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
This action is a final rejection
Claims 1-4, 6-7, 9-12 are pending
Claims 5, 8 were cancelled
Claims 1, 9-11 were amended
Claims 1-4, 6-7, 9-12 are rejected under 35 USC § 101
Claims 1-4, 6-7, 9-11 are rejected under 35 USC § 102
Claim 12 is rejected under 35 USC § 103
Priority
Acknowledgement is made of Applicant’s claim for a foreign priority date of 7-13-2021
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7-13-2022, 5-15-2025 and 10-9-2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The 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.
Claim 1 meets the three-prong test and therefore invokes § 112(f). The words of the claim do not rebut the presumption that the claim limitation is to be interpreted under § 112(f).
Regarding claim 1
The Examiner interprets “the acquirer acquires second medically-related information created by a user on a basis of the medical information” as invoking 35 U.S.C. §112(f). Consistent with “The data acquirer 33 is configured as a network interface” of Applicant specification (0018), the Examiner is interpreting “the acquirer acquires second medically-related information created by a user on a basis of the medical information” as executed by a network interface.
The Examiner interprets “An analysis device comprising an outputter” as invoking 35 U.S.C. §112(f). Consistent with “The data outputter 34 is for outputting information processed by the analysis device 3 to an external destination. For example, a network interface for communicating with the radiology terminal 4, the image server 5, or the like, a connector for connecting an external device (such as a display device or a printer not illustrated, for example), or a port for any of various media such as USB memory is applicable as the data outputter 34” of Applicant specification (0019), the Examiner is interpreting “An analysis device comprising an outputter” as executed by a network interface.
Regarding claim 9
The Examiner interprets “an acquirer into which a confirmed diagnosis information is input by a user” as invoking 35 U.S.C. §112(f). Consistent with “The data acquirer 33 is configured as a network interface” of Applicant specification (0018), the Examiner is interpreting “an acquirer into which a confirmed diagnosis information is input by a user” as executed by a network interface.
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 1-7, 9-12 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-7, 9-12 the claims recite an abstract idea.
Independent Claim 1 is rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claim 1 recites an analysis device to acquire first and second medically-related information followed by statistical information output based on the first and second medically-related information.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
acquires first medically-related information obtained through processing performed on medical information,
acquires second medically-related information created by a user on a basis of the medical information,
compares the acquired first medically-related information and the second medically-related information acquired
outputs statistical information on a basis of the first medically- related information and the second medically-related information
the statistical information is configured to assess a reliability of the first medically-related information compared to second medically-related information;
acquires third medically-related information obtained through second ...processing, being different from the first ... processing, performed on the medical information and
outputs a correct answer ratio of the first medically-related information as statistical information on a basis of the third ground truth data
wherein an output format of the statistical information is changed according to one of a ratio of agreement or a ratio of non-agreement between the first medically-related information and the second medically-related information or between the first medically-related information and the third medical-related information
belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “acquiring first and second medically-related information followed by statistical information output based on the first and second medically-related information”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). Claim 1 recites:
medical imaging system
image generation device
analysis device operably connected to the image generation device and configured t perform analysis based on first medical information from the image generation device;
a hardware processor;
an acquirer;
an outputter;
computer processing;
first computer;
second computer.
Amounting to no more than mere instructions to apply the exception using a generic computer, or merely using a computer as a tool to implement the abstract idea as even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole 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 (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two, claim 1 recites:
medical imaging system
image generation device
analysis device operably connected to the image generation device and configured t perform analysis based on first medical information from the image generation device;
a hardware processor;
an acquirer;
an outputter;
computer processing;
first computer;
second computer.
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)) Accordingly, the additional elements alone, and in combination do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Independent Claim 9 is rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claim 9 recites an analysis device to acquire first medically-related information and confirmed diagnosis information followed by statistical information output based on the first medically-related information and the confirmed diagnosis information.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
A prediction method comprising:
acquires first medically-related information obtained through processing performed on medical information; and
a confirmed diagnosis information is input by a user,
compares the acquired first medically-related information and the confirmed diagnosis information acquired;
wherein the result of the comparison is utilized to assess a reliability of the first medically-related information compared to the confirmed diagnosis information;
acquires third medically-related information obtained through second processing, being different from the first processing, performed on the medical information;
treats the third medically-related information as third ground truth data;
wherein an output format of the statistical information is changed according to one of a ratio of agreement or a ratio of non-agreement between the first medically- related information and the second medically-related information, or between the first medically-related information and the third medical-related information.
belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “acquiring first medically-related information and confirmed diagnosis information followed by statistical information output based on the first medically-related information and the confirmed diagnosis information”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). Claim 9 recites:
medical imaging system;
image generation device;
an analysis device operably connected to the image generation device and configured to perform analysis based on first medical information from the image generation device
hardware processor;
computer processing;
acquirer;
first computer;
second computer.
Amounting to no more than mere instructions to apply the exception using a generic computer, or merely using a computer as a tool to implement the abstract idea as even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole 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 (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two, claim 9 recites:
medical imaging system;
image generation device;
an analysis device operably connected to the image generation device and configured to perform analysis based on first medical information from the image generation device
hardware processor;
computer processing;
acquirer;
first computer;
second computer.
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)) Accordingly, the additional elements alone, and in combination do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Independent Claims 10 & 11 are rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claims 10 & 11 recite a method and non-transitory computer readable storage medium to acquire first and second medically-related information followed by statistical information output based on the first and second medically-related information.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
A prediction method comprising:
acquiring medical information
analyzing the medical information through first processing to acquire first medically-related information;
acquiring second medically-related information created by a user on a basis of the medical information; and
comparing the first medically-related information and the acquired second medically-related information, wherein the comparing includes outputting statistical information on a basis of the first medically-related information and the second medically-related information
the statistical information configured to assess a reliability of the first medically- related information compared to the second medically-related information;
acquires third medically-related information obtained through second processing, being different from the first processing, performed on the medical information;
treats the third medically-related information as third ground truth data;
outputs a correct answer ratio of the first medically-related information as statistical information on a basis of the third ground truth data
wherein an output format of the statistical information is changed according to one of a ratio of agreement or a ratio of non-agreement between the first `medically-related information and the second medically-related information, or between the first medically-related information and the third medical-related information.
belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “acquiring first and second medically-related information followed by statistical information output based on the first and second medically-related information”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claims 10 &11 recite:
an image generation device
hardware processor
first computer processing;
second computer processing.
Claims 11 recites
A non-transitory computer readable storage medium storing a program.
Amounting to no more than mere instructions to apply the exception using a generic computer, or merely using a computer as a tool to implement the abstract idea as even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole 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 (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two:
Claims 10 &11 recite:
an image generation device
hardware processor
first computer processing;
second computer processing.
Claims 11 recites
A non-transitory computer readable storage medium storing a program;
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)) Accordingly, the additional elements alone, and in combination do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible
Dependent Claims:
Step 2A Prong One: The following dependent claims recites additional limitations that further define the abstract idea of acquiring first and second medically-related information followed by statistical information output based on the first and second medically-related information. The claim limitations include:
Claim 2: wherein the statistical information is a ratio of agreement or a ratio of non-agreement between the first medically-related information and the second medically-related information.
Claim 3: treats the first medically-related information as first ground truth data, and outputs a correct answer ratio of the second medically-related information as statistical information on a basis of the first ground truth data.
Claim 4: treats the second medically-related information as second ground truth data, and outputs a correct answer ratio of the first medically-related information as statistical information on a basis of the second ground truth data.
Claim 5:
acquires third medically-related information obtained through processing performed on the medical information,
treats the third medically-related information as third ground truth data, and
outputs a correct answer ratio of the first medically-related information as statistical information on a basis of the third ground truth data.
Claim 6: wherein the second medically-related information is confirmed diagnosis information.
Claim 7:
wherein the second medically-related information is confirmed diagnosis information,
treats the second medically-related information as second ground truth data, and
outputs a correct answer ratio of the first medically-related information as statistical information on a basis of the second ground truth data.
Claim 12:
wherein the one or more areas of disagreement between the first medically-related medical information and the second medically- related medical information are provided in tabular form
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims include:
Claim 3:
hardware processor;
Claim 4:
hardware processor;
Claim 5:
hardware processor;
computer processing;
Claim 7:
hardware processor;
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claim 3:
hardware processor;
Claim 4:
hardware processor;
Claim 5:
hardware processor;
computer processing;
Claim 7:
hardware processor;
Claim Rejections - 35 USC § 102
The 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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-7, 9-11 are rejected by 35 U.S.C. 102(a)(1) as being anticipated by Jeong et.al (US 10825178 B1) hereinafter “Jeong”
Regarding claim 1 Jeong teaches:
A medical imaging system, comprising: an image generation device; (See at least [column 11, lines 49-50] via: “…The medical image apparatus may include a medical imaging device..”) and
An analysis device operably connected to the image generation device and configured to perform analysis based on first medical information from the image generation device, the analysis device comprising: (See at least [column 6, lines 52-56] via: “… Referring to FIG. 1, the medical image analysis device 100 according to an embodiment of the present disclosure may include a data learning unit 110 and a data recognition unit 120. The aforementioned medical image analysis device 100 may include a processor and a memory…”; in addition see at least [column 11, lines 47-52] via: ... The medical image analysis device 100 may receive a medical image from a medical image apparatus by wire or wirelessly. The medical image apparatus may include a medical imaging device and a picture archiving and communication system. Also, the medical image analysis device 100 may acquire a medical image stored in the memory 220)
a hardware processor; (See at least [column 9, lines 45-58] via: “…at least one of the data acquisition unit 111, the preprocessing unit 112, the training data selection unit 113, the model training unit 114, and the model evaluation unit 115 may be manufactured in the form of at least one hardware chip and installed in an electronic device. For example, at least one of the data acquisition unit 111, the preprocessing unit 112, the training data selection unit 113, the model training unit 114, and the model evaluation unit 115 may be manufactured in the form of a dedicated hardware chip for AI or may be manufactured as a part of an existing general purpose processor (e.g., a CPU or an application processor) or a dedicated graphics processor (e.g., a GPU) and installed in the aforementioned various electronic devices…”)
an acquirer; (See at least [column 7, lines 48-51] via: “…The data acquisition unit 111 may acquire data required for machine learning. Since a lot of data is required for learning, the data acquisition unit 111 may receive a data set including a plurality of pieces of data…”) and
an outputter, (See at least [column 13, lines 33-39] via: “…The medical image analysis device 100 may perform an operation 340 of outputting the result information. The result information may be transmitted by wire or wirelessly to a hospital server, a terminal of the healthcare worker, a terminal of the patient, or a payer server. The result information may be displayed on a display of the medical image analysis device 100 or output through a speaker…”)
wherein the hardware processor acquires first medically-related information obtained through computer processing performed on medical information (receiving a medical image… first lesion information), the acquirer acquires second medically-related information created by a user on a basis of the medical information (receiving report information which is a healthcare worker's judgement result of the medical image… second lesion information) , the hardware processor compares the acquired first medically-related information and the second medically-related information acquired by the acquirer, and the hardware processor outputs statistical information on a basis of the first medically- related information and the second medically-related information (correspondence between first lesion information and second lesion information by applying the first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and the second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, to a third analysis model, and outputting the result information). (See at least [column 2, lines 10-21] via: “…there is provided an image interpretation method including receiving a medical image, receiving report information which is a healthcare worker's judgement result of the medical image, generating result information representing correspondence between first lesion information and second lesion information by applying the first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and the second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, to a third analysis model, and outputting the result information. …”; in addition see at least [column 3, lines 27-39] via: “…there is provided a device for analyzing a medical image, the device including a processor and a memory. On the basis of instructions stored in the memory, the processor performs operations of receiving a medical image, receiving report information which is a healthcare worker's judgement result of the medical image, generating result information representing correspondence between first lesion information and second lesion information by applying the first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and the second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, to a third analysis model, and outputting the result information…”; in addition see at least [column 3, lines 61-67 and column 4, lines 1-15] via: “…On the basis of the instructions stored in the memory, the processor may further perform operations of acquiring the first lesion information by applying the medical image to a first analysis model, and acquiring the second lesion information by applying the report information to a second analysis model. The first analysis model may be a model which has machine-learned correlations between a plurality of past medical images and a plurality of pieces of first past lesion information about the plurality of past medical images, the second analysis model may include a model which has machine-learned correlations between a plurality of pieces of past report information and a plurality of pieces of second past lesion information about the plurality of pieces of past report information, and the third analysis model may be a model which has machine-learned the plurality of pieces of first past lesion information, the plurality of pieces of second past lesion information, and past result information representing correspondence between the plurality of pieces of first past lesion information and the plurality of pieces of second past lesion information…”; in addition see at least [column 27, lines 31-39] via: “…the accuracy of the medical image interpretation of the healthcare worker may be based on the number of times that the first lesion information 412 is identical to the second lesion information 422. Also, the accuracy of the medical image interpretation of the healthcare worker may be based on a ratio of the number of times that the first lesion information 412 is identical to the second lesion information 422 to the total number of medical image interpretation times of the healthcare worker…”)
the statistical information is configured to assess a reliability of the first medically-related information compared to the second medically-related information; (See at least [column 2, lines 10-21] via: “…there is provided an image interpretation method including receiving a medical image, receiving report information which is a healthcare worker's judgement result of the medical image, generating result information representing correspondence between first lesion information and second lesion information by applying the first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and the second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, to a third analysis model, and outputting the result information. …”; in addition see at least [column 3, lines 27-39] via: “…there is provided a device for analyzing a medical image, the device including a processor and a memory. On the basis of instructions stored in the memory, the processor performs operations of receiving a medical image, receiving report information which is a healthcare worker's judgement result of the medical image, generating result information representing correspondence between first lesion information and second lesion information by applying the first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and the second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, to a third analysis model, and outputting the result information…”) The examiner interprets the other of the first medically related information and the second medically related information to be the report information based on the third analysis model to which both the first and second medically related information are compared
the hardware processor acquires third medically-related information obtained through second computer processing, being different from the first computer processing, performed on the medical information, and outputs a correct answer ratio of the first medically-related information as statistical information on a basis of the third ground truth data (See at least [column 17, lines 16-22] via: “…the medical image analysis device 100 may acquire the second lesion type information by applying the report information 421 to the second analysis model 420. The second analysis model 420 may be a model which has machine-learned relations between past report information and type information of a past lesion existing in the past report information…”; in addition see at least [column 14, lines 43-45] via: “…The medical image analysis device 100 may not include the first analysis model 410. In this case, first lesion information 412 may include the medical image 411….”; in addition see at least [column 26, lines 46-51] via: “…Referring to FIG. 6, the medical image analysis device 100 may not include the first analysis model 410. The medical image analysis device 100 may input the medical image 411 to the third analysis model 400 as it is. In other words, the first lesion information 412 may include the medical image 411…”; in addition see at least [column 20, lines 31-38] via: “…The second analysis model 420 may be a model which has machine-learned correlations between a plurality of pieces of past report information 711 and a plurality of pieces of second past lesion information 712 corresponding to the plurality of pieces of past report information 711. The plurality of pieces of past report information 711 and the plurality of pieces of second past lesion information 712 may be ground truth information..”; in addition see at least [column 8, lines 28-37] via: “… The machine teaming model may be built in consideration of the application field thereof, the purpose thereof, the computing performance of a device, or the like. The machine learning model may be based on a neural network. For example, a deep neural network (DNN), a recurrent neural network (RNN), a long short-term memory (LSTM) model, a bidirectional recurrent deep neural network (BRDNN), a convolutional neural network (CNN), etc. may be used as the machine learning model, but the machine learning model is not limited thereto..”; in addition see at least [column 23, lines 46-54] via: “…The medical image analysis device 100 may perform an operation of acquiring the result information 430 by applying the first lesion information 412 and the second lesion information 422 to the third analysis model 400. The first lesion information 412 and the second lesion information 422, which are inputs to the third analysis model 400, and the result information 430, which is an output from the third analysis model 400, have been described in FIG. 4..”; in addition see at least [column 25, lines 65-67, and column 26, lines 1-4] via: “…The third analysis model 400 may receive the first feature information included in the first lesion information 412 and the second feature information included in the second lesion information 422 as inputs. The third analysis model 400 may output the result information 430 representing correspondence between the first feature information and second feature information..”; in addition see at least [column 29, lines 17-25] via: “…The medical image analysis device 100 may perform an operation 1110 of receiving evaluation results of a user regarding first lesion information 412. The user may determine whether the first lesion information 412 .. is accurate or inaccurate and input the information to the medical image analysis device 100. The medical image analysis device 100 may determine whether the first lesion information 412 is accurate or inaccurate on the basis of the user's input…”; in addition see at least [column 31, lines 66-67 and column 32, lines 1-2] via: “….When first lesion information differs from second lesion information, the experienced healthcare worker may select inaccurate information between the first lesion information and the second lesion information…”) The examiner interprets the third medically-related information obtained through computer processing performed on the medical information, as third ground truth data equivalent to the second lesion information obtained after the report information goes through the second analysis model. The Examiner interprets “third ground truth” as resulting from one of several machine learning models based on a neural network including a deep neural network (DNN), a recurring neural network (RNN), a long term memory model (LSTM), a bidirectional recurrent deep neural network (BRDNN), or a convolution neural network (CNN) as described by Jeong [column 8, lines 28-37]. This ground truth second lesion information is subsequently compared to the raw first lesion information (that has not gone through the first analysis model) to obtain statistics as to the accuracy of the first lesion information.
wherein an output format of the statistical information is changed according to one of a ratio of agreement or a ratio of non-agreement between the first medically-related information and the second medically-related information, or between the first medically-related information and the third medical-related information (See at least [column 25, lines 65-67 and column 26 lines 1-4] via: “…The third analysis model 400 may receive the first feature information included in the first lesion information 412 and the second feature information included in the second lesion information 422 as inputs. The third analysis model 400 may output the result information 430 representing correspondence between the first feature information and second feature information..”; in addition see at least [column 24, lines 52-64] via: “…the degree of regional coincidence may be determined by the intersection over union (IOU) metric or the like. Also, the medical image analysis device 100 may acquire the result information 430 on the basis of the degree of regional coincidence without the third analysis model 400. The degree of regional coincidence represents how much two different areas overlap. On the basis of the degree of regional coincidence, the medical image analysis device 100 may determine the result information 430 to be close to 1 when the first lesion area 910 is more identical to the second lesion area 920, and may determine the result information 430 to be close to 0 when the first lesion area 910 is less identical to the second lesion area 920. ..”)
Regarding claim 2: Jeong teaches the invention as claimed and detailed above with respect to claim 1. Jeong also teaches:
wherein the statistical information is a ratio of agreement or a ratio of non-agreement between the first medically-related information and the second medically-related information. (See at least [column 12, lines 65-67, column 13, lines 1-12] via: “… the medical image analysis device 100 may represent the similarity between the first lesion information and the second lesion information by a real number or a natural number. For example, the medical image analysis device 100 may represent the similarity by a larger number when the first lesion information is more similar to the second lesion information, and may represent the similarity by a smaller number when the first lesion information is less similar to the second lesion information...”; in addition see at least [column 27, lines 8-30] via: “…The medical image analysis device 100 may perform an operation 1010 of determining the accuracy of a medical image interpretation made by a healthcare worker on the basis of the result information 430. Referring to FIG. 4, the first lesion information 412 may represent diagnosis results which have been automatically generated regarding the medical image 411 by the medical image analysis device 100. Also, the second lesion information 422 may represent diagnosis results generated regarding the medical image 411 by a healthcare worker. The result information 430 may represent correspondence between the first lesion information 412 and the second lesion information 422. The medical image analysis device 100 may cumulatively store result information of healthcare workers. When the reliability of the first lesion information 412 is high, the medical image analysis device 100 may determine the accuracy of the medical image interpretation of the healthcare worker on the basis of the result information 430. In other words, since it is possible to rely on the first lesion information 412, the accuracy of the medical image interpretation of the healthcare worker is determined on the basis of the first lesion information 412 ..”; in addition see at least [column 27, lines 31-39] via: “…Specifically, the accuracy of the medical image interpretation of the healthcare worker may be based on the number of times that the first lesion information 412 is identical to the second lesion information 422. Also, the accuracy of the medical image interpretation of the healthcare worker may be based on a ratio of the number of times that the first lesion information 412 is identical to the second lesion information 422 to the total number of medical image interpretation times of the healthcare worker…”; in addition see at least [column 31, lines 35-58] via: “…The medical image analysis device 100 may cumulatively store result information 430 in the memory. The medical image analysis device 100 may perform an operation 1311 of generating hospital evaluation information on the basis of the accumulated result information. The hospital evaluation information may represent a ratio at which healthcare workers belonging to the hospital have accurately interpreted medical images. The hospital evaluation information may be acquired on the basis of a ratio of a number of times that first lesion information 412 is identical to second lesion information 422 to a total number of interpretations made by healthcare workers of the hospital. The number of times that the first lesion information 412 is identical to the second lesion information 422 may be acquired on the basis of the third analysis model 400. As described above, the first lesion information 412 is lesion information acquired by interpreting a medical image on the basis of the first analysis model 410, and the second lesion information 422 is lesion information acquired by applying the second analysis model 420 to report information of a healthcare worker. When the first lesion information 412 is identical to the second lesion information 422, the medical image analysis device 100 may determine that the healthcare worker has accurately interpreted the medical image…”)
Regarding claim 3: Jeong teaches the invention as claimed and detailed above with respect to claim 1. Jeong also teaches:
wherein the hardware processor treats the first medically-related information as first ground truth data, and outputs a correct answer ratio of the second medically-related information as statistical information on a basis of the first ground truth data. (See at least [column 14, lines 29-37] via: “…The medical image analysis device 100 may receive a medical image 411. The medical image analysis device 100 may perform an operation of acquiring first lesion information by applying the medical image 411 to a first analysis model 410. The first analysis model 410 may be a model which has machine-learned correlations between a plurality of past medical images and a plurality of pieces of first past lesion information corresponding to the plurality of past medical images…”; in addition see at least [column 15, lines 1-4] via: “…The medical image analysis device 100 may not include the second analysis model 420. In this case, the second lesion information 422 may include the report information 421….”; in addition see at least [column 19, lines 34-43] via: “…The first analysis model 410 may be a model which has machine-learned correlations between a plurality of past medical images 611 and a plurality of pieces of first past lesion information 612 corresponding to the plurality of past medical images 611. The plurality of past medical images 611 and the plurality of pieces of first past lesion information 612 corresponding to the plurality of past medical images 611 may be ground truth information. The ground truth information may be information determined to be correct by an expert….”; in addition see at least [column 27, lines 8-30] via: “…The medical image analysis device 100 may perform an operation 1010 of determining the accuracy of a medical image interpretation made by a healthcare worker on the basis of the result information 430. Referring to FIG. 4, the first lesion information 412 may represent diagnosis results which have been automatically generated regarding the medical image 411 by the medical image analysis device 100. Also, the second lesion information 422 may represent diagnosis results generated regarding the medical image 411 by a healthcare worker. The result information 430 may represent correspondence between the first lesion information 412 and the second lesion information 422. The medical image analysis device 100 may cumulatively store result information of healthcare workers. When the reliability of the first lesion information 412 is high, the medical image analysis device 100 may determine the accuracy of the medical image interpretation of the healthcare worker on the basis of the result information 430. In other words, since it is possible to rely on the first lesion information 412, the accuracy of the medical image interpretation of the healthcare worker is determined on the basis of the first lesion information 412….”; in addition see at least [column 27, lines 31-39] via: “…Specifically, the accuracy of the medical image interpretation of the healthcare worker may be based on the number of times that the first lesion information 412 is identical to the second lesion information 422. Also, the accuracy of the medical image interpretation of the healthcare worker may be based on a ratio of the number of times that the first lesion information 412 is identical to the second lesion information 422 to the total number of medical image interpretation times of the healthcare worker…”; in addition see at least [column 30, lines 20-32] via: “…When the accuracy of the first lesion information 412 is high and the first lesion information 412 differs from the second lesion information 422, the hospital server 1200 may determine that the second lesion information 422 is inaccurate…”) The Examiner explains in more simplistic terms how Jeong teaches this claim. Looking at figure 4 the first lesion information 412 is determined to be ground truth after the medical image 411 is evaluated by the first analysis model 410. On the other hand in this situation the second lesion information 422 is identical to the raw report information that does not go through the second analysis model 420. Subsequently the first lesion information 412 that corresponds to the ground truth of the medical image is compared to the second lesion information 422 that is equivalent to the report information under the third analysis model 400 that determines result information 430 as to how accurate is the second lesion information depending as to how close it is to the first lesion information that corresponds to the ground truth of the medical image.
Regarding claim 4: Jeong teaches the invention as claimed and detailed above with respect to claim 1. Jeong also teaches:
wherein the hardware processor treats the second medically-related information as second ground truth data, and outputs a correct answer ratio of the first medically-related information as statistical information on a basis of the second ground truth data. (See at least [column 17, lines 16-22] via: “…the medical image analysis device 100 may acquire the second lesion type information by applying the report information 421 to the second analysis model 420. The second analysis model 420 may be a model which has machine-learned relations between past report information and type information of a past lesion existing in the past report information…”; in addition see at least [column 14, lines 43-45] via: “…The medical image analysis device 100 may not include the first analysis model 410. In this case, first lesion information 412 may include the medical image 411….”; in addition see at least [column 26, lines 46-51] via: “…Referring to FIG. 6, the medical image analysis device 100 may not include the first analysis model 410. The medical image analysis device 100 may input the medical image 411 to the third analysis model 400 as it is. In other words, the first lesion information 412 may include the medical image 411…”; in addition see at least [column 20, lines 31-38] via: “…The second analysis model 420 may be a model which has machine-learned correlations between a plurality of pieces of past report information 711 and a plurality of pieces of second past lesion information 712 corresponding to the plurality of pieces of past report information 711. The plurality of pieces of past report information 711 and the plurality of pieces of second past lesion information 712 may be ground truth information..”; in addition see at least [column 23, lines 46-54] via: “…The medical image analysis device 100 may perform an operation of acquiring the result information 430 by applying the first lesion information 412 and the second lesion information 422 to the third analysis model 400. The first lesion information 412 and the second lesion information 422, which are inputs to the third analysis model 400, and the result information 430, which is an output from the third analysis model 400, have been described in FIG. 4..”; in addition see at least [column 25, lines 65-67 and column 26, lines 1-4] via: “…The third analysis model 400 may receive the first feature information included in the first lesion information 412 and the second feature information included in the second lesion information 422 as inputs. The third analysis model 400 may output the result information 430 representing correspondence between the first feature information and second feature information..”; in addition see at least [column 29, lines 17-27] via: “…The medical image analysis device 100 may perform an operation 1110 of receiving evaluation results of a user regarding first lesion information 412. The user may determine whether the first lesion information 412 .. is accurate or inaccurate and input the information to the medical image analysis device 100. The medical image analysis device 100 may determine whether the first lesion information 412 is accurate or inaccurate on the basis of the user's input…”; in addition see at least [column 31, lines 66-67 and column 32, lines 1-2] via: “….When first lesion information differs from second lesion information, the experienced healthcare worker may select inaccurate information between the first lesion information and the second lesion information...”) The Examiner explains in more simplistic terms how Jeong teaches this claim. Looking at figure 4 the second lesion information 422 is determined to be ground truth after the report information 421 is evaluated by the second analysis model 422. On the other hand in this situation the first lesion information 412 is identical to the medical image that does not go through the first analysis model 410. Subsequently the second lesion information 422 that corresponds to the ground truth of the report information is compared to the first lesion information 412 that is equivalent to the medical image under the third analysis model 400 that determines result information 430 as to how accurate is the first lesion information depending as to how close it is to the second lesion information that corresponds to the ground truth of the report information.
Regarding claim 6: Jeong teaches the invention as claimed and detailed above with respect to claim 1. Jeong also teaches:
wherein the second medically-related information is confirmed diagnosis information. (See at least [column 17, lines 16-22] via: “…the medical image analysis device 100 may acquire the second lesion type information by applying the report information 421 to the second analysis model 420. The second analysis model 420 may be a model which has machine-learned relations between past report information and type information of a past lesion existing in the past report information…”; in addition see at least [column 20. Lines 31-38] via: “…The second analysis model 420 may be a model which has machine-learned correlations between a plurality of pieces of past report information 711 and a plurality 2of pieces of second past lesion information 712 corresponding to the plurality of pieces of past report information 711. The plurality of pieces of past report information 711 and the plurality of pieces of second past lesion information 712 may be ground truth information..”; The Examiner notes that within the context of medical diagnosis, “ground truth” essentially represents the correct diagnosis, acting as a reference standard against which other diagnostic methods are compared. It is considered the most reliable diagnosis for a given condition, often established by a gold standard test or expert.
Regarding claim 7: Jeong teaches the invention as claimed and detailed above with respect to claim 1. Jeong also teaches:
information, the hardware processor treats the second medically-related information as second ground truth data, and outputs a correct answer ratio of the first medically-related information as statistical information on a basis of the second ground truth data. (See at least [column 17, lines 16-22] via: “…the medical image analysis device 100 may acquire the second lesion type information by applying the report information 421 to the second analysis model 420. The second analysis model 420 may be a model which has machine-learned relations between past report information and type information of a past lesion existing in the past report information…”; in addition see at least [column 20. Lines 31-38] via: “…The second analysis model 420 may be a model which has machine-learned correlations between a plurality of pieces of past report information 711 and a plurality of pieces of second past lesion information 712 corresponding to the plurality of pieces of past report information 711. The plurality of pieces of past report information 711 and the plurality of pieces of second past lesion information 712 may be ground truth information..”; in addition see at least [column 12, lines -65-67, and column 13, lines 1-12] via: “… the medical image analysis device 100 may represent the similarity between the first lesion information and the second lesion information by a real number or a natural number. For example, the medical image analysis device 100 may represent the similarity by a larger number when the first lesion information is more similar to the second lesion information, and may represent the similarity by a smaller number when the first lesion information is less similar to the second lesion information..”)
Regarding claim 9: Jeong teaches:
A medical imaging system, comprising: an image generation device; (See at least [column 11, lines 49-50] via: “…The medical image apparatus may include a medical imaging device..”) and
an analysis device operably connected to the image generation device and configured to perform analysis based on first medical information from the image generation device, the analysis device comprising: (See at least [column 6, lines 52-56] via: “… Referring to FIG. 1, the medical image analysis device 100 according to an embodiment of the present disclosure may include a data learning unit 110 and a data recognition unit 120. The aforementioned medical image analysis device 100 may include a processor and a memory…”; in addition see at least [column 11, lines 47-52] via: ... The medical image analysis device 100 may receive a medical image from a medical image apparatus by wire or wirelessly. The medical image apparatus may include a medical imaging device and a picture archiving and communication system. Also, the medical image analysis device 100 may acquire a medical image stored in the memory 220)
a hardware processor that acquires first medically-related information obtained through computer processing performed on medical information; (See at least [column 2, lines 10-12] via: “…there is provided an image interpretation method including receiving a medical image …”; in addition see at least [column 3, lines 27-30] via: “…there is provided a device for analyzing a medical image, the device including a processor and a memory. On the basis of instructions stored in the memory, the processor performs operations of receiving a medical image..”)and
an acquirer into which a confirmed diagnosis information is input by a user, wherein the hardware processor compares the acquired first medically-related information and the confirmed diagnosis information acquired by the acquirer. (See at least [column 17, lines 16-22] via: “…the medical image analysis device 100 may acquire the second lesion type information by applying the report information 421 to the second analysis model 420. The second analysis model 420 may be a model which has machine-learned relations between past report information and type information of a past lesion existing in the past report information…”; in addition see at least [column 14, lines 43-45] via: “…The medical image analysis device 100 may not include the first analysis model 410. In this case, first lesion information 412 may include the medical image 411….”; in addition see at least [column 26, lines 46-51] via: “…Referring to FIG. 6, the medical image analysis device 100 may not include the first analysis model 410. The medical image analysis device 100 may input the medical image 411 to the third analysis model 400 as it is. In other words, the first lesion information 412 may include the medical image 411…”; in addition see at least [column 20, lines 31-38] via: “…The second analysis model 420 may be a model which has machine-learned correlations between a plurality of pieces of past report information 711 and a plurality of pieces of second past lesion information 712 corresponding to the plurality of pieces of past report information 711. The plurality of pieces of past report information 711 and the plurality of pieces of second past lesion information 712 may be ground truth information..”; in addition see at least [column 23, lines 46-54] via: “…The medical image analysis device 100 may perform an operation of acquiring the result information 430 by applying the first lesion information 412 and the second lesion information 422 to the third analysis model 400. The first lesion information 412 and the second lesion information 422, which are inputs to the third analysis model 400, and the result information 430, which is an output from the third analysis model 400, have been described in FIG. 4..”; in addition see at least [column 25, lines 65-67 and column 26, lines 1-4] via: “…The third analysis model 400 may receive the first feature information included in the first lesion information 412 and the second feature information included in the second lesion information 422 as inputs. The third analysis model 400 may output the result information 430 representing correspondence between the first feature information and second feature information..”; in addition see at least [column 29, lines 17-27] via: “…The medical image analysis device 100 may perform an operation 1110 of receiving evaluation results of a user regarding first lesion information 412. The user may determine whether the first lesion information 412 .. is accurate or inaccurate and input the information to the medical image analysis device 100. The medical image analysis device 100 may determine whether the first lesion information 412 is accurate or inaccurate on the basis of the user's input…”; in addition see at least [column 31, lines 66-67 and column 31 lines 1-6] via: “….When first lesion information differs from second lesion information, the experienced healthcare worker may select inaccurate information between the first lesion information and the second lesion information. When the first lesion information is inaccurate, the medical image analysis device 100 may update the first analysis model 410..”)
the result of the comparison is utilized to assess a reliability of the first medically-related information compared to the confirmed diagnosis information; (See at least [column 2, lines 10-21] via: “…there is provided an image interpretation method including receiving a medical image, receiving report information which is a healthcare worker's judgement result of the medical image, generating result information representing correspondence between first lesion information and second lesion information by applying the first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and the second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, to a third analysis model, and outputting the result information. …”; in addition see at least [column 3, lines 27-39] via: “…there is provided a device for analyzing a medical image, the device including a processor and a memory. On the basis of instructions stored in the memory, the processor performs operations of receiving a medical image, receiving report information which is a healthcare worker's judgement result of the medical image, generating result information representing correspondence between first lesion information and second lesion information by applying the first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and the second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, to a third analysis model, and outputting the result information…”)
the hardware processor acquires third medically-related information obtained through second computer processing, being different from the first computer processing, performed on the medical information; the hardware processor treats the third medically-related information as third ground truth data; and outputs a correct answer ratio of the first medically-related information as statistical information on a basis of the third ground truth data (See at least [column 17, lines 16-22] via: “…the medical image analysis device 100 may acquire the second lesion type information by applying the report information 421 to the second analysis model 420. The second analysis model 420 may be a model which has machine-learned relations between past report information and type information of a past lesion existing in the past report information…”; in addition see at least [column 14, lines 43-45] via: “…The medical image analysis device 100 may not include the first analysis model 410. In this case, first lesion information 412 may include the medical image 411….”; in addition see at least [column 26, lines 46-51] via: “…Referring to FIG. 6, the medical image analysis device 100 may not include the first analysis model 410. The medical image analysis device 100 may input the medical image 411 to the third analysis model 400 as it is. In other words, the first lesion information 412 may include the medical image 411…”; in addition see at least [column 20, lines 31-38] via: “…The second analysis model 420 may be a model which has machine-learned correlations between a plurality of pieces of past report information 711 and a plurality of pieces of second past lesion information 712 corresponding to the plurality of pieces of past report information 711. The plurality of pieces of past report information 711 and the plurality of pieces of second past lesion information 712 may be ground truth information..”; in addition see at least [column 8, lines 28-37] via: “… The machine teaming model may be built in consideration of the application field thereof, the purpose thereof, the computing performance of a device, or the like. The machine learning model may be based on a neural network. For example, a deep neural network (DNN), a recurrent neural network (RNN), a long short-term memory (LSTM) model, a bidirectional recurrent deep neural network (BRDNN), a convolutional neural network (CNN), etc. may be used as the machine learning model, but the machine learning model is not limited thereto..”; in addition see at least [column 23, lines 46-54] via: “…The medical image analysis device 100 may perform an operation of acquiring the result information 430 by applying the first lesion information 412 and the second lesion information 422 to the third analysis model 400. The first lesion information 412 and the second lesion information 422, which are inputs to the third analysis model 400, and the result information 430, which is an output from the third analysis model 400, have been described in FIG. 4..”; in addition see at least [column 25, lines 65-67, and column 26, lines 1-4] via: “…The third analysis model 400 may receive the first feature information included in the first lesion information 412 and the second feature information included in the second lesion information 422 as inputs. The third analysis model 400 may output the result information 430 representing correspondence between the first feature information and second feature information..”; in addition see at least [column 29, lines 17-25] via: “…The medical image analysis device 100 may perform an operation 1110 of receiving evaluation results of a user regarding first lesion information 412. The user may determine whether the first lesion information 412 .. is accurate or inaccurate and input the information to the medical image analysis device 100. The medical image analysis device 100 may determine whether the first lesion information 412 is accurate or inaccurate on the basis of the user's input…”; in addition see at least [column 31, lines 66-67 and column 32, lines 1-2] via: “….When first lesion information differs from second lesion information, the experienced healthcare worker may select inaccurate information between the first lesion information and the second lesion information…”) The examiner interprets the third medically-related information obtained through computer processing performed on the medical information, as third ground truth data equivalent to the second lesion information obtained after the report information goes through the second analysis model. The Examiner interprets “third ground truth” as resulting from one of several machine learning models based on a neural network including a deep neural network (DNN), a recurring neural network (RNN), a long term memory model (LSTM), a bidirectional recurrent deep neural network (BRDNN), or a convolution neural network (CNN) as described by Jeong [column 8, lines 28-37]. This ground truth second lesion information is subsequently compared to the raw first lesion information (that has not gone through the first analysis model) to obtain statistics as to the accuracy of the first lesion information.
wherein an output format of the statistical information is changed according to one of a ratio of agreement or a ratio of non-agreement between the first medically-related information and the second medically-related information, or between the first medically-related information and the third medical-related information (See at least [column 25, lines 65-67 and column 26 lines 1-4] via: “…The third analysis model 400 may receive the first feature information included in the first lesion information 412 and the second feature information included in the second lesion information 422 as inputs. The third analysis model 400 may output the result information 430 representing correspondence between the first feature information and second feature information..”; in addition see at least [column 24, lines 52-64] via: “…the degree of regional coincidence may be determined by the intersection over union (IOU) metric or the like. Also, the medical image analysis device 100 may acquire the result information 430 on the basis of the degree of regional coincidence without the third analysis model 400. The degree of regional coincidence represents how much two different areas overlap. On the basis of the degree of regional coincidence, the medical image analysis device 100 may determine the result information 430 to be close to 1 when the first lesion area 910 is more identical to the second lesion area 920, and may determine the result information 430 to be close to 0 when the first lesion area 910 is less identical to the second lesion area 920. ..”)
Regarding claims 10 & 11: Jeong teaches:
A non-transitory computer readable storage medium storing a program causing a computer to perform: (See at least [column 34, lines 12-16] via: “…the above-described embodiments of the present disclosure can be written as a program that can be executed in a computer and can be implemented by a general-use digital computer which runs the program using computer-readable recording media...”)
acquiring medical information from an image generation device; (See at least [column 11, lines 49-50] via: “…The medical image apparatus may include a medical imaging device..”)
Analyzing the medical information through first computer processing to acquire first medically-related information; acquiring second medically-related information created by a user on a basis of the medical information; (See at least [column 2, lines 10-14] via: “…there is provided an image interpretation method including receiving a medical image, receiving report information which is a healthcare worker's judgement result of the medical image …”; in addition see at least [column 3, lines 27-32] via: “…there is provided a device for analyzing a medical image, the device including a processor and a memory. On the basis of instructions stored in the memory, the processor performs operations of receiving a medical image, receiving report information which is a healthcare worker's judgement result of the medical imageand
comparing the first medically-related information and the acquired second medically-related information, wherein the comparing includes outputting statistical information on a basis of the first medically-related information and the second medically-related information (See at least [column 2, lines 14-21] via: “…generating result information representing correspondence between first lesion information and second lesion information by applying the first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and the second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, to a third analysis model, and outputting the result information. …”; in addition see at least [column 3, lines 27-39] via: “…there is provided a device for analyzing a medical image, the device including a processor and a memory. On the basis of instructions stored in the memory, the processor performs operations of receiving a medical image, receiving report information which is a healthcare worker's judgement result of the medical image, generating result information representing correspondence between first lesion information and second lesion information by applying the first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and the second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, to a third analysis model, and outputting the result information…”; in addition see at least [column 3, lines 61-67 and column 4, lines 1-15] via: “…On the basis of the instructions stored in the memory, the processor may further perform operations of acquiring the first lesion information by applying the medical image to a first analysis model, and acquiring the second lesion information by applying the report information to a second analysis model. The first analysis model may be a model which has machine-learned correlations between a plurality of past medical images and a plurality of pieces of first past lesion information about the plurality of past medical images, the second analysis model may include a model which has machine-learned correlations between a plurality of pieces of past report information and a plurality of pieces of second past lesion information about the plurality of pieces of past report information, and the third analysis model may be a model which has machine-learned the plurality of pieces of first past lesion information, the plurality of pieces of second past lesion information, and past result information representing correspondence between the plurality of pieces of first past lesion information and the plurality of pieces of second past lesion information…”; in addition see at least [column 27, lines 31-39] via: “…the accuracy of the medical image interpretation of the healthcare worker may be based on the number of times that the first lesion information 412 is identical to the second lesion information 422. Also, the accuracy of the medical image interpretation of the healthcare worker may be based on a ratio of the number of times that the first lesion information 412 is identical to the second lesion information 422 to the total number of medical image interpretation times of the healthcare worker..”)
the statistical information is configured to assess a reliability of the first medically-related information compared to the second medically-related information; (See at least [column 2, lines 10-21] via: “…there is provided an image interpretation method including receiving a medical image, receiving report information which is a healthcare worker's judgement result of the medical image, generating result information representing correspondence between first lesion information and second lesion information by applying the first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and the second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, to a third analysis model, and outputting the result information. …”; in addition see at least [column 3, lines 27-39] via: “…there is provided a device for analyzing a medical image, the device including a processor and a memory. On the basis of instructions stored in the memory, the processor performs operations of receiving a medical image, receiving report information which is a healthcare worker's judgement result of the medical image, generating result information representing correspondence between first lesion information and second lesion information by applying the first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and the second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, to a third analysis model, and outputting the result information…”) The examiner interprets the other of the first medically related information and the second medically related information to be the report information based on the third analysis model to which both the first and second medically related information are compared.
the hardware processor acquires third medically-related information obtained through second computer processing, being different from the first computer processing, performed on the medical information; the hardware processor treats the third medically-related information as third ground truth data; outputs a correct answer ratio of the first medically-related information as statistical information on a basis of the third ground truth data (See at least [column 17, lines 16-22] via: “…the medical image analysis device 100 may acquire the second lesion type information by applying the report information 421 to the second analysis model 420. The second analysis model 420 may be a model which has machine-learned relations between past report information and type information of a past lesion existing in the past report information…”; in addition see at least [column 14, lines 43-45] via: “…The medical image analysis device 100 may not include the first analysis model 410. In this case, first lesion information 412 may include the medical image 411….”; in addition see at least [column 26, lines 46-51] via: “…Referring to FIG. 6, the medical image analysis device 100 may not include the first analysis model 410. The medical image analysis device 100 may input the medical image 411 to the third analysis model 400 as it is. In other words, the first lesion information 412 may include the medical image 411…”; in addition see at least [column 20, lines 31-38] via: “…The second analysis model 420 may be a model which has machine-learned correlations between a plurality of pieces of past report information 711 and a plurality of pieces of second past lesion information 712 corresponding to the plurality of pieces of past report information 711. The plurality of pieces of past report information 711 and the plurality of pieces of second past lesion information 712 may be ground truth information..”; in addition see at least [column 8, lines 28-37] via: “… The machine teaming model may be built in consideration of the application field thereof, the purpose thereof, the computing performance of a device, or the like. The machine learning model may be based on a neural network. For example, a deep neural network (DNN), a recurrent neural network (RNN), a long short-term memory (LSTM) model, a bidirectional recurrent deep neural network (BRDNN), a convolutional neural network (CNN), etc. may be used as the machine learning model, but the machine learning model is not limited thereto..”; in addition see at least [column 23, lines 46-54] via: “…The medical image analysis device 100 may perform an operation of acquiring the result information 430 by applying the first lesion information 412 and the second lesion information 422 to the third analysis model 400. The first lesion information 412 and the second lesion information 422, which are inputs to the third analysis model 400, and the result information 430, which is an output from the third analysis model 400, have been described in FIG. 4..”; in addition see at least [column 25, lines 65-67, and column 26, lines 1-4] via: “…The third analysis model 400 may receive the first feature information included in the first lesion information 412 and the second feature information included in the second lesion information 422 as inputs. The third analysis model 400 may output the result information 430 representing correspondence between the first feature information and second feature information..”; in addition see at least [column 29, lines 17-25] via: “…The medical image analysis device 100 may perform an operation 1110 of receiving evaluation results of a user regarding first lesion information 412. The user may determine whether the first lesion information 412 .. is accurate or inaccurate and input the information to the medical image analysis device 100. The medical image analysis device 100 may determine whether the first lesion information 412 is accurate or inaccurate on the basis of the user's input…”; in addition see at least [column 31, lines 66-67 and column 32, lines 1-2] via: “….When first lesion information differs from second lesion information, the experienced healthcare worker may select inaccurate information between the first lesion information and the second lesion information…”) The examiner interprets the third medically-related information obtained through computer processing performed on the medical information, as third ground truth data equivalent to the second lesion information obtained after the report information goes through the second analysis model. The Examiner interprets “third ground truth” as resulting from one of several machine learning models based on a neural network including a deep neural network (DNN), a recurring neural network (RNN), a long term memory model (LSTM), a bidirectional recurrent deep neural network (BRDNN), or a convolution neural network (CNN) as described by Jeong [column 8, lines 28-37]. This ground truth second lesion information is subsequently compared to the raw first lesion information (that has not gone through the first analysis model) to obtain statistics as to the accuracy of the first lesion information.
wherein an output format of the statistical information is changed according to one of a ratio of agreement or a ratio of non-agreement between the first medically-related information and the second medically-related information, or between the first medically-related information and the third medical-related information (See at least [column 25, lines 65-67 and column 26 lines 1-4] via: “…The third analysis model 400 may receive the first feature information included in the first lesion information 412 and the second feature information included in the second lesion information 422 as inputs. The third analysis model 400 may output the result information 430 representing correspondence between the first feature information and second feature information..”; in addition see at least [column 24, lines 52-64] via: “…the degree of regional coincidence may be determined by the intersection over union (IOU) metric or the like. Also, the medical image analysis device 100 may acquire the result information 430 on the basis of the degree of regional coincidence without the third analysis model 400. The degree of regional coincidence represents how much two different areas overlap. On the basis of the degree of regional coincidence, the medical image analysis device 100 may determine the result information 430 to be close to 1 when the first lesion area 910 is more identical to the second lesion area 920, and may determine the result information 430 to be close to 0 when the first lesion area 910 is less identical to the second lesion area 920. ..”)
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or
non-obviousness.
Claim 12 is rejected under 35 U.S.C. 103 as being un-patentable by Jeong; in view of Haider et.al. (US 20070282633 A1) hereinafter “Haider”
Regarding claim 12 Jeong teaches the invention as claimed and detailed above with respect to claim 1: wherein the one or more areas of disagreement between the first medically-related medical information and the second medically- related medical information [are provided in tabular form]: (See at least [column 3, lines 29-39] via: “…the processor performs operations of receiving a medical image, receiving report information which is a healthcare worker's judgement result of the medical image, generating result information representing correspondence between first lesion information and second lesion information by applying the first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and the second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, to a third analysis model, and outputting the result information…”; in addition see at least [column 25, lines 65-67 and column 26 lines 1-4] via: “…The third analysis model 400 may receive the first feature information included in the first lesion information 412 and the second feature information included in the second lesion information 422 as inputs. The third analysis model 400 may output the result information 430 representing correspondence between the first feature information and second feature information..”; in addition see at least [column 24, lines 52-64] via: “…the degree of regional coincidence may be determined by the intersection over union (IOU) metric or the like. Also, the medical image analysis device 100 may acquire the result information 430 on the basis of the degree of regional coincidence without the third analysis model 400. The degree of regional coincidence represents how much two different areas overlap. On the basis of the degree of regional coincidence, the medical image analysis device 100 may determine the result information 430 to be close to 1 when the first lesion area 910 is more identical to the second lesion area 920, and may determine the result information 430 to be close to 0 when the first lesion area 910 is less identical to the second lesion area 920. ..”; in addition see at least [column 30, lines 3-10] via: “…The result information 430 may include information on correspondence between first lesion information 412 and second lesion information 422. When it is determined that the first lesion information 412 differs from the second lesion information 422 on the basis of the result information 430, the hospital server 1200 may determine which one of the first lesion information 412 and the second lesion information 422 is inaccurate. ..”; in addition see at least [column 30, lines 20-32] via: “…When the accuracy of the first lesion information 412 is high and the first lesion information 412 differs from the second lesion information 422, the hospital server 1200 may determine that the second lesion information 422 is inaccurate. Also, the medical image analysis device 100 may determine that the second lesion information 422 is inaccurate on the basis of a user's input. The second lesion information 422 being inaccurate may denote that there is information included in the first lesion information 412 but the information is not included in the second lesion information 422, or there is information included in the second lesion information 422 but the information is not included in the first lesion information 412…”)
However Jeong is silent regarding the following limitation which is taught by Haider:
Information provided in tabular form (See at least [claim 10] via: “…display connected to said retrieval engine which displays data in a chart format in which relevant medical information is assembled for said specific medical problem of said specific patient..”). The Examiner interprets presenting in tabular form to be synonymous with presenting in chart form
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Jeong to incorporate the teachings of Haider. Those in the art would have recognized that Jeong’s teaching regarding the degree of correspondence between the first feature information and second feature information which alternatively teaches the degree of disagreement between the two pieces of medical data could be modified to include Haider’s teaching regarding presenting the results in chart or tabular form. The combination of Jeong and Haider is useful to medical personnel by making it more efficient and easier to identify discrepancy in interpretation of medical data.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
Yu (US 20210100471 A1) - SYSTEMS AND METHODS FOR REDUCED LEAD ELECTROCARDIOGRAM DIAGNOSIS USING DEEP NEURAL NETWORKS AND RULE-BASED SYSTEMS - teaches: Methods and systems are provided for automatically diagnosing a patient based on a reduced lead electrocardiogram (ECG), using one or more deep neural networks. In one embodiment, a method for automatically diagnosing a patient using a reduced lead ECG comprises, acquiring reduced lead ECG data, wherein the reduced lead ECG data comprises less than twelve lead signals, determining a type of each of the less than twelve lead signals, selecting a deep neural network based on the type of each of the less than twelve lead signals, and mapping the less than twelve lead signals to a diagnosis using the deep neural network. In this way, reduced lead ECG data may be mapped to a diagnosis using an intelligently selected deep neural network, wherein the deep neural network was trained on reduced lead ECG data comprising a same set of ECG lead types as the acquired reduced lead ECG data.
ELEN (US 20210383262 A1) - SYSTEM AND METHOD FOR EVALUATING A PERFORMANCE OF EXPLAINABILITY METHODS USED WITH ARTIFICIAL NEURAL NETWORKS - teaches: A computing system configured to perform the steps of dividing both a saliency map and a ground-truth feature map into cells in order to obtain segmented saliency map and a segmented feature map, wherein a relevance score is assigned to each cell based on values of individual pixels within the cells in the saliency map and feature map, selecting, for both the segmented saliency map and segmented feature map, a selected number of selected cells corresponding to the most relevant cells having highest relevance scores within the segmented saliency map and the segmented feature map, respectively, and computing a level of agreement between the segmented saliency map and the segmented feature map by comparing the selected cells having highest relevance scores in the segmented saliency map to the selected cells having highest relevance scores in the segmented feature map
石川 亮 (JP 5501491 B2) - Diagnosis Support Apparatus And Control Method - teaches: a medical diagnosis support system that can be used to improve diagnosis efficiency of medical examination data of a doctor by comparing and contrasting a plurality of pieces of diagnosis support information such as a doctor's findings and computer analysis on medical examination data such as a medical image
Response to Arguments
Applicant's arguments filed 3-9-2026, have been fully considered but they are found not
persuasive.
Applicant amended claims 1, 9-11, deleted claims 5,8 as posted in the above analysis with additions underlined and deletions as .
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 101.
Several steps are taken in the analysis as to whether an invention is rejected under 101. The first step is to determine if the claim falls within a statutory category. In this case it does for claims 1, 9 10 and 11 since the claims recite a device, and method, to acquire first and second medically-related information followed by statistical information output based on the first and second medically-related information.
The second step under 2A prong one is to determine if the claims recite an abstract idea, which would be the case if the invention can be grouped as either: a) mathematical concepts; (b) mental processes; or (c) certain methods of organizing human activity (encompassing (i) fundamental economic principles, (ii) commercial or legal interactions or (iii) managing personal behavior or relationships or interactions between people). The current invention is classified as an abstract idea since it may be grouped as mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “acquiring first and second medically-related information followed by statistical information output based on the first and second medically-related information”. Alternatively it may be grouped as certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “acquiring first and second medically-related information followed by statistical information output based on the first and second medically-related information”.
The third step under 2A Prong Two is to determine if additional elements in the claim imposes a meaningful limit on the abstract idea in order to integrate it into a practical idea. The current invention does not represent a practical idea since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
the fourth step under 2B is to determine if additional elements of the claim provide an inventive concept. An invention may be classified as an inventive concept if a computer-implemented processes is determined to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic, and non-conventional even if generic computer operations on a generic computing device is used to implement the abstract idea. The current invention does not represent an inventive concept since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
Step 2A Prong ONE
The Applicant offers no argument as to whether that the claimed subject matter is directed to an abstract idea.
The method used to select the abstract idea, is to strip the additional elements from the claims. As seen below the recited boldened words constitute the abstract idea after stripping the un-boldened additional elements of amended limitation of claims 1, 9-11:
Claim 1:
An analysis device comprising:
a hardware processor;
an acquirer; and
an outputter,
wherein the hardware processor acquires first medically-related information obtained through computer processing performed on medical information,
the acquirer acquires second medically-related information created by a user on a basis of the medical information,
the hardware processor compares the acquired first medically-related information and the second medically-related information acquired by the acquirer, and
the hardware processor outputs statistical information on a basis of the first medically- related information and the second medically-related information
the statistical information is configured to assess a reliability of one of the first medically-related information compared to the second medically-related information; and
the hardware processor acquires third medically-related information obtained through second computer processing, being different from the first computer processing, performed on the medical information and
Claim 9:
An analysis device comprising:
a hardware processor that acquires first medically-related information obtained through computer processing performed on medical information; and
an acquirer into which a confirmed diagnosis information is input by a user wherein the hardware processor compares the acquired first medically-related information and the confirmed diagnosis information acquired by the acquirer.
the result of the comparison is utilized to assess a reliability of the first medically-related information compared to the confirmed diagnosis information;
the hardware processor acquires third medically-related information obtained through second computer processing, being different from the first computer processing, performed on the medical information;
the hardware processor treats the third medically-related information as third ground truth data.
Claims 10 & 11:
analyzing medical information through computer processing to acquire first medically-related information;
acquiring second medically-related information created by a user on a basis of the medical information; and
comparing the first medically-related information and the acquired second medically-related information, wherein
the comparing includes outputting statistical information on a basis of the first medically-related information and the second medically-related information
the statistical information configured to assess a reliability of one of the first medically- related information compared to the second medically-related information;
the hardware processor acquires third medically-related information obtained through second computer processing, being different from the first computer processing, performed on the medical information;
the hardware processor treats the third medically-related information as third ground truth data; and
outputs a correct answer ratio of the first medically-related information as statistical information on a basis of the third ground truth data
The selected abstract idea (boldened limitations) of claims 1, 9, 10 & 11 belong to the classified as an abstract idea since it may be grouped as mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “acquiring first and second medically-related information followed by statistical information output based on the first and second medically-related information”. Alternatively it may be grouped under certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “acquiring first and second medically-related information followed by statistical information output based on the first and second medically-related information”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
Step 2A Prong TWO
The Applicant argues that claims 1, 9-11 have been amended as being drawn to a medical imaging system, and including an image generation device operably connected to the previously claimed analysis device.
The Applicant submits that the claim amendment more clearly incorporates the claims into a practical application, collection medical information from a patient via the medical imaging device and utilization of the result to improve operation of the Al to thereby improve diagnosis reliability and reduce diagnostic time needed by, for example, a radiologist.
The Applicant requests withdrawal of the rejections under 35 U.S.C. 101..
The Examiner disagrees with the Applicant since the arguments provided are not persuasive. What is required for the invention to be directed to a practical application is a demonstration of improvement to the functioning of a computer, or to any other technology or technical field that the invention has recited.
The Examiner restates that claims 1, 9, 10 & 11 do not integrate the abstract idea into a practical application. Claims 1, 9, 10 & 11 do not recite additional elements that impose a meaningful limit on the abstract idea:
Claim 1 recites the following additional elements:
medical imaging system
image generation device
analysis device operably connected to the image generation device and configured t perform analysis based on first medical information from the image generation device;
a hardware processor;
an acquirer;
an outputter;
computer processing;
first computer;
second computer.
Claim 9 recites the following additional elements:
medical imaging system;
image generation device;
an analysis device operably connected to the image generation device and configured to perform analysis based on first medical information from the image generation device
hardware processor;
computer processing;
acquirer;
first computer;
second computer.
Claims 10 & 11 recite the following additional element:
an image generation device
hardware processor;
computer processing;
acquirer;
first computer;
second computer;
Claims 11 recites
A non-transitory computer readable storage medium storing a program
The elements as recited above for claims 1, 9, 10 & 11 amount to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In order to integrate the abstract idea into a practical idea the Applicant could demonstrate at least one of the conditions enumerated below applies:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
The Applicant has not demonstrated any of the above listed conditions. As a result, the Examiner restates the rejection of the invention under 35 USC §101.
Step 2B
Similar to the analysis under Step 2A Prong Two, the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. The use of generic computer components, in combination, do not perform functions that are not merely generic, and non-conventional even if the generic computer operations on a generic computing device is used to implement the abstract idea. Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
In order evaluate whether the claim recites additional elements that amount to an inventive concept what could be shown is:
Adding a specific limitation (unconventional other than what is well-understood, routine, conventional (WURC) activity in the field - see MPEP 2106.05(d)
The Applicant has not demonstrated the above listed condition.
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 102.
The applicant argues that the following amended limitation of claim 1, 9, 10 & 11 are not disclosed by Jeong, the cited reference :
"wherein an output format of the statistical information is changed according to one of a ratio of agreement or a ratio of non-agreement between the first medically-related information and the second medically-related information, or between the first medically-related information and the third medical-related information";
Specifically the Applicant argues that Jeong fails to disclose or suggest at least this element of Applicant's amended claims 1, 9, 10 and 11, and therefore cannot anticipate the claims. As such, Applicant requests withdrawal of the rejections thereof.
The Examiner disagrees since the Applicants argument is not persuasive. Jeong does teach the above limitations in [column 25, lines 65-67 and column 26 lines 1-4], [column 24, lines 52-64]
Thus the examiner maintains the rejection under 35 USC §102 of claims 1-4, 6-7, 9-11 and claim 12 under 35 USC §103.
For reasons of record and as set forth above, the examiner maintains the rejection of claims 1-4, 6-7, 9-12 as being directed to a judicial exception without significantly more, and thereby being directed to non-statutory subject matter under 35 USC §101 in addition to maintaining the rejection under 35 USC §102 of claims 1-4, 6-7, 9-11 and 35 USC §103 of claim 12 . In reaching this decision, the Examiner considered all evidence presented and all arguments actually made by Applicant.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE L MACCAGNO whose telephone number is (571)270-5408. The examiner can normally be reached M-F 8:00 to 5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571)270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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