to-reach being a later time at which another item of the predicted sensor measurement data is forecasted to reach the threshold value, the second forecasting time point being later in time than the first forecasting time point; (Yang [Abstract]; “for each sensor, predicting a first predicted value of the sensor at a first predicted time after the current time according to the detection value, a weight value preset by the sensor and an exponential smoothing prediction method; and for each sensor, if the first predicted value of the sensor is greater than a preset first alarm threshold value, generating first prompt information for prompting that the machine is about to fail after the first predicted time, and outputting the first prompt information.” wherein the first predicted time for when the predicted value is greater than the first alarm threshold reads on an output forecasted time-to-reach at a first forecasting time point
Yang [Page 8 Line 36]; “The failure prediction apparatus 700 may further include:
the second prediction module is used for predicting a second prediction value of the sensor after the current time according to a detection value of the sensor in a second detection time preset before the current time, a weight value preset by the sensor and an index smooth prediction method if a first prediction value of the sensor is greater than a preset first alarm threshold value aiming at each sensor, wherein the second detection time is less than the first detection time, and the second prediction time is less than the first prediction time” wherein the second prediction value of the sensor and its associated second prediction time reads on the second forecasted time-to-reach being at a time at which another item of the predicted sensor measurement data is forecasted to reach the threshold value; wherein a second prediction time being less than the first prediction time reads on a second forecasted time-to-reach being a later time than the first forecasted time-to-reach)
and under a condition a time period between the first forecasted time-to-reach and the second forecasted time-to- reach is greater than or equal to a predetermined time period, outputs a signal that indicates the forecasted time- to-reach has changed (Yang [Page 7 Line 22]; “The third detection time is less than the second detection time. The specific duration of the third detection time is not limited. If the second detection time is 7 days, the third detection time may be 1 day or 1 hour. In the embodiment of the application, when the second predicted value is larger than the preset first alarm threshold value, the machine is possibly in failure at the second predicted time, and the third predicted value is obtained by selecting to predict according to the detection value of the sensor within the third detection time shorter than the current time, so that the obtained third predicted value is more accurate. In the present application, the scheme of predicting the third predicted value of the sensor after the current time is the same as the scheme of predicting the first predicted value and the second predicted value of the sensor after the current time, and a detailed description is not given in the embodiments of the present application. It is to be understood that the time segment of the calculation time used when predicting the third predicted value may be smaller than the time segment of the calculation time used when predicting the second predicted value. Specifically, if the time segment of the calculation time used when predicting the first predicted value is 1h, the time segment of the calculation time used when predicting the third predicted value may be one detection period, so that the obtained third predicted value is more accurate.” wherein the third prediction time obtained when the machine is possibly in failure due to the second predicted value being larger than the preset first alarm threshold value leading to the initialization of the third prediction value by selecting to predict according to the detection value of the sensor within the third detection time shorter thus reads on a signal indicating a change in the forecasted time-to-reach (initialization of new forecasted time-to-reach of sensor reads on outputting signal indicative of changed forecasted time-to-reach))
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Sakaino et al. (JP2016126718A, hereinafter “Sakaino”) in view of Long et al. (US20210366255A1, hereinafter “Long”) further in view of Iino et al. (JPH05173602A, hereinafter “Iino”) further in view of Adhikari et al. (“Combining Multiple Time Series Models Through a Robust Weighted Mechanism” [2013], hereinafter “Adhikari”) further in view of Nandi et al. (“Improving the Performance of Neural Networks with an Ensemble of Activation Functions” [2020], hereinafter “Nandi”)
Regarding Claim 16,
The combination of Sakaino/Long/Iino/Adhikari teaches the method of Claim 13 (and thus the rejection of Claim 13 is incorporated). The combination of Sakaino/Long/Iino/Adhikari fails to explicitly disclose but Nandi discloses wherein the running includes adding the first trained model and the second trained model using a hyperbolic function; (Nandi [Section III Paragraph 2]; “For the experimental purpose of the proposed method, five activation functions are considered for ensembling. In Fig. 3, af1, af2,…,af5 represents five activation functions where af1 is Sigmoid, af2 is Relu, af3 is hyperbolic tangent, af4 is ELU and finally af5 is LeakyRelu. An SGD optimizer is utilized for training the FFNN (Fig. 1) with different activation functions.” wherein ensembling of trained models using a hyperbolic tangent activation function reads on adding the first and second trained models using a hyperbolic function)
It would have been obvious to use Nandi’s hyperbolic tangent function as an activation function for Sakaino/Long/Iino/Adhikari’s combination of first and second trained models. One would have been motivated to do so because “The property of hyperbolic tangent is similar to sigmoid function. This function has larger output range in (−1, 1)” (Nandi [Section II Subsection B]).
Response to Arguments
The Examiner acknowledges the Applicant’s amendments to Claims 1, 9, 12, 18 and 20.
Applicant’s arguments filed February 11th, 2026, traversing the rejection of claims 1-20 under 35 U.S.C. § 101 have been fully considered and are fully persuasive.
Applicant’s arguments regarding the 35 U.S.C. § 103 rejection of claims 1-20 of the previous office action have been considered, have been fully considered, but are not fully persuasive.
Applicant alleges, on Pages 10-12 of Remarks, that since Ovsiannikov identifies the lengths of earlier streams instead of current streams to be processed, and the proposed claim element recites determining a slice size based on present data lanes with different memory sizes, the cited Ovsiannikov portion can’t teach or suggest the claim element of “determine a slice size based on a memory size difference between the first and second data lanes”. Additionally, applicant alleges that Ovsiannikov describes a comparison between two data streams within a pair, and thus can’t teach or suggest these size comparisons among at least three data lanes.
Examiner respectfully disagrees. Although examiner concedes that applicant’s invention as described in the specification does meaningfully differ from the scope of Ovsiannikov in intent, such differences are not meaningfully conveyed in the broadest reasononable interpretable scope of the currently proposed claim language. Applicant’s claim language does not present any language regarding the first, second, and third data lanes that indicate that the data lanes must be “present” data lanes. There is no indication in applicant’s claim language that Ovisannikov’s relocating of future bits in one bit stream to another cannot be interpreted as applicant’s removal of bits in one stream to another bit stream. By nature, relocating bits from one stream to another is reasonable interpretable as removal of the bits from a current stream location and its placement in another stream location. As such, Ovsiannikov’s disclosure of bitwise comparisons performed between earlier data streams falls under the broadest reasonable interpretation of applicant’s claim language that fails to positively recite the nature of such bit streams.
Regarding applicant’s arguments about Ovsiannikov failing to describe such size comparisons among at least three lanes, examiner maintains that Ovsiannikov’s disclosure of bitwise appending/cropping operations in partition including 4 pairs of bitstreams is sufficient for applicant’s current claim scope. Applicant only describes bitwise operations of “removing” and “appending” and “determining a slice size” between a first and second data lane. Importantly, the third data lane’s only current relation to the first and second data lanes is that its size is smaller than the first data lane and larger than the second data lane, while maintaining its size during aforementioned bitwise operations between the first and second data lanes. Moreso, Ovsiannikov’s bit stream operations being performed in pairwise calculations indicate that such calculations are not performed all at once, but rather updated in pairs within the partition. As such, it is reasonable to interpret such pairwise calculations as teaching bitwise operations between the first and second data lanes while a third lane (of a different pair within the partition of four pairs of bit streams) remains idle and thus maintains its size. Ovsiannikov’s disclosure of bitwise operations between pairs of streams within a partition of 8 bit streams, as such, thus discloses a first, second, and third data lane in line with the broadest reasonable interpretation of the applicant’s claim language due to the very broad scope of the third data lane’s functionality and its functional relationship with the first and second data lanes aside from simple size comparisons.
The rejection of Claim 1 under 35 U.S.C. § 103 has been maintained. Similarly, the rejection of Claims 12 and 20 under 35 U.S.C. § 103 have been maintained.
The rejection of Claims 2-11 under 35 U.S.C. § 103, which depend directly or indirectly from Claim 1, have been maintained.
The rejection of Claims 13-19, which depend directly or indirectly from Claim 12, have been maintained.
Conclusion
Claim 17 has been searched, but no prior art which renders the claimed inventions obvious has been uncovered.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN J KIM whose telephone number is (571)272-0523. The examiner can normally be reached 8-6.
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/JONATHAN J KIM/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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