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 06/10/2024, 06/26/2025 and 10/02/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because the following drawing are blurry: Fig. 2, 6, Fig. 7 (a-b), Fig. 9, and Fig. 12. See screenshot of drawing below as their appear in the specification of the instant application for examiner to review.
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Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The use of the terms “Fitbit, Jawbone Up, Nike+ FuelBand, Apple Watch, Samsung Gear,” in paragraph [0058] of the specification, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
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 first learning unit determining…” in claim 1 & 9 line 3.
“a second learning unit updating…” in claim 1 & 9 line 6.
“an inference unit acquiring…” in claim 9 line 9.
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
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.
Claims 1-8, and 10-17 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.
Independent claim 1 recites the limitation “the device” in line 7. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, examiner is interpreting the limitations as “a device”.
Independent claim 10 recites similar limitation line 6 as claim 1. Thus, it is rejected for reasons set forth in the rejection of claim 1.
Claims 2-8 and 11-17 are dependent on claim 1 and 10 and thus are rejected for reasons set forth in the rejection of claims 1 and 10.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Yao et al. Fully hardware-implemented memristor convolutional neural network as cited in the information disclosure statement (IDS) dated 10/02/2025 in further view of Lee et al. US 11,321,589 B2 (hereinafter Lee).
Regarding claim 1:
Yao discloses A learning apparatus for implementing an edge device using a resistive element, comprising: ( Yao Abstract teaches “Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing”).
a first learning [stage] determining a weight of an artificial neural network through learning based on first training data and reflecting the determined weight in a first resistive element; and (Yao pg. 644, left col., para. 2 and Fig. 3 (a) teaches “First a CNN model is trained ex situ, and then all the determined weights are transferred to the memristor PEs by a closed loop writing method”. That is, Yao discloses a first learning stage where the weights are determined through ex-situ training using a training dataset of input images as shown in Fig. 4 and the determined weights are then transferred to the memristor processing element (i.e., first resistive element). In particular, Fig. 3(b) “Diagram of the experimental mCNN demonstration with hybrid training” teaches the “Weigh of convolutional layers” are subject to “weight transfer” and are unchanged. Therefore, a specific hardware resistive element such as the memristor implementing the convolutional layers has its weight set based on initial ex-situ training and it remains unchanged, thereby functioning as the first resistive element).
a second learning [stage] updating the weight of the artificial neural network through learning based on training data collected and reflecting the updated weight in a second resistive element (Yao pg. 644, left col., para. 3, teaches training data is fetched/collected to realize in-situ training ( i.e., a second learning) and Yao pg. 644, left col., para. 2 and Fig. 3 (a-e) teaches in the second learning stage, “the external input propagates forwards through the mCNN, and only the last fully connected (FC) layer is trained in-situ afterwards to tune the memristor conductance”, that is “the system maintains the kernel weights unchanged and updates only the FC weights through in situ training”. In particular, Fig. 3(b) “Diagram of the experimental mCNN demonstration with hybrid training” teaches the “weight of FC layers” are subject to “Weight update”, thus it teaches different subset of the hardware resistive element such as the memristors implementing the FC layer is used for weight update, thereby functioning as the second resistive element).
Yao does not explicitly teach a learning units and secondary training data collected through the device.
Nonetheless Lee teaches the following:
a first learning unit determining a weight of an artificial neural network through learning based on first training data... ( Lee Fig. 1 element 110 teaches a “primary learning unit” and col. 5: 62-65 teaches the primary learning unit input training data (i.e., first training data) to a deep learning model in order to acquire “temporary weight”).
a second learning unit updating the weight of the artificial neural network through learning based on second training data collected through the device... ( Lee Fig. 1 element 120 teaches a “secondary learning unit” and col. 6: 4-12, teaches the secondary learning unit input secondary training data to the deep learning model in order to obtain output data which is used to update the weights. To add, Lee discloses “secondary training data” is data collected by processing a given MRI image ( see Fig. 1 & col. 5: 25-50). A person skilled in the relevant art will recognize that Magnetic resonance imaging (MRI images) are generated via a medical device called MRI scanner, thus the “secondary training data” is generated by data collected through the device).
Lee is also in the same field of endeavor as Yao (machine learning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of first and second training data as being disclosed and taught by Lee, in the system taught by Yao to yield the predictable results of improving segmentation performance for new image environments and related diseases by quickly and accurately learning characteristics that are not learned in a medical image on the basis of meta learning and active learning techniques (see Lee [0026]).
Regarding claim 10:
Yao discloses A learning method for implementing an edge device using a resistive element, comprising: ( Yao Abstract teaches “Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing”).
determining a weight of an artificial neural network through learning based on first training data and reflecting the determined weight in a first resistive element; and (Yao pg. 644, left col., para. 2 and Fig. 3 (a) teaches “First a CNN model is trained ex situ, and then all the determined weights are transferred to the memristor PEs by a closed loop writing method”. That is, Yao discloses a first learning stage where the weights are determined through ex-situ training using a training dataset of input images as shown in Fig. 4 and the determined weights are then transferred to the memristor processing element (i.e., first resistive element). In particular, Fig. 3(b) “Diagram of the experimental mCNN demonstration with hybrid training” teaches the “Weigh of convolutional layers” are subject to “weight transfer” and are unchanged. Therefore, a specific hardware resistive element such as the memristor implementing the convolutional layers has its weight set based on initial ex-situ training and it remains unchanged, thereby functioning as the first resistive element).
updating the weight of the artificial neural network through learning based on second training data collected and reflecting the updated weight in the second resistive element (Yao pg. 644, left col., para. 3, teaches training data is fetched/collected to realize in-situ training ( i.e., a second learning) and Yao pg. 644, left col., para. 2 and Fig. 3 (a-e) teaches in the second learning stage, “the external input propagates forwards through the mCNN, and only the last fully connected (FC) layer is trained in-situ afterwards to tune the memristor conductance”, that is “the system maintains the kernel weights unchanged and updates only the FC weights through in situ training”. In particular, Fig. 3(b) “Diagram of the experimental mCNN demonstration with hybrid training” teaches the “weight of FC layers” are subject to “Weight update”, thus it teaches different subset of the hardware resistive element such as the memristors implementing the FC layer is used for weight update, thereby functioning as the second resistive element).
Yao does not explicitly teaches collecting a “second training data” through a device.
Nonetheless Lee teaches the following:
...second training data collected through the device... (Lee discloses “secondary training data” is data collected by processing a given MRI image ( see Fig. 1 & col. 5: 25-50). A person skilled in the relevant art will recognize that Magnetic resonance imaging (MRI images) are generated via a medical device called MRI scanner, thus the “secondary training data” is generated by data collected through the device).
Lee is also in the same field of endeavor as Yao (machine learning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of second training data collected through a device as being disclosed and taught by Lee, in the system taught by Yao to yield the predictable results of improving segmentation performance for new image environments and related diseases by quickly and accurately learning characteristics that are not learned in a medical image on the basis of meta learning and active learning techniques (see Lee [0026]).
Claims 2-6 and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Yao, Lee in further view of Choi et al. KR 2021/0157049 A (hereinafter Choi) as disclosed in the information disclosure statement (IDS) dated 06/10/2024.
Regarding claim 2:
Yao and Lee teach The learning apparatus of claim 1.
Neither Yao or Lee explicitly disclose: wherein the first resistive element includes a fixed resistive element, and the second resistive element includes a programmable non-volatile memory device.
Nonetheless, Choi teaches the following:
wherein the first resistive element includes a fixed resistive element, and ( Choi [0009] teaches the first resistive element includes a fixed resistive element such as a “metal oxide”).
the second resistive element includes a programmable non-volatile memory device ( A person skilled in the relevant art will recognize a RRAM crossbar array functions as a programmable non-volatile memory device for which Choi Fig. 2 and [0036] teaches “the present invention includes a resistive change element array having a cross structure”, that is the resistive change element includes a RRAM crossbar array which functions a programmable non-volatile memory device).
Choi is also in the same field of endeavor as Yao and Lee (machine learning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of resistive element including a fixed resistive element and a programmable non-volatile memory device as being disclosed and taught by Choi, in the system taught by Yao and Lee to yield the predictable results of “provide a neural network using a weighted synapse based on a resistance variable memory array that can maximize energy efficiency and accuracy improvement during artificial neural network hardware learning and inference” ( see Choi [0005]).
Regarding claim 3:
Yao and Lee teach The learning apparatus of claim 1.
Neither Yao or Lee explicitly disclose: wherein the first learning unit includes a plurality of first resistive elements arranged in an array form, and the second learning unit includes a plurality of second resistive elements arranged in the array form.
However, Choi teaches the following:
wherein the first learning unit includes a plurality of first resistive elements arranged in an array form, and ( Choi Fig. 3 and [0006] teaches a “neural network using a weighted synapse based on a resistance variable memory array” includes a “first resistive change element array including a first resistive change element”).
the second learning unit includes a plurality of second resistive elements arranged in the array form ( Choi Fig. 3 and [0011] teaches “a neural network using a weighted synapse based on a resistance variable memory array” includes “a second resistive change element array including... a second resistive change element”).
Regarding claim 4:
Yao, Lee and Choi teach The learning apparatus of claim 2. Choi explicitly teaches wherein at least one of a width, a length, and a cross-sectional area of the fixed resistive element is determined based on a size of the determined weight ( Examiner will like to emphasize, the claim as presented recites “wherein at least one of a width, a length, and a cross-sectional area of the fixed resistive element is determined based on a size of the determined weight” (emphasis added). For which Choi teaches wherein the weights are proportional to the area of the cross-section of a resistance change element (see Choi [0006]).
Regarding claim 5:
Yao, Lee and Choi teach The learning apparatus of claim 2. Choi explicitly teaches wherein the fixed resistive element includes a metal oxide layer or is formed of a material selected from the group consisting of Si, Ni, Cr, Al, Ta, and combinations thereof (Examiner will like to emphasize, the claim as presented recites “the fixed resistive element includes a metal oxide layer or is formed of a material selected from the group...” (emphasis added) for which Choi [0009] teaches the first resistance change element (i.e., fixed resistive element) includes a metal oxide).
Regarding claim 6:
Yao, Lee and Choi teach The learning apparatus of claim 2. Choi explicitly teaches wherein the programmable non-volatile memory device includes at least one of a NAND flash memory device, a NOR flash memory device, a phase-change random access memory (PCRAM) device, a resistive random-access memory (RRAM) device, a ferroelectric random access memory (FRAM) device, and a magnetic random access memory (MRAM) device ( A person skilled in the relevant art will recognize a RRAM crossbar array functions as a programmable non-volatile memory device for which Choi Fig. 2 and [0036] teaches “the present invention includes a resistive change element array having a cross structure”, that is the resistive change element includes a RRAM crossbar array which functions a programmable non-volatile memory device).
Regarding claim 11:
Yao and Lee teach The learning method of claim 10. Neither Yao or Lee explicitly teach wherein the first resistive element includes a fixed resistive element, and the second resistive element includes a programmable non-volatile memory device.
Nonetheless, Choi teaches the following:
wherein the first resistive element includes a fixed resistive element, and ( Choi [0009] teaches the first resistive element includes a fixed resistive element such as a “metal oxide”).
the second resistive element includes a programmable non-volatile memory device( A person skilled in the relevant art will recognize a RRAM crossbar array functions as a programmable non-volatile memory device for which Choi Fig. 2 and [0036] teaches “the present invention includes a resistive change element array having a cross structure”, that is the resistive change element includes a RRAM crossbar array which functions a programmable non-volatile memory device).
Regarding claim 12:
Yao and Lee teach The learning method of claim 10. Neither Yao or Lee explicitly teach wherein the first resistive element is provided in plural pieces arranged in an array form, and the second resistive element is provided in plural pieces arranged in the array form.
Nevertheless, Choi teaches the following:
wherein the first resistive element is provided in plural pieces arranged in an array form, and the second resistive element is provided in plural pieces arranged in the array form ( Choi Fig. 3 teaches a first and second resistance change element (i.e., first and second resistive element) provided in plural pieces (dashed boxes in Fig. 3) arranged in an array form. See annotated fig. 3 below.
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Regarding claim 13: is a learning method claim comprising limitations similar to those of claim 4, therefore is rejected under the same rationality as claim 4.
Regarding claim 14: is a learning method claim comprising limitations similar to those of claim 5 , therefore is rejected under the same rationality as claim 5.
Regarding claim 15: is a learning method claim comprising limitations similar to those of claim 6 , therefore is rejected under the same rationality as claim 6.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable Yao, Lee, Choi in further view of Moon et al. US 20080191261 A1 (hereinafter Moon).
Regarding claim 7:
Yao, Lee and Choi teach The learning apparatus of claim 6.
While Choi teaches a RRAM device Fig. 2. Neither Yao, Lee or Choi teaches wherein the RRAM device has a structure in which a first metal layer, an insulator layer, and a second metal layer are vertically stacked.
However, Moon discloses the following:
wherein the RRAM device has a structure in which a first metal layer, an insulator layer, and a second metal layer are vertically stacked ( Moon Abstract, paragraph [0010] and Fig. 1 teaches “RRAM may include an upper metal layer, a lower metal layer, and/or an insulating layer (a resistance layer) therebetween” are vertical stacked).
Moon is analogous in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the affective filling date of the claimed invention to have combined Moon, Yao, Lee and Choi to incorporate the teaching of a RRAM device with two metal layer and an insulation layer in stacked form . The motivation to do so is that Moon provides the architecture structure of the RRAM device which Yao, Lee and Choi requires to perform learning.
Regarding claim 16: is a learning method claim comprising limitations similar to those of claim 7 , therefore is rejected under the same rationality as claim 7.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Yao, Lee in further view of Villongco US 11,547,369 B2.
Regarding claim 8:
Yao and Lee teach The learning apparatus of claim 1. While Lee teaches the data used for training may be one of various kinds of medical data ( see Lee col. 4:35-44). Neither Yao or Lee discloses wherein the first training data and the second training data are electrocardiogram signal data, and the artificial neural network is trained to perform inference to classify characteristics of analysis target data, which is the electrocardiogram signal data collected through the device, into a preset category.
Nevertheless, Villongco teaches the following:
wherein the first training data and the second training data are electrocardiogram signal data, and ( Villongco col. 4: 26-31 teaches “generating data representing electromagnetic states ( e.g., normal sinus rhythm versus ventricular fibrillation) of an electromagnetic source ( e.g., a heart) within a body for various purposes such as medical, scientific, research, and engineering purposes”. Further, claim 63 teaches “the first training data generated based on running simulations of electrical activity of a heart, the first training data having simulated cardiograms generated from the simulations of electrical activity... the second training data having cardiograms” thus the first and second training data are cardiograms such as an electrocardiogram (see col. 45: 2)).
the artificial neural network is trained to perform inference to classify characteristics of analysis target data, which is the electrocardiogram signal data collected through the device, into a preset category (Villongco claim 1 teaches applying a patient classifier (i.e., artificial neural network) to the received patient derived electromagnetic data (i.e., the electrocardiogram signal data collected through the device) to generate a patient classification for the target patient (i.e., to perform inference to classify characteristics of analysis target data into a preset category)).
Villongco is also in the same field of endeavor as Tao and Lee (machine learning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of training data including electrocardiogram signal data and a classifier to perform inference based on target data , as being disclosed and taught by Villongco, in the system taught by Yao and Lee to yield the predictable results of “use the modeled electromagnetic output for various purposes such as identifying locations of disorders within the electromagnetic source of a patient's body, guiding a procedure to repair (e.g., directed gene therapy) or modify (e.g., ablate) the electromagnetic source, predicting results of a procedure on the electromagnetic source, analyzing genetic defects, and so on” (see Villongco col. 4: 46-52).
Regarding claim 17: is a learning method claim comprising limitations similar to those of claim 8 , therefore is rejected under the same rationality as claim 8.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yao, Lee in further view of Iino et al. US 12,340,574 B2 (hereinafter Iino).
Regarding claim 9:
Yao discloses An analysis apparatus based on an artificial neural network using a resistive element, comprising: ( Yao Abstract teaches “Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing”).
a first learning [stage] determining a weight of the artificial neural network through learning based on first training data and reflecting the determined weight in a first resistive element; (Yao pg. 644, left col., para. 2 and Fig. 3 (a) teaches “First a CNN model is trained ex situ, and then all the determined weights are transferred to the memristor PEs by a closed loop writing method”. That is, Yao discloses a first learning stage where the weights are determined through ex-situ training using a training dataset of input images as shown in Fig. 4 and the determined weights are then transferred to the memristor processing element (i.e., first resistive element). In particular, Fig. 3(b) “Diagram of the experimental mCNN demonstration with hybrid training” teaches the “Weigh of convolutional layers” are subject to “weight transfer” and are unchanged. Therefore, a specific hardware resistive element such as the memristor implementing the convolutional layers has its weight set based on initial ex-situ training and it remains unchanged, thereby functioning as the first resistive element).
a second learning [stage] updating the weight of the artificial neural network through learning based on second training data collected and reflecting the updated weight in a second resistive element; and (Yao pg. 644, left col., para. 3, teaches training data is fetched/collected to realize in-situ training ( i.e., a second learning) and Yao pg. 644, left col., para. 2 and Fig. 3 (a-e) teaches in the second learning stage, “the external input propagates forwards through the mCNN, and only the last fully connected (FC) layer is trained in-situ afterwards to tune the memristor conductance”, that is “the system maintains the kernel weights unchanged and updates only the FC weights through in situ training”. In particular, Fig. 3(b) “Diagram of the experimental mCNN demonstration with hybrid training” teaches the “weight of FC layers” are subject to “Weight update”, thus it teaches different subset of the hardware resistive element such as the memristors implementing the FC layer is used for weight update, thereby functioning as the second resistive element).
Yao does not explicitly teach a learning units and secondary training data collected through the device and an inference unit acquiring analysis target data using the device and performing inference on the analysis target data based on the artificial neural network.
Nonetheless Lee teaches the following:
a first learning unit determining a weight of an artificial neural network through learning based on first training data... ( Lee Fig. 1 element 110 teaches a “primary learning unit” and col. 5: 62-65 teaches the primary learning unit input training data (i.e., first training data) to a deep learning model in order to acquire “temporary weight”).
a second learning unit updating the weight of the artificial neural network through learning based on second training data collected through the device... ( Lee Fig. 1 element 120 teaches a “secondary learning unit” and col. 6: 4-12, teaches the secondary learning unit input secondary training data to the deep learning model in order to obtain output data which is used to update the weights. To add, Lee discloses “secondary training data” is data collected by processing a given MRI image ( see Fig. 1 & col. 5: 25-50). A person skilled in the relevant art will recognize that Magnetic resonance imaging (MRI images) are generated via a medical device called MRI scanner, thus the “secondary training data” is generated by data collected through the device).
Lee is also in the same field of endeavor as Yao (machine learning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of first and second training data as being disclosed and taught by Lee, in the system taught by Yao to yield the predictable results of improving segmentation performance for new image environments and related diseases by quickly and accurately learning characteristics that are not learned in a medical image on the basis of meta learning and active learning techniques (see Lee [0026]).
Neither Yao and Lee teaches an inference unit acquiring analysis target data using the device and performing inference on the analysis target data based on the artificial neural network.
Nonetheless, Iino teaches the following:
an inference unit acquiring analysis target data using the device and performing inference on the analysis target data based on the artificial neural network ( Iino Abstract teaches “utilizing-side inference unit that uses a utilizing-side inference network to perform inference from target data”).
Iino is also in the same field of endeavor as Yao and Lee (machine learning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of inference unit as being disclosed and taught by Iino in the system taught by Yao and Lee to yield the predictable results of “improve recognition accuracy by using data the correct answer of which is unknown” (see Iino col. 4 sec: Effect of the Invention).
Regarding claim 18:
Yao discloses An analysis method based on an artificial neural network using a resistive element, comprising: ( Yao Abstract teaches “Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing”).
determining a weight of an artificial neural network through learning based on first training data and reflecting the determined weight in a first resistive element; and (Yao pg. 644, left col., para. 2 and Fig. 3 (a) teaches “First a CNN model is trained ex situ, and then all the determined weights are transferred to the memristor PEs by a closed-loop writing method”, that is a first learning stage where weights are determined through ex-situ (offline) training using a training dataset of input images (see Fig. 4) and the determined weights are then transferred to the memristor PEs (i.e., first resistive element)).
updating the weight of the artificial neural network through learning based on training data collected and reflecting the updated weight in the second resistive element (Yao pg. 644, left col., para. 3, teaches training data is fetched/collected to realize in-situ training ( i.e., a second learning) and Yao pg. 644, left col., para. 2 and Fig. 3 (a-e) teaches in the second learning stage, “the external input propagates forwards through the mCNN, and only the last fully connected (FC) layer is trained in-situ afterwards to tune the memristor conductance”, that is “the system maintains the kernel weights unchanged and updates only the FC weights through in situ training”. Fig. 3(b) further teaches, the “weight of FC layers” are subject to “Weight update”, thus it teaches different subset of the hardware resistive element ( the memristors implementing the FC layer) is used for weight update, thereby functioning as the second resistive element).
Yao does not explicitly teaches collecting a “second training data” through a device.
Nonetheless Lee teaches the following:
...second training data collected through the device... (Lee discloses “secondary training data” is data collected by processing a given MRI image ( see Fig. 1 & col. 5: 25-50). A person skilled in the relevant art will recognize that Magnetic resonance imaging (MRI images) are generated via a medical device called MRI scanner, thus the “secondary training data” is generated by data collected through the device).
Lee is also in the same field of endeavor as Yao (machine learning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of second training data collected through the device as being disclosed and taught by Lee, in the system taught by Yao to yield the predictable results of improving segmentation performance for new image environments and related diseases by quickly and accurately learning characteristics that are not learned in a medical image on the basis of meta learning and active learning techniques (see Lee [0026]).
Neither Yao and Lee teaches acquiring analysis target data using the device and performing inference on the analysis target data based on the artificial neural network.
Nonetheless, Iino teaches the following:
acquiring analysis target data using the device and performing inference on the analysis target data based on the artificial neural network ( Iino Abstract teaches “utilizing-side inference unit that uses a utilizing-side inference network to perform inference from target data”).
Iino is also in the same field of endeavor as Yao and Lee (machine learning). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of inference unit as being disclosed and taught by Iino in the system taught by Yao and Lee to yield the predictable results of “improve recognition accuracy by using data the correct answer of which is unknown” (see Iino col. 4 sec: Effect of the Invention).
Conclusion
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/G.G.F./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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