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 .
Response to Arguments
Remarks pages 11-12, Applicant contends:
By incorporating claims 2 and 3 into independent claims, the claims clarify the use of a learning model that has been trained from data (pressure, resistance, and length) for an elastic body. Thus the application is directed to a concrete application to physical control that enables an improvement in accuracy. The application is not directed to data processing on a general purpose computer, but is a concrete technical solution for improving a specific hardware configuration.
Response:
Estimating an amount is seen as an abstract idea, for predicting a value is something can be performed within the human mind
(MPEP 2106.04(a)(2)(3): “An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that ‘with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper’.”)
and applying the abstract idea of predicting a value involves data of a member does not integrate into a practical application as the application appears to simply be having an abstract idea be performed by a computer
(MPEP 2106.05(f): "The Supreme Court has identified additional elements as mere instructions to apply an exception in several cases. For instance, in Mayo, the Supreme Court concluded that a step of determining thiopurine metabolite levels in patients’ blood did not amount to significantly more than the recited laws of nature, because this additional element simply instructed doctors to apply the laws by measuring the metabolites in any way the doctors (or medical laboratories) chose to use. 566 U.S. at 79, 101 USPQ2d at 1968. In Alice Corp., the claim recited the concept of intermediated settlement as performed by a generic computer. The Court found that the recitation of the computer in the claim amounted to mere instructions to apply the abstract idea on a generic computer. 573 U.S. at 225-26, 110 USPQ2d at 1984. The Supreme Court also discussed this concept in an earlier case, Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), where the claim recited a process for converting binary-coded-decimal (BCD) numerals into pure binary numbers. The Court found that the claimed process had no meaningful practical application except in connection with a computer. Benson, 409 U.S. at 71-72, 175 USPQ at 676. The claim simply stated a judicial exception (e.g., law of nature or abstract idea) while effectively adding words that "apply it" in a computer.").
No specific hardware is in the claims, thus the claim does not use a particular machine (MPEP 2145(VI) states: “Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims.”). Claim 1 is also noted for being software per se as a result of not including hardware in a claim directed to a device. The specification of the data used for input and the data that is output does not integrate into a practical application as those elements are directed to mere input or mere output as written in the previous 101 rejections. As a result, the claims are not seem as satisfying the conditions for 101.
Adding particular hardware, such as elements of a physical reservoir, or particular methods, such as how the model is trained to do the model’s task, could be possible methods of trying to satisfy the requirements under 101.
Remarks pages 12-13, Applicant contends:
The cited references do not disclose the combination of the physical amounts and the roles of the constituent elements in the present claims.
Response:
[Maselli Results and Discussions page 3 column 2] is noted in previous office action claim 1 as reciting the wanted phsyical amounts. [Maselli Introduction page 2 column 1] is noted in claim 1 to teach estimating a target physical amount. Claim 2, using Maselli, goes over how these physical amounts are already known to have existing relations between them ([Maselli Introduction page 1 column 2]). [Maselli Results and Discussions page 3 column 2] then goes over in claim 2 how some of the physical amounts can be used to predict or measure the target physical amount. The combination with Nakajima brings in the use of a machine learning model for such tasks, thus the claims are noted as disclosing the physical amounts and the roles of elements within the claims. The idea of unknown values is taught through the use of a machine learning model predicting something, as the prediction fulfills the want of acquiring a value without a known/measured answer ([Current Application 0061]: “As described above, in accordance with the present disclosure, the length of the rubber actuator 2 can be estimated from the unknown first input data 3 (pressure) and second input data 4 (electrical resistance) for the rubber actuator 2. Namely, the length of the rubber actuator 2 can be estimated without directly measuring the non-linear deformation of the rubber actuator 2 that deforms non-linearly.”). Nakajima notes about physical reservoir in claim 6 and recurrent networks in claim 5, and as the claims do not give much detail on the requirements for a particular physical reservoir under BRI Nakajima is interpreted as teaching the claim limitations noted in the previous office action as taught by Nakajima. As a result of arguments from the applicant not being convincing, the rejections under 103 are sustained.
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, 4-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more.
In regards to Claim 1:
Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter?
No, the claim is directed towards software per se as no hardware is present in the claim.
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) an abstract idea.
Claim 1 recites the following abstract ideas:
and estimates an unknown target physical amount, which corresponds to the two unknown physical amounts of the object of estimation
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation.
and the learned model uses the first physical amount and the second physical amount as inputs, and outputs the target physical amount
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 1 recites the following additional elements:
An estimation device comprising: an estimation section and a learning model that is trained by using, as learning data, a plurality of physical amounts comprising at least three physical amounts, which are of different types, which vary in accordance with a member deforming linearly or non-linearly, and which include a target physical amount with which time-series information is associated
At a high level of generality, this is an activity of using learning data and a learned model as an “apply it” use (see MPEP 2106.05(f)).
wherein: the learning model is configured to receive at least two physical amounts other than the target physical amount and output the target physical amount, the estimation section inputs, to the learning model, two unknown physical amounts of an object of estimation which correspond to the at least two physical amounts other than the target physical amount
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
the member is an elastic body having an interior that is formed to be hollow, a pressurized fluid being supplied to the hollow interior, and the elastic body generating contracting force in a predetermined direction
This limitation is directed towards continuing the additional elements that do not integrate the abstract idea into a practical application.
an electrical characteristic of the elastic body varies in accordance with the deformation, and the at least three physical amounts include a first physical amount that deforms the elastic body, a second physical amount expressing the electrical characteristic that varies in accordance with the deformation of the elastic body, and a target physical amount expressing an amount of deformation of the elastic body
This limitation is directed towards linking or indicating a field of use or technological environment (see MPEP 2106.05(h)).
the first physical amount is a pressure value expressing a supplied state of the pressurized fluid that is supplied to the elastic body, the second physical amount is an electrical resistance value of the elastic body, and the target physical amount is a distance of the elastic body in the predetermined direction
This limitation is directed towards continuing the additional elements that do not integrate the abstract idea into a practical application.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 1 recites the following additional elements:
An estimation device comprising: an estimation section and a learning model that is trained by using, as learning data, a plurality of physical amounts comprising at least three physical amounts, which are of different types, which vary in accordance with a member deforming linearly or non-linearly, and which include a target physical amount with which time-series information is associated
At a high level of generality, this is an activity of using learning data and a learned model as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “learned by using” using learning data and a learned model does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
wherein: the learning model is configured to receive at least two physical amounts other than the target physical amount and output the target physical amount, the estimation section inputs, to the learning model, two unknown physical amounts of an object of estimation which correspond to the at least two physical amounts other than the target physical amount
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
the member is an elastic body having an interior that is formed to be hollow, a pressurized fluid being supplied to the hollow interior, and the elastic body generating contracting force in a predetermined direction
This limitation is directed towards continuing the additional elements that do not integrate the abstract idea into a practical application. Limitations that amount to merely giving more details on the data that is utilized as input or output, such as describing the member that is deformed, do not amount to significantly more than the exception itself and simply continue the additional elements.
an electrical characteristic of the elastic body varies in accordance with the deformation, and the at least three physical amounts include a first physical amount that deforms the elastic body, a second physical amount expressing the electrical characteristic that varies in accordance with the deformation of the elastic body, and a target physical amount expressing an amount of deformation of the elastic body
This limitation is directed towards linking or indicating a field of use or technological environment (see MPEP 2106.05(h)). Limitations that amount to merely linking/indicating to a field of use or technological environment, such as using a learned model for estimation of deformation (see MPEP 2106.05(h)(x)), do not amount to significantly more than the exception itself.
the first physical amount is a pressure value expressing a supplied state of the pressurized fluid that is supplied to the elastic body, the second physical amount is an electrical resistance value of the elastic body, and the target physical amount is a distance of the elastic body in the predetermined direction
This limitation is directed towards continuing the additional elements that do not integrate the abstract idea into a practical application. Limitations that amount to merely giving more details on the data that is utilized as input or output, such as describing the member that is deformed, do not amount to significantly more than the exception itself and simply continue the additional elements.
In regards to Claim 4:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 4 recites the following additional elements:
wherein the learned model is a model generated by learning using a recurrent neural network
At a high level of generality, this is an activity of using a recurrent neural network as an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 4 recites the following additional elements:
wherein the learned model is a model generated by learning using a recurrent neural network
At a high level of generality, this is an activity of using a recurrent neural network as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “generate the learned model” using a recurrent neural network does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
In regards to Claim 5:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 5 recites the following additional elements:
wherein the learned model is a model generated by learning using a network in accordance with reservoir computing
At a high level of generality, this is an activity of using a recurrent neural network as an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 5 recites the following additional elements:
wherein the learned model is a model generated by learning using a network in accordance with reservoir computing
At a high level of generality, this is an activity of using reservoir computing as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “generate the learned model” using reservoir computing does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
In regards to Claim 6:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 6 recites the following additional elements:
wherein the learned model is a model generated by learning using a network in accordance with physical reservoir computing that uses a reservoir that accumulates the at least three physical amounts of a member that deforms non-linearly
At a high level of generality, this is an activity of using a recurrent neural network as an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 6 recites the following additional elements:
wherein the learned model is a model generated by learning using a network in accordance with physical reservoir computing that uses a reservoir that accumulates the at least three physical amounts of a member that deforms non-linearly
At a high level of generality, this is an activity of using reservoir computing as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “generate the learned model” using reservoir computing does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
In regards to Claim 7:
Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter?
Yes, the claim is directed towards a process
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) an abstract idea.
Claim 7 recites the same abstract ideas as analogous claim 1.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 7 recites the same additional elements as analogous claim 1.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 7 recites the same additional elements as analogous claim 1.
In regards to Claim 8:
Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter?
Yes, the claim is directed towards a medium, so manufacture.
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) an abstract idea.
Claim 8 recites the same abstract ideas as analogous claim 1.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 8 recites the same additional elements as analogous claim 1.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 8 recites the same additional elements as analogous claim 1.
In regards to Claim 9:
Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter?
No, the claim is directed towards software per se as no hardware is present in the supposed device claim.
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) an abstract idea.
Claim 9 recites the following abstract ideas:
and the learned model uses the first physical amount and the second physical amount as inputs, and outputs the target physical amount
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 9 recites the following additional elements:
an acquisition section that acquires a plurality of physical amounts comprising at least three physical amounts, which are of different types, which vary in accordance with deformation at a member deforming linearly or non-linearly, and which include a target physical amount with which time-series information is associated
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
and a learning model generation section that, on the basis of results of acquisition by the acquisition section, generates a learning model configured to receive at least two physical amounts other than the target physical amount
At a high level of generality, this is an activity of using learning data and a learned model as an “apply it” use (see MPEP 2106.05(f)).
the learning model being trained so as to output the target physical amount
At a high level of generality, this is an activity of training as an “apply it” use (see MPEP 2106.05(f)).
the member is an elastic body having an interior that is formed to be hollow, a pressurized fluid being supplied to the hollow interior, and the elastic body generating contracting force in a predetermined direction
This limitation is directed towards continuing the additional elements that do not integrate the abstract idea into a practical application.
an electrical characteristic of the elastic body varies in accordance with the deformation, and the at least three physical amounts include a first physical amount that deforms the elastic body, a second physical amount expressing the electrical characteristic that varies in accordance with the deformation of the elastic body, and a target physical amount expressing an amount of deformation of the elastic body
This limitation is directed towards linking or indicating a field of use or technological environment (see MPEP 2106.05(h)).
the first physical amount is a pressure value expressing a supplied state of the pressurized fluid that is supplied to the elastic body, the second physical amount is an electrical resistance value of the elastic body, and the target physical amount is a distance of the elastic body in the predetermined direction
This limitation is directed towards continuing the additional elements that do not integrate the abstract idea into a practical application.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 9 recites the following additional elements:
an acquisition section that acquires a plurality of physical amounts comprising at least three physical amounts, which are of different types, which vary in accordance with deformation at a member deforming linearly or non-linearly, and which include a target physical amount with which time-series information is associated
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
and a learning model generation section that, on the basis of results of acquisition by the acquisition section, generates a learning model configured to receive at least two physical amounts other than the target physical amount
At a high level of generality, this is an activity of using results of acquisition and a learned model as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “generates a learned model” using the basis of results of acquisition does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
the learning model being trained so as to output the target physical amount
At a high level of generality, this is an activity of training as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “trained” to output the target physical amount does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it” or generic training.
the member is an elastic body having an interior that is formed to be hollow, a pressurized fluid being supplied to the hollow interior, and the elastic body generating contracting force in a predetermined direction
This limitation is directed towards continuing the additional elements that do not integrate the abstract idea into a practical application. Limitations that amount to merely giving more details on the data that is utilized as input or output, such as describing the member that is deformed, do not amount to significantly more than the exception itself and simply continue the additional elements.
an electrical characteristic of the elastic body varies in accordance with the deformation, and the at least three physical amounts include a first physical amount that deforms the elastic body, a second physical amount expressing the electrical characteristic that varies in accordance with the deformation of the elastic body, and a target physical amount expressing an amount of deformation of the elastic body
This limitation is directed towards linking or indicating a field of use or technological environment (see MPEP 2106.05(h)). Limitations that amount to merely linking/indicating to a field of use or technological environment, such as using a learned model for estimation of deformation (see MPEP 2106.05(h)(x)), do not amount to significantly more than the exception itself.
the first physical amount is a pressure value expressing a supplied state of the pressurized fluid that is supplied to the elastic body, the second physical amount is an electrical resistance value of the elastic body, and the target physical amount is a distance of the elastic body in the predetermined direction
This limitation is directed towards continuing the additional elements that do not integrate the abstract idea into a practical application. Limitations that amount to merely giving more details on the data that is utilized as input or output, such as describing the member that is deformed, do not amount to significantly more than the exception itself and simply continue the additional elements.
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, 4-7 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maselli et al (“A piezoresistive flexible sensor to detect soft actuator deformation”), referred to as Maselli in this document, and further in view of Nakajima et al (“Exploiting the Dynamics of Soft Materials for Machine Learning”), referred to as Nakajima in this document.
Regarding Claim 1:
Maselli teaches:
as learning data, a plurality of physical amounts comprising at least three physical amounts, which are of different types Three physical amounts of strain, resistance, and length/deformation are noted in the quote to teach this limitation.
and which include a target physical amount with which time-series information is associated
wherein: the learning model is configured to receive at least two physical amounts other than the target physical amount The two physical amounts (physical amount 1 and physical amount 2) are two physical amounts other than the target (physical amount 3).
and output the target physical amount
the estimation section inputs, to the learning model, two unknown physical amounts of an object of estimation which correspond to the at least two physical amounts other than the target physical amount, The two physical amounts (physical amount 1 and physical amount 2) are two physical amounts other than the target (physical amount 3). The “unknown” aspect is explained more in the combination with Nakajima.
[Maselli Results and Discussions page 3 column 2]: “The dependency on the strain rate is shown in Fig. 3. The relationship between strain [physical amount 1] and resistance [physical amount 2] obtained at v1, v2, v3, v4, and v5 is hysteretic and, as the strain rate increases, the loop rotates counterclockwise around its low-left corner. Moreover, each loop is traveled clockwise, as marked by the arrows. This result is in agreement with the literature evidence [22] [28], and it is important for the design since it means that sensor could be used within a wide range of velocities, but with different calibration procedures. This study aims at monitoring the axial length [physical amount 3] [and output the target physical amount] of the actuator during its dynamic contraction at a variable speed [and which include a target physical amount with which time-series information is associated]; therefore, it was necessary both a sensor calibration at a variable speed and one taking into account a range of strain rates.”
which vary in accordance with a member deforming linearly or non-linearly
and estimates an unknown target physical amount, which corresponds to the two unknown physical amounts of the object of estimation
[Maselli Introduction page 2 column 1]: “Firstly, we fabricated and tested the McKibben actuator to be sensorized; secondly, we characterized the sensor by extracting the useful electro-mechanical characteristics and by evaluating to what extent it affects the contracting abilities [and estimates an unknown target physical amount, which corresponds to the two unknown physical amounts of the object of estimation where the two physical amounts are noted earlier as taught by Maselli and the “unknown” aspect is explained more in the combination with Nakajima] of the artificial muscle. Then, we evaluated a simple model to compensate its nonlinearities [which vary in accordance with a member deforming linearly or non-linearly]. Finally, we fixed the conductive textile sensor over the actuator and tested its performances as monitoring system, in dynamic conditions, i.e. actuator contracting at different speeds. The tests results underlined the advantages of this design, as the low-cost and the possibility to be implemented on any type of McKibben actuator without affecting the existing form factors or shapes.”
Support for the estimation and other aspects is given by the description in [Maselli Abstract]: “For this reason, flexible and stretchable sensors are necessary. In this study, a flexible sensor using conductive textile is proposed to actively measure the length of manufactured McKibben actuators. Firstly, the electro-mechanical characteristics of the proposed sensor were obtained and a model to compensate its nonlinearities was evaluated”
Parts rolled up from claim 2 taught by Maselli:
an electrical characteristic of the elastic body varies in accordance with the deformation
[Maselli Introduction page 1 column 2]: “Consequently, there has been increasing interest in embedding sensing capabilities into McKibben actuators in order to produce more controllable soft devices. Wakimoto developed a McKibben actuator with a build-in flexible electro-conductive rubber sensor, which can measure the length by actuator itself [14]: the overall system contraction [an electrical characteristic of the elastic body varies in accordance with the deformation] induces a measurable electrical resistance variation”
and the at least three physical amounts (taught by there being 3 physical amounts noted) include a first physical amount that deforms the elastic body (physical amount 1 notes strain), a second physical amount expressing the electrical characteristic that varies in accordance with the deformation of the elastic body (physical amount 2 notes resistance), and a target physical amount expressing an amount of deformation of the elastic body (physical amount 3 notes the length),
and the learned model uses the first physical amount and the second physical amount as inputs, and outputs the target physical amount
[Maselli Results and Discussions page 3 column 2]: “The dependency on the strain rate is shown in Fig. 3. The relationship between strain [physical amount 1] and resistance [physical amount 2] obtained at v1, v2, v3, v4, and v5 is hysteretic and, as the strain rate increases, the loop rotates counterclockwise around its low-left corner. Moreover, each loop is traveled clockwise, as marked by the arrows. This result is in agreement with the literature evidence [22] [28], and it is important for the design since it means that sensor could be used within a wide range of velocities, but with different calibration procedures. This study aims at monitoring the axial length [physical amount 3] [and the learned model uses the first physical amount and the second physical amount as inputs, and outputs the target physical amount where the learned model is taught more later in claim 1, as Maselli notes a model (noted in Abstract of Maselli) and Nakajima notes a machine learning model] of the actuator during its dynamic contraction at a variable speed; therefore, it was necessary both a sensor calibration at a variable speed and one taking into account a range of strain rates.”
Parts rolled up from claim 3 taught by Maselli:
the member is an elastic body having an interior that is formed to be hollow, a pressurized fluid being supplied to the hollow interior, and the elastic body generating contracting force in a predetermined direction
[Maselli Introduction Page 1 column 1]: “Fiber-reinforced fluidic actuators are very much used in the soft robotics field [1]. This class of actuators includes devices that can bend [2], twist [3], curl [4], and extend [5] upon pressurization. This group includes also devices that can contract along their length like biological muscles [6], known as pneumatic artificial muscles (PAMs) or McKibben muscles.” [the member is an elastic body having an interior that is formed to be hollow, a pressurized fluid being supplied to the hollow interior, and the elastic body generating contracting force in a predetermined direction] is seen as taught by the above quote, as McKibben muscles appear to match the description of limitation and the specification of the current application notes rubber actuators [0089] which also appear to match the description of a McKibben muscle.
the first physical amount is a pressure value expressing a supplied state of the pressurized fluid that is supplied to the elastic body, the second physical amount is an electrical resistance value of the elastic body, and the target physical amount is a distance of the elastic body in the predetermined direction where the physical amounts are seen as taught by the 3 physical amounts, as physical amount 1 denotes strain for the first physical amount, physical amount 2 denotes resistance for the second physical amount, and physical amount 3 denotes the length for the target physical amount.
[Maselli Results and Discussions page 3 column 2]: “The dependency on the strain rate is shown in Fig. 3. The relationship between strain [physical amount 1] and resistance [physical amount 2] obtained at v1, v2, v3, v4, and v5 is hysteretic and, as the strain rate increases, the loop rotates counterclockwise around its low-left corner. Moreover, each loop is traveled clockwise, as marked by the arrows. This result is in agreement with the literature evidence [22] [28], and it is important for the design since it means that sensor could be used within a wide range of velocities, but with different calibration procedures. This study aims at monitoring the axial length [physical amount 3] of the actuator during its dynamic contraction at a variable speed; therefore, it was necessary both a sensor calibration at a variable speed and one taking into account a range of strain rates.”
Maselli does not explicitly teach:
An estimation device comprising: an estimation section and a learning model that is trained by using
Nakajima teaches:
An estimation device comprising: an estimation section and a learning model that is trained by using
[Nakajima Introduction page 1 column 1]: “Compared with rigid materials, soft materials exhibit rich dynamics including a variety of properties, such as nonlinearity, elasticity, and high dimensionality. In this article, we demonstrate that these dynamic properties constitute an asset that can be effectively employed for machine learning purposes [An estimation device comprising: an estimation section and a learning model that is trained by using]. Our approach is based on a technique called reservoir computing,11–13 which is a framework rooted in recurrent neural network learning. When a high-dimensional dynamical system, which is referred to as the reservoir, is driven with input streams, it generates transient dynamics that operate as a type of temporal and finite kernel that facilitates the separation of the input states. If the dynamics involve short-term memory and nonlinear processing of the input stream, then nonlinear dynamical systems can be learned by adjusting a linear, static readout from the high-dimensional state space of the reservoir. We exploit the rich physical dynamics of soft materials directly as a reservoir for temporal machine learning problems.”
The idea of “unknown” values or amounts is seen as referring to the physical amounts, but the implication of a prediction or finding a non-measured answer. As a result, the premise of the physical amounts is still seen as being taught by Maselli, but the combination with Nakajima is seen as supporting the implication as is noted by the specification ([Current Application 0061]: “As described above, in accordance with the present disclosure, the length of the rubber actuator 2 can be estimated from the unknown first input data 3 (pressure) and second input data 4 (electrical resistance) for the rubber actuator 2. Namely, the length of the rubber actuator 2 can be estimated without directly measuring the non-linear deformation of the rubber actuator 2 that deforms non-linearly.”). Nakajima noting the use of machine learning is seen as predicting something via learning ([Nakajima Introduction page 1 column 1]: “If the dynamics involve short-term memory and nonlinear processing of the input stream, then nonlinear dynamical systems can be learned by adjusting a linear, static readout from the high-dimensional state space of the reservoir.”), so the Nakajima fulfills the want of acquiring a value without a known/measured answer.
One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Maselli and Nakajima. Maselli and Nakajima are in the same field of endeavor of predicting physical properties or material science. One of ordinary skill in the art would have been motivated to combine Maselli and Nakajima in order to be able to take advantage of the properties of the material ([Nakajima Introduction page 1 column 2]: “We exploit the rich physical dynamics of soft materials directly as a reservoir for temporal machine learning problems”) and nature of the problem being an effective use of machine learning that includes recurrent neural networks with reservoirs ([Nakajima Introduction page 1 column 2]: “In this article, we demonstrate that these dynamic properties constitute an asset that can be effectively employed for machine learning purposes”).
Regarding Claim 4:
The method of claim 1 is taught by Maselli and Nakajima.
Nakajima teaches:
wherein the learned model is a model generated by learning using a recurrent neural network
[Nakajima Introduction page 1 column 1]: “Compared with rigid materials, soft materials exhibit rich dynamics including a variety of properties, such as nonlinearity, elasticity, and high dimensionality. In this article, we demonstrate that these dynamic properties constitute an asset that can be effectively employed for machine learning purposes. Our approach is based on a technique called reservoir computing,11–13 which is a framework rooted in recurrent neural network learning [wherein the learned model is a model generated by learning using a recurrent neural network]. When a high-dimensional dynamical system, which is referred to as the reservoir, is driven with input streams, it generates transient dynamics that operate as a type of temporal and finite kernel that facilitates the separation of the input states. If the dynamics involve short-term memory and nonlinear processing of the input stream, then nonlinear dynamical systems can be learned by adjusting a linear, static readout from the high-dimensional state space of the reservoir. We exploit the rich physical dynamics of soft materials directly as a reservoir for temporal machine learning problems.”
Motivation to combine with Nakajima is the same motivation to combine with Nakajima in claim 1.
Regarding Claim 5:
The method of claim 1 is taught by Maselli and Nakajima.
Nakajima teaches:
wherein the learned model is a model generated by learning using a network in accordance with reservoir computing
[Nakajima Introduction page 1 column 1]: “Compared with rigid materials, soft materials exhibit rich dynamics including a variety of properties, such as nonlinearity, elasticity, and high dimensionality. In this article, we demonstrate that these dynamic properties constitute an asset that can be effectively employed for machine learning purposes. Our approach is based on a technique called reservoir computing [wherein the learned model is a model generated by learning using a network in accordance with reservoir computing],11–13 which is a framework rooted in recurrent neural network learning. When a high-dimensional dynamical system, which is referred to as the reservoir, is driven with input streams, it generates transient dynamics that operate as a type of temporal and finite kernel that facilitates the separation of the input states. If the dynamics involve short-term memory and nonlinear processing of the input stream, then nonlinear dynamical systems can be learned by adjusting a linear, static readout from the high-dimensional state space of the reservoir. We exploit the rich physical dynamics of soft materials directly as a reservoir for temporal machine learning problems.”
Motivation to combine with Nakajima is the same motivation to combine with Nakajima in claim 1.
Regarding Claim 6:
The method of claim 1 is taught by Maselli and Nakajima.
Nakajima teaches:
wherein the learned model is a model generated by learning using a network in accordance with physical reservoir computing that uses a reservoir that accumulates the at least three physical amounts of a member that deforms non-linearly
[Nakajima Introduction page 1 column 1]: “Compared with rigid materials, soft materials exhibit rich dynamics including a variety of properties, such as nonlinearity, elasticity, and high dimensionality. In this article, we demonstrate that these dynamic properties constitute an asset that can be effectively employed for machine learning purposes. Our approach is based on a technique called reservoir computing,11–13 which is a framework rooted in recurrent neural network learning. When a high-dimensional dynamical system, which is referred to as the reservoir, is driven with input streams, it generates transient dynamics that operate as a type of temporal and finite kernel that facilitates the separation of the input states. If the dynamics involve short-term memory and nonlinear processing of the input stream, then nonlinear dynamical systems can be learned by adjusting a linear, static readout from the high-dimensional state space of the reservoir. We exploit the rich physical dynamics of soft materials directly as a reservoir [wherein the learned model is a model generated by learning using a network in accordance with physical reservoir computing that uses a reservoir that accumulates the at least three physical amounts of a member that deforms non-linearly where the three physical amounts are taught in detail in claim 1] for temporal machine learning problems.”
Motivation to combine with Nakajima is the same motivation to combine with Nakajima in claim 1.
Regarding Claim 7:
Claim 7 is analogous to claim 1, besides the limitations taught below.
Maselli teaches:
An estimation method in which a computer
[Maselli Conclusions page 5 column 2]: “In our study, we show that a simple hysteresis compensation model, characterized by a low computational cost [An estimation method in which a computer as computational cost is seen as teaching the use of a computer], combined with an adequate processing, can enhance the sensor performances in case of dynamic measurements.”
Regarding Claim 9:
Claim 9 is analogous to claim 1.
Claims 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maselli et al (“A piezoresistive flexible sensor to detect soft actuator deformation”), referred to as Maselli in this document, and further in view of Nakajima et al (“Exploiting the Dynamics of Soft Materials for Machine Learning”), referred to as Nakajima in this document, and further in view of Katsuki et al (US 20170193138 A1), referred to as Katsuki in this document.
Regarding Claim 8:
Claim 8 is analogous to claim 1, besides the limitations taught below.
Maselli does not explicitly teach:
A non-transitory storage medium storing a program executable by a computer
Katsuki teaches:
A non-transitory storage medium storing a program executable by a computer
[Katsuki 0005]: “A first aspect of the innovations may be an apparatus including a processor and one or more computer readable mediums [A non-transitory storage medium storing a program executable by a computer where Katsuki 0089 notes that such mediums are not be construed as transitory] collectively including instructions.”
One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Maselli and Katsuki to incorporate a computer readable medium. Maselli and Katsuki are in the same field of endeavor of predicting material properties or material science. One of ordinary skill in the art, prior to the effective filing date would have been motivated to combine Maselli and Katsuki in order to be able to distribute or store the invention as instructions for a computer for a computer to perform the task ([Katsuki 0005]: “A first aspect of the innovations may be an apparatus including a processor and one or more computer readable mediums collectively including instructions.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Matos et al (“Application of machine learning to predict the multiaxial strain-sensing response of CNT-polymer composites”) is considered relevant art as the reference teaches uses for sensing and predicting properties of materials, such as deformation’s relation to electrical resistance.
Ling et al (“Machine learning strategies for systems with invariance properties”) is considered relevant art as Ling et al teaches the use of machine learning to estimate properties of a material, which include things such as conductivity.
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.
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/C.D.D./Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129