DETAILED ACTION
This action is made in response to the amendments/remarks filed on December 8, 2025. This action is made final.
Claims 1-6 and 10-11 are pending. Claims 7-9 and 12 have been previously cancelled. Claims 1 and 11 have been amended. Claims 1, and 11 are independent claims.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments filed August 13, 2025 with respect to the previous art rejection has been fully considered but is moot in light of the new grounds of rejection.
Applicant’s arguments with respect to the 101 rejection has been fully considered but is not persuasive.
Applicant argues the claims recite additional elements that integrate the exception into a practical application and that the claimed subject matter constitutes a technological improvement. Applicant states the claimed solution is an improvement in a technological field, namely computer-assisted medical diagnostics. However, the examiner respectfully disagrees.
While the claimed invention makes use of a computer, the claims are nonetheless directed to medical diagnostics. The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to make a medical diagnosis, which is abstract. While Applicant argues the claims improve another technology, the examiner respectfully disagrees. MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves another technology. See also MPEP 2106.05(a)(II). Applicant’s claimed invention recites the additional element(s) of (“an information processing apparatus”, “a[n] apparatus”, “a memory”, “terminal apparatus”, “medical image diagnosis apparatus”, “processing circuitry”, “non-transitory computer readable medium”. These additional elements are recited at high levels of generality and merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
Applicant alleges the “automatic” selection of the model without requiring repeated user-driven model configuration amounts to a technical improvement of the device. However, the examiner, respectfully disagrees. The “automatic” nature of the steps does not disqualify the limitation from being categorized as an abstract idea. Language such as concurrently, automatically, instantly, or simultaneously to describe the automation of a manual process is not enough to overcome a subject matter eligibility rejection (MPEP § 2106.05(a)(I) Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality no. (iii) mere automation of manual processes). Examiner also notes that language such as this is not restricted to computer processes, humans can automatically/instantly/simultaneously complete different tasks (see MPEP § 2106.04(a)(2)(III) stating that the mental processes may be completely by humans plural – not just a singular human mind).
Furthermore, any purported improvement, such as refining disease predictions and reducing unnecessary detailed examinations are not improvements on the computer or technology, but at best, are purported improvements to the abstract idea of medical diagnosing. As such, these additional elements are not improved through implementation of the abstract idea and a practical application is not present.
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 1-6, 10, and 11 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.
Claims 1-6 and 10 recite an apparatus for determining a disease, which is within the statutory category of a machine. Claim 11 recites a non-transitory computer readable memory performing instructions for determining a disease, which is within the statutory class of a manufacture.
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. ___ (2014). Claims 1-6, 10, and 11, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
MPEP 2106 Step 2A – Prong 1:
The bolded limitations of:
Claims 1, and 11 (claim 1 being representative)
acquire, from a sample examination apparatus configured to analyze a physical sample collected from an examination target subject, an examination value of a biomarker collected from an examination target subject; store the acquired examination value in a memory; determine a first disease type among a plurality of first disease types of a disease with which the subject is potentially affected based on an inference result of a first trained model, the inference result being obtained by inputting the acquired examination value to the first trained model, the first trained model being configured to output, upon inputting of the examination value, a numerical value indicating a possibility of being affected for each of the plurality of first disease types as the inference result; receive, via a terminal apparatus connected to the medical information processing apparatus, input of an examination result related to the first disease type determined of the subject and obtained through examination by a medical image diagnosis apparatus different from the sample examination apparatus; when the examination result of the subject defies the first disease type determined, select, based on the first disease type defined, at least one second trained model used for determination of a second disease type with which the subject is potentially affected other than the first disease type determined among a plurality of second trained models each trained with learning data excluding an examination value of a subject who is affected with one certain disease type and each configured to output, upon inputting of the examination value, a numerical value indicating a possibility of being affected for each of a plurality of disease types excluding the certain disease type, the at least one second trained model selected being trained with learning data excluding an examination value of a subject who is affected with the first disease type defied and configured to output, upon inputting of the examination value, a numerical value indicating a possibility of being affected for each of a plurality of disease types excluding the first disease type defied; determine a second disease type among the plurality of disease types of the disease with which the subject is potentially affected based on an inference result of the at least one trained model selected, the inference result being obtained by inputting the examination value acquired to the at least one second trained model selected; receive, via the terminal apparatus, input of an examination result of a detailed examination related to the second disease type determined of the subject and obtained through examination by the medical image diagnosis apparatus different from the sample examination apparatus; and determine, based on the examination result of the detailed examination related to the second disease type determined of the subject whether the at least one second disease type determined is defied or affirmed.
as presently drafted, under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for the recitation of generic computer components. But for the noted computer elements, the claim encompasses a person following rules or instructions to analyze data in the manner described in the abstract idea, such as a clinician analyzing test results to determine a diagnosis probability and performing additional testing to confirm or deny the diagnosis and re-analyzing the data to determine another possible diagnosis when the first diagnosis is incorrect. The examiner further notes that “methods of organizing human activity” includes a person’s interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). If the claim limitation, under its broadest reasonable interpretation, covers managing persona behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
MPEP 2106 Step 2A – Prong 2:
This judicial exception is not integrated into a practical application because there are no meaningful limitations that transform the exception into a patent eligible application. The additional elements merely amount to instructions to apply the exception using generic computer components (“an information processing apparatus”, “a[n] apparatus”, “a memory”, “terminal apparatus”, “medical image diagnosis apparatus”, “processing circuitry”, “non-transitory computer readable medium” all recited at a high level of generality). Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claim further recites the additional elements of using a trained model with learning data. When given the broadest reasonable interpretation in light of the nonexistent description of model training in the disclosure, training of a machine model with the noted data amounts to a mathematical concept that creates data associations. As such, this training of the model is interpreted to be subsumed within the identified abstract idea and the use of the trained model provides nothing more than mere instructions to implement the abstract idea, supra. July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of item (c) at Pgs. 7-9. Furthermore, the use of the trained model provides nothing more than mere instructions to implement an abstract idea on a generic computer (“apply it”). See MPEP 2106.05(f). MPEP 2106.05(f); July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of items (d) and (e) at Pgs. 8-9.
The claims only manipulate abstract data elements into another form. They do not set forth improvements to another technological field or the functioning of the computer itself and instead use computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. None of the additional elements recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
At the levels of abstraction described above, the claims do not readily lend themselves to a finding that they are directed to a nonabstract idea. Therefore, the analysis proceeds to step 2B. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016) ("The Enfish claims, understood in light of their specific limitations, were unambiguously directed to an improvement in computer capabilities. Here, in contrast, the claims and their specific limitations do not readily lend themselves to a step-one finding that they are directed to a nonabstract idea. We therefore defer our consideration of the specific claim limitations’ narrowing effect for step two.") (citations omitted).
MPEP 2106 Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as presented in Step 2A Prong 2. Moreover, the additional elements recited are known and conventional generic computing elements (“an information processing apparatus”, “a[n] apparatus”, “a memory”, “terminal apparatus”, “medical image diagnosis apparatus”, “processing circuitry”, “non-transitory computer readable medium” -- see Specification Fig. 1, page 6 describing the various components as general purpose, common, standard, known to one of ordinary skill, and at a high level of generality, and in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements). Therefore, these additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept that amounts to significantly more. See MPEP 2106.05(f).
The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, translating, and displaying data—see Specification above as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the model and the use of the trained model were considered to be part of the abstract idea and “apply it,” respectively. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. The training of the model is considered part of the abstract idea and thus cannot provide a practical application. Furthermore, the use of the trained model represented saying “apply it.” The use of the trained model has been revaluated under the “significantly more” analysis and does not provide “significantly more” to the abstract idea. MPEP 2106.05(A) indicates also indicates that merely adding the words “apply it” or equivalent use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Dependent Claims
The limitations of dependent but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. Claims 2-6, and 10 further recite excluding a parameter for consideration in the disease determination, which covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1-6, 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cohen et al. (USPPN: 2020/0005901; hereinafter Cohen) in further view of Jessen et al. (USPPN: 2023/0145258; hereinafter Jessen), and Fields et al. (USPPN: 2020/0365229; hereinafter Fields)
As to claim 1, Cohen teaches A medical information processing apparatus comprising a circuitry (e.g., see Abstract) configured to:
acquire, from a sample examination apparatus configured to analyze a physical sample collected from an examination target subject, an examination value of a biomarker collected from an examination target subject (e.g., see Abstract, [0050], [0067], [0112], [0129] wherein biomarker measurements and values are obtained from a patient sample using various methodologies for measurement of a physical samples from a patient);
store the acquired examination value in a memory (e.g., see [0156] wherein the patient data is stored in a memory);
determine a first disease type of a disease with which the subject is potentially affected based on an inference result of a first trained model, the inference result being obtained by inputting the acquired examination value to the first trained model, the first trained model being configured to infer the first disease type of an affected disease upon inputting of the examination value (e.g., see [0092], [0103], [0129], [0130] wherein a patient can be identified as having a risk of having a specific cancer of many cancers based on their biomarker data and a machine learning model);
receive, via a terminal apparatus configured to receive input of various kinds of examination results and transmit the input to the medical information processing apparatus, input of an examination result of a detailed examination related to the first disease type determined of the subject and obtained through examination by a medical image diagnosis apparatus different from the sample examination apparatus (e.g., see Fig. 4, [0135], [0148], [0152], [0156], [0196] wherein diagnostic indicators are further collected, the diagnostic indicators being obtained from diagnostic tests such as biopsies, x-ray, scans, etc. connected to/accessible by the system (i.e., medical image diagnosis different from the patient sample biomarker)); and
when the examination result of the subject defies the first disease type determined, select, based on the first disease type defined, at least one second trained model used for determination of a second disease type with which the subject is potentially affected other than the first disease type determined among a plurality of second trained models each trained with learning data excluding an examination value of a subject who is affected with one certain disease type and each configured to output, upon inputting of the examination value, a numerical value indicating a possibility of being affected for each of a plurality of disease types excluding the certain disease type, the at least one second trained model selected being trained with learning data excluding an examination value of a subject who is affected with the first disease type defied and configured to output, upon inputting of the examination value, a numerical value indicating a possibility of being affected for each of a plurality of disease types excluding the first disease type defied (While Cohen fails to explicitly teach determining a second disease type, Cohen does teach biomarkers can be indicative of numerous cancer types wherein one or more potential cancers can be determined using a machine learning model and further improving upon the model by using the diagnostic test results, which confirm or deny, the disease diagnosis of the model to further train the machine learning system e.g., see [0015], [0086], [0153], [0187], tables 8 &9. Accordingly, it would have at least been obvious, that in the event that a test confirms the model diagnosis to be incorrect, in order for the model to be improved upon, the incorrect diagnosis would be excluded as a possible diagnosis and, therefore, a second diagnosis would be output. See also See also MPEP 2144.04 wherein omission of an element and its function is obvious if the function of the element is not desired and wherein duplication of parts is obvious).
determine a second disease type among the plurality of second disease types of the disease with which the subject is potentially affected based on an inference result of the at least one second trained model selected, the inference result being obtained by inputting the examination value to the at least one second trained model (e.g., see [0057], [0060], [0148], [0153] wherein parameters including biomarkers from affirmative indicator that the patient has the disease are used to further train the machine learning model to make a prediction of a potential cancer out of a plurality of potential cancers);
receive, via the terminal apparatus, input of an examination result of a detailed examination related to the second disease type determined of the subject and obtained through examination by the medical image diagnosis apparatus different from the sample examination apparatus (e.g., see [0015], [0086], [0152]-[0153] wherein the system can receive test results obtained from the diagnostic testing); and
determine, based on the examination result of the detailed examination related to the second disease type determined of the subject whether the at least one second disease type determined is defied or affirmed (e.g., see [0015], [0086], [0152]-[0153] wherein the system can receive test results obtained from the diagnostic testing, which confirm or deny the presence of cancer).
While Cohen teaches an iterative process for confirming/denying the predicted diagnosis output by a machine learning model, wherein the model is continuously updated and improved the obtain the correct/confirmed result, wherein it would have been obvious to remove an incorrect diagnosis from the training data; for the purposes of compact prosecution and in the same filed of endeavor of predictive models, Jessen teaches the claimed limitation.
Jessen teaches when the examination result of the subject defies the first disease type determined, select, based on the first disease type defined, at least one second trained model used for determination of a second disease type with which the subject is potentially affected other than the first disease type determined among a plurality of second trained models each trained with learning data excluding an examination value of a subject who is affected with one certain disease type and each configured to output, upon inputting of the examination value, a numerical value indicating a possibility of being affected for each of a plurality of disease types excluding the certain disease type, the at least one second trained model selected being trained with learning data excluding an examination value of a subject who is affected with the first disease type defied and configured to output, upon inputting of the examination value, a numerical value indicating a possibility of being affected for each of a plurality of disease types excluding the first disease type defied (e.g., see Fig. 1, [0066], [0067], [0099], [0126] teaching a plurality of machine learning models which are trained for a given task or endpoint, wherein a model configured for a particular task or endpoint is selected, the model omitting test results that may bias the prediction, such as those from another disease, from the training dataset). Accordingly, it would have been obvious to modify Cohen in view of Jessen before the effective date of the present application with a reasonable expectation of success. One would have been motivated to make the modification in order to improve data quality in generating models by identifying and discarding bias data (e.g., see [0099] of Jessen).
While Cohen teaches determining one or more potential cancers, Cohen fails to explicitly teach output a numerical value indicating a possibility of being affected for each of the plurality of first/second disease types.
However, in the same field of endeavor predictive models for disease states, Fields teaches output a numerical value indicating a possibility of being affected for each of the plurality of first/second disease types (e.g., see Fig. 6, [0203] wherein a numerical value can be output for each of the plurality of disease types). Accordingly, it would have been obvious to modify Cohen-Jessen in view of Fields before the effective date of the present application with a reasonable expectation of success. One would have been motivated to make the modification in order to quickly and easily visualize the ranking of the plurality of diseases.
As to claim 2, the rejection of claim 1 is incorporated. Cohen further teaches wherein the processing circuitry is further configured to determine the second disease type of the disease with which the subject is potentially affected by using the at least one second trained model selected which is obtained by excluding an element related to inference of the first disease type defied from the first trained model (While Cohen fails to explicitly teach determining a second disease type by excluding an element…of the first disease, Cohen does teach biomarkers can be indicative of numerous cancer types wherein one or more potential cancers can be determined using a machine learning model and further improving upon the model by using the diagnostic test results, which confirm or deny, the disease diagnosis of the model to further train the machine learning system. Cohen further teaches the iterative training includes a different subset of the parameters to generate the desired output. e.g., see [0015], [0086], [0151], [0153], [0187], tables 8 &9. Accordingly, it would have at least been obvious, that in the event that a test confirms the model diagnosis to be incorrect, in order for the model to be improved upon, the incorrect diagnosis, and its associated input parameters, would be excluded as a possible diagnosis. See also MPEP 2144.04 wherein omission of an element and its function is obvious if the function of the element is not desired).
Nonetheless, for the purposes of compact prosecution, Jessen teaches excluding an element related to inference of the first disease type defied from the first trained model (e.g., see [0099, [0126]-[0036] wherein test results that may bias the prediction are removed from the training dataset). Accordingly, it would have been obvious to modify Cohen in view of Jessen with a reasonable expectation of success. One would have been motivated to make the modification in order to improve data quality in generating models by identifying and discarding outlier data (e.g., see [0099] of Jessen).
As to claim 3, the rejection of claim 1 is incorporated. Cohen further teaches wherein the processing circuitry is further configured to determine the second disease type of the disease with which the subject is potentially affected by using the at least one second trained model selected which has been trained with examination values which exclude examination value of a subject affected with a disease of the first disease type defied among examination values of biomarkers collected from a plurality of subjects (While Cohen fails to explicitly teach the model being trained with examination values which exclude examination value…of the first disease, Cohen does teach biomarkers can be indicative of numerous cancer types wherein one or more potential cancers can be determined using a machine learning model and further improving upon the model by using the diagnostic test results, which confirm or deny, the disease diagnosis of the model to further train the machine learning system. Cohen further teaches the iterative training includes a different subset of the parameters to generate the desired output. e.g., see [0015], [0086], [0151], [0153], [0187], tables 8 &9. Accordingly, it would have at least been obvious, that in the event that a test confirms the model diagnosis to be incorrect, in order for the model to be improved upon, the incorrect diagnosis, and its associated input parameters such as a biomarker, would be excluded as a possible diagnosis. See also MPEP 2144.04 wherein omission of an element and its function is obvious if the function of the element is not desired).
Nonetheless, for the purposes of compact prosecution, Jessen teaches using the at least one second trained model which has been trained with examination values which exclude examination value (e.g., see [0099, [0126]-[0036] wherein test results that may bias the prediction are removed from the training dataset). Accordingly, it would have been obvious to modify Cohen in view of Jessen with a reasonable expectation of success. One would have been motivated to make the modification in order to improve data quality in generating models by identifying and discarding outlier data (e.g., see [0099] of Jessen).
As to claim 4, the rejection of claim 1 is incorporated. Cohen further teaches wherein the processing circuitry is further configured to determine the second disease type of the disease with which the subject is potentially affected by using the at least one second trained model selected which has been trained with an examination values of a kind of biomarker except for, among examination values of a plurality of kinds of biomarkers collected from a plurality of subjects excluding, the examination values of a kind of biomarker corresponding to a disease of the first disease type defied (While Cohen fails to explicitly teach the model being trained with examination values which exclude examination value of a kind of biomarker…of the first disease, Cohen does teach biomarkers can be indicative of numerous cancer types wherein one or more potential cancers can be determined using a machine learning model and further improving upon the model by using the diagnostic test results, which confirm or deny, the disease diagnosis of the model to further train the machine learning system. Cohen further teaches the iterative training includes a different subset of the parameters to generate the desired output. e.g., see [0015], [0086], [0151], [0153], [0187], tables 8 &9. Accordingly, it would have at least been obvious, that in the event that a test confirms the model diagnosis to be incorrect, in order for the model to be improved upon, the incorrect diagnosis, and its associated input parameters such as a kind of biomarker, would be excluded as a possible diagnosis. See also MPEP 2144.04 wherein omission of an element and its function is obvious if the function of the element is not desired).
Nonetheless, for the purposes of compact prosecution, Jessen teaches excluding the values of a kind corresponding to a type defied (e.g., see [0099, [0126]-[0036] wherein test results that may bias the prediction are removed from the training dataset). Accordingly, it would have been obvious to modify Cohen in view of Jessen with a reasonable expectation of success. One would have been motivated to make the modification in order to improve data quality in generating models by identifying and discarding outlier data (e.g., see [0099] of Jessen).
As to claim 5, the rejection of claim 1 is incorporated. Cohen further teaches wherein when the examination result of the subject defies the second disease type determined, the processing circuitry is further configured to determine a new second disease type by using a trained model obtained by excluding an element related to inference of the second disease type defied from the trained model used to determine the second disease type defied (While Cohen fails to explicitly teach determining a third disease type by excluding an element…of the first disease, Cohen does teach biomarkers can be indicative of numerous cancer types wherein one or more potential cancers can be determined using a machine learning model and further improving upon the model by using the diagnostic test results, which confirm or deny, the disease diagnosis of the model to further train the machine learning system. Cohen further teaches the iterative training includes a different subset of the parameters to generate the desired output. e.g., see [0015], [0086], [0151], [0153], [0187], tables 8 &9. Accordingly, it would have at least been obvious, that in the event that a test confirms the model diagnosis to be incorrect, in order for the model to be improved upon, the incorrect diagnosis, and its associated input parameters, would be excluded as a possible diagnosis. See also See also MPEP 2144.04 wherein omission of an element and its function is obvious if the function of the element is not desired and wherein duplication of parts is obvious).
Nonetheless, for the purposes of compact prosecution, Jessen teaches excluding an element related to inference of the type defied from the trained model used to determine the second type defied (e.g., see [0099, [0126]-[0036] wherein test results that may bias the prediction are removed from the training dataset). Accordingly, it would have been obvious to modify Cohen in view of Jessen with a reasonable expectation of success. One would have been motivated to make the modification in order to improve data quality in generating models by identifying and discarding outlier data (e.g., see [0099] of Jessen).
As to claim 6, the rejection of claim 1 is incorporated. Cohen further teaches wherein the processing circuitry is further configured to determine whether the subject is affected with the disease based on an inference result of a third trained model, the inference result being obtained by inputting the acquired examination value to the third trained model, the third trained model being configured to output existence of the disease as an inference result upon inputting of the examination value, and when having determined that the subject is affected with the disease, the processing circuitry is further configured to input the acquired examination value to the first trained model (e.g., see [0057], [0060], [0148], [0153] wherein parameters including biomarkers from affirmative indicator that the patient has the disease are used to further train the machine learning model).
As to claim 10, the rejection of claim 1 is incorporated. Cohen further teaches wherein the disease is cancer (e.g., see [0092] wherein the disease is one or more cancers).
As to claim 11, the claim is directed to the non-transitory computer medium implemented on the apparatus of claim 1 and is similarly rejected.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Relevant Art not Cited
As a courtesy, the following prior art documents have been found during the course of examination and deemed relevant to applicant’s disclosure. Applicant is strongly encouraged to review the following references prior to any amendments/remarks:
Cohen et al. (USPPN: 2018/0068083): Methods and machine learning systems for predicting the likelihood or risk of having cancer
Shi et al. (USPPN: 2023/0263477): Universal pan cancer classifier models, machine learning systems and methods of use.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/STELLA HIGGS/Primary Examiner, Art Unit 3681