DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is responsive to the communication received on 2023-07-27. The claims 1-9 are pending, of which the claim(s) 1, 5, & 6 is/are in independent form.
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.
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:
in claims 1- 4:
“an image obtaining unit”: shown as item 110, spec para. [043, 060].
“a trained model application unit”: shown as item 120
“a determination unit”: shown as item 130
In claim 3:
“a trained model generation unit”: shown as item 140
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.
Claim 5 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.
Regarding claim 5, the claim recites “alternating current in wires based on deep learning” in line 2 and “alternating current in wires based on deep learning configured to obtain a wire image” in line 4. However, the claim fails to clarify the relationship between these two deep learning recitations thereby rendering the scope of the claim indefinite. That is, the claim fails to clarify whether the second deep learning corresponds to first deep learning or not.
For the examination purpose, the second recitation of “deep learning” of line 4 is interpreted as “the deep learning”.
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- 2, 4 & 6 rejected under 35 U.S.C. 101 because the claimed invention is directed to Judicial Exception (“abstract idea”) without significantly more.
As to claim 1
1. An apparatus for determining electrical melting traces by direct current and alternating current in wires based on deep learning, the apparatus comprising:
[a] an image obtaining unit configured to obtain a wire image including an electrical melting trace;
[b] a trained model application unit configured to calculate a probability (determination probability) of determining the electrical melting trace as an electrical melting trace by direct current or alternating current by applying a pre-trained model; and
[c] a determination unit configured to determine the electrical melting trace according to a set determination condition.
1. Step 1: Yes. The claim is to an apparatus, which is one of the four categories of patent eligible subject matter.
2. Step 2A, Prong 1: Yes. The claim(s) recite(s) limitations [b] and [c]. These limitation(s) cover abstract idea because, under broadest reasonable interpretations (BRI), they can be practically performed in human’s mind, hence are “Mental Processes”. While these limitations are drafted as being performed by respective computer elements of “a trained model application unit” and “a determination unit”, these computer elements are not necessarily required to perform the function. Put differently, nothing in the claim, other than by “a trained model application unit” and “a determination unit” preclude the method steps from practically being performed in the human’s mind. The ¶¶ [047-048] of the applicant’s own specification clearly show using simple percentage value as determining probability values and using these probability values as a set determination condition to determine the cause of melting trace. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components like a trained model application unit and a determination unit as in this case, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
3. Step 2A, Prong 2: No. This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element(s) of limitation [a] and the elements of “a trained model application unit”, and “a determination unit”.
As to the limitation [a], it is merely akin to adding of an insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) hence cannot provide a practical application. This is so because obtaining image step is data gathering step required for the subsequent step of calculating probability and determining melting trace. As to elements of “trained model application unit” and “a determination unit”, they are also merely recited as using computer as tool to perform the abstract idea (since these computer elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component) hence cannot provide a practical application. The specification in paras. [044-045] discloses that any types of the CNN based models can be used to calculate the probability. Hence, using of the particular CNN model is not required here. Even when considering the additional elements together, they continue to remain using computing elements as tool to execute an abstract idea and data gathering step. Accordingly, the individual/combination of additional element(s) fail(s) to integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the above abstract idea other than adding of extra solution activity and using computer as a tool. The claim is directed to an abstract idea.
4. Step 2B: No. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of limitation [a] amount(s) to no more than adding of well-understood, routine, and conventional data gathering step and examiner takes an Official notice to that effect based on the evidence of the cited arts (See US 20200109873 A1, para. 020; CN 112465002 A, Abstract as example evidences) as part of Berkheimer memo. As stated above, the using of the elements of “a trained model application unit” and “a determination unit” amount to no more than mere instructions to apply the exception using respective generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept as in this case. Accordingly, the additional elements when considered separately and in combination, do not add significantly more (also known as an “inventive concept”) to the exception. The claim is not patent eligible.
Regarding claim 2, this claim depends on claim 1 and recites the same abstract idea and additional elements set forth above in claim 1. The claim 2 recites another limitation of “set determination condition determines an electrical melting trace having a higher determination probability and greater than or equal to a reference value from among electrical melting traces by direct current or alternating current.” However, this limitation too can be practically performed in human’s mind. Hence this limitation is a mental processes based abstract idea. Accordingly, the claim 2 fails to provide a practical application and inventive step. The claim 2 is not patent eligible.
Regarding claim 4, the claim depends on claim 1 and hence recites the same abstract idea and additional elements of the claim 1. The claim also clarifies that “the pre-trained model is a convolutional neural network (CNN)-based deep learning model.” Here, this limitation specifies the type of the model hence is an additional elements. However, CNN based deep learning is well-known generic computer elements used as a tool to determine the melting trace. Thus, mere using the CNN based deep learning computing element as a tool to perform the abstract idea individually or in combination with other additional limitations cannot provide a practical application and an inventive step. The claim 4 is not patent eligible.
Regarding claim 6,
The rejection set forth above in claim 1 are incorporated. Thus, only in summary, following analysis is performed again per 2019 PEG.
Step 1, Yes. This claim is to a method which is one of the four categories of patent eligible subject matter.
Step 2A, Prong 1: Yes. The limitations of “calculating a probability (determination probability) of determining the electrical melting trace as an electrical melting trace by direct current or alternating current by applying a pre-trained model; and determining the electrical melting trace according to a set determination condition” can be practically performed in human’s mind for the similar reasons set forth above in claim 1. Therefore, the claim 6 recites “mental processes” based abstract idea. That is, the claim is directed to an abstract idea.
Step 2A, Prong 2: No. This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element(s) of “obtaining a wire image including an electrical melting trace”. This step is merely used for data gathering, hence is akin to adding of an insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Mere adding of an insignificant extra solution activity cannot provide a practical application. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B. No. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “obtaining a wire image including an electrical melting trace” is a data gathering step hence is an insignificant extra-solution activity to the judicial exception. Furthermore, this extra-solution activity is well-understood, routine, and conventional activity as can be demonstrated by the cited arts under Berkheimer memo as well. The additional element is not an indicative of an inventive concept (aka “significantly more”). The claim is not patent eligible.
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.
Claim(s) 1- 4 & 6- 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Applicant Admitted Prior Arts (hereinafter “AAPA”) in view of Chen et al. (CN 112465002 A).
Regarding claim 1, AAPA teaches a means for determining electrical melting traces by direct current and alternating current in wires means comprising:
trace by direct current often has an elongated melting portion, and an electrical melting trace by alternating current often has a short melting portion”, “so it is difficult to determine whether an electrical melting trace of the wire is due to direct current or alternating current.” While with AAPA’s technique, it is hard to determine melting trace, it does teach/suggest the determining step] the electrical melting trace as an electrical melting trace by direct current or alternating current to characteristics of difference in external form formed on wires ([003-008]).
In summary, AAPA teaches a technique that clearly identifies whether the melting trace is due to one of the two suspects ([008]), but it fails to tell how it determines the melting trace is due to AC or DC current. Therefore AAPA does not teach its means for determining is “an apparatus” comprising:
an image obtaining unit configured to obtain a wire image including an electrical
melting trace; a trained model application unit configured to calculate a probability (determination probability) and a determination unit configured to determine the electrical melting trace according to a set determination condition.
Chen teaches, using a trained model, for identifying the melting traces on a copper wire suspects either to a short circuits or to a fire (Abstract). Specifically, Chen teaches an apparatus comprising various units to easily distinguishing the cause of the melting traces between two possible suspects by training a neural network model with labeled data about two suspects (in analogous manner in AAPA’s para. 008) and using the trained neural network model to determine the cause of the fire. Additionally, Chen teaches collecting multiple images of first type of the causes of melting trace and second types of the causes of the melting trace to train a learning model via a supervised learning (that uses data (images) and label (cause of melting) both). After the learning model is trained, Chen inputs newly received images of the melting trace to the model, in order to determine the cause of the melting trace. More specifically, Chen teaches An apparatus [“As shown in FIG. 2, the device comprises” of fig. 10] for determining electrical melting traces by short-circuit and fire in wires based on deep learning, the apparatus comprising: (Page 10);
an image obtaining unit [Fig. 2, “collecting module 201,for collecting the first image related to the fire scene”] configured to obtain a wire image [“the first image related to the fire scene and copper wire melting mark”] including an electrical melting trace (Page 7: “step 101, collecting the first image, and converting it into the corresponding pixel matrix”, page 10);
a trained model application unit configured to calculate a probability (determination probability) [“obtain the two-dimensional confidence vector”. The confidence score indicates probability because higher the confidence vector value, higher the probability] of determining the electrical melting trace as an electrical melting trace by short circuit or burn by applying a pre-trained model (Fig. 2, pages 8-10); and
a determination unit configured to determine [Fig. 1, step 103] the electrical melting trace according to a set determination condition (Fig. 2, pages 8, 10).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Chen and AAPA because they both related to analyzing data pairs of input data for an electrical melting traces and associated labels and (2) modify the melting trace determining means of AAPA to incorporate the apparatus with image obtaining unit, trained model application unit and determination unit (just like for short circuit and fire in Chen, train and use a neural network model with AAPA’s images of melting traces with “elongated melting portion” and “short melting portion”) of Chen to determine its electrical melting traces is either by the direct current or alternating current in order to solve the difficulty in determining the cause of the melting between AC or DC current.
That is, doing so would avoid manual/participation of professional or one or more professional special instruments to distinguish the cause of the melting trace with AC current or DC current in the AAPA thereby avoiding the difficulty of determining the cause of melting trace in AAPA (Chen, Abstract, AAPA [005]). Accordingly, when AAPA’s system/method is modified (not the cited references individually), to use the known technique of Chen to easily and quickly distinguishing the cause of the melting, the modified AAPA teaches/suggests each limitation of the claim and renders invention thereof obvious to PHOSITA.
Regarding claim 2, AAPA in view of Chen teaches/suggests the apparatus of claim 1, wherein the set determination condition determines an electrical melting trace having a higher determination probability and greater than or equal to a reference value [“the preset threshold value”] from among electrical melting traces by direct current or alternating current (AAPA [003- 008]; Chen, page 8).
Regarding claim 3, AAPA in view of Chen teaches/suggests the apparatus of claim 1, further comprising: a trained model generation unit configured to generate the pre-trained model by using wire images including electrical melting traces by direct current and alternating current used in an appraisal report as training data (AAPA [003- 008]; Chen, page 8).
Regarding claim 4, AAPA in view of Chen teaches/suggests the apparatus of claim 1, wherein the pre-trained model [“the neural network model for training”] is a convolutional neural network (CNN)-based deep learning model (Chen, page 8).
Regarding claim 6, AAPA in view of Chen teaches/suggests a method of determining electrical melting traces by direct current and alternating current in wires based on deep learning for the similar reasons set forth above in claim 1.
Regarding claim 7, AAPA in view of Chen teaches/suggests the method of claim 6, further comprising: generating the pre-trained model [“neural network model is trained by a large number of copper wire melting mark related image” technique using for images of “elongated melting Portion” and “short melting portion” in AAPA] by using wire images including electrical melting traces by direct current and alternating current used in an appraisal report as training data (Chen, claim 6, page 3; AAPA, [003-008]).
Regarding claim 8, AAPA in view of Chen teaches/suggests the method of claim 7, wherein the generating of the pre-trained model comprises: obtaining wire images including electrical melting traces by direct current and alternating current used in an appraisal report; and generating a CNN-based deep learning model by using the wire images including electrical melting traces by direct current and alternating current as training data (Chen, pages 8-10; AAPA [003- 008]).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Applicant Admitted Prior Arts (hereinafter “AAPA”) in view of Lorenz et al. (US 20200109873 A1).
Regarding claim 5, AAPA teaches a system for determining electrical melting traces by direct current and alternating current in wires
an means for determining [“an electrical melting trace by direct current often has an elongated melting portion, and an electrical melting trace by alternating current often has a short melting portion”, “so it is difficult to determine whether an electrical melting trace of the wire is due to direct current or alternating current.”] electrical melting traces by direct current and alternating current in wires based on
AAPA teaches of determining electrical melting traces is either by a direct current and alternating current in wires with a difficult technique based on “elongated melting” characteristics and “a short melting portion” characteristics of the wires. However, AAPA does not teaches means for determining (albeit difficult way) whether an electrical melting trace of the wire is due to direct current or alternating current to use deep learning.
AAPA does not teach the limitation shown with strikethrough emphasis but these deficiencies are suggested by Lorenz.
That is, Lorenz teaches a system for determining wiring types in a thermostat based on capturing an image of the wires using a user device 2041 and inputting the captured wire image into a deep learning model 308 that was trained with pluralities of the images of the wires and associated labels to determine additional detailed information about the wires by analyzing the images of the wires (Abstract, figs. 2-3, [020]). Specifically, Lorenz teaches a system for determining electrical wiring detailed information based on deep learning [“pre-processing is needed to initially train the machine learning algorithm with input images as well as expected output results”], the system comprising:
an apparatus [apparatus 202] for determining electrical melting traces an image 214 captured by handheld user computing device 202. Image analyzer 210 is configured to identify and pre-process wire image elements”] including an electrical melting trace and to determine the electrical melting trace included in the wire image as an electrical melting trace Server computing device 208”] configured to receive user input text information [“analyzer 210 is configured to identify and pre-process wire image elements and language character elements in captured image 214”] related to an electrical melting trace in the wire and to provide [the server 208 returning request results (Step 512 of fig. 5) of the requested image and language query to the user device 202: “If the confidence is high, wirelist 222 is transmitted to handheld user computing device 202.”] a wire image including the electrical melting trace based on the user input text information (Figs. 2- 5, [019-025]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Lorenz and AAPA because they both related to analyzing wire melting traces data (label and input data) based on the wires characteristics and (2) modify the system of AAPA to include missing limitations (based on deep learning to easily determine whether an electrical melting trace of the wire is due to direct current or alternating current using an apparatus and a server) as suggested by Lorenz. Doing so would avoid the situation of having to use a difficult technique to determine cause of the melting trace and possible misidentifying the cause of the melting trace in the system of AAPA (Lorenz, [002] & AAPA [005]). Therefore, the combination of AAPA and Lorenz (not the cited references individually) teach each elements of the claim and renders invention of this claim obvious to PHOSITA.
Allowable Subject Matter
Claim 9 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Jin (CN 108267652 B) teaches when the electrical interference is detected, determining whether the electrical interference is caused by an AC component or due to the DC component based on a difference between the second sample value and a third sample value (Claim 1).
2) Yang et al. (CN 204203370 U) teaches direct contact part melted trace circuit and discharge arc trace can powerfully shows direct current circuit of whether the fire site occurring over the two facts of abnormal discharge, providing objective scientific basis to determine direct abnormal discharge electric fire (Abstract).
3) Lee Ui-pyeong (NPL article, “Cause and investigation of fire in wiring equipment”) teaches obtaining wire image including an electrical melting traces at different types of the power wiring (Figs. 7-11, “Case of fire caused by poor contact between the outlet holder and the plug blade”, “Disassembled view of the outlet (T-shaped, recessed) and surrounding area”, “example damage to the main switch contacts of a multi-outlet due to arcing”)
Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANTOSH R. POUDEL whose telephone number is (571)272-2347. The examiner can normally be reached Monday - Friday (8:30 am - 5:00 pm).
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115