DETAILED ACTION
This action is responsive to the claims filed on 06/13/2023. Claims 1-19 are pending for examination.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/22/2024 and 06/13/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a detection unit that detects an electrical property…” and “an estimation unit that inputs, to a learning model…” in claim 1
“a detection unit that detects an electrical property…” in claim 6
“a detection unit that detects an electrical property…” in claim 7
“an acquisition unit that acquires”, “a detection unit that detects” and “a learning model generation unit that generates” in claim 8
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
Claims 17 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 17 recites the limitation “The estimation device of claim 6…”. There is insufficient antecedent basis for this limitation in the claim.
Claim 18 recites the limitation “The estimation device of claim 7…”. There is insufficient antecedent basis for this limitation in the claim.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 12 and 16 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 4 and its dependent 12 recites substantially identical limitations where a further limitation cannot clearly be identified. Claim 5 and its dependent 16 recites substantially identical limitations where a further limitation cannot clearly be identified. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Statutory Categories
Claims 1-5 and 9-16 are directed to a device.
Claim 6 and 17 are directed to an method.
Claim 7 and 18 are directed to a computer-readable medium.
Claim 8 and 19 are directed to a device.
Independent Claims – Claim 1
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes. Independent claims 1 recites limitations that are abstract ideas in the form of mental processes:
Claim 1 recites:
detects an electrical property between a plurality of detection points in a conductive flexible material; (this limitation merely amounts to detecting an electrical property at a high level of generality for a conductive material and is being interpreted as a mental process of evaluation which can reasonably be performed in human mind or with aid of pen and paper)
Claim 1 also recites the following additional elements for the purposes of Step 2A Prong Two analysis:
An estimation device, comprising: a detection unit that (For the purposes of Step 2A prong 2: the recitation of an estimation device used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and an estimation unit that (For the purposes of Step 2A prong 2: the recitation of an estimation unit used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
inputs… as learning data, an electrical property that changes chronologically in response to deformation of the flexible material, and shape information representing shapes, in the flexible material, of pressure stimuli that impart deformation to the flexible material, (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
to a learning model that has been trained to use (For the purposes of Step 2A prong 2: the recitation of a trained learning model used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and to receive the electrical property as input (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
and to output the shape information, the electrical property of an estimation target object detected by the detection unit, and that estimates shape information of the estimation target object. (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
The additional limitations fail step 2A Prong 2 of the 101 analysis because they do not transform the claim into a practical application. These limitations are too abstract or lack technical improvement that would make the concept practically useful. Without clear utility or integration into a specific field, the claim does not relate to any particular application. It does not meet the requirements of Step 2A Prong 2, as it fails to make the concept meaningfully applicable in practice.
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
This claim recites the following additional elements for the purposes of Step 2B analysis:
An estimation device, comprising: a detection unit that (For the purposes of Step 2B: the recitation of an estimation device used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and an estimation unit that (For the purposes of Step 2B: the recitation of an estimation unit used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
inputs… as learning data, an electrical property that changes chronologically in response to deformation of the flexible material, and shape information representing shapes, in the flexible material, of pressure stimuli that impart deformation to the flexible material, (Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);)
to a learning model that has been trained to use (For the purposes of Step 2B: the recitation of a trained learning model used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and to receive the electrical property as input (Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);))
and to output the shape information, the electrical property of an estimation target object detected by the detection unit, and that estimates shape information of the estimation target object. (Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);))
The claim also fails Step 2B of the analysis because the additional limitations do not amount to significantly more than the abstract idea itself. The additional limitations do not enhance the claim in a way that would move it beyond its abstract ideas as they minimally elaborate on the core concept without adding any inventive or technical substance. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Dependents of Claim 1
The remaining dependent claims corresponding to independent claims 1 do not recite additional elements, whether considered individually or in combination, that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. The analysis of which is shown below:
The claims below recite additional limitations which fail step 2A Prong 2 of the 101 analysis because they do not transform the claim into a practical application. These limitations are too abstract or lack technical improvement that would make the concept practically useful. Without clear utility or integration into a specific field, the claim does not relate to any particular application. It does not meet the requirements of Step 2A Prong 2, as it fails to make the concept meaningfully applicable in practice.
The claims also fails Step 2B of the analysis because the additional limitations do not amount to significantly more than the abstract idea itself. The additional limitations do not enhance the claim in a way that would move it beyond its abstract ideas as they minimally elaborate on the core concept without adding any inventive or technical substance. The claims are unpatentable.
Claim 2 recites the additional limitation of:
The estimation device of claim 1, wherein: the flexible material is a material having an electrical property that changes in response to the deformation, (For the purposes of step 2A Prong II or Step 2B: this limitation is generally linking the use of a judicial exception to a particular technological field or field of use, see MPEP 2106.05(h))
and the learning model is trained to output shape information corresponding to the detected electrical property. (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);))
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 3 recites the additional limitation of:
The estimation device of claim 1, wherein the electrical property of the flexible material is volume resistance. (For the purposes of step 2A Prong II or Step 2B: this limitation is generally linking the use of a judicial exception to a particular technological field or field of use, see MPEP 2106.05(h))
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 4 recites the additional limitation of:
The estimation device of claim 1, wherein the flexible material is a material in which conductivity is imparted to a urethane material having a structure having a fibrous skeleton or a structure having a plurality of microscopic air bubbles scattered inside. (For the purposes of step 2A Prong II or Step 2B: this limitation is generally linking the use of a judicial exception to a particular technological field or field of use, see MPEP 2106.05(h))
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 5 recites the additional limitation of:
The estimation device of claim 1, wherein the learning model is a model generated by training the model using, with the flexible material as a reservoir, a network obtained by reservoir computing using the reservoir. (For the purposes of step 2A Prong II or Step 2B: this limitation is generally linking the use of a judicial exception to a particular technological field or field of use, see MPEP 2106.05(h))
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 9 recite limitations substantially similar to claim 3, as such a similar analysis applies.
Claims 10-12 recite limitations substantially similar to claim 4, as such a similar analysis applies.
Claims 13-15 recite limitations substantially similar to claim 5, as such a similar analysis applies.
Independent Claims – Claim 6
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes. Independent claims 6 recites limitations that are abstract ideas in the form of mental processes:
Claim 6 recites:
detects an electrical property between a plurality of detection points in a conductive flexible material, (this limitation merely amounts to detecting an electrical property at a high level of generality for a conductive material and is being interpreted as a mental process of evaluation which can reasonably be performed in human mind or with aid of pen and paper)
Claim 6 also recites the following additional elements for the purposes of Step 2A Prong Two analysis:
An estimation method, comprising, by a computer: acquiring, from a detection unit that (For the purposes of Step 2A prong 2: the recitation of an estimation device used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and inputting… as learning data, an electrical property with which is associated time-series information that changes in response to deformation of the flexible material, and shape information of pressure stimuli that impart deformation to the flexible material, (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
to a learning model that has been trained to use (For the purposes of Step 2A prong 2: the recitation of a trained learning model used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and to receive the electrical property as input (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
and to output the shape information, the acquired electrical property of an estimation target object, and estimating shape information of the estimation target object. (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
The additional limitations fail step 2A Prong 2 of the 101 analysis because they do not transform the claim into a practical application. These limitations are too abstract or lack technical improvement that would make the concept practically useful. Without clear utility or integration into a specific field, the claim does not relate to any particular application. It does not meet the requirements of Step 2A Prong 2, as it fails to make the concept meaningfully applicable in practice.
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
This claim recites the following additional elements for the purposes of Step 2B analysis:
An estimation method, comprising, by a computer: acquiring, from a detection unit that (For the purposes of Step 2B: the recitation of an estimation device used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and inputting… as learning data, an electrical property with which is associated time-series information that changes in response to deformation of the flexible material, and shape information of pressure stimuli that impart deformation to the flexible material, (Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);))
to a learning model that has been trained to use (For the purposes of 2B: the recitation of a trained learning model used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and to receive the electrical property as input (Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);))
and to output the shape information, the acquired electrical property of an estimation target object, and estimating shape information of the estimation target object. (Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);))
The claim also fails Step 2B of the analysis because the additional limitations do not amount to significantly more than the abstract idea itself. The additional limitations do not enhance the claim in a way that would move it beyond its abstract ideas as they minimally elaborate on the core concept without adding any inventive or technical substance. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Dependents of Claim 6
The remaining dependent claims corresponding to independent claim 6 do not recite additional elements, whether considered individually or in combination, that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. The analysis of which is shown below:
The claims below recite additional limitations which fail step 2A Prong 2 of the 101 analysis because they do not transform the claim into a practical application. These limitations are too abstract or lack technical improvement that would make the concept practically useful. Without clear utility or integration into a specific field, the claim does not relate to any particular application. It does not meet the requirements of Step 2A Prong 2, as it fails to make the concept meaningfully applicable in practice.
The claims also fails Step 2B of the analysis because the additional limitations do not amount to significantly more than the abstract idea itself. The additional limitations do not enhance the claim in a way that would move it beyond its abstract ideas as they minimally elaborate on the core concept without adding any inventive or technical substance. The claims are unpatentable.
Claim 17 recites limitations substantially similar to claim 5, as such a similar analysis applies.
Independent Claims – Claim 7
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes. Independent claims 7 recites limitations that are abstract ideas in the form of mental processes:
Claim 7 recites:
detects an electrical property between a plurality of detection points in a conductive flexible material, (this limitation merely amounts to detecting an electrical property at a high level of generality for a conductive material and is being interpreted as a mental process of evaluation which can reasonably be performed in human mind or with aid of pen and paper)
Claim 7 also recites the following additional elements for the purposes of Step 2A Prong Two analysis:
A non-transitory computer-readable medium storing a program for causing a computer to (Under Step 2A Prong II: this limitation is invoking computers or other machinery merely as a tool to perform an existing process, see MPEP 2106.05(f))
Acquiring… the electrical property; (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
from a detection unit, (For the purposes of Step 2A prong 2: the recitation of an detection device used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and inputting… as learning data, an electrical property with which is associated time-series information that changes in response to deformation of the flexible material, and shape information of pressure stimuli that impart deformation to the flexible material, (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
to a learning model that has been trained to use (For the purposes of Step 2A prong 2: the recitation of a trained learning model used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and to receive the electrical property as input (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
and to output the shape information, the acquired electrical property of an estimation target object, and estimating shape information of the estimation target object. (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
The additional limitations fail step 2A Prong 2 of the 101 analysis because they do not transform the claim into a practical application. These limitations are too abstract or lack technical improvement that would make the concept practically useful. Without clear utility or integration into a specific field, the claim does not relate to any particular application. It does not meet the requirements of Step 2A Prong 2, as it fails to make the concept meaningfully applicable in practice.
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
This claim recites the following additional elements for the purposes of Step 2B analysis:
A non-transitory computer-readable medium storing a program for causing a computer to (Under Step 2B: this limitation is invoking computers or other machinery merely as a tool to perform an existing process, see MPEP 2106.05(f))
Acquiring… the electrical property; (Under Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);)
from a detection unit (Under Step 2B: the recitation of an detection device used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and inputting… as learning data, an electrical property with which is associated time-series information that changes in response to deformation of the flexible material, and shape information of pressure stimuli that impart deformation to the flexible material, (Under Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);)
to a learning model that has been trained to use (Under Step 2B: the recitation of a trained learning model used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and to receive the electrical property as input (Under Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);)
and to output the shape information, the acquired electrical property of an estimation target object, and estimating shape information of the estimation target object. (Under Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);)
The claim also fails Step 2B of the analysis because the additional limitations do not amount to significantly more than the abstract idea itself. The additional limitations do not enhance the claim in a way that would move it beyond its abstract ideas as they minimally elaborate on the core concept without adding any inventive or technical substance. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Dependents of Claim 7
The remaining dependent claims corresponding to independent claim 7 do not recite additional elements, whether considered individually or in combination, that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. The analysis of which is shown below:
The claims below recite additional limitations which fail step 2A Prong 2 of the 101 analysis because they do not transform the claim into a practical application. These limitations are too abstract or lack technical improvement that would make the concept practically useful. Without clear utility or integration into a specific field, the claim does not relate to any particular application. It does not meet the requirements of Step 2A Prong 2, as it fails to make the concept meaningfully applicable in practice.
The claims also fails Step 2B of the analysis because the additional limitations do not amount to significantly more than the abstract idea itself. The additional limitations do not enhance the claim in a way that would move it beyond its abstract ideas as they minimally elaborate on the core concept without adding any inventive or technical substance. The claims are unpatentable.
Claim 18 recites limitations substantially similar to claim 5, as such a similar analysis applies.
Independent Claims – Claim 8
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes. Independent claims 8 recites limitations that are abstract ideas in the form of mental processes:
Claim 8 recites:
detects an electrical property between a plurality of detection points in a conductive flexible material, (this limitation merely amounts to detecting an electrical property at a high level of generality for a conductive material and is being interpreted as a mental process of evaluation which can reasonably be performed in human mind or with aid of pen and paper)
Claim 8 also recites the following additional elements for the purposes of Step 2A Prong Two analysis:
A learning model generation device, comprising: (For the purposes of Step 2A prong 2: the recitation of learning model device used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
acquires, from a detection unit (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
an acquisition unit that acquires, (For the purposes of Step 2A prong 2: the recitation of an acquisition device used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and acquires shape information of pressure stimuli that impart deformation to the flexible material; (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
and a learning model generation unit that generates, based on acquisition results of the acquisition unit, a learning model (For the purposes of Step 2A prong 2: the recitation of a trained learning model used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
that has been trained to receive, as input, an electrical property with which is associated time-series information that changes in response to deformation of the flexible material, (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
and to output shape information of a target object. (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g).)
The additional limitations fail step 2A Prong 2 of the 101 analysis because they do not transform the claim into a practical application. These limitations are too abstract or lack technical improvement that would make the concept practically useful. Without clear utility or integration into a specific field, the claim does not relate to any particular application. It does not meet the requirements of Step 2A Prong 2, as it fails to make the concept meaningfully applicable in practice.
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
This claim recites the following additional elements for the purposes of Step 2B analysis:
A learning model generation device, comprising: (Step 2B: the recitation of a learning model generation device used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
acquires, from a detection unit (Under Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);)
an acquisition unit that acquires, (Step 2B: the recitation of an acquisition device used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and acquires shape information of pressure stimuli that impart deformation to the flexible material; (Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);)
and a learning model generation unit that generates, based on acquisition results of the acquisition unit, a learning model (Step 2B: the recitation of a trained learning model used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
that has been trained to receive, as input, an electrical property with which is associated time-series information that changes in response to deformation of the flexible material, (Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);)
and to output shape information of a target object. (Step 2B: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);)
The claim also fails Step 2B of the analysis because the additional limitations do not amount to significantly more than the abstract idea itself. The additional limitations do not enhance the claim in a way that would move it beyond its abstract ideas as they minimally elaborate on the core concept without adding any inventive or technical substance. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Dependents of Claim 8
The remaining dependent claims corresponding to independent claim 8 do not recite additional elements, whether considered individually or in combination, that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. The analysis of which is shown below:
The claims below recite additional limitations which fail step 2A Prong 2 of the 101 analysis because they do not transform the claim into a practical application. These limitations are too abstract or lack technical improvement that would make the concept practically useful. Without clear utility or integration into a specific field, the claim does not relate to any particular application. It does not meet the requirements of Step 2A Prong 2, as it fails to make the concept meaningfully applicable in practice.
The claims also fails Step 2B of the analysis because the additional limitations do not amount to significantly more than the abstract idea itself. The additional limitations do not enhance the claim in a way that would move it beyond its abstract ideas as they minimally elaborate on the core concept without adding any inventive or technical substance. The claims are unpatentable.
Claim 18 recites limitations substantially similar to claim 5, as such a similar analysis applies.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 and 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al., (Park, H., Lee, H., Park, K., Mo, S., & Kim, J. (2019, November). Deep neural network approach in electrical impedance tomography-based real-time soft tactile sensor. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7447-7452). IEEE.),), hereafter referred to as Park in view of Reyes et al. (Costilla-Reyes, O., Scully, P., & Ozanyan, K. B. (2017). Deep neural networks for learning spatio-temporal features from tomography sensors. IEEE Transactions on Industrial Electronics, 65(1), 645-653.), hereafter referred to as Reyes.
Claim 1: Park teaches:
An estimation device, comprising: a detection unit that detects an electrical property between a plurality of detection points in a conductive flexible material; (Park, page 7448, col. 2, section B, paragraph 1, “The resistance of this fabric sensitively changes from normal pressure and lateral stretch…. In total, 16 electrodes were attached on the boundary of the piezoresistive fabric.”
Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”, Park describes a sensing arrangement in which a conductive, stretchable/piezoresistive fabric (i.e., a “conductive flexible material”) is provided with “16 electrodes” positioned “on the boundary,” which are discrete “detection points.” Park further explains that the fabric’s “resistance… sensitively changes” under “normal pressure” and “lateral stretch,” confirming an “electrical property” that varies with deformation. Park also expressly measures “boundary voltage measurements” between these electrodes, which constitutes detecting an electrical property between a plurality of detection points in the conductive flexible material as claimed.)
and an estimation unit that inputs, to a learning model that has been trained to use, as learning data, an electrical property… in response to deformation of the flexible material, (Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”,
Park, page 7451, col. 2, section D, paragraph 1, “Using the piezoresistive tactile sensor and the electronic equipment, the real boundary voltages were measured. The boundary voltages were applied to the trained DNN model to estimate the conductivity distribution, which corresponds to the external normal pressure.”, Park’s “boundary voltage measurements” are the detected “electrical property” acquired by the electrode-based detection arrangement. Park then discloses an “estimation” (inference) component implemented by a “DNN” that is trained to “estimate the conductivity distribution” from the boundary voltage measurements (input) to an inferred output (conductivity/pressure distribution). Thus, Park teaches an estimation unit that inputs the detected electrical property (boundary voltage measurements) to a trained learning model for inference regarding the state of the material/target. )
shape information representing shapes, in the flexible material, of pressure stimuli that impart deformation to the flexible material, (Park, page 7451, col. 2, section D, paragraph 1, “To show the applicability of the proposed model, the piezoresistive tactile sensor was demonstrated to estimate external normal pressure. Using the piezoresistive tactile sensor and the electronic equipment, the real boundary voltages were measured. The boundary voltages were applied to the trained DNN model to estimate the conductivity distribution, which corresponds to the external normal pressure”,
Park, page 7449, col. 2, paragraph 1, “As shown in Figure 3, the first one is an imposition of conductivity difference on singe element to obtain individual effects of each elements. Next one is a shape of square that contributes on reconstruction of areal distribution. The last one is a selection of random elements. In this method, the random distribution was made as few as possible by inducing each element to be activated the same number of times in all datasets. Different amounts of conductivity differences were given for each distribution to obtain the ability to estimate the magnitude of the force input. As a result, total 76,776 dataset was generated.”, Park’s training framework expressly uses predefined spatial distributions (“preset distribution”) that include a specific area ”whose shape and can be moved, which constitutes “shape information representing shapes … in the flexible material.” Park further ties the conductivity distribution to applied loading by stating the inferred “conductivity distribution … corresponds to the external surface normal pressures,” i.e., pressure stimuli that deform the fabric. Accordingly, Park teaches training that uses learning data including shape-characterizing spatial patterns corresponding to pressure stimuli that impart deformation.)
and to receive the electrical property as input and to output the shape information, (Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”,
Park, page 7449, col. 2, section B, paragraph 2, “The training maps input of 208 voltage measurements to output of n × n conductivity distribution… The output layer represents the reconstructed conductivity and the distribution can be visualized through reshaping”,
Park, page 7449, col. 2, paragraph 1, “As shown in Figure 3, the first one is an imposition of conductivity difference on singe element to obtain individual effects of each elements. Next one is a shape of square that contributes on reconstruction of areal distribution”, Park expressly discloses that the trained DNN receives “boundary voltage measurement” as the model input and produces an output “conductivity distribution” (“mapping boundary voltage measurement to conductivity distribution”), where the conductivity distribution “corresponds to external surface normal pressures,” i.e., the pressure pattern imposed on the tactile fabric. Park further clarifies the input/output relationship structurally: “input of 208 voltage measurements” is mapped to an “output of n × n conductivity distribution,” and the distribution is “visualized through reshaping,” meaning the output is a spatially arranged (image-like) distribution encoding the shape of the pressure/contact region across the material. That spatial output constitutes the claimed “shape information,” and Park’s dataset construction reinforces that the output distribution is shape-representative because it includes explicit geometric patterns such as a “shape of square” used to support “reconstruction of areal distribution,” i.e., reconstruction of a 2D shaped region.)
the electrical property of an estimation target object detected by the detection unit, (Park, page 7448, col. 2, section B, paragraph 1, “The resistance of this fabric sensitively changes from normal pressure and lateral stretch…. In total, 16 electrodes were attached on the boundary of the piezoresistive fabric.”
Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”, Park explains that the fabric’s “resistance… sensitively changes” under “normal pressure” and “lateral stretch,” confirming an “electrical property” that varies with deformation. Park also expressly measures “boundary voltage measurements” by between these electrodes, which constitutes detecting an electrical property between a plurality of detection points in the conductive flexible material as claimed.)
and that estimates shape information of the estimation target object. (Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”,
Park, page 7450, col. 1, section A, paragraph 1, “The reconstruction performance of the proposed DNN model was evaluated using two predefined reference conductivity distributions which are a simple square and a complex geometry… From the forward solving, corresponding boundary voltages were obtained. These boundary voltage was used to reconstruct the conductivity distribution from two different conventional reconstruction methods… As shown in Figure 5 (a) and (c), the proposed model as well as the nonlinear iterative model (PDIPM) successfully reconstructed the reference conductivity distribution compared to the linear model”, Park describes the trained DNN as performing estimation/reconstruction of a spatial distribution from measured electrical data: Park obtains “boundary voltages” and uses them to “reconstruct the conductivity distribution”. For generating a shape, Park evaluates reconstruction on reference distributions having explicit geometric forms e.g. “a simple square and a complex geometry”, and reports that the proposed model “successfully reconstructed the reference conductivity distribution”. Because Park also states the reconstructed conductivity distribution “corresponds to external surface normal pressures,” the reconstructed (estimated) distribution is not merely a scalar pressure value, but a spatial pressure/contact pattern whose geometry (e.g., square vs. complex shape) is the claimed “shape information” of the pressed/loaded target on the material.)
Reyes, in the same field of spatio-temporal feature analysis, teaches the following limitations which Park fails to teach:
an electrical property that changes chronologically in response to deformation of the flexible material, and (Reyes, page 647, col. 1, paragraph 1, “The floor sensor geometry allows capturing the spatial distribution of pressure, while the time component is captured as frames acquired at 256 Hz, thus, providing the time evolution of the spatial component”
Reyes, page 646, col. 2, section C, “iMAGiMAT, an experimental floor sensor system demonstrator for gait analysis [8], was employed to acquire the UoM-Gait13 dataset used for the analysis in this paper. Deformation of plastic optical fibres (POFs) due to applied footstep pressure, modulates their transmission properties and, therefore, the intensity of the transmitted light.”,
Reyes, page 647, col. 2, section 4, paragraph 3, “Each sample contains 116 sensors signals obtained in a time window of 5.4 s (1400 frames at 256 Hz)”,
Reyes, page 647, col. 1, section D.III, “Fig. 2 shows the floor sensor system raw signals in the time domain for four gait experiments. Fig. 2(a) shows the amplitude response over time of the 116 spatial sensor signals of 1 sample of the normal gait experiment. Each signal has a different amplitude response that is determined by the physical characteristics of each POF sensor and the executed gait pattern type.”, Reyes discloses sensor outputs that vary over time as a result of physical loading/deformation: applied footstep pressure causes deformation of plastic optical fibres (POFs)” which “modulates” transmission properties and the sensed intensity, meaning the measured sensor signal changes as deformation/pressure changes. Reyes then explicitly treats those sensor signals as chronological/time-series data: the “time component” is captured as “frames acquired at 256 Hz,” providing “time evolution,” and Reyes visualizes the raw data as “amplitude response over time” with an explicit definition where “t represents the time component in frames”. Thus, Reyes teaches that a measured sensor/electrical output signal (sensor “amplitude response”) changes chronologically (frame-by-frame over time) in response to deformation caused by applied pressure.)
Park already teaches reconstructing/estimating a spatial pressure-correspondent distribution from boundary electrical measurements via a trained DNN. Reyes teaches explicitly capturing and using time series spatio-temporal analysis, i.e., handling sensor data that changes “chronologically.” Because Park’s pressure-mapping sensor is intended for real interactions (pressures/contacts) that inherently vary over time, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Reyes’s explicit spatio-temporal framing (chronological frames/time evolution) into Park’s learning setup to improve inference robustness and/or capture dynamic deformation patterns, a motivation of which would have been to incorporate chronological (time-evolving) sensor information into the learning model’s training/inference pipeline. (Reyes, page 647, col. 1, paragraph 1, “the time component is captured as frames acquired at 256 Hz, thus, providing the time evolution of the spatial component.” )
Claim 2: Park and Reyes teaches the limitations of claim 1, Park further teaches:
The estimation device of claim 1, wherein: the flexible material is a material having an electrical property that changes in response to the deformation, (Park, page 7448, col. 2, section B, paragraph 1, “To implement EIT-based tactile sensors, conductive medium and boundary electrodes are required. In this study, a stretchable piezoresistive fabric (COM-14112, Eeonyx, USA) is used to ensure large-scale manufacturability for whole-body tactile sensing in the future. The resistance of this fabric sensitively changes from normal pressure and lateral stretch.”, Park expressly states that the “resistance” (electrical property) of the fabric “sensitively changes” due to “normal pressure” and “lateral stretch,” both of which are forms of deformation. This directly teaches that the flexible material has an electrical property that changes in response to deformation.)
and the learning model is trained to output shape information corresponding to the detected electrical property. (Park, page 7451, col. 2, section V, paragraph 1, “In this paper, we proposed a novel DNN model to handle nonlinear EIT algorithm. In this study, the dataset including three types of conductivity distribution trained the DNN model for capability of reconstructing arbitrary shape of distribution, and this model improved reconstruction accuracy, computation time, and noise robustness compared to the conventional approaches.”
Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”, Park trains a DNN so that the detected electrical property (“boundary voltage measurements”) is mapped to an output “conductivity distribution.” Park further explains this conductivity distribution “corresponds to the external surface normal pressures,” meaning it corresponds to the spatial pressure pattern/shape imposed on the material. Thus, Park teaches a learning model trained so that detected electrical property measurements correspond to, and generate, output shape/pressure-distribution information.)
Claim 6: Park teaches:
An estimation method, comprising, by a computer: acquiring, from a detection unit that detects an electrical property between a plurality of detection points in a conductive flexible material, the electrical property; (Park, page 7448, col. 2, section B, paragraph 1, “The resistance of this fabric sensitively changes from normal pressure and lateral stretch…. In total, 16 electrodes were attached on the boundary of the piezoresistive fabric.”
Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”, Park describes a sensing arrangement in which a conductive, stretchable/piezoresistive fabric (i.e., a “conductive flexible material”) is provided with “16 electrodes” positioned “on the boundary,” which are discrete “detection points.” Park further explains that the fabric’s “resistance… sensitively changes” under “normal pressure” and “lateral stretch,” confirming an “electrical property” that varies with deformation. Park also expressly measures “boundary voltage measurements” between these electrodes, which constitutes detecting an electrical property between a plurality of detection points in the conductive flexible material as claimed.)
and inputting, to a learning model that has been trained to use, as learning data, an electrical property… that changes in response to deformation of the flexible material, (Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”,
Park, page 7451, col. 2, section D, paragraph 1, “Using the piezoresistive tactile sensor and the electronic equipment, the real boundary voltages were measured. The boundary voltages were applied to the trained DNN model to estimate the conductivity distribution, which corresponds to the external normal pressure.”, Park’s “boundary voltage measurements” are the detected “electrical property” acquired by the electrode-based detection arrangement. Park then discloses an “estimation” (inference) component implemented by a “DNN” that is trained to “estimate the conductivity distribution” from the boundary voltage measurements (input) to an inferred output (conductivity/pressure distribution). Thus, Park teaches an estimation unit that inputs the detected electrical property (boundary voltage measurements) to a trained learning model for inference regarding the state of the material/target. )
and shape information of pressure stimuli that impart deformation to the flexible material, (Park, page 7451, col. 2, section D, paragraph 1, “To show the applicability of the proposed model, the piezoresistive tactile sensor was demonstrated to estimate external normal pressure. Using the piezoresistive tactile sensor and the electronic equipment, the real boundary voltages were measured. The boundary voltages were applied to the trained DNN model to estimate the conductivity distribution, which corresponds to the external normal pressure”,
Park, page 7449, col. 2, paragraph 1, “As shown in Figure 3, the first one is an imposition of conductivity difference on singe element to obtain individual effects of each elements. Next one is a shape of square that contributes on reconstruction of areal distribution. The last one is a selection of random elements. In this method, the random distribution was made as few as possible by inducing each element to be activated the same number of times in all datasets. Different amounts of conductivity differences were given for each distribution to obtain the ability to estimate the magnitude of the force input. As a result, total 76,776 dataset was generated.”, Park’s training framework expressly uses predefined spatial distributions (“preset distribution”) that include a specific area ”whose shape and can be moved, which constitutes “shape information representing shapes … in the flexible material.” Park further ties the conductivity distribution to applied loading by stating the inferred “conductivity distribution … corresponds to the external surface normal pressures,” i.e., pressure stimuli that deform the fabric. Accordingly, Park teaches training that uses learning data including shape-characterizing spatial patterns corresponding to pressure stimuli that impart deformation.)
and to receive the electrical property as input and to output the shape information, (Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”,
Park, page 7449, col. 2, section B, paragraph 2, “The training maps input of 208 voltage measurements to output of n × n conductivity distribution… The output layer represents the reconstructed conductivity and the distribution can be visualized through reshaping”,
Park, page 7449, col. 2, paragraph 1, “As shown in Figure 3, the first one is an imposition of conductivity difference on singe element to obtain individual effects of each elements. Next one is a shape of square that contributes on reconstruction of areal distribution”, Park expressly discloses that the trained DNN receives “boundary voltage measurement” as the model input and produces an output “conductivity distribution” (“mapping boundary voltage measurement to conductivity distribution”), where the conductivity distribution “corresponds to external surface normal pressures,” i.e., the pressure pattern imposed on the tactile fabric. Park further clarifies the input/output relationship structurally: “input of 208 voltage measurements” is mapped to an “output of n × n conductivity distribution,” and the distribution is “visualized through reshaping,” meaning the output is a spatially arranged (image-like) distribution encoding the shape of the pressure/contact region across the material. That spatial output constitutes the claimed “shape information,” and Park’s dataset construction reinforces that the output distribution is shape-representative because it includes explicit geometric patterns such as a “shape of square” used to support “reconstruction of areal distribution,” i.e., reconstruction of a 2D shaped region.)
the acquired electrical property of an estimation target object, (Park, page 7448, col. 2, section B, paragraph 1, “The resistance of this fabric sensitively changes from normal pressure and lateral stretch…. In total, 16 electrodes were attached on the boundary of the piezoresistive fabric.”
Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”, Park explains that the fabric’s “resistance… sensitively changes” under “normal pressure” and “lateral stretch,” confirming an “electrical property” that varies with deformation. Park also expressly measures “boundary voltage measurements” by between these electrodes, which constitutes detecting an electrical property between a plurality of detection points in the conductive flexible material as claimed.)
and estimating shape information of the estimation target object. (Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”,
Park, page 7450, col. 1, section A, paragraph 1, “The reconstruction performance of the proposed DNN model was evaluated using two predefined reference conductivity distributions which are a simple square and a complex geometry… From the forward solving, corresponding boundary voltages were obtained. These boundary voltage was used to reconstruct the conductivity distribution from two different conventional reconstruction methods… As shown in Figure 5 (a) and (c), the proposed model as well as the nonlinear iterative model (PDIPM) successfully reconstructed the reference conductivity distribution compared to the linear model”, Park describes the trained DNN as performing estimation/reconstruction of a spatial distribution from measured electrical data: Park obtains “boundary voltages” and uses them to “reconstruct the conductivity distribution”. For generating a shape, Park evaluates reconstruction on reference distributions having explicit geometric forms e.g. “a simple square and a complex geometry”, and reports that the proposed model “successfully reconstructed the reference conductivity distribution”. Because Park also states the reconstructed conductivity distribution “corresponds to external surface normal pressures,” the reconstructed (estimated) distribution is not merely a scalar pressure value, but a spatial pressure/contact pattern whose geometry (e.g., square vs. complex shape) is the claimed “shape information” of the pressed/loaded target on the material.)
Reyes, in the same field of spatio-temporal feature analysis, teaches the following limitations which Park fails to teach:
an electrical property with which is associated time-series information (Reyes, page 647, col. 1, paragraph 1, “The floor sensor geometry allows capturing the spatial distribution of pressure, while the time component is captured as frames acquired at 256 Hz, thus, providing the time evolution of the spatial component”
Reyes, page 646, col. 2, section C, “iMAGiMAT, an experimental floor sensor system demonstrator for gait analysis [8], was employed to acquire the UoM-Gait13 dataset used for the analysis in this paper. Deformation of plastic optical fibres (POFs) due to applied footstep pressure, modulates their transmission properties and, therefore, the intensity of the transmitted light.”,
Reyes, page 647, col. 2, section 4, paragraph 3, “Each sample contains 116 sensors signals obtained in a time window of 5.4 s (1400 frames at 256 Hz)”,
Reyes, page 647, col. 1, section D.III, “Fig. 2 shows the floor sensor system raw signals in the time domain for four gait experiments. Fig. 2(a) shows the amplitude response over time of the 116 spatial sensor signals of 1 sample of the normal gait experiment. Each signal has a different amplitude response that is determined by the physical characteristics of each POF sensor and the executed gait pattern type.”, Reyes discloses sensor outputs that vary over time as a result of physical loading/deformation: applied footstep pressure causes deformation of plastic optical fibres (POFs)” which “modulates” transmission properties and the sensed intensity, meaning the measured sensor signal changes as deformation/pressure changes. Reyes then explicitly treats those sensor signals as chronological/time-series data: the “time component” is captured as “frames acquired at 256 Hz,” providing “time evolution,” and Reyes visualizes the raw data as “amplitude response over time” with an explicit definition where “t represents the time component in frames”. Thus, Reyes teaches that a measured sensor/electrical output signal (sensor “amplitude response”) changes chronologically (frame-by-frame over time) in response to deformation caused by applied pressure.)
A motivation for combining Park and Reyes would be similar to that as applied for claim 1 above.
Claim 7: Park teaches:
A non-transitory computer-readable medium storing a program for causing a computer to (Reyes, page 651, col. 2, section F.2, “The models in Table III were trained at the CPU level, except for the optimal spatio-temporal RSM model that was trained at the GPU level. Here longer training times are observed for the SVM models with the reconstructed image features. This effect is caused mainly due to the high dimensionality of the feature vector since the execution time is strongly dependent on the number of features and increases in parallel with the memory consumption of each training sample.”, it is interpreted by the examiner that the use of a processing unit (CPU) and memory encompasses the non-transitory computer-readable medium storing a program for causing the computer to perform, as recited in the claim.)
perform processing, the processing comprising: acquiring, from a detection unit that detects an electrical property between a plurality of detection points in a conductive flexible material, the electrical property; (Park, page 7448, col. 2, section B, paragraph 1, “The resistance of this fabric sensitively changes from normal pressure and lateral stretch…. In total, 16 electrodes were attached on the boundary of the piezoresistive fabric.”
Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”, Park describes a sensing arrangement in which a conductive, stretchable/piezoresistive fabric (i.e., a “conductive flexible material”) is provided with “16 electrodes” positioned “on the boundary,” which are discrete “detection points.” Park further explains that the fabric’s “resistance… sensitively changes” under “normal pressure” and “lateral stretch,” confirming an “electrical property” that varies with deformation. Park also expressly measures “boundary voltage measurements” between these electrodes, which constitutes detecting an electrical property between a plurality of detection points in the conductive flexible material as claimed.)
and inputting, to a learning model that has been trained to use, as learning data, an electrical property… that changes in response to deformation of the flexible material, (Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”,
Park, page 7451, col. 2, section D, paragraph 1, “Using the piezoresistive tactile sensor and the electronic equipment, the real boundary voltages were measured. The boundary voltages were applied to the trained DNN model to estimate the conductivity distribution, which corresponds to the external normal pressure.”, Park’s “boundary voltage measurements” are the detected “electrical property” acquired by the electrode-based detection arrangement. Park then discloses an “estimation” (inference) component implemented by a “DNN” that is trained to “estimate the conductivity distribution” from the boundary voltage measurements (input) to an inferred output (conductivity/pressure distribution). Thus, Park teaches an estimation unit that inputs the detected electrical property (boundary voltage measurements) to a trained learning model for inference regarding the state of the material/target. )
and shape information of pressure stimuli that impart deformation to the flexible material, (Park, page 7451, col. 2, section D, paragraph 1, “To show the applicability of the proposed model, the piezoresistive tactile sensor was demonstrated to estimate external normal pressure. Using the piezoresistive tactile sensor and the electronic equipment, the real boundary voltages were measured. The boundary voltages were applied to the trained DNN model to estimate the conductivity distribution, which corresponds to the external normal pressure”,
Park, page 7449, col. 2, paragraph 1, “As shown in Figure 3, the first one is an imposition of conductivity difference on singe element to obtain individual effects of each elements. Next one is a shape of square that contributes on reconstruction of areal distribution. The last one is a selection of random elements. In this method, the random distribution was made as few as possible by inducing each element to be activated the same number of times in all datasets. Different amounts of conductivity differences were given for each distribution to obtain the ability to estimate the magnitude of the force input. As a result, total 76,776 dataset was generated.”, Park’s training framework expressly uses predefined spatial distributions (“preset distribution”) that include a specific area ”whose shape and can be moved, which constitutes “shape information representing shapes … in the flexible material.” Park further ties the conductivity distribution to applied loading by stating the inferred “conductivity distribution … corresponds to the external surface normal pressures,” i.e., pressure stimuli that deform the fabric. Accordingly, Park teaches training that uses learning data including shape-characterizing spatial patterns corresponding to pressure stimuli that impart deformation.)
and to receive the electrical property as input and to output the shape information, (Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”,
Park, page 7449, col. 2, section B, paragraph 2, “The training maps input of 208 voltage measurements to output of n × n conductivity distribution… The output layer represents the reconstructed conductivity and the distribution can be visualized through reshaping”,
Park, page 7449, col. 2, paragraph 1, “As shown in Figure 3, the first one is an imposition of conductivity difference on singe element to obtain individual effects of each elements. Next one is a shape of square that contributes on reconstruction of areal distribution”, Park expressly discloses that the trained DNN receives “boundary voltage measurement” as the model input and produces an output “conductivity distribution” (“mapping boundary voltage measurement to conductivity distribution”), where the conductivity distribution “corresponds to external surface normal pressures,” i.e., the pressure pattern imposed on the tactile fabric. Park further clarifies the input/output relationship structurally: “input of 208 voltage measurements” is mapped to an “output of n × n conductivity distribution,” and the distribution is “visualized through reshaping,” meaning the output is a spatially arranged (image-like) distribution encoding the shape of the pressure/contact region across the material. That spatial output constitutes the claimed “shape information,” and Park’s dataset construction reinforces that the output distribution is shape-representative because it includes explicit geometric patterns such as a “shape of square” used to support “reconstruction of areal distribution,” i.e., reconstruction of a 2D shaped region.)
the acquired electrical property of an estimation target object, (Park, page 7448, col. 2, section B, paragraph 1, “The resistance of this fabric sensitively changes from normal pressure and lateral stretch…. In total, 16 electrodes were attached on the boundary of the piezoresistive fabric.”
Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”, Park explains that the fabric’s “resistance… sensitively changes” under “normal pressure” and “lateral stretch,” confirming an “electrical property” that varies with deformation. Park also expressly measures “boundary voltage measurements” by between these electrodes, which constitutes detecting an electrical property between a plurality of detection points in the conductive flexible material as claimed.)
and estimating shape information of the estimation target object. (Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”,
Park, page 7450, col. 1, section A, paragraph 1, “The reconstruction performance of the proposed DNN model was evaluated using two predefined reference conductivity distributions which are a simple square and a complex geometry… From the forward solving, corresponding boundary voltages were obtained. These boundary voltage was used to reconstruct the conductivity distribution from two different conventional reconstruction methods… As shown in Figure 5 (a) and (c), the proposed model as well as the nonlinear iterative model (PDIPM) successfully reconstructed the reference conductivity distribution compared to the linear model”, Park describes the trained DNN as performing estimation/reconstruction of a spatial distribution from measured electrical data: Park obtains “boundary voltages” and uses them to “reconstruct the conductivity distribution”. For generating a shape, Park evaluates reconstruction on reference distributions having explicit geometric forms e.g. “a simple square and a complex geometry”, and reports that the proposed model “successfully reconstructed the reference conductivity distribution”. Because Park also states the reconstructed conductivity distribution “corresponds to external surface normal pressures,” the reconstructed (estimated) distribution is not merely a scalar pressure value, but a spatial pressure/contact pattern whose geometry (e.g., square vs. complex shape) is the claimed “shape information” of the pressed/loaded target on the material.)
Reyes, in the same field of spatio-temporal feature analysis, teaches the following limitations which Park fails to teach:
an electrical property with which is associated time-series information (Reyes, page 647, col. 1, paragraph 1, “The floor sensor geometry allows capturing the spatial distribution of pressure, while the time component is captured as frames acquired at 256 Hz, thus, providing the time evolution of the spatial component”
Reyes, page 646, col. 2, section C, “iMAGiMAT, an experimental floor sensor system demonstrator for gait analysis [8], was employed to acquire the UoM-Gait13 dataset used for the analysis in this paper. Deformation of plastic optical fibres (POFs) due to applied footstep pressure, modulates their transmission properties and, therefore, the intensity of the transmitted light.”,
Reyes, page 647, col. 2, section 4, paragraph 3, “Each sample contains 116 sensors signals obtained in a time window of 5.4 s (1400 frames at 256 Hz)”,
Reyes, page 647, col. 1, section D.III, “Fig. 2 shows the floor sensor system raw signals in the time domain for four gait experiments. Fig. 2(a) shows the amplitude response over time of the 116 spatial sensor signals of 1 sample of the normal gait experiment. Each signal has a different amplitude response that is determined by the physical characteristics of each POF sensor and the executed gait pattern type.”, Reyes discloses sensor outputs that vary over time as a result of physical loading/deformation: applied footstep pressure causes deformation of plastic optical fibres (POFs)” which “modulates” transmission properties and the sensed intensity, meaning the measured sensor signal changes as deformation/pressure changes. Reyes then explicitly treats those sensor signals as chronological/time-series data: the “time component” is captured as “frames acquired at 256 Hz,” providing “time evolution,” and Reyes visualizes the raw data as “amplitude response over time” with an explicit definition where “t represents the time component in frames”. Thus, Reyes teaches that a measured sensor/electrical output signal (sensor “amplitude response”) changes chronologically (frame-by-frame over time) in response to deformation caused by applied pressure.)
A motivation for combining Park and Reyes would be similar to that as applied for claim 1 above.
Claim 8: Park teaches:
A learning model generation device, comprising: an acquisition unit that acquires, from a detection unit that detects an electrical property between a plurality of detection points in a conductive flexible material, the electrical property, (Park, page 7448, col. 2, section B, paragraph 1, “The resistance of this fabric sensitively changes from normal pressure and lateral stretch…. In total, 16 electrodes were attached on the boundary of the piezoresistive fabric.”
Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”, Park describes a sensing arrangement in which a conductive, stretchable/piezoresistive fabric (i.e., a “conductive flexible material”) is provided with “16 electrodes” positioned “on the boundary,” which are discrete “detection points.” Park further explains that the fabric’s “resistance… sensitively changes” under “normal pressure” and “lateral stretch,” confirming an “electrical property” that varies with deformation. Park also expressly measures “boundary voltage measurements” between these electrodes, which constitutes detecting an electrical property between a plurality of detection points in the conductive flexible material as claimed.)
and acquires shape information of pressure stimuli that impart deformation to the flexible material; (Park, page 7451, col. 2, section D, paragraph 1, “To show the applicability of the proposed model, the piezoresistive tactile sensor was demonstrated to estimate external normal pressure. Using the piezoresistive tactile sensor and the electronic equipment, the real boundary voltages were measured. The boundary voltages were applied to the trained DNN model to estimate the conductivity distribution, which corresponds to the external normal pressure”,
Park, page 7449, col. 2, paragraph 1, “As shown in Figure 3, the first one is an imposition of conductivity difference on singe element to obtain individual effects of each elements. Next one is a shape of square that contributes on reconstruction of areal distribution. The last one is a selection of random elements. In this method, the random distribution was made as few as possible by inducing each element to be activated the same number of times in all datasets. Different amounts of conductivity differences were given for each distribution to obtain the ability to estimate the magnitude of the force input. As a result, total 76,776 dataset was generated.”, Park’s training framework expressly uses predefined spatial distributions (“preset distribution”) that include a specific area ”whose shape and can be moved, which constitutes “shape information representing shapes … in the flexible material.” Park further ties the conductivity distribution to applied loading by stating the inferred “conductivity distribution … corresponds to the external surface normal pressures,” i.e., pressure stimuli that deform the fabric. Accordingly, Park teaches training that uses learning data including shape-characterizing spatial patterns corresponding to pressure stimuli that impart deformation.)
and a learning model generation unit that generates, based on acquisition results of the acquisition unit, a learning model that has been trained to receive, as input, an electrical property… that changes in response to deformation of the flexible material, (Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”,
Park, page 7451, col. 2, section D, paragraph 1, “Using the piezoresistive tactile sensor and the electronic equipment, the real boundary voltages were measured. The boundary voltages were applied to the trained DNN model to estimate the conductivity distribution, which corresponds to the external normal pressure.”, Park’s “boundary voltage measurements” are the detected “electrical property” acquired by the electrode-based detection arrangement. Park then discloses an “estimation” (inference) component implemented by a “DNN” that is trained to “estimate the conductivity distribution” from the boundary voltage measurements (input) to an inferred output (conductivity/pressure distribution). Thus, Park teaches an estimation unit that inputs the detected electrical property (boundary voltage measurements) to a trained learning model for inference regarding the state of the material/target. )
and to output shape information of a target object. (Park, page 7448, col 1, paragraph 3, “The deep neural network is trained from nonlinear EIT dataset and it is used to mapping boundary voltage measurement to conductivity distribution, which corresponds to external surface normal pressures (See Figure 1 (b)).”,
Park, page 7450, col. 1, section A, paragraph 1, “The reconstruction performance of the proposed DNN model was evaluated using two predefined reference conductivity distributions which are a simple square and a complex geometry… From the forward solving, corresponding boundary voltages were obtained. These boundary voltage was used to reconstruct the conductivity distribution from two different conventional reconstruction methods… As shown in Figure 5 (a) and (c), the proposed model as well as the nonlinear iterative model (PDIPM) successfully reconstructed the reference conductivity distribution compared to the linear model”, Park describes the trained DNN as performing estimation/reconstruction of a spatial distribution from measured electrical data: Park obtains “boundary voltages” and uses them to “reconstruct the conductivity distribution”. For generating a shape, Park evaluates reconstruction on reference distributions having explicit geometric forms e.g. “a simple square and a complex geometry”, and reports that the proposed model “successfully reconstructed the reference conductivity distribution”. Because Park also states the reconstructed conductivity distribution “corresponds to external surface normal pressures,” the reconstructed (estimated) distribution is not merely a scalar pressure value, but a spatial pressure/contact pattern whose geometry (e.g., square vs. complex shape) is the claimed “shape information” of the pressed/loaded target on the material.)
Reyes, in the same field of spatio-temporal feature analysis, teaches the following limitations which Park fails to teach:
an electrical property with which is associated time-series information (Reyes, page 647, col. 1, paragraph 1, “The floor sensor geometry allows capturing the spatial distribution of pressure, while the time component is captured as frames acquired at 256 Hz, thus, providing the time evolution of the spatial component”
Reyes, page 646, col. 2, section C, “iMAGiMAT, an experimental floor sensor system demonstrator for gait analysis [8], was employed to acquire the UoM-Gait13 dataset used for the analysis in this paper. Deformation of plastic optical fibres (POFs) due to applied footstep pressure, modulates their transmission properties and, therefore, the intensity of the transmitted light.”,
Reyes, page 647, col. 2, section 4, paragraph 3, “Each sample contains 116 sensors signals obtained in a time window of 5.4 s (1400 frames at 256 Hz)”,
Reyes, page 647, col. 1, section D.III, “Fig. 2 shows the floor sensor system raw signals in the time domain for four gait experiments. Fig. 2(a) shows the amplitude response over time of the 116 spatial sensor signals of 1 sample of the normal gait experiment. Each signal has a different amplitude response that is determined by the physical characteristics of each POF sensor and the executed gait pattern type.”, Reyes discloses sensor outputs that vary over time as a result of physical loading/deformation: applied footstep pressure causes deformation of plastic optical fibres (POFs)” which “modulates” transmission properties and the sensed intensity, meaning the measured sensor signal changes as deformation/pressure changes. Reyes then explicitly treats those sensor signals as chronological/time-series data: the “time component” is captured as “frames acquired at 256 Hz,” providing “time evolution,” and Reyes visualizes the raw data as “amplitude response over time” with an explicit definition where “t represents the time component in frames”. Thus, Reyes teaches that a measured sensor/electrical output signal (sensor “amplitude response”) changes chronologically (frame-by-frame over time) in response to deformation caused by applied pressure.)
A motivation for combining Park and Reyes would be similar to that as applied for claim 1 above.
Claims 3-4 and 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Park in view of Reyes, and in further view of Chen et al., (Lv, B., Chen, X., & Liu, C. (2020). A highly sensitive piezoresistive pressure sensor based on graphene oxide/polypyrrole@ polyurethane sponge. Sensors, 20(4), 1219.), hereafter referred to as Chen.
Claim 3: Park and Reyes teaches the limitations of claim 1, Chen, in the same field of electrical analysis over flexible materials, teaches the following which Park and Reyes fail to teach:
The estimation device of claim 1, wherein the electrical property of the flexible material is volume resistance. (Chen, page 2, section 2.3, paragraph 1, “The resistance of conductive sponges was measured with 124 oscilloscope”,
Chen, page 7, paragraph 4, “The volume resistance and volume parameters of the conductive sponge can be described by the Equation (3):”, Chen identifies the relevant electrical property of the conductive sponge as “volume resistance,”. Chen further discloses actual measurement of this resistance (“The resistance of conductive sponges was measured …”). Accordingly, Chen teaches that the electrical property of the flexible material is “volume resistance.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Park and Reyes by incorporating the teachings of Chen, where Park and Reyes teach using deformation-responsive electrical measurements from a flexible sensing material (and using time-evolving measurements per Reyes), and Chen teaches characterizing the resistive electrical property of a deformable conductive sponge specifically as “volume resistance” defined using volume parameters like thickness and cross-sectional area. Incorporating Chen’s volume-resistance teaching into Park/Reyes would have predictably provided a well-defined bulk resistance metric that directly tracks deformation-induced geometric changes (e.g., thickness change under compression), thereby improving calibration/normalization and repeatability of the electrical property used as the learning model input over time, a motivation of which would have been to use a geometry-aware volume-resistance formulation so the electrical property of the flexible material more consistently reflects deformation across measurements and over time. (Chen, page 7, paragraph 4, “The volume resistance and volume parameters of the conductive sponge can be described by the Equation (3):
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”)
Claim 4: Park and Reyes teaches the limitations of claim 1, Chen, in the same field of electrical analysis over flexible materials, teaches the following which Park and Reyes fail to teach:
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Figure 2 of Chen
The estimation device of claim 1, wherein the flexible material is a material in which conductivity is imparted to a urethane material having a structure having a fibrous skeleton or a structure having a plurality of microscopic air bubbles scattered inside. (Chen, abstract, “In this work, polyurethane sponge is employed as the structural substrate of the sensor.”, Chen teaches the claimed conductive urethane material with the recited skeleton/porous internal structure as shown above in figure 2.)
The motivation for combining Park/Reyes with Chen is similar to that as applied for claim 3 above.
Claim 9 recite limitations substantially similar to claim 3, as such a similar analysis applies.
Claims 10-12 recite limitations substantially similar to claim 4, as such a similar analysis applies.
Claims 5 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Park in view of Reyes, and in further view of Nakajima et al., (Nakajima, K., Hauser, H., Li, T., & Pfeifer, R. (2015). Information processing via physical soft body. Scientific reports, 5(1), 10487.), hereafter referred to as Nakajima.
Claim 5: Park and Reyes teaches the limitations of claim 1, Nakajima, in the same field of electrical analysis over flexible materials, teaches the following which Park and Reyes fail to teach:
The estimation device of claim 1, wherein the learning model is a model generated by training the model using, with the flexible material as a reservoir, a network obtained by reservoir computing using the reservoir. (Nakajima, figure 1, “Platform setup for a soft silicone arm and schematics showing the information processing scheme using the arm… Schematics expressing an analogy between a conventional reservoir computing system and our system. In a conventional reservoir system, randomly coupled abstract computational units are used for the reservoir, whereas our system exploits a physical reservoir whose units are sensors that are coupled through a soft silicone material.”,
Nakajima, page 2, paragraph 4, “By generating passive body dynamics resulting from the interaction between the water and the soft silicone material29,30, we will show that the sensory time series reflected in the body dynamics can be used to emulate the desired nonlinear dynamical systems, which are often targeted with a recurrent neural network learning or reservoir computing approach… For this purpose, we first need to define how to provide inputs I t( ) to the system and how to generate corresponding outputs O t( + 1). In this study, we apply the motor command as an input, and the output is generated by a weighted sum of the 10 sensory values and a constant valued bias set to 1.0 Fig. 1(a)”, Nakajima explicitly frames the disclosed system as “reservoir computing,” where the system “exploits a physical reservoir that are coupled through a soft silicone material” (a flexible body serving as the reservoir) training/deriving the readout by collecting “target outputs over time,” and generating outputs via a “weighted sum of the sensory values,” which is interpreted by the examiner to be reservoir-computing approach (fixed reservoir dynamics + trained network output). Therefore, Nakajima teaches generating a learning model by training a reservoir-computing network that uses a flexible physical material as the reservoir. )
Park uses a learned mapping from electrical measurements to a spatial distribution, and Reyes emphasizes spatio-temporal representations and time evolution of sensor signals; Nakajima teaches a reservoir-computing paradigm that explicitly leverages the dynamics of a flexible physical body as a “physical reservoir” and trains based on time-varying sensor outputs. Because Park/Reyes involve time-varying deformation signals and require mapping those signals to output patterns, it would have been obvious to implement the learning model using Nakajima’s reservoir-computing approach (physical reservoir + trained readout) to exploit the body’s inherent dynamics for computation and memory, a motivation of which would have been to use the flexible material’s dynamics as a reservoir to efficiently learn from time-varying deformation signals. (Nakajima, page 3, figure 1, “our system exploits a physical reservoir whose units are sensors that are coupled through a soft silicone material.”)
Claims 13-19 recite limitations substantially similar to claim 5, as such a similar analysis applies.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Yao, A., Yang, C. L., Seo, J. K., & Soleimani, M. (2013). EIT‐Based Fabric Pressure Sensing. Computational and mathematical methods in medicine, 2013(1), 405325.
Duan, X., Taurand, S., & Soleimani, M. (2019). Artificial skin through super-sensing method and electrical impedance data from conductive fabric with aid of deep learning. Scientific reports, 9(1), 8831.
Liu, S., Cao, R., Huang, Y., Ouypornkochagorn, T., & Jia, J. (2020). Time sequence learning for electrical impedance tomography using Bayesian spatiotemporal priors. IEEE Transactions on Instrumentation and Measurement, 69(9), 6045-6057.
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/H.B.Y./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146