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 .
Status of Claims
Claims 1-13 are hereby under examination.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed on 03/01/2023 has been considered except where lined through. References were lined through because they did not have an English abstract as indicated in the IDS or they were not provided.
Claim Objections
Claim 3 is objected to because of the following informalities: “data portion on the first converted data” should read “data portion of the converted data” and "determination on one of a brain disease" should read "determination of one of a brain disease". Appropriate correction is required.
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:
First acquisition unit in claim 1
First conversion unit in claim 1
Identification unit in claim 1
Display control unit in claim 2
Second acquisition unit in claim 7
Second conversion unit in claim 7
Learning unit in claim 7
Standardization unit 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. All instances of a “unit” are herein interpreted to be a functional unit of a personal computer or any 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 the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claims 1, 12, and 13, the claims recite “a deep learning model constructed by predetermined deep learning”. Page 30 of the specification as filed states “a deep learning model that is constructed by predetermined deep learning”. However, the specification as filed fails to disclose how the deep learning model is actually constructed by predetermined deep learning, what is meant by predetermined deep learning, or provide any details with regards to the deep learning model. Therefore, claims 1, 12, and 13 fail to comply with the written description requirement, and are rejected under 35 U.S.C. 112(a). Claims 2-11 are rejected due to their dependence on claim 1.
Regarding Claim 4, the claim recites “display the identification result with respect to same- dimensional data as the first converted data”. Pages 30-31 of the specification as filed state “the display control unit 211 displays the identification result with respect to the same-dimensional data as the converted data that is input to the deep learning model. With this configuration, it is possible to visualize the identification result with respect to the same-dimensional data as the visualized data (converted data) that is input to the deep learning model". The specification fails to disclose what is meant by same-dimensional data or provide any details with regards to the same-dimensional data. Therefore, claim 4 fails to comply with the written description requirement, and is further rejected under 35 U.S.C. 112(a).
Regarding Claim 7, the claim recites “a learning unit configured to construct a deep learning model through a learning process based on the deep learning”. Page 17 of the specification as filed states “the learning unit 206 performs a learning process by internally constructing a neural network based on an algorithm, such as a convolutional neural network (CNN), to extract a feature on spatial information, and constructing a neural network based on an algorithm, such as a recurrent neural network (RNN) or an attention, to extract a feature on temporal information.”. The specification discloses details of the learning process, however, the specification as filed fails to disclose what is meant by the deep learning or how the learning process is based on the deep learning. Therefore, claim 7 fails to comply with the written description requirement, and is further rejected under 35 U.S.C. 112(a).
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 4-8, and 11 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.
Regarding Claim 4, the claim recites “display the identification result with respect to same- dimensional data as the first converted data being the input to the deep learning model”. It is unclear what is meant by “same-dimensional data”. The specification does not provide any clarification with regards to same-dimensional data. For the purposes of examination, “same-dimensional data” is herein interpreted to be any data that has at least a time and a space as dimensions. Furthermore, it is unclear what “being the input to the deep learning model” means in the context of that entire limitation. For the purposes of examination, “being the input to the deep learning model” is herein interpreted to mean “that is used as the input to the deep learning model”. Due to the aforementioned reasons, claim 4 is rendered indefinite.
Regarding Claim 5, the claim recites “a signal intensity of a frequency”. It is unclear what is meant by this limitation as “a frequency” has not been properly defined. For the purposes of examination, “a signal intensity of a frequency” is herein interpreted to be “a signal intensity of the brain function data at a frequency”. The claim also recites “a corresponding brain disease region”. Claim 1, from which claim 1 is dependent, recites “identifying a brain disease region”. It is unclear if “a corresponding brain disease region” is the same as or different than the brain disease region that is identified in claim 1. For the purposes of examination, “a corresponding brain disease region” is herein interpreted to be the same as or different than the brain disease region that is identified in claim 1. The claim also recites “each specific brain disease”. This phrase lacks proper antecedent basis. Claim 1, from which claim 5 is dependent, only recites “a brain disease”, whereas “each specific brain disease” implies multiple diseases. For the purposes of examination, “each specific brain disease” is herein interpreted to be “the brain disease”. Due to the aforementioned reasons, claim 5 is rendered indefinite.
Regarding Claim 6, the claim recites “each disease type of each brain disease”. This phrase lacks proper antecedent basis. Claim 1, from which claim 5 is dependent, only recites “a brain disease”, whereas “each disease type of each brain disease” implies multiple diseases. For the purposes of examination, “each disease type of each brain disease” is herein interpreted to be “a disease type of the brain disease”. Therefore, Claim 6 is rendered indefinite.
Regarding Claim 7, the claim recites “acquire brain function data”. It is unclear if “brain function data” is the same as or different than “brain function data” recited in claim 1. For the purposes of examination, “brain function data” is herein interpreted to be the same as or different than “brain function data” recited in claim 1. It is unclear what it means for a label to “indicate content” of “one of a brain disease and a healthy state”. The claim also recites “the disease label indicating content of one of a brain disease and a healthy state”. It is also unclear if “a brain disease” is the same as or different than “a brain disease” recited in claim 1. For the purposes of examination, “a brain disease” is herein interpreted to be the same as or different than “a brain disease” recited in claim 1. The claim further recites “based on the deep learning”. There is insufficient antecedent basis for “the deep learning”. For the purposes of examination, “the deep learning” is herein interpreted to be any form of deep learning. The claim also recites “using the second converted data”. There is insufficient antecedent basis for “the second converted data”. For the purposes of examination, “the second converted data” is herein interpreted to be the “converted data” recited in claim 7. Due to the aforementioned reasons, claim 7 is rendered indefinite. Claim 8 is rejected due to its dependence on claim 7.
Regarding Claim 8, the phrase “the deep learning model” lacks proper antecedent basis. It is unclear if “the deep learning model” of claim 8 refers to the deep learning model of claim 1 or the deep learning model of claim 7. For the purposes of examination, “the deep learning model” is herein interpreted to be the deep learning model of claim or the deep learning model of claim 7. Therefore, claim 8 is rendered indefinite.
Regarding Claim 11, the claim recites “the deep learning”. There is insufficient antecedent basis for “the deep learning”. It is unclear if “the deep learning” refers to “predetermined deep learning” recited in claim 1. For the purposes of examination, “the deep learning” is herein interpreted to be the “predetermined deep learning” recited in claim 1. Therefore, claim 11 is rendered indefinite.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 12 follows.
STEP 1
Regarding claim 12, the claim recites a series of steps or acts, including acquiring brain function data including a temporal change, and indicating a brain function state measured by a measurement apparatus. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of performing an identification process of determining a brain disease and identifying a brain disease region, using the first converted data as an input of a deep learning model constructed by predetermined deep learning sets forth a judicial exception. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 12 fails to recite any limitations that provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the Abstract Idea, nor does the method use a particular machine to perform the Abstract Idea. Examiner notes that reciting a deep learning model does not qualify as an improvement to the technological field or using a particular machine. The claim fails to recite any specifics of the deep learning. Therefore, the human mind can perform the identification process as the human mind can interpreted to be a deep learning model.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of acquiring brain function data including a temporal change, indicating a brain function state measured by a measurement apparatus and converting the acquired brain function data to first converted data including information on at least a time and a space as dimensions. Acquiring data (brain function data) in order to diagnose a brain disease is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the acquiring and converting steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding claim 1, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The acquisition unit is a generic processing unit configured to perform pre-solutional data gathering activity, the first conversion unit is a generic processing unit configured to perform pre-solution data processing activity, and the identification unit is a generic processing unit is configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
Regarding Claim 13, the device recited in the claim is a generic computer device configured to perform the Abstract Idea. The recited non-transitory computer-readable medium including programmed instructions is a generic component of a computer configured to perform pre-solutional data gathering activity, pre-solution data processing activity, and perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to data gathering, data processing, and the display of data. The acquiring, converting, and identifying steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
Claim Rejections - 35 USC § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 7-10 and 12-13 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kim et al. (US Patent Pub. No. 20220151540) hereinafter Kim.
Regarding Claim 1, Kim discloses a brain function determination apparatus (the XAI system 200 [0065]; fig 2) comprising:
a first acquisition unit configured to acquire brain function data including a temporal change, indicating a brain function state measured by a measurement apparatus (the XAI system 200 may receive the brain wave of the patient, which is measured in the hospital, through the communication unit 210 [0069]; A brain wave refers to the recording of potentials on the vertical axis and time on the horizontal axis [0066]; the term “unit” as used herein means, but is not limited to, a software or hardware component, such as field-programmable gate array (FPGA) or application-specific integrated circuit (ASIC), which performs certain tasks. However, the “unit” is not limited to software or hardware. The “unit” may be configured to reside on the addressable storage medium and configured to execute on one or more processors. Thus, as an example, the “unit” may include elements, such as software elements, object-oriented software elements, class elements and task elements, processes, functions, attributes, procedures, subroutines, segments of program codes, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. The functionality provided for in the elements and “unit” or may be combined into fewer elements and “units” or further separated into additional elements and “units”. [0041]);
a first conversion unit configured to convert the brain function data acquired by the first acquisition unit, to first converted data including information on at least a time and a space as dimensions (The brain wave may change in time and space depending on activity of the brain [0066]; the processor 230 may extract power for each frequency by means of spectral density analysis and may extract a quantitative brain wave feature using a linear or non-linear network analysis, complex system network analysis, or the like [0084]);
and an identification unit configured to perform an identification process of determining a brain disease and identifying a brain disease region (When at least one first brain wave feature is extracted, the processor 230 may diagnose mental diseases by means of at least one second brain wave feature necessary to diagnose mental diseases of a patient and a weight of at least one second brain wave feature, using a machine learning model learned for diagnosis of mental diseases. Furthermore, the processor 230 may determine a brain region corresponding to the diagnosed mental diseases among cerebral regions of the patient [0087]), using the first converted data as an input of a deep learning model constructed by predetermined deep learning (the machine learning model may be learned to diagnose mental diseases using learning data including at least one of a brain wave for each age, analysis data of the brain wave, and mental diseases corresponding to a feature of the brain wave. The machine learning model may use a brain wave for each age, a preprocessed brain wave feature included in the brain wave for each age, and mental diseases corresponding to a brain wave feature for each channel among the brain wave features as learning data [0061]; The deep learning may refer to a machine learning method based on an artificial neural network [0051]).
Regarding Claim 2, Kim discloses the invention as discussed above in claim 1. Kim further discloses a display control unit configured to display, on a display device, an identification result of the identification process by the identification unit (the XIA system 200 may provide the medical team with the diagnosed result, a decision-making structure 510 of FIG. 5, description information 520 of FIG. 5, the channel 620, and the like by means of the display [0165]).
Regarding Claim 3, Kim discloses the invention as discussed above in claim 2. Kim further discloses the display control unit is configured to display the identification result by the identification unit such that a data portion on the first converted data is identifiable (the processor 230 may determine the decision-making structure as description information for describing the diagnosed result and a basis for diagnosis and may visualize the generated decision-making structure to provide visual information to the medical team [0088]; fig 5; Examiner notes the brain wave feature is a data portion of the first converted data and the displayed decision-making structure makes the brain wave feature identifiable), the data portion being a basis for determination on one of a brain disease and a healthy state (the processor 230 may extract a brain wave feature [0085]; When at least one first brain wave feature is extracted, the processor 230 may diagnose mental diseases by means of at least one second brain wave feature necessary to diagnose mental diseases of a patient [0087]).
Regarding Claim 4, Kim discloses the invention as discussed above in claim 2. Kim further discloses the display control unit is configured to display the identification result with respect to same- dimensional data as the first converted data being the input to the deep learning model (the XIA system 200 may provide the medical team with the diagnosed result, a decision-making structure 510 of FIG. 5, description information 520 of FIG. 5, the channel 620, and the like by means of the display [0165]; When at least one first brain wave feature is extracted, the processor 230 may diagnose mental diseases by means of at least one second brain wave feature necessary to diagnose mental diseases of a patient [0087]; Examiner notes that since the first converted data is being used to determine the identification result, the display control unit is configured to display the identification result with respect to same- dimensional data as the first converted data being the input to the deep learning model).
Regarding Claim 7, Kim discloses the invention as discussed above in claim 1. Kim further discloses a second acquisition unit configured to acquire brain function data measured by the measurement apparatus and having a disease label added (The memory 220 may include a brain wave signal received by the communication unit 210 and may store analysis data of the brain wave signal, a type of mental diseases corresponding to a specific brain wave signal [0074]), the disease label indicating content of one of a brain disease or a healthy state (a type of mental diseases corresponding to a specific brain wave signal [0074]); a second conversion unit configured to convert the brain function data acquired by the second acquisition unit, to converted data including information on at least a time and a space as dimensions (The machine learning model may use a brain wave for each age and a brain wave feature for each channel, the brain wave feature being included in the brain wave for each age, as learning data to diagnose the mental diseases. [0027]; the processor 230 may extract at least one brain wave feature from the preprocessed brain wave. In this case, the processor 230 may extract power for each frequency by means of spectral density analysis and may extract a quantitative brain wave feature using a linear or non-linear network analysis, complex system network analysis, or the like. [0084]); and a learning unit configured to construct a deep learning model through a learning process based on the deep learning, using the second converted data to which the disease label is added, as an input (The machine learning model may use a brain wave for each age, a preprocessed brain wave feature included in the brain wave for each age, and mental diseases corresponding to a brain wave feature for each channel among the brain wave features as learning data. In detail, the machine learning model may use mental diseases of a patient, which is finally derived as the diagnosed result by substituting the brain waver feature for each channel into a decision-making structure which will be described below, as learning data. [0061]; The machine learning model according to an embodiment of the inventive concept may be composed of an artificial neural network which is in the form of an advanced variational autoencoder and may be learned such that the at least one first brain wave feature extracted from the preprocessed brain wave 430 is an input of the present artificial neural network [0109]).
Regarding Claim 8, Kim discloses the invention as discussed above in claim 7. Kim further discloses a standardization unit configured to perform a predetermined standardization process on the second converted data (the processor 230 may determine a weight for each of the second brain wave features together using the machine learning model, as well as the at least one second brain wave feature [0123]; Examiner notes weighting brain wave features is a form of standardization), wherein the learning unit is configured to construct the deep learning model (The machine learning model may use a brain wave for each age, a preprocessed brain wave feature included in the brain wave for each age, and mental diseases corresponding to a brain wave feature for each channel among the brain wave features as learning data. In detail, the machine learning model may use mental diseases of a patient, which is finally derived as the diagnosed result by substituting the brain waver feature for each channel into a decision-making structure which will be described below, as learning data. [0061]; Examiner notes since the processor inputs data to the machine learning model, the processor also constructs the machine learning model), using the second converted data subjected to the standardization process, as an input (When the second brain wave feature and the weight are determined, in S450, the processor 230 may diagnose mental diseases of the patient by means of the decision-making structure. [0124]).
Regarding Claim 9, Kim discloses the invention as discussed above in claim 1. Kim further discloses the brain function data includes electro-encephalography data and magneto-encephalography data (Brain wave signals such as EEG, MEG, and ECoG are exemplified in the specification [0067]).
Regarding Claim 10, Kim discloses the invention as discussed above in claim 1. Kim further discloses the first conversion unit is configured to convert the brain function data acquired by the first acquisition unit, to the first converted data including information on a frequency as a dimension (a first brain wave extracted by means of the processor 230 may include brain wave (e.g., γ wave, α wave, β wave, δ wave, θ wave, or the like) power for each frequency of a patient, a frequency, connectivity between channels, and the like [0086]).
Regarding Claim 12, Kim discloses a brain function determination method (operations of a method for diagnosing mental diseases according to the inventive concept [0151]) comprising:
acquiring brain function data including a temporal change, indicating a brain function state measured by a measurement apparatus (the XAI system 200 may receive the brain wave of the patient, which is measured in the hospital, through the communication unit 210 [0069]; A brain wave refers to the recording of potentials on the vertical axis and time on the horizontal axis [0066]);
converting the acquired brain function data to first converted data including information on at least a time and a space as dimensions (The brain wave may change in time and space depending on activity of the brain [0066]; the processor 230 may extract power for each frequency by means of spectral density analysis and may extract a quantitative brain wave feature using a linear or non-linear network analysis, complex system network analysis, or the like [0084]);
and performing an identification process of determining a brain disease and identifying a brain disease region (When at least one first brain wave feature is extracted, the processor 230 may diagnose mental diseases by means of at least one second brain wave feature necessary to diagnose mental diseases of a patient and a weight of at least one second brain wave feature, using a machine learning model learned for diagnosis of mental diseases. Furthermore, the processor 230 may determine a brain region corresponding to the diagnosed mental diseases among cerebral regions of the patient [0087]), using the first converted data as an input of a deep learning model constructed by predetermined deep learning (the machine learning model may be learned to diagnose mental diseases using learning data including at least one of a brain wave for each age, analysis data of the brain wave, and mental diseases corresponding to a feature of the brain wave. The machine learning model may use a brain wave for each age, a preprocessed brain wave feature included in the brain wave for each age, and mental diseases corresponding to a brain wave feature for each channel among the brain wave features as learning data [0061]; The deep learning may refer to a machine learning method based on an artificial neural network [0051]).
Regarding Claim 13, Kim discloses a non-transitory computer-readable medium including programmed instructions (Various embodiments of the inventive concept may be implemented as software including one or more instructions stored in a storage medium (e.g., a memory) readable by a machine (e.g., the XAI system 200 or a computer)… The machine-readable storage medium may be provided in the form of a non-transitory storage medium [0166]) that cause a computer to execute:
acquiring brain function data including a temporal change, indicating a brain function state measured by a measurement apparatus (the XAI system 200 may receive the brain wave of the patient, which is measured in the hospital, through the communication unit 210 [0069]; A brain wave refers to the recording of potentials on the vertical axis and time on the horizontal axis [0066]);
converting the acquired brain function data to first converted data including information on at least a time and a space as dimensions (The brain wave may change in time and space depending on activity of the brain [0066]; the processor 230 may extract power for each frequency by means of spectral density analysis and may extract a quantitative brain wave feature using a linear or non-linear network analysis, complex system network analysis, or the like [0084]);
and performing an identification process of determining a brain disease and identifying a brain disease region (When at least one first brain wave feature is extracted, the processor 230 may diagnose mental diseases by means of at least one second brain wave feature necessary to diagnose mental diseases of a patient and a weight of at least one second brain wave feature, using a machine learning model learned for diagnosis of mental diseases. Furthermore, the processor 230 may determine a brain region corresponding to the diagnosed mental diseases among cerebral regions of the patient [0087]), using the first converted data as an input of a deep learning model constructed by predetermined deep learning (the machine learning model may be learned to diagnose mental diseases using learning data including at least one of a brain wave for each age, analysis data of the brain wave, and mental diseases corresponding to a feature of the brain wave. The machine learning model may use a brain wave for each age, a preprocessed brain wave feature included in the brain wave for each age, and mental diseases corresponding to a brain wave feature for each channel among the brain wave features as learning data [0061]; The deep learning may refer to a machine learning method based on an artificial neural network [0051]).
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 5 is/are rejected under 35 U.S.C. 103 as being obvious over Kim (US Patent Pub. No. 20220151540), as applied to claim 2 above, in view of Shinohara et al. (US Patent Pub. No. 20190236824) hereinafter Shinohara.
Regarding Claim 5, Kim discloses the invention as discussed above in claim 2. Kim fails to disclose the display control unit is configured to display, as the identification result, a heat map representing a specific time and a signal intensity of a frequency, to be superimposed on a corresponding brain disease region on a brain image, for each specific brain disease.
However, Shinohara teaches displaying a heat map representing a specific time and a signal intensity of a frequency, to be superimposed on a corresponding brain disease region on a brain image, for each specific brain disease (On each of the cross-sectional views 641 to 643; a heat map (different than the heat map 611) (a third intensity distribution), which represents the distribution of signal intensities of the biosignals at the time and the frequency corresponding to the position (a point or a range) specified in the heat map 611, is displayed in a superimposed manner. [0167]; As a result, it becomes possible to understand the positional relationship between the heat map representing the conservation site indicated by the stereoscopic image 644 and the dipole representing the site of disorder (the target site). That information can be put to use during a surgery. [0172]; fig 32).
Kim and Shinohara are considered analogous art to present invention because they are directed towards the same field of endeavor.
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to have modified the brain function determination apparatus of Kim such that the display control unit is configured to display, as the identification result, a heat map representing a specific time and a signal intensity of a frequency, to be superimposed on a corresponding brain disease region on a brain image, for each specific brain disease, as taught by Shinohara, because doing so would provide useful information regarding brain activity and a corresponding brain disease region.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US Patent Pub. No. 20220151540) as applied to claim 2 above, and further in view of Morimoto et al. (US Patent Pub. No. 20150272461) hereinafter Morimoto.
Regarding Claim 6, Kim discloses the invention as discussed above in claim 2. Kim further discloses the identification unit is configured to calculate, as the identification result, probabilities of each disease type of each brain disease (the processor 230 may visualize and provide a diagnosed result 530 [0138]; fig 5), based on an output of the deep learning model (the explainable XAI process may refer to a series of processing of diagnosing mental diseases using the machine learning model [0091]), and the display control unit is configured to display the probabilities (the processor 230 may visualize and provide a diagnosed result 530 [0138]; fig 5).
Kim fails to disclose the identification unit is configured to calculate, as the identification result, a probability of a healthy state.
However, Morimoto teaches an identification unit (the present invention provides a biomarker apparatus for generating an output as a biomarker by computer analysis [0075]) is configured to calculate, as an identification result, a probability of a healthy state (the “disease discriminant label” as the biomarker output may include a probability that the subject has the disease (or probability that the subject is healthy) [0174]; If an indication such as “probability of how healthy you are: 00 %” is given in the state before the onset of a disease mentioned above, it is possible to indicate the user of his/her health conditions by an objective numerical value.[0221]).
Morimoto is considered analogous art to the present invention because it is directed towards the same field of endeavor.
It would have been obvious to one having ordinary skill in the art at the time of the effective date to have modified the brain function determination apparatus of Kim such that the identification unit is configured to calculate, as the identification result, a probability of a healthy state, as taught by Morimoto, because it would provide a patient with an objective numeral value indicative of his/her heath.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US Patent Pub. No. 20220151540) as applied to claim 1 above, and further in view of Kim et al. (KR 102344378 B1) hereinafter Kim 2. US Patent Pub. No. 20220323003 is being used as the English translation of the Kim 2 reference.
Regarding Claim 11, Kim discloses the invention as discussed above in claim 1. Kim fails to disclose the deep learning model is constructed by the deep learning with a time series analysis function.
However, Kim 2 teaches a learning model with a time series analysis function (using a deep learning model [0005]; The learning model may be a model for processing the time-series data [0041]).
Kim 2 is considered analogous art to the present application because it is reasonably pertinent to a problem faced by the inventor.
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to have modified the brain function determination apparatus of Kim such that the deep learning model is constructed by the deep learning with a time series analysis function, as taught by Kim 2, because the brain function data analyzed by the brain function determination apparatus is time series data.
Conclusion
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/JANKI M BAVA/Examiner, Art Unit 3791
/ETSUB D BERHANU/Primary Examiner, Art Unit 3791