DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-16 and 20-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1
Claims 1-16 and 20-23 are within the four statutory categories. However, as will be shown below, Claims 1-16 and 20-23 are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1, 9, and 20 are representative of the inventive concept and recite:
Claim 1
An apparatus for a disease prediction, comprising at least one processor configured to:
acquire disease-related information for predicting a disease for each subject of disease prediction,
predict a disease that the subject of disease prediction may contract in the future in each of a plurality of body parts,
based on the disease-related information, to generate disease prediction information,
and output the disease prediction information.
*Claims 9 and 20 recite similar limitations as claim 1, but for a method and non-transitory computer-readable medium, respectively.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of
predicting and generating) or using pen and paper. Other than reciting generic computer
components/functions such as “apparatus” and “processor”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components/functions, then it falls within “Mental Processes”
grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not
take the claim limitation out of the mental process grouping. Thus, the claim recites a mental process.
The recitation of generic computer components/functions of generating and acquiring also covers behavioral or interactions between people (i.e. a computer and user interface), and/or managing
personal behavior or relationships or interactions between people (i.e. social activities, teaching, and
following rules or instructions), hence the claim falls under “Certain Methods of Organizing Human
Activity”.
Dependent claims 2-8, 10-16, and 21-23 recite additional subject matter which further narrows or
defines the abstract idea embodied in the claims.
Step 2A Prong Two
This judicial exception is no integrated into a practical application. In particular, the claims recite the
following additional limitations:
Claim 1 recites: “apparatus”, “processor”, and “output the disease prediction information”.
In particular, the additional elements do no integrate the abstract idea into a practical application, other
than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations of
are recited as being performed by a “apparatus”, “processor” , and “non-transitory computer-readable medium”. They are recited at a high level of generality and amount to no more than mere instructions to apply the exception using a generic computer.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of “output the disease prediction information”.
Dependent claims 3-7, 11-15, and 22-23 recite “output”
In particular, the additional elements do no integrate the abstract idea into a practical application, other
than the abstract idea per se, because the additional elements amount to no more limitations which:
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of “output”.
Dependent claims 2, 8, 10, 16, and 21 do not include any additional elements beyond those
already recited in independent claims 1, 9, and 20 and dependent claims 3-7, 11-15, and 22-23, hence do not integrate the aforementioned abstract idea into a practical application. 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 any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1, 9, and 20 do not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to discussion of integration of the
abstract idea into a practical application, the additional elements: An apparatus in claim 1; amount to no
more than mere instructions to apply an exception to the abstract idea. Additionally, the additional
limitations, other than the abstract idea per se, amount to no more than limitations which amount to
elements that have been recognized as well-understood, routine, and conventional activity in particular
fields as demonstrated by the recitation of:
Output, which refers to the process of a computer system to present or display processed information (Para 0068, Shahid(US 20220300824 A1) discloses: “Output interface 616 may comprise one or more conventional mechanisms that output information to the operator or user, such as a display 640, etc.) in a manner that would be well-understood, routine, and conventional.
Dependent claims 2, 8, 10, 16, and 21 do not include any additional elements beyond those
already recited in independent claims 1, 9, and 20 and dependent claims 3-7, 11-15, and 22-23. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1, 9, and 20, hence do not amount to “significantly more” than the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-16 and 20-23 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by
Tschulena(US20200258639A1).
Claim 1
Tschulena discloses:
An apparatus for a disease prediction(Para 0001, Tschulena discloses an apparatus for predicting occurrence of health conditions), comprising at least one processor(Para 0010, Tschulena discloses microprocessors) configured to: acquire disease-related information for predicting a disease for each subject of disease prediction(Figure 4, Tschulena discloses the acquisition of patient record information for predicting a health condition), predict a disease that the subject of disease prediction may contract in the future in each of a plurality of body parts(Para 0093, Tschulena discloses predicting occurrence of a health condition), based on the disease-related information, to generate disease prediction information(Figure 4, Tschulena discloses generation of prediction information), output the disease prediction information(Para 0006, Tschulena discloses outputting of a score indicating risk of occurrence [PREDICTION INFORMATION]).
Claim 2
Tschulena discloses:
The apparatus according to claim 1, wherein the disease prediction information includes risk score information indicating a likelihood of disease incidence(Para 0006, Tschulena discloses outputting of a score indicating risk of occurrence [RISK SCORE INFORMATION]).
Claim 3
Tschulena discloses:
The apparatus according to claim 2, wherein: the processor is further configured to: generate recommended examination information related to an examination recommended for the subject of disease prediction based on the risk score for each of the plurality of body parts, and output the recommended examination information(Para 0007, Tschulena discloses the providing of therapy recommendations based on predicted health condition or risk score).
Claim 4
Tschulena discloses:
The disease prediction apparatus according to claim 1, wherein the processor is configured to: refer to past disease prediction information(Para 0009, Tschulena discloses characterizing features from past time instants), extract a content that has changed in comparison with the past disease prediction information from current disease prediction information(Para 0009, Tschulena discloses characterizing features from past time instants indicating change over time), generate the content as disease prediction change information, and output the disease prediction change information(Para 0094, Tschulena discloses updating a model as new publications/data become available).
Claim 5
Tschulena discloses:
The apparatus according to claim 1, wherein: the processor is further configured to: statistically analyze the disease prediction information of a plurality of subjects of disease prediction(Para 0015, Tschulena discloses using probabilistic statistical models to analyze health condition frequency in a population), analyze an association between a type of disease and an attribute of the subject of disease prediction(Para 0036, Tschulena discloses assessing contribution of patient health state attributes to risk), and generate attribute-related information, and output the attribute-related information(Para 0036, Tschulena discloses a model generating attribute-related information).
Claim 6
Tschulena discloses:
The disease prediction apparatus according to claim 5, wherein the processor is configured to: further extract an attribute with a high likelihood of disease incidence(Figure 1, #106, Tschulena discloses extracting risk factor prevalence[RISK FACTOR CAN BE AN ATTRIBUTE], and adverse outcome incidence), based on the attribute-related information, to generate high-risk attribute information(Figure 1, #112, Tschulena discloses a model created to generate high-risk attribute information), and output the high-risk attribute information(Figure 1, #112, Tschulena discloses a model created to generate high-risk attribute information, which inherently outputs high-risk attribute information).
Claim 7
Tschulena discloses:
The disease prediction apparatus according to claim 1, wherein: the processor is further configured to: analyze the disease prediction information of a plurality of subjects of disease prediction(Para 0015, Tschulena discloses using probabilistic statistical models to analyze health condition frequency in a population), analyze a correlation between diseases(Para 0009, Tschulena discloses comorbidity as a characterizing feature to be analyzed for predictive models), and generate disease correlation information, and output the disease correlation information(Para 0009, Tschulena discloses comorbidity as a characterizing feature to be analyzed for predictive models. Models that analyze comorbidities inherently will generate and output correlation information).
Claim 8
Tschulena discloses:
The apparatus according to claim 1, wherein the processor is configured to: predict a disease that the subject of disease prediction may contract in the future in each of a plurality of body parts(Figure 4, Tschulena discloses the acquisition of patient record information for predicting[PREDICTION RELATES TO FUTURE EVENTS] a health condition, which can be contracted ), based on the disease-related information and statistical information on risk of disease incidence(Para 0015, Tschulena discloses using probabilistic statistical models to analyze health condition frequency in a population) after a certain period has elapsed(Para 0060, Tschulena discloses a prediction time period), to generate disease prediction information(Figure 4, Tschulena discloses generation of prediction information).
Claim 9
Claim 9 recites similar limitations as claim 1. See claim 1 analysis.
Claim 10
Claim 10 recites similar limitations as claim 2. See claim 2 analysis.
Claim 11
Claim 11 recites similar limitations as claim 3. See claim 3 analysis.
Claim 12
Claim 12 recites similar limitations as claim 4. See claim 4 analysis.
Claim 13
Claim 13 recites similar limitations as claim 5. See claim 5 analysis.
Claim 14
Claim 14 recites similar limitations as claim 6. See claim 6 analysis.
Claim 15
Claim 15 recites similar limitations as claim 7. See claim 7 analysis.
Claim 16
Claim 16 recites similar limitations as claim 8. See claim 8 analysis.
Claim 20
Claim 20 recites similar limitations as claim 1. See claim 1 analysis.
Claim 21
Claim 21 recites similar limitations as claim 2. See claim 2 analysis.
Claim 22
Claim 22 recites similar limitations as claim 3. See claim 3 analysis.
Claim 23
Claim 23 recites similar limitations as claim 4. See claim 4 analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Park(US20240127950A1) discloses a disease prediction method and apparatus utilizing AI.
Hatakeyama(US20220415508A1) discloses a prediction device which utilizes a trained model.
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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