DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/18/2023 and 10/20/2023 has/have been considered by the examiner.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it has phrases “In one embodiment … comprising”. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 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 system … configured to …” in claim 1, lines 1-2.
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 § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 5, 7-8, 11-12, 15, 17-18, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lahivaara et al (arXiv:2009.05297v1 2020), hereinafter Lahivaara.
-Regarding claim 1, Lahivaara discloses a system, comprising (Abstract; FIGS. 1-12): a neural network, configured to (FIG. 7): receive electromagnetic field measurement data from an object of interest as input to the neural network (FIG. 7; Page 4, Sec. 3., 1st paragraph; Page 5, 4th paragraph, “the electric field measurements are simulated at 3 GHz frequency and are measured in terms of complex-valued S-parameter data”; Page 8, Sec. 4., 1st paragraph), the neural network trained on labeled data (Abstract, “a database containing different moisture content distribution scenarios and corresponding electromagnetic wave responses are build and used to train the machine learning algorithm”; Page 2, 4th paragraph; Page 10, 1st paragraph, “trained using a dataset comprising of moisture content distribution … and corresponding S-parameters …”); and reconstruct a three-dimensional (3D) distribution image of a physical property of the object of interest from the received electromagnetic field measurement data (Abstract; FIGS. 5, 7; Page 2, 2nd paragraph, “three-dimensional (3D) microwave tomography (MWT) system is proposed … estimating the material properties of an object from the measured data of scattered electromagnetic field around the object”; equations (2)-(5); Procedure 1), the reconstruction implemented without performing a forward solve during the reconstruction (Abstract, “a neural network based approach”; Page 2, 4th paragraph, “real-time estimation … recover the moisture content in real-time”).
-Regarding claim 11, Lahivaara discloses a method, comprising (Abstract; FIGS. 1-12): a neural network, configured to (FIG. 7): receiving electromagnetic field measurement data from an object of interest as input to the neural network (FIG. 7; Page 4, Sec. 3., 1st paragraph; Page 5, 4th paragraph, “the electric field measurements are simulated at 3 GHz frequency and are measured in terms of complex-valued S-parameter data”; Page 8, Sec. 4., 1st paragraph), the neural network trained on labeled data (Abstract, “a database containing different moisture content distribution scenarios and corresponding electromagnetic wave responses are build and used to train the machine learning algorithm”; Page 2, 4th paragraph; Page 10, 1st paragraph, “trained using a dataset comprising of moisture content distribution … and corresponding S-parameters …”); and reconstructing a three-dimensional (3D) distribution image of a physical property of the object of interest from the received electromagnetic field measurement data (Abstract; FIGS. 5, 7; Page 2, 2nd paragraph, “three-dimensional (3D) microwave tomography (MWT) system is proposed … estimating the material properties of an object from the measured data of scattered electromagnetic field around the object”; equations (2)-(5); Procedure 1), the reconstruction implemented without performing a forward solve during the reconstruction (Abstract, “a neural network based approach”; Page 2, 4th paragraph, “real-time estimation … recover the moisture content in real-time”).
-Regarding claim 20, Lahivaara discloses a non-transitory, computer readable medium comprising instructions, that when executed by one or more processors, causes the one or more processors to (Abstract; FIGS. 1-12; one or more memory and processors has to be used in order to implement the method as shown in Lahivaara’s FIG. 7, equations (2)-(5) and procedure 1): a neural network, configured to (FIG. 7): receive electromagnetic field measurement data from an object of interest as input to the neural network (FIG. 7; Page 4, Sec. 3., 1st paragraph; Page 5, 4th paragraph, “the electric field measurements are simulated at 3 GHz frequency and are measured in terms of complex-valued S-parameter data”; Page 8, Sec. 4., 1st paragraph), the neural network trained on labeled data (Abstract, “a database containing different moisture content distribution scenarios and corresponding electromagnetic wave responses are build and used to train the machine learning algorithm”; Page 2, 4th paragraph; Page 10, 1st paragraph, “trained using a dataset comprising of moisture content distribution … and corresponding S-parameters …”); and reconstruct a three-dimensional (3D) distribution image of a physical property of the object of interest from the received electromagnetic field measurement data (Abstract; FIGS. 5, 7; Page 2, 2nd paragraph, “three-dimensional (3D) microwave tomography (MWT) system is proposed … estimating the material properties of an object from the measured data of scattered electromagnetic field around the object”; equations (2)-(5); Procedure 1), the reconstruction implemented without performing a forward solve during the reconstruction (Abstract, “a neural network based approach”; Page 2, 4th paragraph, “real-time estimation … recover the moisture content in real-time”).
-Regarding claim 2 and 12, Lahivaara discloses the system of claim 1 and the method of claim 11. Lahivaara further discloses wherein the object of interest comprises contents within a container (FIG. 1; Page 3, last paragraph, “ … the moisture content present in the polymer foam …”; Page 16, 5th paragraph).
-Regarding claim 5 and 15, Lahivaara discloses the system of claim 1 and the method of claim 11. Lahivaara further discloses wherein the labeled data comprises only synthetic data (Abstract; Page 2, 5th paragraph; Page 4, Sec. 3., 1st paragraph).
-Regarding claim 7 and 17, Lahivaara discloses the system of claim 1 and the method of claim 11. Lahivaara further discloses wherein the neural network is trained based on a plurality of forward solves (FIG. 2; Page 2, 4th paragraph; Page 4, Sec. 3.; Page 5, 4th paragraph; Page 16, 5th paragraph).
-Regarding claim 8 and 18, Lahivaara discloses the system of claim 1 and the method of claim 11. Lahivaara further discloses wherein the neural network comprises a two-stage convolutional decoder, wherein a first stage comprises a stack of fully connected layers configured to transform inputted scattered field data to a 3D distribution image of the physical property, wherein a second stage comprises successive deconvolutional and up-sampling layers configured to provide a reconstructed 3D volume of the physical property (FIG. 7). Note: using decoder architecture with fully connected layers and up-sampling layers are common practice for 3D reconstruction with deep neural network (see Han et al (arXiv:1906.0654 2019): Sec. 4.2.; Table 4; Kavasidis et al (MM’17 2017): Sec.3.; FIG. 5). The recited claim limitations are not inventive concepts.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3-4, 6, 13-14, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lahivaara et al (arXiv:2009.05297v1 2020), hereinafter Lahivaara in view of Bartley et al (IEEE Transactions on Instrumentation and Measurement, Vol 7, Issue 1, pp. 123-126, 1998), hereinafter Bartley.
-Regarding claim 3 and 13, Lahivaara discloses the system of claim 2 and the method of claim 12.
Lahivaara does not disclose wherein the contents comprises grain.
In the same field of endeavor, Bartley teaches a method to determine the moisture content of hard, red winter wheat using an artificial neural network (ANN) (Bartley: Abstract; FIGS. 1-4). Bartley further teaches wherein the contents comprises grain (Bartley: Abstract; Sec. I.) .
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Lahivaara with the teaching of Bartley by using neural network based approach for estimating moisture content of wheat in real-time in order to provide another real-world application.
-Regarding claim 4 and 14, Lahivaara in view of Bartley teaches the system of claim 3 and the method of claim 13. The combination further teaches wherein the neural network is configured to implement the reconstruction without reconstructing an image of a complex valued permittivity of the grain (Lahivaara: Abstract; FIGS. 5, 7-12).
-Regarding claim 6 and 16, Lahivaara discloses the system of claim 1 and the method of claim 11.
Lahivaara does not disclose wherein the labeled data comprises measured data.
In the same field of endeavor, Bartley teaches a method to determine the moisture content of hard, red winter wheat using an artificial neural network (ANN) (Bartley: Abstract; FIGS. 1-4). Bartley further teaches wherein the labeled data comprises measured data (Bartley: Abstract; Sec. II.; FIG. 1; Page 124, 2nd Col., 1st paragraph). Note: It is a common practice to train neural networks using measure data, or synthetic data (data augmentation cab be used for synthetic data generation based on measured data), or combination of the two. The recited claim limitation is not an inventive concept.
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Lahivaara with the teaching of Bartley by using both measured data and synthetic data for neural network training in order to achieve better performance with limited available measured data.
Allowable Subject Matter
Claims 9-10 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/XIAO LIU/Primary Examiner, Art Unit 2664