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 .
Claim Objections
Claim 8 objected to because of the following informalities: applicant stated ". Appropriate correction is required.
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.
Claim 4-6, 8-11, 13 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 4 (which incorporate the limitations from claims 1 and 2),
Step 2A Prong 1: this claim recites the following abstract ideas:
acquire the training data including actual measurement data of the measurement item in addition to data based on the simulation result. (this limitation describes collecting and combing data from multiple sources, which is a mental process implemented in the human mind)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
a memory configured to store instructions; and (this limitation is directed to mere instruction to apply the abstract idea using generic computer component, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
a processor configured to execute the instructions to: (this limitation is directed to mere instruction to apply the abstract idea using generic computer component, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
acquire training data, the training data including time-series data of a measurement item regarding a target and time-series data of an item that influences the target (this limitation describes data collection/receiving, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
train, by using the training data, a model that receives an input of time-series data of the measurement item and outputs time-series data of the item that influences the target. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
wherein the processor is configured to execute the instructions to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 5,
Step 2A Prong 1: this claim recites the following abstract ideas:
receive designation of a non-estimation target item among items influencing the target (the limitation describes selecting or identifying an item based on received information, which is a mental process implemented in the human mind.)
and estimates a value of an item that is not the non-estimation target. (the limitation describes preforming an estimation which is a mental process implemented in the human mind)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
wherein the processor is configured to execute the instructions to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 6,
Step 2A Prong 1: this claim recites the following abstract ideas:
estimate the value of the item that is not the non-estimation target (the limitation describes preforming an estimation which is a mental process implemented in the human mind)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
wherein the processor is configured to execute the instructions to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
using the model that is adjusted so that the value of the non-estimation target item is a predetermined value for the item. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 8,Step 2A Prong 1: this claim recites the following abstract ideas:
learn a method of adjusting the model for each item that has a possibility of being designated as the non-estimation target item or each combination thereof. ( the limitation describe learning or determining a method for adjusting a model which is a mental process)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
wherein the processor is configured to execute the instructions to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 9,
Step 2A Prong 1: this claim recites the following abstract ideas:
determine validity of output data of the model, based on input data to the model. ( the limitation describes determining a or evaluating the results by comparing the output data with input data, which is a mental process implemented in the human mind)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
wherein the processor is configured to execute the instructions to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 10, Step 2A Prong 1: this claim recites the following abstract ideas:
determine the validity of the output data of the model, based on a consistency between an inference result obtained by qualitative inference using a qualitative expression of the output data of the model, and the input data to the model. (the limitation describes a evaluating or determining inconsistency between inference results and input data, which is a mental process implemented in the human mind)Step 2A Prong 2 and 2B: the claim recited the following additional elements:
wherein the processor is configured to execute the instructions to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 11, Step 2A Prong 1: this claim recites the following abstract ideas:
determine the validity of the output data of the model by comparing a simulation result obtained by inputting the output data of the model into a simulator of the target, with the input data to the model. (the limitation describes determining evaluation by comparison of results, which is a mental process implemented in the human mind)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
wherein the processor is configured to execute the instructions to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 13,Step 2A Prong 1: this claim recites the following abstract ideas:
estimate an abnormality related to the target, based on output data calculated by inputting actual measurement data of the measurement item into the model. ( the limitation describes determining an abnormality based on model output, which is a mental process implemented in the human mind)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
wherein the processor is configured to execute the instructions to (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
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) 1-2, 4-10, 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Keeler et al. (US 6985781 B2 ) in view of Green et al. (US 20200276680 A1).
As per claim 1, Keeler teaches an information processing device
a memory configured to store instructions; and (see Keeler [Col 13 L 47] “the residual states are effectively frozen with a latch 113 that is controlled by a LATCH signal. … The latch is set and these values are then clamped for the next operation”)
a processor configured to execute the instructions to: (see Keeler [Col 5 L 32] “This is effected through a control block 22 that is controlled by a control/optimizer block 24. The control/optimizer block receives the outputs from the predicted model 10 in addition to a desired output signal and changes the plant inputs.”)
acquire training data, the training data including time-series data of a measurement item regarding a target and time-series data of an item that influences the target; and (see Keeler [Col 5 L 6] “there is illustrated a diagrammatic view of a predicted model 10 of a plant 12. The plant 12 is any type of physical, chemical, biological, electronic or economic process with inputs and outputs. … The input of the model 10 is comprised of an input vector 20 of known plant inputs, which inputs comprise in part manipulated variables referred to as "control" variables, and in part measured or non-manipulated variables referred to as "state" variables. The control variables are the input to the plant 12. When the inputs are applied to the plant 12, an actual output results. … In addition to the control inputs, the plant 12 also has some unmeasured unknown plant inputs, referred to as "external disturbances", which represent unknown relationships, etc. that may exist in any given plant such as humidity, feed-stock variations, etc … input nodes 14 are comprised of N nodes… operable to receive an input vector x(t)”and see Keeler [Col 7 L 55] “Initially, the pattern y(t) is provided as a time series output of a plant for a time series input x(t). The first network… is trained on this pattern as the target…. the network with the time series x(t) to generate a predicted output o.sup.1(t).”)
train, by using the training data, a model that receives an input of time-series data of the measurement item and (see keeler [Col 10 L 5] “An error is generated between the desired and the predicted outputs and input to an inverse plant model 76 … operated by back propagating the error … This iteration continues until the error is reduced below a predetermined value. The final value is then output as the new predicted control variables c(t+1)…. the inverse plant model for back propagating the error to determine the control variable determines these control variables independent of the state variables”)
Keeler does not explicitly teach “a memory configured to store instructions”, “a processor configured to execute the instructions”, “time-series data of a measurement”, and “outputs time-series data of the item that influences the target”
However, Green teaches
a memory configured to store instructions; and (see Green paragraph [0026] “The system may also include wireless connectivity, a memory storage device”)
a processor configured to execute the instructions to: (see Green paragraph [0057] “a processor (Processing Device [114])”)
the training data including time-series data of a measurement item regarding a target (see Green paragraph [0014] “anonymous time series data collected through use of the power tool is transmitted using the mobile device running the mobile application for power tool safety to a cloud-based deep-learning machine learning model and used to improve detection of future hazardous conditions.”, and see Green paragraph [0023] “Using a neural network that is trained on labeled training data to differentiate normal from abnormal power tool motion, the system can identify uncharacteristic or undesirable motion of a power tool from real-time usage data gathered while the tool is operating. The system can also capture data, such as motion profiles under various conditions, i.e., time of day, temperature, humidity, continuo---us hours of usage, etc.”)
outputs time-series data of the item that influences the target. (see Green paragraph [0041] “determines whether detected power tool motion is normal given specific sensor data, e.g., data from the power tool itself, and optionally, data from the user and/or from external sensors. The system measures multiple sensing modalities in three-dimensions to calculate the total kinematic motion of a power tool while in use. … a pre-trained neural network to determine if the sensed motion from the power tool is desirable or undesirable for the particular power tool.”, and see Green paragraph [0057] “a pre-trained LSTM running a sequence-to-sequence time-series prediction”)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of keeler to include neural network trained on time series data to predict abnormal motion given certain usage scenarios (Green, [0023]).
As per claim 2, Keeler as modified by Green taches an information processing device according to claim 1
wherein the processor is configured to execute the instructions to acquire the training data based on a simulation result of the target using a simulator. (See Green paragraph [0067] “In addition, a 3D model of the tool and physics-based simulation may be used to generate synthetic training data for normal use.”)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of keeler to include abnormal data generated by simulator because it is unsafe to request users to intentionally misuse a power tool. Green [0068]
As per claim 4, Keeler as modified by Green taches an information processing device according to claim 2
wherein the processor is configured to execute the instructions to acquire the training data including actual measurement data of the measurement item in addition to data based on the simulation result. (See Green paragraph [0069] “As an example, a circular saw may be used to rip half-way down the length of a board, then pinched in a material using a clamp. The trigger is fastened in the on position such that when power is applied, the back edge of the blade immediately gains purchase on the board and causes the tool to jump from the board. Such a scenario is not 100% representative of a real-world event, but sufficient to train the neural network for the machine learning model…”)
As per claim 5, Keeler as modified by Green taches an information processing device according to claim 1
wherein the processor is configured to execute the instructions to receive designation of a non-estimation target item among items influencing the target, and estimates a value of an item that is not the non-estimation target. (See Keeler [Col 5 L6] “Referring now to FIG. 1, there is illustrated a diagrammatic view of a predicted model 10 of a plant 12. The plant 12 is any type of physical, chemical, biological, electronic or economic process with inputs and outputs. The predicted model is a neural network”, and see [Col 14 L 40] “The output variables y(t) are functions of the control variables c(t), the measured state variables s(t) and the external influences E(t)”, and see Keeler [Col 15 L 34] “useful information that is captured by the measured state variables, and that implicitly contains the external disturbances, is not discarded. Note that since the neural network learning state variable predictions can learn non-linear functions, this is a fully general non-linear projection to f(c(t)). Furthermore, by calculating the residuals, an excellent estimation of the external variations has been provided.”)
As per claim 6, Keeler as modified by Green taches an information processing device according to claim 5
wherein the processor is configured to execute the instructions to estimate the value of the item that is not the non-estimation target, using the model that is adjusted so that the value of the non-estimation target item is a predetermined value for the item. (See Keeler [Col 5 L6] “Referring now to FIG. 1, there is illustrated a diagrammatic view of a predicted model 10 of a plant 12. The plant 12 is any type of physical, chemical, biological, electronic or economic process with inputs and outputs. The predicted model is a neural network”, and see Keeler [Col 6 L 33] “Referring now to FIG. 3…. The time series represents the actual output of a plant, which is referred to as y(t). As will be described in more detail hereinbelow, a first network is provided for making a first prediction, and then the difference between that prediction and the actual output y(t) is then determined to define a second time series representing the residual.”, and see Keeler [Col 14 L 40] “The output variables y(t) are functions of the control variables c(t), the measured state variables s(t) and the external influences E(t)”, and see Keeler [Col 15 L 34] “useful information that is captured by the measured state variables, and that implicitly contains the external disturbances, is not discarded. Note that since the neural network learning state variable predictions can learn non-linear functions, this is a fully general non-linear projection to f(c(t)). Furthermore, by calculating the residuals, an excellent estimation of the external variations has been provided.”)
As per claim 7, Keeler as modified by Green taches an information processing device according to claim 6
wherein the model is configured using a neural network, and wherein the processor is configured to execute the instructions to use the model that is adjusted by performing at least any one of: switching an input value to one or more nodes of the neural network to a constant value; biasing an input to one or more nodes of the neural network; rewriting a weight coefficient of one or more edges of the neural network; and switching part or all of the neural network to another neural network. (see Keeler [Col 13 L 5] “The residual states s.sup.r(t) in layer 102 are calculated after the weights in the network labelled NET 1 are frozen. This network is referred to as the "state prediction" net. The values in the residual layer 102 are referred to as the "residual activation" of the state variables.”, and see keeler [Col 13 L 18] “Referring now to FIG. 12, there is illustrated the next step in building the network, wherein the overall residual network is built…. until the output reaches a desired output or until a given number of BPA iterations has been achieved.”)
As per claim 8, Keeler as modified by Green taches an information processing device according to claim 6
wherein the processor is configured to execute the instructions to learn a method of adjusting the model for each item that has a possibility of being designated as the non-estimation target item or each combination thereof. (see Keeler [Col 13 L 5] “The residual states s.sup.r(t) in layer 102 are calculated after the weights in the network labelled NET 1 are frozen. This network is referred to as the "state prediction" net. The values in the residual layer 102 are referred to as the "residual activation" of the state variables. These residuals represent a good estimation of the external variables that affect the plant operation. This is important additional information for the network as a whole, and it is somewhat analogous to noise estimation in Weiner and Kahlman filtering, wherein the external perturbations can be viewed as noise and the residuals are the optimal (non-linear) estimate of this noise.”)
Note: “each combination thereof” this optional claim limitation is not addressed in this rejection, also see objection above.
As per claim 9, Keeler as modified by Green taches an information processing device according to claim 1
wherein the processor is configured to execute the instructions to determine validity of output data of the model, based on input data to the model. (See Keeler [Col 5 L 20] “The input of the model 10 is comprised of an input vector 20 of known plant inputs, which inputs comprise in part manipulated variables referred to as "control" variables, and in part measured or non-manipulated variables referred to as "state" variables. The control variables are the input to the plant 12. When the inputs are applied to the plant 12, an actual output results. By comparison, the output of the model 10 is a predicted output. To the extent that the model 10 is an accurate model, the actual output and the predicted output will be essentially identical. However, whenever the actual output is to be varied to a set point, the plant control inputs must be varied.”)
As per claim 10, Keeler as modified by Green taches an information processing device according to claim 9
wherein the processor is configured to execute the instructions to determine the validity of the output data of the model, based on a consistency between an inference result obtained by qualitative inference using a qualitative expression of the output data of the model, and the input data to the model. (see Green paragraph [0089] “The system then compares extracted time series data with known, labelled data from power tools to determine whether the sensed motion is normal or abnormal for the given power tool and user. The machine learning model outputs a decimal value between 0 and 1”, and see Green paragraph [0070] “The time-series data representing abnormal motion events are labeled and manually annotated with a timestamp of the exact event. For example, a time-series that includes a kickback event may be 250 ms with the actual kickback event occurring 150 ms into the time series. The entire time-series data of 250 ms can be labeled as representing abnormal motion with the actual event at 150 ms being labeled as the kickback event. This labelling process is done by manually inspecting the data and determining when the event occurred.”)
As per claim 12, Keeler as modified by Green taches an information processing device according to claim 1
wherein the processor is configured to execute the instructions to update the model, using actual measurement data of the measurement item. (See Green paragraph [0080] “Neural network models can then be continually refined by retraining on additional real-world data… time series data collected … and used to improve detection of future hazardous conditions. New versions of the neural network can be deployed to the system…” )
As per claim 13, Keeler as modified by Green taches an information processing device according to claim 1
wherein the processor is configured to execute the instructions to estimate an abnormality related to the target, based on output data calculated by inputting actual measurement data of the measurement item into the model. (see Green paragraph [0060] “The system 100, sensor data is collected in real-time and processed by processing device 114, e.g., a microcontroller. The processing device 114 can include one or more of the following components: a memory 110 for storing sensor data, an environment sensor 106d for collecting information about the workspace 110 environment, wireless connectivity 117, a general purpose input/output 118, a battery 119, a battery charger 120, and a machine learning model 112 that is trained to determine abnormal motion of a power tool.”)
As per claim 14, this is directed to a method that corresponds to the device of claim 1, See the rejection for claim 1 above, which also applies to claim 14.
As to claim 15, this is directed to a computer-program embodiment that corresponds to device claim 1. See the rejection for claim 1 above, which also applies to claim 15.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Keeler et al. (US 6985781 B2 ) in view of Green et al. (US 20200276680 A1) and Tremblay et al. (Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization).
As per claim 3, Keeler as modified by Green taches an information processing device according to claim 2
wherein the processor is configured to execute the instructions to acquire the training data including data obtained by adding noise to data of the simulation result. (See Tremblay [4.1. Object detection] “During training, we applied the following data augmentations: random brightness, random contrast, and random Gaussian noise. We also included more classic augmentations to our training process, such as random flips, random resizing, box jitter, and random crop.”, and see Tremblay [5. Conclusion] “We have demonstrated that domain randomization (DR) is an effective technique to bridge the reality gap. Using synthetic DR data alone, we have trained a neural network to accomplish complex tasks like object detection with performance comparable to more labor-intensive (and therefore more expensive) datasets.”)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of keeler-Green by including obtained data by simulation as taught by Green with added noise to bridge the reality gap.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Keeler et al. (US 6985781 B2 ) in view of Green et al. (US 20200276680 A1) and Nakaya et al. (US 20050240382 A1).
As per claim 11, Keeler as modified by Green taches an information processing device according to claim 9,
wherein the processor is configured to execute the instructions to determine the validity of the output data of the model by comparing a simulation result obtained by inputting the output data of the model into a simulator of the target, with the input data to the model. (see Nakaya paragraph [0040] “plant diagnosis means 110 checks consistency between data calculated by process simulation means 102 and the actual data obtained from actual plant 30 and, if the difference between the two exceeds a predetermined tolerance, displays the difference as a plant abnormality on display means 107.”)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the invention of keeler-Green by including instructions to determine the validity of the output data of the model by comparing simulation results to reflect the status of the actual plant in the simulation model consecutively so that operations of the actual plant can be predicted in a highly accurate manner. Nakaya [0066]
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDULLAH K ABOUD whose telephone number is (571)272-0025. The examiner can normally be reached Mon-Fri 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen, can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDULLAH KHALED ABOUD/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121