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 the Claims
The office action is in response to the claims filed on July 24, 2025 for the application filed on August 24, 2023, which is a U.S. National Stage of International Application No. PCT/JP2022/002489 filed on January 25, 2022, which claims priority to Japanese Application No. JP2021-030858 filed on February 26, 2021. Claims 1, 15, 20, 23-24, and 26 have been amended, and claim 14 has been canceled without prejudice or disclaimer. Claims 1-8, 10-12, 15, and 19-26 are currently pending and have been examined as discussed below.
Priority
The certified copy of the foreign priority application was filed on August 24, 2023, and it is a non-English language JP application (JP2021-030858). The certified copy of the PCT priority application was filed on August 24, 2023, and it is a non-English language PCT application (PCT/JP2022/002489). When a claim to priority and the certified copy of the foreign application are received while the application is pending before the examiner, the examiner should review the certified copy to see that it contains no obvious formal defects and that it corresponds in number, date and country to the application identified in the application data sheet for an application filed on or after September 16, 2012, or oath or declaration or application data sheet for an application filed prior to September 16, 2012. See MPEP 215(I).
Applicant cannot rely upon the certified copy of the foreign priority application to overcome the rejections because a translation of said application has not been made of record in accordance with 37 CFR 1.55. When an English language translation of a non-English language application is required, the translation must be that of the certified copy (of the foreign application as filed) submitted together with a statement that the translation of the certified copy is accurate. See MPEP 215 and 216.
Accordingly, the Office requires that English language translations of the associated non-English language foreign application and non-English language PCT application be filed, with the translations being that of the associated certified copies (of the foreign application and PCT application as filed) and submitted together with a statement that each translation of the associated certified copy is accurate.
Claim Objection
Claim 1 is objected to because of the following informalities: the limitation “target image management apparatus” should be replaced with “the image capture apparatus.” Appropriate correction is required.
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 1-8, 10-12, 15 and 19-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Examiners should determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance with the following flowchart. See MPEP 2016(III).
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As shown in the flowchart, Step 1 relates to the statutory categories and ensures that the first criterion is met by confirming that the claim falls within one of the four statutory categories of invention. Id. If the claim as a whole falls within one or more statutory categories (Step 1: YES), the claim must be further analyzed to determine whether it qualifies as eligible at Pathway A or requires further analysis at Step 2A to determine if the claim is directed to a judicial exception. See MPEP 2106.03(II).
For purposes of efficiency in examination, examiners may use a streamlined eligibility analysis (Pathway A) when the eligibility of the claim is self-evident, e.g., because the claim clearly improves a technology or computer functionality. See MPEP 2016.06. However, if there is doubt as to whether the applicant is effectively seeking coverage for a judicial exception itself, the full eligibility analysis (the Alice/Mayo test described in MPEP 2106(III)) should be conducted to determine whether the claim integrates the judicial exception into a practical application or recites significantly more than the judicial exception. Id. Of particular interest, claims that could have been found eligible at Pathway A (streamlined analysis), but are subjected to further analysis at Steps 2A or Step 2B, will ultimately be found eligible at Pathways B or C. See MPEP 2016(III). Thus, if the examiner is uncertain about whether a streamlined analysis is appropriate, the examiner is encouraged to conduct a full eligibility analysis. Id.
Step 2, which is the Supreme Court’s Alice/Mayo test, is a two-part test to identify claims that are directed to a judicial exception (Step 2A) and to then evaluate if additional elements of the claim provide an inventive concept (Step 2B) (also called "significantly more" than the recited judicial exception). See MPEP 2106(III).
Eligibility Step 1:
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether each claim as a whole falls within one of the statutory categories of invention (i.e., a process, machine, manufacture, or composition of matter). See MPEP 2106.03. In the instant application, claims 1-8, 10-12, 15, and 19-23 appear to be directed to a determination system (i.e., a machine), claim 24 appears to be directed to a method (i.e., a process), and claims 25 and 26 appear to be directed to a non-transitory computer-readable medium (i.e., a manufacture).
While each one of claims 1-8, 10-12, 15, and 19-26 appears to fall within one of the statutory categories of invention, the Office has determined that the full eligibility analysis is required because there is doubt as to whether the applicant is effectively seeking coverage for a judicial exception itself. The eligibility of each claim is not self-evident at least because each claim as a whole did not appear to clearly improve a technology or computer functionality. To the contrary, each claim as a whole appeared to merely apply one or more judicial exceptions to a computer.
Accordingly, it has been determined that each one of claims 1-8, 10-12, 15, and 19-26 as a whole falls within one or more statutory categories under Step 1, and the Office proceeds with the full eligibility analysis (the Alice/Mayo test described in MPEP 2106(III)) as discussed below.
Eligibility Step 2A, Prong One:
Under Step 2A, Prong One of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether each claim is directed to one or more of the judicial exceptions (i.e., an abstract idea, law of nature, or natural phenomenon). See MPEP 2106.04(II)(A)(1). After evaluation, it has been determined that claims 1-8, 10-12, 15, and 19-26 are directed to judicial exceptions because claims 1-8, 10-12, 15, and 19-26 recite abstract ideas.
The abstract idea exception includes three groupings: (i) mathematical concepts, (ii) certain methods of organizing human activities (“CMOHA”), and (iii) mental processes. See MPEP 2106.04(a). The CMOHA grouping includes three sub-groupings: (i) "fundamental economic practices" or "fundamental economic principles," (ii) "commercial interactions" or "legal interactions"; and (iii) "managing personal behavior or relationships or interactions between people." See MPEP 2106.04(a). The sub-grouping "managing personal behavior or relationships or interactions between people" includes social activities, teaching, and following rules or instructions. See MPEP 2106.04(a)(2)(II)(C). The "mental processes" grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. See MPEP 2106.04(a)(2)(III). The courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. See MPEP 2106.04(a)(2)(III). Claims can recite a mental process even if they are claimed as being performed on a computer. See MPEP 2106.04(a)(2)(III)(c).
Claims 1, 2, and 23-26 are determined to be directed to a judicial exception because abstract ideas (i.e., CMOHAs and mental processes) are recited in the claims. Representative claim 1 recites the abstract ideas identified in bold as:
A determination system comprising:
an image capture apparatus configured to capture a target image representing a target site of a body of a subject, wherein the target image is one of an X-ray image, a computed tomography image, a magnetic resonance imaging image, a positron emission tomography image, a radio isotope image, a mammographic image, an ultrasonic image, an endoscopic image, or an angiographic image;
an acquisition unit configured to control a target image management apparatus to obtain the target image representing the target site of the body of the subject;
a first generator configured to receive the target image and generate first determination information indicating a condition of the target site from the target image (CMOHA and/or mental process);
a second generator configured to receive the target image and generate feature information indicating features related to a condition of the body of the subject based on the target image used to generate the first determination information (CMOHA and/or mental process), wherein the second generator comprises a feature generation model configured to generate the feature information, the feature generation model comprising a second neural network trained using a plurality of training images of a portion of a body of a subject with identification information corresponding to one or more features in each training image; and
a display controller configured to:
receive the first determination information and the feature information and
control a display apparatus to display the first determination information and the feature information.
With further respect to claims 2 and 23, representative claim 2 further recites an abstract idea identified in bold as:
a. a third generator configured to generate region-of-interest information indicating a region of interest related to the first determination information and being a partial region of the target image (CMOHA and/or mental process),
b. wherein the display controller is configured to acquire the first determination information, the feature information, and the region-of-interest information and cause the display apparatus to display the first determination information, the feature information, and the region-of-interest information.
CMOHA:
The limitations “generate first determination information indicating a condition of the target site from the target image,” “generate feature information indicating features related to a condition of the body of the subject based on the target image used to generate the first determination information,” and “generate region-of-interest information indicating a region of interest related to the first determination information and being a partial region of the target image,” individually and in combination, amount to following rules or instructions, which is an activity of following rules or instructions included in the sub-grouping "managing personal behavior or relationships or interactions between people" encompassed by the CMOHA grouping. Accordingly, claims 1, 2, and 23-26 recite an abstract idea exception falling within the CMOHA grouping, and it is therefore determined that claims 1, 2, and 23-26 are directed to one or more judicial exceptions under Step 2A, Prong One.
Mental Process:
The limitations identified in bold in representative claims 1 and 2 above, individually and in combination, be practically performed in the human mind using observations, evaluations, judgments, and opinions. See MPEP 2106.04(a)(2)(III). With the exception of generic computer-implemented steps (i.e., the first generator, the second generator, etc.), there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper. Accordingly, claims 1, 2, and 23-26 recite an abstract idea merely performed in a computer environment and falling within the mental process grouping, and it is therefore determined that claims 1, 2, and 23-26 are directed to a judicial exception under Step 2A, Prong One.
Dependent claims 3-8, 10-12, 15, and 19-22 are directed to one or more judicial exceptions (i.e., abstract idea exceptions falling within the CMOHA grouping and/or the mental process grouping) under Step 2A, Prong One of the full eligibility analysis as follows:
Claim 3 is directed to one or more judicial exceptions (i.e., abstract ideas) under Step 2A, Prong One because abstract ideas (i.e., CMOHAs and/or mental processes) are recited in the claims. Claim 3 depends from claim 2 and carries over the abstract ideas identified in bold as:
wherein the region of interest comprises a region of interest in a process of generating the first determination information from the target image, and (CMOHA and/or mental process)
the region-of-interest information comprises information indicating a position of the region of interest in the target image.
These abstract ideas in bold, individually or in combination, simply define the associated abstract idea exceptions in claims 1 and 2. More specifically, the abstract idea “a process of generating the first determination information” is carried over from the abstract idea “generate first determination information” in claim 1, and the abstract idea “region-of-interest information” is carried over from the abstract idea “region-of-interest information” in claim 2. Accordingly, for the same reasons discussed above in relation to “generate first determination information” in claim 1 and “region-of-interest information” in claim 2, it is determined that the same abstract ideas carried over from associated claims 1 and 2 to claim 3 fall within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., an observation, evaluation, judgment, or opinion that can practically be performed in the human mind under MPEP 2106.04(a)(2)(III); a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; a mental process performed on a computer under MPEP 2106.04(a)(2)(III) citing Intellectual Ventures LLC; a mental process performed in a computer environment under MPEP 2106.04(a)(2)(III)(C)(2) citing FairWarning; etc.). Accordingly, for the same reasons discussed above, these limitations also fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 3 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Claim 4 is directed to one or more judicial exceptions (i.e., abstract ideas) under Step 2A, Prong One because abstract ideas (i.e., CMOHAs and/or mental processes) are recited in the claim. Regarding claim 4, the abstract ideas are identified in bold as the following combination of limitations “wherein the feature information comprises identification information comprising a name of each of the features detected from the target image and position information indicating a position of each of the features in the target image”. This abstract idea, individually or in combination, simply defines the associated abstract idea exception in claim 1. More specifically, the abstract idea “the feature information” is carried over from the abstract idea “generate feature information” in claim 1. Accordingly, for the same reasons discussed above in relation to “the feature information” in claim 1, it is determined that the same abstract ideas carried over to claim 3 fall within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., an observation, evaluation, judgment, or opinion that can practically be performed in the human mind under MPEP 2106.04(a)(2)(III); a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; a mental process performed on a computer under MPEP 2106.04(a)(2)(III) citing Intellectual Ventures LLC; a mental process performed in a computer environment under MPEP 2106.04(a)(2)(III)(C)(2) citing FairWarning; etc.). Accordingly, for the same reasons discussed above, these limitations also fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 4 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Claim 5 is directed to one or more judicial exceptions (i.e., abstract ideas) under Step 2A, Prong One because abstract ideas (i.e., CMOHAs and/or mental processes) are recited in the claim. Regarding claim 5, the abstract ideas are identified in bold as:
an image-of-interest generator configured to generate an image of interest where the region of interest is superimposed on the target image (CMOHA and/or mental process),
wherein the display controller is further configured to cause the display apparatus to display the image of interest (CMOHA).
The abstract idea “generate an image of interest” individually or in combination falls within at least the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., an observation, evaluation, judgment, or opinion that can practically be performed in the human mind under MPEP 2106.04(a)(2)(III); a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; a mental process performed on a computer under MPEP 2106.04(a)(2)(III) citing Intellectual Ventures LLC; a mental process performed in a computer environment under MPEP 2106.04(a)(2)(III)(C)(2) citing FairWarning; etc.). Accordingly, for the same reasons discussed above, these limitations also fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 5 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
The abstract idea “display the image of interest” individually or in combination falls within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)). Furthermore, the abstract idea “display the image of interest” in combination with the abstract idea “generate an image of interest where the region of interest is superimposed on the target image” falls within at least the mental process grouping (i.e., collecting, analyzing, and displaying information with the data analysis step being recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group). The broadest reasonable interpretation of “the target image” includes the target image acquired by the acquisition unit in claim 1 and thus amounts to collected information. The broadest reasonable interpretation of “generate an image of interest” includes the activity of determining an image of interest, i.e., based on an analysis of information (i.e., analyzing the target image and the region of interest). This data analysis step is recited at a high level of generality at least because the claim does not recite any particular way how to generate the image of interest. Instead, the claim merely recites as an idea of solution or outcome. The broadest reasonable interpretation of “display the image of interest” amounts to displaying the results of the collection and analysis of information (i.e., the region of interest and the target image). Accordingly, the abstract idea “display the image of interest” in combination with the abstract idea “generate an image of interest where the region of interest is superimposed on the target image” falls within at least the mental process grouping, and it is determined that claim 5 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Claim 6 is directed to one or more judicial exceptions (i.e., abstract ideas) under Step 2A, Prong One because abstract ideas (i.e., CMOHAs and/or mental processes) are recited in the claim. Regarding claim 6, the abstract ideas are identified in bold as:
a feature image generator configured to generate a first feature image where the feature information is superimposed on the target image and/or a second feature image where the feature information and the region-of-interest information are superimposed on the target image, (CMOHA and/or mental process)
wherein the display controller is configured to cause the display apparatus to display the first feature image and/or the second feature image. (CMOHA)
The abstract idea “generate a first feature image… and/or a second feature image” individually or in combination falls within at least the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., an observation, evaluation, judgment, or opinion that can practically be performed in the human mind under MPEP 2106.04(a)(2)(III); etc.). The combination of limitations “a feature image generator configured to generate a first feature image … and/or a second feature image” individually or in further combination falls within at least the mental process grouping (a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; a mental process performed on a computer under MPEP 2106.04(a)(2)(III) citing Intellectual Ventures LLC; a mental process performed in a computer environment under MPEP 2106.04(a)(2)(III)(C)(2) citing FairWarning; etc.). Accordingly, these limitations also fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 6 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
The abstract idea “display the first feature image and/or the second feature image” individually or in combination falls within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., display step combined with a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; etc.). Accordingly, these limitations fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 6 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Claim 7 is directed to one or more judicial exceptions (i.e., abstract ideas) under Step 2A, Prong One because abstract ideas (i.e., CMOHAs and/or mental processes) are recited in the claim. Regarding claim 7, the abstract ideas are identified in bold as: “wherein the display controller is further configured to cause the display apparatus to display the image of interest and the second feature image alongside each other.”
The abstract idea “display the image of interest and the second feature image alongside each other” individually or in combination falls within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., display step combined with a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; etc.). Accordingly, these limitations fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 7 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Claim 8 is directed to one or more judicial exceptions (i.e., abstract ideas) under Step 2A, Prong One because abstract ideas (i.e., CMOHAs and/or mental processes) are recited in the claim. Regarding claim 8, the abstract ideas are identified in bold as: “wherein the display controller is configured to cause the display apparatus to simultaneously display (1) the first determination information, (2) the region-of-interest information, and (3) the feature information.” The abstract idea “simultaneously display (1) the first determination information, (2) the region-of-interest information, and (3) the feature information” individually or in combination falls within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., display step combined with a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; etc.). Accordingly, these limitations fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 8 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Claim 10 is directed to a judicial exception (i.e., abstract idea) under Step 2A, Prong One because an abstract idea (i.e., a CMOHA and/or mental process) is recited in the claim. Claim 10 depends from claim 1 and carries over the abstract idea identified in bold: “wherein the first determination information comprises diagnostic information comprising diagnostic results related to whether the subject has a disorder.” This abstract idea simply defines the abstract idea “generate first determination information” in claim 1 as discussed above. Accordingly, the abstract idea falls within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., an observation, evaluation, judgment, or opinion that can practically be performed in the human mind under MPEP 2106.04(a)(2)(III); a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; a mental process performed on a computer under MPEP 2106.04(a)(2)(III) citing Intellectual Ventures LLC; a mental process performed in a computer environment under MPEP 2106.04(a)(2)(III)(C)(2) citing FairWarning; etc.). Accordingly, for the same reasons discussed above, these limitations also fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 10 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Claim 11 is directed to a judicial exception (i.e., an abstract idea) under Step 2A, Prong One because an abstract idea (i.e., a CMOHA and/or mental process) is recited in the claim. Regarding claim 11, the abstract idea is identified in bold as: “wherein the first generator comprises a determination information generation model configured to generate the first determination information by using the target image of the subject.” This abstract idea, individually or in combination, falls within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., an observation, evaluation, judgment, or opinion that can practically be performed in the human mind under MPEP 2106.04(a)(2)(III); a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; a mental process performed on a computer under MPEP 2106.04(a)(2)(III) citing Intellectual Ventures LLC; a mental process performed in a computer environment under MPEP 2106.04(a)(2)(III)(C)(2) citing FairWarning; etc.). Accordingly, these limitations also fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 11 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Claim 12 is directed to a judicial exception (i.e., an abstract idea) under Step 2A, Prong One because the abstract idea (i.e., CMOHA and/or mental process) is recited in the claim. Claim 12 depends from claim 11 and recites the abstract idea identified in bold as: “wherein the determination information generation model comprises a first neural network trained by using patient information, as teaching data, related to a plurality of patients each having a disorder in a target site, and the patient information comprises diagnostic information indicating diagnostic results related to a condition of a target site and a medical image of each of the plurality of patients.” The abstract idea individually or in combination simply defines “determination information generation model configured to generate the first determination information by using the target image of the subject” in claim 11. Accordingly, the abstract idea falls within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., an observation, evaluation, judgment, or opinion that can practically be performed in the human mind under MPEP 2106.04(a)(2)(III); a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; a mental process performed on a computer under MPEP 2106.04(a)(2)(III) citing Intellectual Ventures LLC; a mental process performed in a computer environment under MPEP 2106.04(a)(2)(III)(C)(2) citing FairWarning; etc.). Accordingly, for the same reasons discussed above, these limitations also fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 12 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Claim 15 is directed to one or more judicial exceptions (i.e., abstract ideas) under Step 2A, Prong One because abstract ideas (i.e., CMOHAs and/or mental processes) are recited in the claim. Claim 15 depends from claim 1 and recites the abstract ideas identified in bold as:
each of the plurality of training images is further associated with identification information comprising a name and an annotation of each of the features detected in the respective training image from the patient image and position information indicating a position of each of the features in the respective training image (CMOHA and mental process), and
wherein machine learning using backpropagation with application of an R-CNN is performed on the feature generation model (mathematical concept).
These limitations, individually or in combination, define the abstract idea of “the feature generation model configured to generate feature information, the feature generation model comprising a second neural network trained using a plurality of training images of a portion of a body of a subject with identification information corresponding to one or more features in each training image” in claim 1. Accordingly, the abstract idea falls within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., an observation, evaluation, judgment, or opinion that can practically be performed in the human mind under MPEP 2106.04(a)(2)(III); a mental process performed in a computer environment under MPEP 2106.04(a)(2)(III)(C)(2) citing FairWarning; etc.). Furthermore, the limitation of “machine learning using backpropagation with application of an R-CNN is performed on the feature generation model,” individually in combination with the other limitations, is a mathematical calculation and thus amounts to a mathematical concept. Accordingly, these limitations fall within the CMOHA grouping, the mental process grouping, and the mathematical concept grouping, and it is determined that claim 15 recites an abstract idea exception under Step 2A, Prong One.
Claim 19 is directed to a judicial exception (i.e., an abstract idea) under Step 2A, Prong One because an abstract idea (i.e., a CMOHA and/or mental process) is recited in the claim. Claim 19 depends from claim 1 and carries over the abstract idea identified in bold as: “wherein the first determination information comprises information used to determine the condition of the target site at a second time after elapse of a predetermined period since a first time when the target image is captured.” This limitation individually or in combination defines “generate first determination information” in claim 1. Accordingly, the abstract idea falls within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., an observation, evaluation, judgment, or opinion that can practically be performed in the human mind under MPEP 2106.04(a)(2)(III); a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; a mental process performed on a computer under MPEP 2106.04(a)(2)(III) citing Intellectual Ventures LLC; a mental process performed in a computer environment under MPEP 2106.04(a)(2)(III)(C)(2) citing FairWarning; etc.). Accordingly, for the same reasons discussed above, these limitations also fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 19 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Claim 20 is directed to one or more judicial exceptions (i.e., abstract ideas) under Step 2A, Prong One because abstract ideas (i.e., CMOHAs and/or mental processes) are recited in the claim. Regarding claim 20, the abstract ideas are identified in bold as:
a fourth generator configured to generate, in response to the first generation instruction, from the first determination information, second determination information indicating a method of medical intervention for the subject and an effect of the medical intervention, (CMOHA and mental process)
wherein the display controller is configured to cause the display apparatus to display the first determination information, the feature information generated from the target image used to generate the first determination information, and the second determination information.
The abstract idea “generate, from the first determination information, second determination information” individually or in combination falls within at least the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., an observation, evaluation, judgment, or opinion that can practically be performed in the human mind under MPEP 2106.04(a)(2)(III); etc.). The combination of limitations “a fourth generator configured to generate, from the first determination information, second determination information” individually or in further combination falls within at least the mental process grouping (i.e., a display step combined with a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; a mental process performed on a computer under MPEP 2106.04(a)(2)(III) citing Intellectual Ventures LLC; a mental process performed in a computer environment under MPEP 2106.04(a)(2)(III)(C)(2) citing FairWarning; etc.). Accordingly, for the same reasons discussed above, these limitations also fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 20 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
The abstract idea “display the first determination information, the feature information …, and the second determination information” individually or in combination falls within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., display step combined with a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; etc.). Accordingly, for the same reasons discussed above, these limitations fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 20 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Claim 21 is directed to one or more judicial exceptions (i.e., abstract ideas) under Step 2A, Prong One because abstract ideas (i.e., CMOHAs and/or mental processes) are recited in the claim. Regarding claim 21, the abstract ideas are identified in bold as: “wherein the fourth generator comprises an intervention effect determination model configured to receive input of the first determination information of the subject and output the second determination information indicating a method of intervention for the subject and an effect of the intervention.” These abstract ideas, individually or in combination, simply define abstract idea exceptions in claims 1 and 20. More specifically, this abstract idea individually or in combination defines the abstract idea “a first generator configured to generate first determination information” in claim 1 and “second determination information” in claim 20. These abstract ideas individually or in combination fall within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., an observation, evaluation, judgment, or opinion that can practically be performed in the human mind under MPEP 2106.04(a)(2)(III); a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; a mental process performed on a computer under MPEP 2106.04(a)(2)(III) citing Intellectual Ventures LLC; a mental process performed in a computer environment under MPEP 2106.04(a)(2)(III)(C)(2) citing FairWarning; etc.). Accordingly, for the same reasons discussed above, these limitations also fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 21 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Claim 22 is directed to one or more judicial exceptions (i.e., abstract ideas) under Step 2A, Prong One because abstract ideas (i.e., CMOHAs and/or mental processes) are recited in the claim. Regarding claim 22, the abstract ideas are identified in bold as: “wherein the intervention effect determination model comprises a third neural network trained by using effect information, as teaching data, of each of a plurality of patients who have undergone intervention for a disorder in the target site, and the effect information comprises information where the intervention provided for the target site of each of the plurality of patients and intervention effect information indicating the effect of the intervention are associated for each of the plurality of patients.” The abstract idea individually or in combination simply defines “the intervention effect determination model configured to receive input of the first determination information of the subject and output the second determination information” in claim 21. This abstract idea individually or combination falls within the CMOHA grouping (i.e., following rules or instructions under MPEP 2106.04(a)(2)(II)) and the mental process grouping (i.e., an observation, evaluation, judgment, or opinion that can practically be performed in the human mind under MPEP 2106.04(a)(2)(III); a data analysis step recited at a high level of generality such that it could practically be performed in the human mind under MPEP 2106.04(a)(2)(III) citing Electric Power Group; a mental process performed on a computer under MPEP 2106.04(a)(2)(III) citing Intellectual Ventures LLC; a mental process performed in a computer environment under MPEP 2106.04(a)(2)(III)(C)(2) citing FairWarning; etc.). Accordingly, for the same reasons discussed above, these limitations also fall within the CMOHA grouping and the mental process grouping, and it is determined that claim 22 recites abstract ideas and therefore is directed to judicial exceptions under Step 2A, Prong One.
Eligibility Step 2A, Prong Two:
Under Step 2A, Prong Two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the claims recite any additional limitations individually or in combination that integrate a judicial exception (i.e., the identified abstract ideas) into a practical application. After evaluation, it has been determined that claims 1-8, 10-12, 15, and 19-26 do not recite any additional elements individually or in combination that integrate the abstract ideas into a practical application.
A number of considerations provided in MPEP 2106.04(d)(2), MPEP 2106.05(a) through (c), and MPEP 2106.05(e) through (h) are relevant to determining whether any additional elements individually or in combination integrate the judicial exception (i.e., the abstract ideas defined as mathematical concepts, mental processes, and/or CMOHAs) into a practical application. See MPEP 2106.04(d). These considerations require an evaluation of the claims to determine whether the additional limitations individually or in combination:
effect a particular treatment or prophylaxis for a disease or medical condition under MPEP 2106.04(d)(2);
reflect an improvement to the functioning of a computer, or an improvements to any other technology or technical field under MPEP 2106.05(a);
implement the judicial exception with, or using the judicial exception in connection with, a particular machine or manufacture that is integral to the claim under MPEP 2106.05(b);
effect a transformation or reduction of a particular article to a different state or thing under MPEP 2106.05(c);
apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition under MPEP 2106.05(e);
are mere instructions to implement an abstract idea on a computer under MPEP 2106.05(f);
amount to no more than a recitation of insignificant extra-solution activity to the judicial exception under MPEP 2106.05(g); and
apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment under MPEP 2106.05(h), such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Regarding claims 1 and 23-26, the additional elements of representative claim 1 are identified in bold as:
a. A determination system comprising:
b. an image capture apparatus configured to capture a target image representing a target site of a body of a subject, wherein the target image is one of an X-ray image, a computed tomography image, a magnetic resonance imaging image, a positron emission tomography image, a radio isotope image, a mammographic image, an ultrasonic image, an endoscopic image, or an angiographic image;
c. an acquisition unit configured to control a target image management apparatus to obtain the target image representing the target site of the body of the subject;
d. a first generator configured to receive the target image and generate first determination information indicating a condition of the target site from the target image;
e. a second generator configured to receive the target image and generate feature information indicating features related to a condition of the body of the subject based on the target image used to generate the first determination information, wherein the second generator comprises a feature generation model configured to generate the feature information, the feature generation model comprising a second neural network trained using a plurality of training images of a portion of a body of a subject with identification information corresponding to one or more features in each training image; and
f. a display controller configured to:
g. receive the first determination information and the feature information and
h. control a display apparatus to display the first determination information and the feature information.
Regarding claims 2 and 23, the additional limitations of representative claim 2 are identified in bold as:
a third generator configured to generate region-of-interest information indicating a region of interest related to the first determination information and being a partial region of the target image,
wherein the display controller is configured to acquire the first determination information, the feature information, and the region-of-interest information and cause the display apparatus to display the first determination information, the feature information, and the region-of-interest information.
Regarding the consideration under MPEP 2106.04(d)(2), claims 1 and 23-26 include the additional limitations identified in bold as “a determination system,” “an image capture apparatus configured to capture a target image representing a target site of a body of a subject, wherein the target image is one of an X-ray image, a computed tomography image, a magnetic resonance imaging image, a positron emission tomography image, a radio isotope image, a mammographic image, an ultrasonic image, an endoscopic image, or an angiographic image,” “an acquisition unit configured to control a target image management apparatus to obtain the target image representing the target site of the body of the subject,” “a first generator configured to receive the target image,” “a second generator configured to receive the target image,” “the second generator comprises a feature generation model configured to generate the feature information, the feature generation model comprising a second neural network trained using a plurality of training images of a portion of a body of a subject with identification information corresponding to one or more features in each training image,” and “a display controller configured to receive the first determination information and the feature information and control a display apparatus to display the first determination information and the feature information.” Each one of claims 1 and 23-26 as a whole does not effect a particular treatment or prophylaxis, and is instead merely instructions to “apply” the abstract idea in a generic way. Thus, each one of the claims as whole does not integrate the exception into a practical application.
Regarding the consideration under MPEP 2106.05(a), each one of claims 1 and 23-26 as a whole does not “purport to improve the functioning of the computer itself" or "any other technology or technical field.” It is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. See MPEP 2106.05(a)(II). Each claim as a whole does not include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. Thus, it is determined that each of the claims as a whole and the additional elements, individually or in combination, fail to integrate the abstract ideas into a practical application under Step 2A, Prong Two.
Regarding the consideration under MPEP 2106.05(b), each one of claims 1 and 23-26 as a whole merely adds generic computer components (i.e., the determination system, the image capture apparatus, the acquisition unit, the first generator, the second generator, the feature generation model, the second neural network, the display controller, the display apparatus, etc.) to perform conventional computer functions and thus do not qualify as a particular machine. See MPEP 2106.05(b)(1). Thus, each one of the claims as whole does not integrate the exception into a practical application.
Regarding the consideration under MPEP 2106.05(f), each one of the additional limitations in bold above is determined to be mere instructions to apply an abstract idea. These limitations are used to implement the abstract ideas recited at a high level of generality and are determined to be no more than mere instructions to implement the abstract ideas (i.e., CMOHA and mental processes) on generic computer components including the determination system, the image capture apparatus, the acquisition unit, the first generator, the second generator, the feature generation model, the second neural network, the display controller, the display apparatus. Accordingly, for these additional reasons, each one of claims 1 and 23-26 as a whole does not recite additional elements which integrate the abstract idea into a practical application.
Regarding the consideration under MPEP 2106.05(g), the limitations of “an image capture apparatus configured to capture a target image representing a target site of a body of a subject, wherein the target image is one of an X-ray image, a computed tomography image, a magnetic resonance imaging image, a positron emission tomography image, a radio isotope image, a mammographic image, an ultrasonic image, an endoscopic image, or an angiographic image” and “an acquisition unit configured to control a target image management apparatus to obtain the target image representing the target site of the body of the subject” are insignificant extra-solution activities, i.e., well-known pre-solution activities (e.g., necessary data gathering), and are incidental to the primary process and thus merely a nominal or tangential addition to the claim. The limitation of “a display controller configured to receive the first determination information and the feature information and control a display apparatus to display the first determination information and the feature information” is an insignificant extra-solution activity, i.e., a well-known post-solution activity (e.g., necessary data outputting), and is incidental to the primary process and thus merely a nominal or tangential addition to the claim. Accordingly, for these additional reasons, each one of claims 1 and 23-26 as a whole does not recite additional elements which integrate the abstract idea into a practical application.
Regarding the consideration under MPEP 2106.05(h), the additional limitations, individually or in combination, also amount to merely indicating a field of use or technological environment in which to apply the judicial exception. In the instant application, the additional limitations of “a determination system,” “an image capture apparatus configured to capture a target image representing a target site of a body of a subject, wherein the target image is one of an X-ray image, a computed tomography image, a magnetic resonance imaging image, a positron emission tomography image, a radio isotope image, a mammographic image, an ultrasonic image, an endoscopic image, or an angiographic image,” “an acquisition unit configured to control a target image management apparatus to obtain the target image representing the target site of the body of the subject,” “a first generator configured to receive the target image,” “a second generator configured to receive the target image,” “the second generator comprises a feature generation model configured to generate the feature information, the feature generation model comprising a second neural network trained using a plurality of training images of a portion of a body of a subject with identification information corresponding to one or more features in each training image,” and “a display controller configured to receive the first determination information and the feature information and control a display apparatus to display the first determination information and the feature information” do no more than link the abstract ideas (i.e., the mental processes and/or CMOHAs identified above) to a particular technological environment, i.e., the field of biomedical image inspection and computer-aided diagnosis (as opposed to any other field of data mining). Thus, the additional limitations fail to add an inventive concept to the claims.
Accordingly, in view of these considerations, the Office has determined that each one of claims 1 and 23-26 as a whole does not have one or more additional limitations, individually or in combination, that integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Dependent claims 3-8, 10-12, 15, and 19-22 either simply define judicial exceptions in claim 1 or recite one or more additional elements that, individually or in combination, fail to integrate the judicial exceptions (i.e., the abstract idea exceptions identified in Step 2A, Prong One above) into a practical application under Step 2A Prong Two of the full eligibility analysis as follows:
Claim 3 recites additional limitations that, individually or in combination, fail to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Claim 3 includes the additional limitations identified in bold as:
wherein the region of interest comprises a region of interest in a process of generating the first determination information from the target image, and
the region-of-interest information comprises information indicating a position of the region of interest in the target image.
After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitations individually or in combination fail to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As one non-limiting exemplary consideration, the additional elements individually or in combination do not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). More specifically, each one of the two additional limitations in bold amounts to selecting a particular data source or type of data to be manipulated, which is insignificant extra-solution activity. See MPEP 2106.05(g)(3). In still another non-limiting example, the additional limitations individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). Limiting application of the abstract idea to biomedical image inspection and computer-aided diagnosis (i.e., more specifically, the additional limitations in bold) is simply an attempt to limit the use of the abstract idea to a particular technological environment. Accordingly, in view of at least these considerations, the additional limitations individually or in combination do not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Claim 4 recites additional limitations that, individually or in combination, fail to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Regarding claim 4, the additional limitations are identified in bold as the following combination of limitations “wherein the feature information comprises identification information comprising a name of each of the features detected from the target image and position information indicating a position of each of the features in the target image.” After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitations individually or in combination fail to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As but one non-limiting exemplary consideration, the additional limitations individually or in combination do not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). More specifically, each one of the additional limitations merely amounts to selecting a particular data source or type of data to be manipulated (e.g., identification information comprising a name of each of the features detected from the target image and position information indicating a position of each of the features in the target image), which is insignificant extra-solution activity. See MPEP 2106.05(g)(3). In still another non-limiting example, the additional limitations in bold individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). Limiting application of the abstract idea to biomedical image inspection and computer-aided diagnosis (i.e., more specifically, the additional limitations in bold) is simply an attempt to limit the use of the abstract idea to a particular technological environment. Accordingly, in view of at least these considerations, the additional limitations individually or in combination do not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Claim 5 recites additional limitations that, individually or in combination, fail to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Regarding claim 5, the additional limitations are identified in bold as:
an image-of-interest generator configured to generate an image of interest where the region of interest is superimposed on the target image,
wherein the display controller is further configured to cause the display apparatus to display the image of interest.
After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitations individually or in combination fail to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As but one non-limiting exemplary consideration, claim 5 merely adds generic computer components (i.e., the image-of-interest generator and display controller) to perform conventional computer functions. See MPEP 2106.05(b)(1). Because the additional limitations amount to generic computer components that apply the abstract idea exceptions by use of conventional computer functions, the abstract idea exceptions do not qualify as being applied with, or by use of, a particular machine. See MPEP 2106.05(b)(1). As another non-limiting example, the additional limitations individually or in combination do not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). One example of activities that courts have found to be insignificant extra-solution activities is selecting or adding a limitation directed to a particular type of data to be manipulated. The broadest reasonable interpretation of “an image of interest where the region of interest is superimposed on the target image” includes unstructured data such as image documents (i.e., where a region of interest may be an inset image that is smaller than the target image and placed within the boundary of the larger target image), as opposed to other types of data. This activity is well known, nominally or tangentially related to the invention, and necessary data outputting. See MPEP 2106.05(g)(1) through (3). In still another non-limiting example, the additional limitations individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). See MPEP 2106.05(h) citing Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). In Electric Power Group, the court found that the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis was limited to data related to the electric power grid, and the court held that limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment. Claim 5 is directed to the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis, and the claim is limited to data related to biomedical image inspection and computer-aided diagnosis of patients. Claim 5 depends from claim 2, which in turn depends from claim 1. Claim 1 recites the abstract ideas “acquire a target image” (i.e., a step for collecting information) and “generate first determination information indicating a condition of the target site from the target image” (i.e., a step for analyzing information). The broadest reasonable interpretation of “a target” reads on a biomedical image, such as an X-ray image of target site on a patient’s body, and the broadest reasonable interpretation of “generate first determination” includes determining a diagnosis or diagnostic results for the patient. Claim 2 recites “generate region-of-interest” (i.e., a step for analyzing information). The broadest reasonable interpretation of “region of interest” includes the partial region of the target image that shows a portion of the target site on the patient’s body related to the diagnosis or diagnostic results. Claim 5 recites the abstract ideas “generate an image of interest” and “display the image of interest” (i.e., steps for displaying results of collection and analysis). The broadest reasonable interpretation of “an image of interest where the region of interest is superimposed on the target image” encompasses the region of interest as an inset image positioned within the boundary of the target image and being an enlarged view of a portion of the target image. Accordingly, it is determined that claim 5 is limited to data related to biomedical image inspection and computer-aided diagnostic systems. Accordingly, in view of at least these considerations, the additional limitations individually or in combination do not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Claim 6 recites additional limitations that, individually or in combination, fail to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Regarding claim 6, the additional limitations are identified in bold as:
a feature image generator configured to generate a first feature image where the feature information is superimposed on the target image and/or a second feature image where the feature information and the region-of-interest information are superimposed on the target image,
wherein the display controller is configured to cause the display apparatus to display the first feature image and/or the second feature image.
After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitations individually or in combination fail to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As but one non-limiting exemplary consideration, claim 3 merely add generic computer component (i.e., a feature image generator, the display controller, the display apparatus, etc.) to perform conventional computer functions. See MPEP 2106.05(b)(1). Because the additional limitations amount to generic computer components that apply the abstract idea exceptions by use of conventional computer functions, the abstract idea exceptions do not qualify as being applied with, or by use of, a particular machine. See MPEP 2106.05(b)(1). As another non-limiting example, the additional limitations individually or in combination do not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). More specifically, claim 6 includes the following combinations of limitations (i.e., with the limitations in italics being abstract ideas and the limitations in bold being additional limitations) “a first feature image where the feature information is superimposed on the target image” and “a second feature image where the feature information and the region-of-interest information are superimposed on the target image,” This combination merely amounts to selecting a particular data source or type of data to be manipulated, which is insignificant extra-solution activity. See MPEP 2106.05(g)(3). In still another non-limiting example, the additional limitations individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). See MPEP 2106.05(h) citing Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). In Electric Power Group, the court found that the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis was limited to data related to the electric power grid, and the court held that limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment. Similarly, claim 6 recites the abstract ideas of “generate a first feature image and/or a second feature image” and “display the first feature image and/or the second feature image” (CMOHA and/or mental process), which amount to collecting information, analyzing it, and displaying certain results of the collection and analysis. The additional limitations limit these abstract ideas to data related to biomedical image inspection and computer-aided diagnosis of patients. Limiting application of the abstract idea to biomedical image inspection and computer-aided diagnosis is simply an attempt to limit the use of the abstract idea to a particular technological environment. Accordingly, in view of at least these considerations, the additional limitations individually or in combination do not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Claim 7 recites an additional limitation that, individually or in combination, fails to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Regarding claim 7, the additional limitations are identified in bold as: “wherein the display controller is further configured to cause the display apparatus to display the image of interest and the second feature image alongside each other.” After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitation individually or in combination fails to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As but one non-limiting exemplary consideration, claim 7 merely adds generic computer components (i.e., the display controller, the display apparatus, etc.) to perform conventional computer functions. See MPEP 2106.05(b)(1). Because the additional elements amount to generic computer components that apply the abstract idea exceptions by use of conventional computer functions, the abstract idea exceptions do not qualify as being applied with, or by use of, a particular machine. See MPEP 2106.05(b)(1). As another non-limiting example, the additional element individually or in combination does not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). More specifically, claim 7 includes the following combination of limitations “cause the display apparatus to display the image of interest and the second feature image alongside each other.” This combination merely amounts to selecting a type of data to be manipulated, which is insignificant extra-solution activity. See MPEP 2106.05(g)(3). In still another non-limiting example, the additional limitations individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). See MPEP 2106.05(h) citing Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). Claim 7 recites the abstract idea of “the display apparatus to display the image of interest and the second feature image alongside each other” (CMOHA and/or mental process), which in combination amounts to collecting information, analyzing it, and displaying certain results of the collection and analysis. These additional limitations limit these abstract ideas to data related to biomedical image inspection and computer-aided diagnosis of patients. Limiting application of the abstract idea to biomedical image inspection and computer-aided diagnosis is simply an attempt to limit the use of the abstract idea to a particular technological environment. Accordingly, in view of at least these considerations, the additional limitation individually or in combination does not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Claim 8 recites additional limitations that, individually or in combination, fail to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Regarding claim 8, the additional limitations are identified in bold as: “wherein the display controller is configured to cause the display apparatus to simultaneously display (1) the first determination information, (2) the region-of-interest information, and (3) the feature information.” After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitation individually or in combination fails to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As but one non-limiting exemplary consideration, claim 8 merely add a generic computer component (i.e., the display controller, the display apparatus, etc.) to perform conventional computer functions. See MPEP 2106.05(b)(1). Because the additional limitations amount to generic computer components that apply the abstract idea exceptions by use of conventional computer functions, the abstract idea exceptions do not qualify as being applied with, or by use of, a particular machine. See MPEP 2106.05(b)(1). As another non-limiting example, the additional limitations individually or in combination do not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). More specifically, claim 8 includes the following combination of limitations (i.e., (1) the first determination information, (2) the region-of-interest information, and (3) the feature information.” This combination merely amounts to selecting a particular type of data to be manipulated, which is insignificant extra-solution activity. See MPEP 2106.05(g)(3). In still another non-limiting example, the additional limitations individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). See MPEP 2106.05(h) citing Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). In Electric Power Group, the court found that the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis was limited to data related to the electric power grid, and the court held that limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment. Similarly, claim 8 recites the abstract ideas “simultaneously display (1) the first determination information, (2) the region-of-interest information, and (3) the feature information” (CMOHA and/or mental process), which in combination with other limitations “acquire the target image,” “generate first determination information,” “generate the region-of-interest information,” and “generate feature information” amount to collecting information, analyzing it, and displaying certain results of the collection and analysis. The additional limitations limit these abstract ideas to data related to biomedical image inspection and computer-aided diagnosis of patients. Limiting application of the abstract idea to biomedical image inspection and computer-aided diagnosis is simply an attempt to limit the use of the abstract idea to a particular technological environment. Accordingly, in view of at least these considerations, the additional limitations individually or in combination do not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Claim 10 recites additional limitations that, individually or in combination, fail to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Regarding claim 10, the abstract idea is identified in bold as: “wherein the first determination information comprises diagnostic information comprising diagnostic results related to whether the subject has a disorder.” After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitations individually or in combination fail to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As but one non-limiting exemplary consideration, the additional limitations individually or in combination do not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). More specifically, each one of the additional limitations merely amounts to selecting a particular type of data to be manipulated (e.g., diagnostic information comprising diagnostic results related to whether the subject has a disorder), which is insignificant extra-solution activity. See MPEP 2106.05(g)(3). In still another non-limiting example, the additional limitations in bold individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). Limiting application of the abstract idea to biomedical image inspection and computer-aided diagnosis (i.e., more specifically, the additional limitations in bold) is simply an attempt to limit the use of the abstract idea to a particular technological environment. Accordingly, in view of at least these considerations, the additional limitations individually or in combination do not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Claim 11 recites additional limitations that, individually or in combination, fail to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Regarding claim 11, the abstract idea is identified in bold as: “wherein the first generator comprises a determination information generation model configured to generate the first determination information by using the target image of the subject.” After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitations individually or in combination fail to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As but one non-limiting exemplary consideration, claim 11 merely adds a generic computer component (i.e., “the first generator,” “a determination information generation model,” etc.) to perform conventional computer functions. See MPEP 2106.05(b)(1). Because the additional limitations amount to generic computer components that apply the abstract idea exceptions by use of conventional computer functions, the abstract idea exceptions do not qualify as being applied with, or by use of, a particular machine. See MPEP 2106.05(b)(1). As another non-limiting example, the additional limitations individually or in combination do not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). More specifically, claim 11 includes the following combination of limitations “generate the first determination information by using the target image of the subject” with the limitation in italics being the abstract idea and the limitation in bold being an additional limitation. This combination merely amounts to selecting a particular data source or type of data to be manipulated, which is insignificant extra-solution activity. See MPEP 2106.05(g)(3). In still another non-limiting example, the additional limitations individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). See MPEP 2106.05(h) citing Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). In Electric Power Group, the court found that the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis was limited to data related to the electric power grid, and the court held that limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment. Similarly, claim 11 recites the combination of limitations “generate the first determination information by using the target image of the subject” which in combination with other limitations “acquire the target image,” “generate feature information,” and “display the first determination information and the feature information” amount to collecting information, analyzing it, and displaying certain results of the collection and analysis. The additional limitations limit the abstract idea to data related to biomedical image inspection and computer-aided diagnosis of patients. Limiting application of the abstract idea to biomedical image inspection and computer-aided diagnosis is simply an attempt to limit the use of the abstract idea to a particular technological environment. Accordingly, in view of at least these considerations, the additional limitations individually or in combination do not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Claim 12 recites additional limitations that, individually or in combination, fail to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Regarding claim 12, the abstract ideas are identified in bold as: “wherein the determination information generation model comprises a first neural network trained by using patient information, as teaching data, related to a plurality of patients each having a disorder in a target site, and the patient information comprises diagnostic information indicating diagnostic results related to a condition of a target site and a medical image of each of the plurality of patients.” After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitations individually or in combination fails to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As but one non-limiting exemplary consideration, claim 12 merely adds generic computer components (i.e., the determination information generation model, a first neural network, etc.) to perform conventional computer functions. See MPEP 2106.05(b)(1). Because the additional limitations amount to generic computer components that apply the abstract idea exceptions by use of conventional computer functions, the abstract idea exceptions do not qualify as being applied with, or by use of, a particular machine. See MPEP 2106.05(b)(1). As another non-limiting example, the additional limitations individually or in combination do not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). More specifically, claim 12 includes the following combination of limitations “using patient information, as teaching data, related to a plurality of patients each having a disorder in a target site, and the patient information comprises diagnostic information indicating diagnostic results related to a condition of a target site and a medical image of each of the plurality of patients” with the limitations in italics being abstract ideas and the limitations in bold being additional limitations. This combination merely amounts to selecting a particular data source or type of data to be manipulated, which is insignificant extra-solution activity. See MPEP 2106.05(g)(3). In still another non-limiting example, the additional limitations individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). See MPEP 2106.05(h). Claim 12 recites the combination of limitations “the determination information generation model comprises a first neural network trained by using patient information, as teaching data” which in combination with other limitations “acquire the target image,” “generate first determination information,” “generate the region-of-interest information,” and “generate feature information” amount to collecting information, analyzing it, and displaying certain results of the collection and analysis. The additional limitations limit these abstract ideas to data related to biomedical image inspection and computer-aided diagnosis of patients. Limiting application of the abstract idea to biomedical image inspection and computer-aided diagnosis is simply an attempt to limit the use of the abstract idea to a particular technological environment. Accordingly, in view of at least these considerations, the additional limitations individually or in combination do not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Claim 15 does not recite additional limitations.
Claim 19 recites additional limitations that, individually or in combination, fail to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Regarding claim 19, the additional elements are identified in bold as: “wherein the first determination information comprises information used to determine the condition of the target site at a second time after elapse of a predetermined period since a first time when the target image is captured.” After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitations individually or in combination fail to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As but one non-limiting exemplary consideration, the additional limitations individually or in combination do not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). More specifically, claim 19 includes the following combination of limitations “the first determination information comprises information used to determine the condition of the target site at a second time after elapse of a predetermined period since a first time when the target image is captured.” This combination merely amounts to selecting a particular data source or type of data to be manipulated, which is insignificant extra-solution activity. See MPEP 2106.05(g)(3). In still another non-limiting example, the additional limitations individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). See MPEP 2106.05(h). Claim 19 recites the combination of limitations “the first determination information comprises information used to determine the condition of the target site at a second time” which in combination with other limitations “acquire the target image,” “generate first determination information,” “generate feature information,” and “generate feature information” amount to collecting information, analyzing it, and displaying certain results of the collection and analysis. The additional limitations limit these abstract ideas to data related to biomedical image inspection and computer-aided diagnosis of patients. Limiting application of the abstract idea to biomedical image inspection and computer-aided diagnosis is simply an attempt to limit the use of the abstract idea to a particular technological environment. Accordingly, in view of at least these considerations, the additional limitations individually or in combination do not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Claim 20 recites additional limitations that, individually or in combination, fail to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Regarding claim 20, the additional elements are identified in bold as:
a fourth generator configured to generate, in response to the first generation instruction, from the first determination information generated by the first generator, second determination information indicating a method of medical intervention for the subject and an effect of the medical intervention,
wherein the display controller is configured to cause the display apparatus to display the first determination information, the feature information generated from the target image used to generate the first determination information, and the second determination information.
After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitations individually or in combination fail to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As but one non-limiting exemplary consideration, claim 20 merely adds a generic computer component (i.e., a. a fourth generator, the display controller, the display controller, the display apparatus, etc.) to perform conventional computer functions. See MPEP 2106.05(b)(1). Because the additional limitations amount to generic computer components that apply the abstract idea exceptions by use of conventional computer functions, the abstract idea exceptions do not qualify as being applied with, or by use of, a particular machine. See MPEP 2106.05(b)(1). As another non-limiting example, the additional limitations individually or in combination do not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). More specifically, claim 20 includes the following two combinations of limitations “second determination information indicating a method of medical intervention for the subject and an effect of the medical intervention” and “the feature information generated from the target image used to generate the first determination information.” Each one of these combinations merely amounts to selecting a particular data source or type of data to be manipulated, which is insignificant extra-solution activity. See MPEP 2106.05(g)(3). In still another non-limiting example, the additional limitations individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). See MPEP 2106.05(h). Claim 20 recites the limitations “generate, from the first determination information, second determination information” and “cause the display apparatus to display the first determination information, the feature information…, and the second determination information,” which in combination with “acquire the target image,” “generate first determination information,” “generate feature information,” and “generate feature information” amount to collecting information, analyzing it, and displaying certain results of the collection and analysis. The additional limitations limit these abstract ideas to data related to biomedical image inspection and computer-aided diagnosis of patients. Limiting application of the abstract idea to biomedical image inspection and computer-aided diagnosis is simply an attempt to limit the use of the abstract idea to a particular technological environment. Accordingly, in view of at least these considerations, the additional limitation individually or in combination does not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Claim 21 recites additional limitations that, individually or in combination, fail to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Regarding claim 21, the additional elements are identified in bold as: “wherein the fourth generator comprises an intervention effect determination model configured to receive input of the first determination information of the subject and output the second determination information indicating a method of intervention for the subject and an effect of the intervention.” After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitations individually or in combination fail to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As but one non-limiting exemplary consideration, claim 21 merely adds generic computer components (i.e., the fourth generator, an intervention effect determination model, etc.) to perform conventional computer functions. See MPEP 2106.05(b)(1). Because the additional limitations amount to generic computer components that apply the abstract idea exceptions by use of conventional computer functions, the abstract idea exceptions do not qualify as being applied with, or by use of, a particular machine. See MPEP 2106.05(b)(1). As another non-limiting example, the additional limitations individually or in combination do not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). More specifically, claim 21 includes the following combination of limitations “an intervention effect determination model configured to receive input of the first determination information of the subject and output the second determination information indicating a method of intervention for the subject and an effect of the intervention” merely amounts to selecting particular data sources or types of data to be manipulated, which is insignificant extra-solution activity. See MPEP 2106.05(g)(3). In still another non-limiting example, the additional limitations individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). See MPEP 2106.05(h). Claim 21 recites the limitations “the fourth generator comprises an intervention effect determination model configured to receive input of the first determination information of the subject and output the second determination information” which in combination with other limitations “acquire the target image,” “generate feature information,” and “display the first determination information and the feature information” amount to collecting information, analyzing it, and displaying certain results of the collection and analysis. The additional limitations limit these abstract ideas to data related to biomedical image inspection and computer-aided diagnosis of patients. Limiting application of the abstract idea to biomedical image inspection and computer-aided diagnosis is simply an attempt to limit the use of the abstract idea to a particular technological environment. Accordingly, in view of at least these considerations, the additional limitation individually or in combination does not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Claim 22 recites an additional limitation that, individually or in combination, fails to integrate the judicial exception (i.e., abstract exceptions) into a practical application under Step 2A, Prong Two. Regarding claim 22, the additional limitations are identified in bold as: “wherein the intervention effect determination model comprises a third neural network trained by using effect information, as teaching data, of each of a plurality of patients who have undergone intervention for a disorder in the target site, and the effect information comprises information where the intervention provided for the target site of each of the plurality of patients and intervention effect information indicating the effect of the intervention are associated for each of the plurality of patients.” After evaluating the considerations in MPEP 2106.04(d)(I), 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h), the Office has determined that the additional limitations individually or in combination fail to contribute an inventive concept (i.e., integrate the abstract idea exceptions into a practical application) under Step 2A, Prong Two. As but one non-limiting exemplary consideration, claim 22 merely adds generic computer components (i.e., the intervention effect determination model, a third neural network, etc.) to perform conventional computer functions. See MPEP 2106.05(b)(1). Because the additional element amounts to generic computer components that apply the abstract idea exceptions by use of conventional computer functions, the abstract idea exceptions do not qualify as being applied with, or by use of, a particular machine. See MPEP 2106.05(b)(1). As another non-limiting example, the additional limitations individually or in combination do not amount to more than a recitation of insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). More specifically, claim 22 includes the following combination of limitations “a third neural network trained by using effect information, as teaching data, of each of a plurality of patients who have undergone intervention for a disorder in the target site, and the effect information comprises information where the intervention provided for the target site of each of the plurality of patients and intervention effect information indicating the effect of the intervention are associated for each of the plurality of patients.” This combination merely amounts to selecting a particular data source or type of data to be manipulated, which is insignificant extra-solution activity. See MPEP 2106.05(g)(3). In still another non-limiting example, the additional limitations individually or in combination amount to merely indicating a field of use or technological environment in which to apply the judicial exception (i.e., the abstract idea exception). See MPEP 2106.05(h). Claim 22 recites the limitations “the intervention effect determination model comprises a third neural network trained by using effect information, as teaching data” which in combination with other limitations “acquire the target image,” “generate feature information,” and “display the first determination information and the feature information” amount to collecting information, analyzing it, and displaying certain results of the collection and analysis. The additional limitations limit these abstract ideas to data related to biomedical image inspection and computer-aided diagnosis of patients. Limiting application of the abstract idea to biomedical image inspection and computer-aided diagnosis is simply an attempt to limit the use of the abstract idea to a particular technological environment. Accordingly, in view of at least these considerations, the additional limitation individually or in combination does not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Eligibility Step 2B:
Under Step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the claims include an element or a combination of elements that are sufficient to amount to significantly more than the judicial exception (i.e., whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry). See MPEP 2106.05(II).
This evaluation is made with respect to the considerations that the Supreme Court has identified as relevant to the eligibility analysis, which are discussed in detail in MPEP § 2106.05(a) through (h). See MPEP 2106.05(I)(A). Many of these considerations overlap, and often more than one consideration is relevant to analysis of an additional element. See MPEP 2106.05(II). Not all considerations will be relevant to every element, or every claim. Id. Although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two. Id. Thus, in Step 2B, examiners should:
Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two;
Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP 2106.05(a) through (c), (e), (f) and (h);
Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and
Evaluate whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d). Id.
Regarding claims 1, 2, and 23-26, the Office carries over its identification of the additional elements from Step 2A, Prong Two so as to apply the same additional elements in Step 2B. See MPEP 2106.05(II). The Office further carries over conclusions from Step 2A, Prong Two on the considerations discussed in MPEP 2106.05(a) through (c), (e), (f) and (h) so as to apply the same considerations for Step 2B.
Claims 1, 2, and 23-26 recite limitations that are not enough to qualify as “significantly more” because those limitations simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer (i.e., the “determination system,” “image capture apparatus,” “first generator,” “second generator,” “feature generation model,” “second neural network,” “display controller,” and “display apparatus”) to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry. See MPEP 2106.05(d) and 2106.05(I)(A). Because re-evaluation under MPEP 2106.05(d) did not result in the Office determining that the additional element individually or in combination is unconventional, this finding cannot indicate that the additional element is no longer considered to be insignificant. See MPEP 2106.05(g).
Regarding dependent claims 3-8, 10-12, 15, and 19-22, the Office carries over its identification of the additional element(s) in claims 3-8, 10-12, 15, and 19-22 (or lack thereof) from Step 2A, Prong Two so as to apply the same additional elements (or lack thereof) in associated claims 1 and 2 for Step 2B. See MPEP 2106.05(II). The Office further carries over conclusions from Step 2A, Prong Two on the considerations discussed in MPEP 2106.05(a) through (c), (e), (f) and (h) so as to apply the same considerations for Step 2B.
Claims 3-8, 10-12, 15, and 19-22 do not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination are well-understood, routine, conventional activities previously known to the industry. Put another way, the additional limitations individually or in combination are not more than what is well-understood, routine, conventional activity in the field. See MPEP 2106.05(d). Because re-evaluation under MPEP 2106.05(d) did not result in the Office determining that the additional element individually or in combination is unconventional, this finding cannot indicate that the additional element is no longer considered to be insignificant. See MPEP 2106.05(g).
These limitations (i.e., when viewed individually, as a whole, and as an ordered combination) are simply taking the well-understood process of managing prior authorizations for insurance claims, which does not qualify as significantly more. The limitations (i.e., when viewed individually, as a whole, and as an ordered combination) represent insignificant conventional activities well-understood in the industry of medical data mining and managing prior authorizations for insurance claims, and narrowing the idea to using a computer to perform those activities is merely an attempt to limit the use of the abstract idea to a particular technological environment. Furthermore, the additional elements or combination of elements in the dependent claims (claims 3-8, 10-12, 15, and 19-22), other than the abstract idea per se, amount to no more than a recitation of:
A) Generic computer structure that serves to perform generic computer functions that serve to merely link the abstract idea to a particular technological environment (i.e., the “system,” “processor,” “memory,” and “computer readable medium”).
B) Generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e., determining, analyzing, and grouping).
Therefore, claims 3-8, 10-12, 15, and 19-22 include additional elements that, individually or in combination, are not sufficient to amount to significantly more than the judicial exception (i.e., the additional elements individually or in combination are well-understood, routine, conventional activities previously known to the industry.
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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8, 10-12, 19, and 23-26 are rejected under 35 U.S.C. 103(a) as being unpatentable over Makino (U.S. Pub. No. 2021/0407077 A1) in view of Chang (U.S. Pub. No. 2022/0058839 A1).
Regarding claims 1, 2, and 23-26, Makino discloses the limitations of representative claim 1 identified in bold as:
a. A determination system comprising: (Abstract of Makino, To provide an information processing device and the like for presenting a determination reason together with a determination result regarding a disease.)
b. an image capture apparatus configured to capture a target image representing a target site of a body of a subject, wherein the target image is one of an X-ray image, a computed tomography image, a magnetic resonance imaging image, a positron emission tomography image, a radio isotope image, a mammographic image, an ultrasonic image, an endoscopic image, or an angiographic image (Paragraph [0062] of Makino, FIG. 1 is an explanatory diagram for explaining an outline of a diagnostic support system 10. An endoscope image 49 photographed using an endoscope 14 (see FIG. 2) is input to a first model 61 and a second model 62. In the instant application, the broadest reasonable interpretation of “an image capture apparatus configured to capture a target image representing a target site of a body of a subject, wherein the target image is one of an X-ray image, a computed tomography image, a magnetic resonance imaging image, a positron emission tomography image, a radio isotope image, a mammographic image, an ultrasonic image, an endoscopic image, or an angiographic image” reads on the endoscope 14 in Makino (Paragraph [0062]) configured to capture endoscope image 49 representing a target site of a body of a subject.);
c. an acquisition unit configured to control [the image capture apparatus] to obtain the target image representing the target site of the body of the subject (Paragraph [0072] of Makino, The endoscope 14 is connected to the processor 11 for endoscope via an endoscope connector 15. The processor 11 for endoscope receives a video signal from the image sensor 141, performs various image processing, and generates the endoscope image 49 suitable for observation by a doctor. That is, the processor 11 for endoscope functions as an image generation unit that generates the endoscope image 49 based on the video signal acquired from the endoscope 14. In the instant application, the broadest reasonable interpretation of “an acquisition unit configured to control [the image capture apparatus] to obtain the target image representing the target site of the body of the subject” reads on the processor 11 of Makino (Paragraph [0072]) for endoscope, with processor 11 receiving a video signal from the image sensor 141, performing various image processing, and generating the endoscope image 49 suitable for observation by a doctor.);
d. a first generator configured to receive the target image and generate first determination information indicating a condition of the target site based on the target image (Paragraph [0062] of Makino, An endoscope image 49 photographed using an endoscope 14 (see FIG. 2) is input (from the processor 11 for endoscope) to … a second model 62. The second model 62 outputs a diagnosis prediction regarding a state of ulcerative colitis when the endoscope image 49 is input. In the example illustrated in FIG. 1, the diagnosis prediction that the probability that the ulcerative colitis is normal, that is, the ulcerative colitis is not an affected area is 70%, and the probability that the ulcerative colitis is light is 20% is output. Details of the second model 62 will be described later. Paragraph [0084] of Makino, FIG. 7, The second model 62 outputs the diagnosis prediction of ulcerative colitis when the endoscope image 49 is input. The diagnosis prediction is a prediction of how a skilled specialist diagnoses the ulcerative colitis when the skilled specialist looks at the endoscope image 49. Paragraph [0085] of Makino, The second model 62 of the present embodiment is a learning model generated by the machine learning using, for example, the CNN. The second model 62 includes the input layer 531, the intermediate layer 532, the output layer 533, and a neural network model 53 having the convolutional layer and the pooling layer (not illustrated). In the instant application, the broadest reasonable interpretation of “a first generator configured to generate first determination information indicating a condition of the target site from the target image” reads on the second model 62 of Makino (Paragraphs [0062] and [0084]) receiving the endoscope image 49 from the processor 11 for endoscope and outputting a diagnosis state of ulcerative colitis when the endoscope image 49 is input.);
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e. a second generator configured to receive the target image and generate feature information indicating features related to a condition of the body of the subject based on the target image used to generate the first determination information (Paragraph [0062] of Makino, An endoscope image 49 photographed using an endoscope 14 (see FIG. 2) is input (from the processor 11 for endoscope) to a first model 61. Paragraph [0063] of Makino, The first model 61 includes a first score learning model 611, a second score learning model 612 and a third score learning model 613.
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In the following description, when there is no particular need to distinguish from the first score learning model 611 to the third score learning model 613, the first score learning model 611 to the third score learning model 613 may be simply described as the first model 61. Paragraph [0064] of Makino, The first score learning model 611 outputs a predicted value of a first score obtained by digitizing evaluation regarding the degree of reddishness when the endoscope image 49 is input. The second score learning model 612 outputs a predicted value of a second score obtained by digitizing evaluation regarding the degree of blood vessel transparency when the endoscope image 49 is input. The third score learning model 613 outputs a predicted value of a third score which quantifies the evaluation regarding the degree of ulcer when the endoscope image 49 is input. Paragraph [0065] of Makino, The degree of reddishness, the degree of blood vessel transparency, and the degree of ulcer are examples of diagnostic criteria items included in the diagnostic criteria used when a doctor diagnoses the condition of ulcerative colitis. The predicted values of the first to third scores are examples of diagnosis criteria prediction regarding the diagnostic criteria of ulcerative colitis. In the instant application, the broadest reasonable interpretation of “a second generator configured to generate feature information indicating features related to a condition of the body of the subject from the target image used to generate the first determination information” reads on the first model 61 of Makino (Paragraphs [0063] through [0065]) configured to receive the endoscope image 49 from the processor 11 for endoscope and generate diagnosis criteria prediction output.), wherein the second generator comprises a feature generation model configured to generate the feature information, the feature generation model comprising a second neural network trained using a plurality of training images of a portion of a body of a subject with identification information corresponding to one or more features in each training image; and
f. a display controller configured to (Paragraph [0071] and FIGS. 2 and 7 of Makino, FIG. 2 is an explanatory diagram for explaining a configuration of the diagnostic support system 10. The diagnostic support system 10 includes an endoscope 14, a processor 11 for endoscope, and an information processing device 20. The information processing device 20 includes a control unit 21, a main storage device 22, an auxiliary storage device 23, a communication unit 24, a display device I/F (interface) 26, an input device I/F 27, and a bus. In the instant application, it is determined that the broadest reasonable interpretation of “a display controller” reads on the display device I/F 26 of Makino (Paragraph [0071]).):
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g. receive the first determination information and the feature information (Paragraph [0076] of Makino, The display device I/F 26 is an interface that connects the information processing device 20 and the display device 16. The display device 16 is an example of an output unit that outputs the diagnosis criteria prediction acquired from the first model 61 and the diagnosis prediction acquired from the second model 62. However, as more accurately illustrated in FIG. 7 copied below, Makino discloses that the display device 16 is an example of an output unit that outputs the diagnosis criteria prediction acquired from the first model 61 (i.e., indirectly via the display device I/F 26) and the diagnosis prediction acquired from the second model 62 (i.e., indirectly via the display device I/F 26). In the instant application, it is determined that the broadest reasonable interpretation of “acquire the first determination information and the feature information” reads on the display device I/F 26 of Makino being an interface that connects the information processing device 20 and the display device 16, with the display device 16 acquiring the diagnosis criteria prediction from the first model 61 and the diagnosis prediction from the second model 62 such that the display device 16 acquires the diagnosis criteria prediction and the diagnosis prediction from the display device I/F 26.) and
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h. control a display apparatus to display the first determination information and the feature information (Paragraph [0076] of Makino, The display device I/F 26 is an interface that connects the information processing device 20 and the display device 16. The display device 16 is an example of an output unit that outputs the diagnosis criteria prediction acquired from the first model 61 and the diagnosis prediction acquired from the second model 62. In the instant application, it is determined that the broadest reasonable interpretation of “cause a display apparatus to display the first determination information and the feature information” reads on the display device 16 of Makino (Paragraph [0076]) that outputs the diagnosis criteria prediction acquired from the first model 61 and the diagnosis prediction acquired from the second model 62.).
With further respect to claims 2 and 23, Makino also discloses the limitations of representative claim 2 identified in bold as:
a. a third generator configured to generate region-of-interest information indicating a region of interest related to the first determination information and being a partial region of the target image (Paragraph [0218] of Makino, FIG. 29 is an explanatory diagram for explaining an outline of a diagnostic support system 10 according to the fifth embodiment. An endoscope image 49 photographed using an endoscope 14 is input to the second model 62. A second model 62 outputs an area prediction that predicts a range of legion region 74 that is predicted to have a lesion such as a polyp or cancer when an endoscope image 49 is input, and a diagnosis prediction such as whether the lesion is positive or malignant. In FIG. 29, it is predicted that the probability that a polyp in the legion region 74 is “malignant” is 5% and the probability that it is “positive” is 95%. Paragraph [0224] of Makino, The legion region 74 may be surrounded by a circle, an ellipse, or any closed curve. In such a case, the peripheral area is masked with black or white, and thus the image corrected to a shape suitable for input to the first model 61 is input to the first model 61. For example, when multiple polyps are close to each other, the region including one polyp can be cut out and the score can be calculated by the first model 61. In the instant application, the broadest reasonable interpretation of “a third generator configured to generate region-of-interest information indicating a region of interest related to the first determination information and being a partial region of the target image” reads on the second model 62 of Makino (Paragraphs [0218] and [0224]) outputs an area prediction that predicts a range of legion region 74 that is predicted to have a lesion such as a polyp or cancer when an endoscope image 49 is input).
b. wherein the display controller is configured to acquire the first determination information, the feature information, and the region-of-interest information and cause the display apparatus to display the first determination information, the feature information, and the region-of-interest information (Paragraph [0073] of Makino, The control unit 21 is an arithmetic control device that executes the program of the present embodiment. One or more central processing units (CPUs), graphics processing units (GPUs), or multi-core CPUs, and the like are used for the control unit 21. The control unit 21 is connected to each part of hardware constituting the information processing device 20 via the bus. Paragraph [0076] of Makino, The display device I/F 26 is an interface that connects the information processing device 20 and the display device 16. The display device 16 is an example of an output unit that outputs the diagnosis criteria prediction acquired from the first model 61 and the diagnosis prediction acquired from the second model 62. Paragraph [0068] of Makino, The endoscope image 49 photographed using the endoscope 14 is displayed in the endoscope image field 73 in real time. The diagnosis criteria prediction output from the first model 61 is listed in the first result field 71. The diagnosis prediction output from the second model 62 is displayed in the second result field 72. Paragraph [0095] of Makino, According to the present embodiment, it is possible to provide the diagnostic support system 10 that displays the diagnosis criteria prediction output from the first model 61 and the diagnosis prediction output from the second model 62 together with the endoscope image 49. In the instant application, the broadest reasonable interpretation of the first determination information, the feature information, and the region-of-interest information reads on an associated one of the diagnosis criteria prediction output from the first model 61 and the diagnosis prediction output from the second model 62 together with the endoscope image 49 of Makino (Paragraphs [0068], [0073], and [0095]).).
Makino does not appear to explicitly disclose, but Chang teaches the limitation in bold identified as “the second generator comprises a feature generation model configured to generate the feature information, the feature generation model comprising a second neural network trained using a plurality of training images of a portion of a body of a subject with identification information corresponding to one or more features in each training image” (Paragraph [0037] of Chang, [T]he Deep Learning (DL) model utilized by the image translation system 106 includes conditional generative adversarial networks (cGANs) configured to generate the estimated selective image 114. In various cases, the DL model can be referred to as a “SHIFT” model. For example, the image translation system 106 can utilize the bipartite, cGAN-driven technique pix2pix (Isola, et al.) to learn how to and to perform translation of the nonselective image 112 into the estimated selective image 114. The cGANs are represented, in FIG. 1, by a discriminator 122 and a generator 124. The generator 124 includes a model (e.g., a convolutional neural network (CNN)) that is trained and/or generated using at least a portion of the training data 116 in order to translate nonselective images (e.g., the nonselective image 112) into corresponding estimated selective images (e.g., the estimated selective image 114). Various parameters of the model are optimized to accurately represent conversions of the at least one nonselective image (e.g., at least one H&E image) in the training data 116 into its corresponding selective image(s) in the training data 116. According to some examples, the images for the training data can be selected according to the feature-guided training set selector 120. Paragraph [0047] of Chang, Accordingly, the predetermined number of the most representative samples in the nonselective images in the training data 116 can be identified. The predetermined number may correspond to the maximum number of ground truth selective images that can be obtained, for instance, in a limited resource setting (e.g., with a limited amount of dyes, imaging equipment, personnel availability, or the like). Selective imaging of the most representative samples can be prioritized, such that the training data 116 may be obtained to include nonselective and selective images of the most representative samples identified by the feature-guided training set selector 120. In the instant application, the broadest reasonable interpretation of “the second generator comprises a feature generation model configured to generate the feature information, the feature generation model comprising a second neural network trained using a plurality of training images of a portion of a body of a subject with identification information corresponding to one or more features in each training image” reads on the generator 124 of Chang (Paragraphs [0037] and [0047]) including a model (e.g., a convolutional neural network (CNN)) that is trained and/or generated using at least a portion of the training data 116, and further on the most representative samples of Chang (Paragraph [0047]), identified by the feature-guided training set selector 120, in the nonselective images in the training data 116 and obtained in a limited resource setting (e.g., with a limited amount of dyes).).
Therefore, it would have been obvious to one of ordinary skill in the art of health informatics and computer-aided diagnosis at the time of filing to modify the system of Makino such that the second generator comprises a feature generation model configured to generate the feature information, the feature generation model comprising a second neural network trained using a plurality of training images of a portion of a body of a subject with identification information corresponding to one or more features in each training image, as taught by Chang (Paragraphs [0037] and [0047]) in order to improve our understanding of the mapping of histological and morphological profiles into protein expression profiles, and also greatly increase the efficiency of diagnostic and prognostic decision-making (Paragraph [0023] of Chang).
Regarding claim 3, Makino as modified by Chang and modified by claim 2 discloses the limitations in bold as:
wherein the region of interest comprises a region of interest in a process of generating the first determination information from the target image (Paragraph [0218] of Makino, An endoscope image 49 photographed using an endoscope 14 is input to the second model 62. A second model 62 outputs an area prediction that predicts a range of legion region 74 that is predicted to have a lesion such as a polyp or cancer when an endoscope image 49 is input.) , and
the region-of-interest information comprises information indicating a position of the region of interest in the target image (Paragraph [0271] of Makino, By displaying the endoscope image field 73 and the area of interest field 78 separately, the user can observe the color and texture of the endoscope image 49 without being hindered by the area of interest indicator 781. By displaying the endoscope image field 73 and the area of interest field 78 on the same scale, the user can more intuitively grasp the positional relationship between the endoscope image 49 and the area of interest indicator 781. Paragraph [0273] of Makino, According to the present embodiment, the user can intuitively grasp the positional relationship between the endoscope image 49 and the area of interest indicator 781. In addition, by looking at the endoscope image field 73, the endoscope image 49 can be observed without being disturbed by the area of interest indicator 781.).
Regarding claim 4, Makino as modified by Chang and modified by claim 1 discloses the limitations in bold as wherein the feature information comprises identification information comprising a name of each of the features detected from the target image and position information indicating a position of each of the features in the target image (Paragraph [0095] of Makino, According to the present embodiment, it is possible to provide the diagnostic support system 10 that displays the diagnosis criteria prediction output from the first model 61 and the diagnosis prediction output from the second model 62 together with the endoscope image 49. While observing the endoscope image 49, a doctor can check the diagnosis criteria prediction and the diagnosis prediction that predicts the diagnosis when a skilled specialist looks at the same endoscope image 49. Paragraph [0222] of Makino, The outputs of the first model 61 and the second model 62 are acquired by a first acquisition unit and a second acquisition unit, respectively. Based on outputs acquired by a first acquisition unit and a second acquisition unit, a screen illustrated at the bottom of FIG. 29 is displayed on a display device 16. Since the displayed screen is the same as the screen described in the first embodiment, the description thereof will be omitted.
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Paragraph [0224] of Makino, The legion region 74 may be surrounded by a circle, an ellipse, or any closed curve. In such a case, the peripheral area is masked with black or white, and thus the image corrected to a shape suitable for input to the first model 61 is input to the first model 61. For example, when multiple polyps are close to each other, the region including one polyp can be cut out and the score can be calculated by the first model 61.).
Regarding claim 5, Makino as modified by Chang and modified by claim 2 discloses the limitations in bold as:
an image-of-interest generator configured to generate an image of interest where the region of interest is superimposed on the target image (Paragraph [0218] of Makino, FIG. 29 is an explanatory diagram for explaining an outline of a diagnostic support system 10 according to the fifth embodiment. An endoscope image 49 photographed using an endoscope 14 is input to the second model 62. A second model 62 outputs an area prediction that predicts a range of legion region 74 that is predicted to have a lesion such as a polyp or cancer when an endoscope image 49 is input, and a diagnosis prediction such as whether the lesion is positive or malignant. In FIG. 29, it is predicted that the probability that a polyp in the legion region 74 is “malignant” is 5% and the probability that it is “positive” is 95%. Paragraph [0224] of Makino, The legion region 74 may be surrounded by a circle, an ellipse, or any closed curve. In such a case, the peripheral area is masked with black or white, and thus the image corrected to a shape suitable for input to the first model 61 is input to the first model 61. For example, when multiple polyps are close to each other, the region including one polyp can be cut out and the score can be calculated by the first model 61.), and
wherein the display controller is further configured to cause the display apparatus to display the image of interest (Paragraph [0068] of Makino, The endoscope image 49 photographed using the endoscope 14 is displayed in the endoscope image field 73 in real time. The diagnosis criteria prediction output from the first model 61 is listed in the first result field 71. The diagnosis prediction output from the second model 62 is displayed in the second result field 72. Paragraph [0095] of Makino, According to the present embodiment, it is possible to provide the diagnostic support system 10 that displays the diagnosis criteria prediction output from the first model 61 and the diagnosis prediction output from the second model 62 together with the endoscope image 49. While observing the endoscope image 49, a doctor can check the diagnosis criteria prediction and the diagnosis prediction that predicts the diagnosis when a skilled specialist looks at the same endoscope image 49.
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Paragraph [0222] of Makino, The outputs of the first model 61 and the second model 62 are acquired by a first acquisition unit and a second acquisition unit, respectively. Based on outputs acquired by a first acquisition unit and a second acquisition unit, a screen illustrated at the bottom of FIG. 29 is displayed on a display device 16. Since the displayed screen is the same as the screen described in the first embodiment, the description thereof will be omitted.).
Regarding claim 6, Makino as modified by Chang and modified by claim 5 discloses the limitations in bold as:
a feature image generator configured to generate a first feature image where the feature information is superimposed on the target image and/or a second feature image where the feature information and the region-of-interest information are superimposed on the target image (Paragraph [0218] of Makino, FIG. 29 is an explanatory diagram for explaining an outline of a diagnostic support system 10 according to the fifth embodiment. An endoscope image 49 photographed using an endoscope 14 is input to the second model 62. A second model 62 outputs an area prediction that predicts a range of legion region 74 that is predicted to have a lesion such as a polyp or cancer when an endoscope image 49 is input, and a diagnosis prediction such as whether the lesion is positive or malignant. In FIG. 29, it is predicted that the probability that a polyp in the legion region 74 is “malignant” is 5% and the probability that it is “positive” is 95%. Paragraph [0224] of Makino, The legion region 74 may be surrounded by a circle, an ellipse, or any closed curve. In such a case, the peripheral area is masked with black or white, and thus the image corrected to a shape suitable for input to the first model 61 is input to the first model 61. For example, when multiple polyps are close to each other, the region including one polyp can be cut out and the score can be calculated by the first model 61.), and
wherein the display controller is configured to cause the display apparatus to display the first feature image and/or the second feature image (FIG. 29 of Makino).
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Regarding claim 7, Makino as modified by Chang and modified by claim 6 discloses the limitations in bold as: wherein the display controller is further configured to cause the display apparatus to display the image of interest and the second feature image alongside each other (Paragraph [0106] of Makino, The display device 16 includes a first display device 161 and a second display device 162. The first display device 161 is connected to a display device I/F 26. The second display device 162 is connected to a processor 11 for endoscope. It is preferable that the first display device 161 and the second display device 162 are arranged adjacent to each other. Paragraph [0107] of Makino, The endoscope image 49 generated by the processor 11 for endoscope is displayed on the first display device 161 in real time. The diagnosis prediction and the diagnosis criteria prediction acquired by the control unit 21 are displayed on the second display device 162.).
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Regarding claim 8, Makino as modified by Chang and modified by claim 2 discloses the limitations in bold as: wherein the display controller is configured to cause the display apparatus to simultaneously display (1) the first determination information, (2) the region-of-interest information, and (3) the feature information (Paragraph [0073] of Makino, The control unit 21 is an arithmetic control device that executes the program of the present embodiment. One or more central processing units (CPUs), graphics processing units (GPUs), or multi-core CPUs, and the like are used for the control unit 21. The control unit 21 is connected to each part of hardware constituting the information processing device 20 via the bus. Paragraph [0076] of Makino, The display device I/F 26 is an interface that connects the information processing device 20 and the display device 16. The display device 16 is an example of an output unit that outputs the diagnosis criteria prediction acquired from the first model 61 and the diagnosis prediction acquired from the second model 62.
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Paragraph [0095] of Makino, According to the present embodiment, it is possible to provide the diagnostic support system 10 that displays the diagnosis criteria prediction output from the first model 61 and the diagnosis prediction output from the second model 62 together with the endoscope image 49. While observing the endoscope image 49, a doctor can check the diagnosis criteria prediction and the diagnosis prediction that predicts the diagnosis when a skilled specialist looks at the same endoscope image 49. Paragraph [0222] of Makino, The outputs of the first model 61 and the second model 62 are acquired by a first acquisition unit and a second acquisition unit, respectively. Based on outputs acquired by a first acquisition unit and a second acquisition unit, a screen illustrated at the bottom of FIG. 29 is displayed on a display device 16. Since the displayed screen is the same as the screen described in the first embodiment, the description thereof will be omitted.).
Regarding claim 10, Makino as modified by Chang and modified by claim 1 discloses the limitations in bold as: wherein the first determination information comprises diagnostic information comprising diagnostic results related to whether the subject has a disorder (Paragraph [0084] of Makino, The second model 62 outputs the diagnosis prediction of ulcerative colitis when the endoscope image 49 is input. The diagnosis prediction is a prediction of how a skilled specialist diagnoses the ulcerative colitis when the skilled specialist looks at the endoscope image 49.Paragraph [0095] of Makino, According to the present embodiment, it is possible to provide the diagnostic support system 10 that displays the diagnosis criteria prediction output from the first model 61 and the diagnosis prediction output from the second model 62 together with the endoscope image 49. While observing the endoscope image 49, a doctor can check the diagnosis criteria prediction and the diagnosis prediction that predicts the diagnosis when a skilled specialist looks at the same endoscope image 49.).
Regarding claim 11, Makino as modified by Chang and modified by claim 1 discloses the limitations in bold as: wherein the first generator comprises a determination information generation model configured to generate the first determination information by using the target image of the subject (Paragraph [0062] of Makino, The second model 62 outputs a diagnosis prediction regarding a state of ulcerative colitis when the endoscope image 49 is input. In the example illustrated in FIG. 1, the diagnosis prediction that the probability that the ulcerative colitis is normal, that is, the ulcerative colitis is not an affected area is 70%, and the probability that the ulcerative colitis is light is 20% is output. Details of the second model 62 will be described later. Paragraph [0084] of Makino, The second model 62 outputs the diagnosis prediction of ulcerative colitis when the endoscope image 49 is input. The diagnosis prediction is a prediction of how a skilled specialist diagnoses the ulcerative colitis when the skilled specialist looks at the endoscope image 49. Paragraph [0085] of Makino, The second model 62 of the present embodiment is a learning model generated by the machine learning using, for example, the CNN. The second model 62 includes the input layer 531, the intermediate layer 532, the output layer 533, and a neural network model 53 having the convolutional layer and the pooling layer (not illustrated). The method for generating the second model 62 will be described later.).
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Regarding claim 12, Makino as modified by Chang and applied to claim 11 discloses the limitations in bold as: wherein the determination information generation model comprises a first neural network trained by using patient information, as teaching data, related to a plurality of patients each having a disorder in a target site, and the patient information comprises diagnostic information indicating diagnostic results related to a condition of a target site and a medical image of each of the plurality of patients (Paragraph [0085] of Makino, The second model 62 of the present embodiment is a learning model generated by the machine learning using, for example, the CNN. The second model 62 includes the input layer 531, the intermediate layer 532, the output layer 533, and a neural network model 53 having the convolutional layer and the pooling layer (not illustrated). Paragraph [0121] of Makino, FIG. 11 is an explanatory diagram for explaining an outline of a process of generating a model. A training data DB 64 (see FIG. 12) records multiple sets of training data in which an endoscope image 49 is associated with determination results of experts such as skilled specialists. The determination results by experts are the diagnosis of ulcerative colitis, the first score, the second score, and the third score based on endoscope image 49. Paragraph [0122] of Makino, A second model 62 is generated by machine learning using the set of endoscope image 49 and diagnosis result as the training data. A first score learning model 611 is generated by machine learning using the set of endoscope image 49 and first score as the training data. A second score learning model 612 is generated by machine learning using the set of endoscope image 49 and second score as the training data. A third score learning model 613 is generated by machine learning using the set of endoscope image 49 and third score as the training data. Paragraph [0138] of Makino, The endoscope image 49 is displayed in the endoscope image field 73. The endoscope image 49 may be an image photographed by the endoscope inspection performed by a specialist or the like who inputs training data, or may be an image delivered from the server 30. A specialist or the like performs a diagnosis regarding “ulcerative colitis” displayed in the disease name field 87 based on the endoscope image 49, and selects a check box provided at a left end of the second input field 82.).
Regarding claim 19, Makino as modified by Chang and modified by claim 1 discloses the limitations in bold as: wherein the first determination information comprises information used to determine the condition of the target site at a second time after elapse of a predetermined period since a first time when the target image is captured (Paragraph [0088] of Makino, FIG. 5 is a time chart for schematically explaining an operation of the diagnostic support system 10.
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FIG. 5A illustrates a timing of capturing by the image sensor 141. FIG. 5B illustrates a timing of generating the endoscope image 49 by the image processing in the processor 11 for endoscope. FIG. 5C illustrates a timing when the first model 61 and the second model 62 output predictions based on the endoscope image 49. FIG. 5D illustrates a timing of display on the display device 16. All horizontal axes from FIGS. 5A to 5D indicate time. Paragraph [0089] of Makino, At time t0, the image sensor 141 captures frame “a”. The video signal is transmitted to the processor 11 for endoscope. The processor 11 for endoscope performs the image processing and generates the endoscope image 49 of “a” at time t1. The control unit 21 acquires the endoscope image 49 generated by the processor 11 for endoscope and inputs the acquired endoscope image 49 to the first model 61 and the second model 62. At time t2, the control unit 21 acquires the predictions output from the first model 61 and the second model 62, respectively. Paragraph [0090] of Makino, At time t3, the control unit 21 outputs the endoscope image 49 and prediction of the frame “a” to the display device 16. As a result, the processing of an image corresponding to one frame photographed by the image sensor 141 is terminated. Similarly, at time t6, the image sensor 141 captures frame “b”. At time t7, the endoscope image 49 of “b” is generated. The control unit 21 acquires the prediction at time t8, outputs the endoscope image 49 of the frame “b” at time t9, and outputs the prediction to the display device 16. Since an operation after frame “c” is also the same, a description thereof will be omitted. As a result, the endoscope image 49 and the predictions made by the first model 61 and the second model 62 are displayed in synchronization with each other. Paragraph [0105] of Makino, The endoscope image 49 may be an image recorded in an electronic medical record system or the like. For example, by inputting each image photographed at the time of the follow-up to the first model 61, it is possible to provide the diagnostic support system 10 that can compare the temporal change of each score.).
Claim 15 is rejected under 35 U.S.C. 103(a) as being unpatentable over Makino as modified by Chang and applied to claim 1, and further in view of Tsukagoshi (U.S. Pub. No. 2017/0319164 A1).
Regarding claim 15, Makino as modified by Chang and applied to claim 1 discloses the limitations identified in bold as:
wherein the feature generation model comprises a second neural network trained by using a patient image, as teaching data, representing the target site of each of a plurality of patients having the disorder in the target site (Paragraph [0121] of Makino, FIG. 11 is an explanatory diagram for explaining an outline of a process of generating a model. A training data DB 64 (see FIG. 12) records multiple sets of training data in which an endoscope image 49 is associated with determination results of experts such as skilled specialists. Paragraph [0303] of Makino, FIG. 45 is a functional block diagram of a server 30 according to a thirteenth embodiment. The server 30 has an acquisition unit 381 and a generation unit 382. The acquisition unit 381 acquires multiple sets of training data in which the endoscope image 49 and the determination result determined for the diagnostic criteria used in the diagnosis of disease are recorded in association with each other. The generation unit 382 uses the training data to generate the first model that outputs the diagnosis criteria prediction that predicts the diagnostic criteria of disease when the endoscope image 49 is input.), and
the patient image is associated with identification information comprising a name and an annotation of each of the features detected from the patient image and position information indicating a position of each of the features in the patient image.
Makino as modified by Chang and applied to claim 1 does not appear to explicitly disclose, but Tsukagoshi teaches that the patient image is associated with identification information comprising a name and an annotation of each of the features detected from the patient image and position information indicating a position of each of the features in the patient image (Paragraph [0076] of Tsukagoshi, That is, the memory circuitry 35 stores therein the coordinates of the landmarks in a coordinate space of a three-dimensional human image and the corresponding identification codes in association with each other. As an example, the memory circuitry 35 stores therein the coordinate of the landmark corresponding to the identification code “V1” illustrated in FIG. 7 in association therewith. Similarly, the memory circuitry 35 stores therein the coordinate of the landmark and the identification code in association therewith. In FIG. 7, although only the lung, the heart, the liver, the stomach, and the kidney are illustrated as the internal organs, more internal organs, bones, blood vessels and nerves are actually included in the virtual subject image. Further, in FIG. 7, only the landmarks corresponding to the identification codes “V1”, “V2”, and “V3” are illustrated. However, more landmarks are included in practice. Paragraph [0077] of Tsukagoshi, The position matching function 37 b matches the landmarks in the volume data of the subject detected by the detecting function 37 a with the landmarks in the virtual subject image described above by using the identification codes, thereby associating the coordinate space of the volume data with the coordinate space of the virtual subject image. FIG. 8 is an explanatory diagram of an example of the matching processing with the position matching function 37 b according to the first embodiment. In FIG. 8, a case where matching is performed by using three pairs of landmarks allocated with the identification codes respectively indicating the same landmark between the landmarks detected from the scanogram image and the landmarks detected from the virtual subject image. However, the first embodiment is not limited thereto, and matching can be performed by using arbitrary pairs of landmarks. Paragraph [0078] of Tsukagoshi, For example, as illustrated in FIG. 8, when the landmarks indicated by the identification codes “V1”, “V2”, and “V3” in the virtual subject image and the landmarks indicated by the identification codes “C1”, “C2”, and “C3” in the scanogram image are matched with each other, the position matching function 37 b performs coordinate transformation so as to minimize a position gap between the same landmarks, thereby associating the coordinate spaces between the images.).
Therefore, it would have been obvious to one of ordinary skill in the art of biomedical image inspection and computer-aided diagnostic systems at the time of the filing to modify the system of Makino as modified by Chang such that the patient image is associated with identification information comprising a name and an annotation of each of the features detected from the patient image and position information indicating a position of each of the features in the patient image, as taught by Tsukagoshi (Paragraphs [0076] through [0078]) in order to provide an information processing device or the like that presents a region that contributes to a determination together with the determination result related to a diagnosis of a disease (Paragraph [0008] of Tsukagoshi).
Claims 20-22 are rejected under 35 U.S.C. 103(a) as being unpatentable over Makino as modified by Chang and applied to claim 1, and further in view of Ohyu (U.S. Pub. No. 2021/0407655 A1).
Regarding claim 20, Makino as modified by Chang and modified by claim 1 discloses the limitations identified in bold as:
a fourth generator configured to generate, from the first determination information, second determination information indicating a method of medical intervention for the subject and an effect of the medical intervention,
wherein the display controller is configured to cause the display apparatus to display the first determination information, the feature information generated from the target image used to generate the first determination information, and the second determination information (Paragraph [0067] of Makino, The outputs of the first model 61 and the second model 62 are acquired by a first acquisition unit and a second acquisition unit, respectively. Based on the outputs obtained by the first acquisition unit and the second acquisition unit, a screen illustrated at the bottom of FIG. 1 is displayed on a display device 16 (see FIG. 2). The screen displayed includes an endoscope image field 73, a first result field 71, a first stop button 711, a second result field 72, and a second stop button 722. Paragraph [0076] of Makino, The display device I/F 26 is an interface that connects the information processing device 20 and the display device 16. The display device 16 is an example of an output unit that outputs the diagnosis criteria prediction acquired from the first model 61 and the diagnosis prediction acquired from the second model 62.)
Makino as modified by Chang and applied to claim 1 does not appear to explicitly disclose, but Ohyu teaches that a fourth generator configured to generate, from the first determination information, second determination information indicating a method of medical intervention for the subject and an effect of the medical intervention (Paragraph [0303] of Ohyu, FIGS. 41A and 41B are examples of predicting the lesion probability in three layers. FIGS. 41C and 41D are examples of predicting the effect of treatment by each treatment method in three layers. FIGS. 41E and 41F are examples of predicting the probability of good QOL and the probability of aggravation in three layers. For such a prediction, the case data recorded in a complex manner is used. In the case data, image data or feature data, other non-imaging examination data, type of lesion, selected treatment method and its effect, good/bad of various QOLs, and occurrence of various seriousness is included. Paragraph [0304] of Ohyu, In the prediction, first, all the cases are divided into four lesion groups (major classification), and the counting range of the feature is set. The number of cases in each major classification of lesions 1 to 4 is calculated. The confidence interval for the probability of each lesion predicts the possibility of each lesion by category. A major classification (type of lesion) is predicted using the confidence interval values (FIGS. 41A and 41B). Paragraph [0305] of Ohyu, Next, the effect of each treatment method is predicted for the cases narrowed down to the cases in the predicted group (assumed to be predicted to be lesion 1). The cases of lesion 1 for which the major classification group was predicted are divided into eight groups (middle classification) of “effective of treatment method 1”, “no effect of treatment method 1”, “effective of treatment method 2”, “no effect of treatment method 2”, “effective of treatment method 3”, “no effect of treatment method 3”, “effective of treatment method 4”, and “no effect of treatment method 4”. By setting the counting range of the feature, the number of cases in each of these middle classification can be acquired. Regarding the presence or absence probability of the effect of the treatment method 1, the probability of the effect and the confidence interval thereof are acquired based on the number of cases of “effective of the treatment method 1” and “no effect of the treatment method 1”. Similarly, for the treatment methods 2 to 4, the probability of effectiveness and the confidence interval are required. Based on these, the possibility of effectiveness is predicted by category. The middle classification is predicted using the confidence interval values (FIGS. 41C and 41D). In the example shown in FIG. 41D, it is assumed that the treatment group 1 is selected.).
Therefore, it would have been obvious to one of ordinary skill in the art of biomedical image inspection and computer-aided diagnostic systems at the time of the filing to modify the system of Makino as modified by Chang to further include a fourth generator configured to generate, from the first determination information, second determination information indicating a method of medical intervention for the subject and an effect of the medical intervention, as taught by Ohyu (Paragraphs [0303] through [0305]) in order to output data on the lesion based on a probability (hereinafter referred to as “lesion probability”) that the subject of the image actually has the lesion and the confidence interval which is an interval indicating the reliability of the lesion probability (Paragraph [0053] of Ohyu).
Regarding claim 21, Makino as modified by Chang and applied to claim 20 does not appear to explicitly disclose, but Ohyu teaches the limitations identified in bold as “the fourth generator comprises an intervention effect determination model configured to receive input of the first determination information of the subject and output the second determination information indicating a method of intervention for the subject and an effect of the intervention” (Paragraph [0158] of Ohyu, FIG. 11 is a diagram showing an example of a pattern setting table. FIG. 11 shows pattern classification by approximating the distribution of the classification measure vector by the normal distribution model. Paragraph [0303] of Ohyu, FIGS. 41A and 41B are examples of predicting the lesion probability in three layers. FIGS. 41C and 41D are examples of predicting the effect of treatment by each treatment method in three layers. FIGS. 41E and 41F are examples of predicting the probability of good QOL and the probability of aggravation in three layers. For such a prediction, the case data recorded in a complex manner is used. In the case data, image data or feature data, other non-imaging examination data, type of lesion, selected treatment method and its effect, good/bad of various QOLs, and occurrence of various seriousness is included. Paragraph [0304] of Ohyu, In the prediction, first, all the cases are divided into four lesion groups (major classification), and the counting range of the feature is set. The number of cases in each major classification of lesions 1 to 4 is calculated. The confidence interval for the probability of each lesion predicts the possibility of each lesion by category. A major classification (type of lesion) is predicted using the confidence interval values (FIGS. 41A and 41B). Paragraph [0305] of Ohyu, Next, the effect of each treatment method is predicted for the cases narrowed down to the cases in the predicted group (assumed to be predicted to be lesion 1). The cases of lesion 1 for which the major classification group was predicted are divided into eight groups (middle classification) of “effective of treatment method 1”, “no effect of treatment method 1”, “effective of treatment method 2”, “no effect of treatment method 2”, “effective of treatment method 3”, “no effect of treatment method 3”, “effective of treatment method 4”, and “no effect of treatment method 4”. By setting the counting range of the feature, the number of cases in each of these middle classification can be acquired. Regarding the presence or absence probability of the effect of the treatment method 1, the probability of the effect and the confidence interval thereof are acquired based on the number of cases of “effective of the treatment method 1” and “no effect of the treatment method 1”. Similarly, for the treatment methods 2 to 4, the probability of effectiveness and the confidence interval are required. Based on these, the possibility of effectiveness is predicted by category. The middle classification is predicted using the confidence interval values (FIGS. 41C and 41D). In the example shown in FIG. 41D, it is assumed that the treatment group 1 is selected.).
Regarding claim 22, Makino as modified by Chang and applied to claim 21 does not appear to explicitly disclose, but Ohyu teaches the limitations identified in bold as: wherein the intervention effect determination model comprises a third neural network trained by using effect information, as teaching data, of each of [[the]] a plurality of patients who have undergone intervention for a disorder in the target site, and the effect information comprises information where the intervention provided for the target site of each of the plurality of patients and intervention effect information indicating the effect of the intervention are associated for each of the plurality of patients (Paragraph [0286] of Ohyu, In addition, various machine learning can be used for regression. FIG. 34 shows a case where SVM (Support vector machine) is used for regression. The regressed parameters can be used as features. Artificial neural networks and random trees can also be applied to regression. The explanation of the graphs U34, M34, and B34 is the same as the explanation of the graphs U15, M15, and B15 shown in FIG. 15, so the description thereof will be omitted. Paragraph [0303] of Ohyu, FIGS. 41A and 41B are examples of predicting the lesion probability in three layers. FIGS. 41C and 41D are examples of predicting the effect of treatment by each treatment method in three layers. FIGS. 41E and 41F are examples of predicting the probability of good QOL and the probability of aggravation in three layers. For such a prediction, the case data recorded in a complex manner is used. In the case data, image data or feature data, other non-imaging examination data, type of lesion, selected treatment method and its effect, good/bad of various QOLs, and occurrence of various seriousness is included. Paragraph [0304] of Ohyu, In the prediction, first, all the cases are divided into four lesion groups (major classification), and the counting range of the feature is set. The number of cases in each major classification of lesions 1 to 4 is calculated. The confidence interval for the probability of each lesion predicts the possibility of each lesion by category. A major classification (type of lesion) is predicted using the confidence interval values (FIGS. 41A and 41B). Paragraph [0305] of Ohyu, Next, the effect of each treatment method is predicted for the cases narrowed down to the cases in the predicted group (assumed to be predicted to be lesion 1). The cases of lesion 1 for which the major classification group was predicted are divided into eight groups (middle classification) of “effective of treatment method 1”, “no effect of treatment method 1”, “effective of treatment method 2”, “no effect of treatment method 2”, “effective of treatment method 3”, “no effect of treatment method 3”, “effective of treatment method 4”, and “no effect of treatment method 4”. By setting the counting range of the feature, the number of cases in each of these middle classification can be acquired. Regarding the presence or absence probability of the effect of the treatment method 1, the probability of the effect and the confidence interval thereof are acquired based on the number of cases of “effective of the treatment method 1” and “no effect of the treatment method 1”. Similarly, for the treatment methods 2 to 4, the probability of effectiveness and the confidence interval are required. Based on these, the possibility of effectiveness is predicted by category. The middle classification is predicted using the confidence interval values (FIGS. 41C and 41D). In the example shown in FIG. 41D, it is assumed that the treatment group 1 is selected.).
Response to Amendment
Applicant’s amendment and argument (Second Paragraph on Page 12 of the Amendment filed July 24, 2025) regarding the objection to the specification have been fully considered and are persuasive. Therefore, the objection to the specification has been withdrawn.
Applicant’s amendment and argument (Third Paragraph on Page 12 of the Amendment filed July 24, 2025) regarding the objection to claims 23 and 26 have been fully considered and are persuasive. Therefore, the objection to claims 23 and 26 has been withdrawn.
Applicant’s amendment and argument (Fourth Paragraph on Page 12 of the Amendment filed July 24, 2025) regarding the double patenting rejection of claims 1, 17, 19, and 20 have been fully considered and are persuasive. Therefore, the double patenting rejection of claims 1, 17, 19, and 20 has been withdrawn.
Applicant’s amendment and argument (Fifth Paragraph on Page 12 to Fifth Paragraph on Page 13 of the Amendment filed July 24, 2025) regarding the rejection of claims 1-8, 10-12, 14, 15, and 19-26 under 35 U.S.C. § 101 have been fully considered but are moot in view of the new grounds of rejection necessitated by the amendment.
In the Applicant’s Amendment (Fourth Paragraph on Page 12 of the Amendment filed July 24, 2025), Applicant argued: “[T]he amended claims integrate any alleged abstract idea into a practical application by implementing the specialized artificial intelligence models. The system utilizes trained neural networks that process patient information, including diagnostic information and medical images, to generate determination information. The implementation of the system goes beyond mere mental processes and represents a specific technological improvement in computer-aided diagnosis.” Section 2106.05(a)(II) of the MPEP states: “To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology.” While Applicant argued that the system performs the method of generating determination information, the claims do not recite how the trained neural networks that process patient information to aid the method, the extent to which the trained neural networks aids the method, or the significance of the trained neural networks to the performance of the method.
Applicant's amendment and arguments (Sixth Paragraph on Page 13 to First Paragraph on Page 15 of the Amendment filed July 24, 2025) regarding the rejection of claims 1-8, 10-12, 14, 19, and 23-26 under 35 U.S.C. § 102 have been fully considered and are persuasive. Therefore, the rejection of claims 1-8, 10-12, 14, 19, and 23-26 under 35 U.S.C. § 102 has been withdrawn.
Applicant's arguments (Second Paragraph on Page 12 to Third Paragraph on Page 14 of the Amendment filed July 24, 2025) regarding the rejections of claims 15 and 20-22 under 35 U.S.C. § 103 have been fully considered but are moot in view of the new grounds of rejection necessitated by the amendment.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/V.C.I./Examiner, Art Unit 3686
/DEVIN C HEIN/Examiner, Art Unit 3686
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