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 .
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.
Claim(s) 1, 2, 4, and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2021/0196565 to Marinakis et al. in view of “ESTIMATING COUNTERFACTUAL TREATMENT OUTCOMES OVER TIME THROUGH ADVERSARIALLY BALANCED REPRESENTATIONS” to Bica et al.
As to claims 1, 9, and 10, Marinakis discloses a medical information processing system comprising a processing circuit configured:
to acquire examination data showing medical examination results with respect to a medical treatment subject and reply data showing reply results of a medical examination by interview with respect to the medical treatment subject (Marinakis [0110], [0147], and [0149]);
input only the examination data of the medical treatment subject to a first machine learning model trained on the basis of a first training data set, the first training data set being a data set in which information on medical treatment of a learning subject is associated with only the examination data of the learning subject as a correct label (Marinakis [0110], [0147], and [0149]);
to estimate information on medical treatment of the medical treatment subject on the basis of information output by the first machine learning model in response to only the examination data of the medical treatment subject being input into the first machine learning model (Marinakis [0110], [0147], and [0149])
to input both the examination data and the reply data of the medical treatment subject to a second machine learning model trained on the basis of a second training data set, the second training data set being a data set in which the information on medical treatment of the learning subject is associated with both the examination data and the reply data of the learning subject as a correct label (Marinakis [0110], [0147], and [0149]); and
to output a first estimation result representing the information on medical treatment estimated using the first machine learning model and a second estimation result representing the information on medical treatment estimated using the second machine learning model via an output unit (Marinakis [0110], [0147], and [0149]).
wherein the processing circuit further compares the first estimation result with the second estimation result to determine whether or not the first estimation result and the second estimation result match (Marinakis [0110] see anomalies outside the bounds of expectations).
To output an alert to remind a user via the output unit if it is determined the first estimation result and the second estimation results do not match (Marinakis [0110])
However, Marinakis does not teach that the first and second machine learning models are neural networks. Bica discloses that the first and second machine learning models are neural networks and compares the first estimation result with the second estimation result to determine whether or not the first estimation result and the second estimation result match (Bica page 9 see comparisons of various neural networks considering treatment response and conclusion).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing of the application by Applicant to utilize neural networks as in Bica in the system of Marinakis to improve model accuracy.
As to claim 2, see the discussion of claim 1, additionally, Marinakis discloses the medical information processing system wherein the output unit includes a display unit (Marinakis [0147]). However, Marinakis does not explicitly teach the processing circuit displays the first estimation result and the second estimation result side by side on the display unit.
However these differences are only found in the data displayed by the device. The display of the first and second estimation results side by side are not functionally related to the functions system. Thus, this descriptive information will not distinguish the claimed invention from the prior art in terms of patentability, see Cf. In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 40, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to display different types of content in the system of Marinakis and Bica because such information does not functionally relate to the computer and merely using different content would have been obvious matter of design choice. See In re Kuhle, 526 F.2d 553, 555, 188 USPQ 7, 9 (CCPA 1975).
As to claim 4, see the discussion of claim 3, additionally, Marinakis discloses the medical information processing system wherein the processing circuit calculates a similarity between the first estimation result and the second estimation result, determines that the first estimation result and the second estimation result match if the similarity is equal to or greater than a threshold value, and determines that the first estimation result and the second estimation result do not match if the similarity is less than the threshold value (Marinakis [0110] see anomalies outside the bounds of expectations).
As to claim 5, see the discussion of claim 3, additionally, Marinakis discloses the medical information processing system wherein the processing circuit outputs an alert via the output unit if it is determined that the first estimation result and the second estimation result do not match (Marinakis [0110] see forward to case manager from an artificial intelligence system).
Claim(s) 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2021/0196565 to Marinakis et al. in view of “ESTIMATING COUNTERFACTUAL TREATMENT OUTCOMES OVER TIME THROUGH ADVERSARIALLY BALANCED REPRESENTATIONS” to Bica et al. in view of U.S. Patent Application Publication 2010/0191100 to Anderson et al.
As to claim 6, see the discussion of claim 3, however, Marinakis does not explicitly teach the medical information processing system wherein the processing circuit weights the reply data of the medical treatment subject at the time of inputting the reply data of the medical treatment subject to the second machine learning model, and estimates information on medical treatment of the medical treatment subject by inputting the weighted reply data and the examination data of the medical treatment subject to the second machine learning model. Anderson discloses wherein the processing circuit weights the reply data of the medical treatment subject at the time of inputting the reply data of the medical treatment subject to the second machine learning model, and estimates information on medical treatment of the medical treatment subject by inputting the weighted reply data and the examination data of the medical treatment subject to the second machine learning model (Anderson [0007], [0059] and [0066]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing of the invention by applicant to weight reply data as in Anderson in the system of Marinakis and Bica to improve the accuracy of the results.
As to claim 7, see the discussion of claim 6, additionally, Anderson discloses the medical information processing system wherein the processing circuit repeatedly estimates information on medical treatment while changing a weighting factor and compares the second estimation results representing a plurality of pieces of information on medical treatment repeatedly estimated using the second model to determine whether or not the plurality of second estimation results match (Anderson [0007]).
As to claim 8, see the discussion of claim 7, additionally, Marinakis discloses the medical information processing system wherein the output unit includes a display unit (Marinakis [0147]). However, Marinakis does not explicitly teach wherein the processing circuit outputs the second estimation result for each weighting factor via the output unit.
However these differences are only found in the data displayed by the device. The display of the first and second estimation results side by side are not functionally related to the functions system. Thus, this descriptive information will not distinguish the claimed invention from the prior art in terms of patentability, see Cf. In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 40, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to display different types of content in the system of Marinakis because such information does not functionally relate to the computer and merely using different content would have been obvious matter of design choice. See In re Kuhle, 526 F.2d 553, 555, 188 USPQ 7, 9 (CCPA 1975).
Response to Arguments
Applicant's arguments filed 1/7/26 have been fully considered but they are not persuasive.
Applicant argues that the references does not outputting an alert based on the result of two neural networks. Marinakis is relied upon for the disclosure of outputting an alert based on the results and Bica for disclosing results of neural networks and performing accuracy analysis on the results (a determination that the results do not match). In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The rejection is therefore maintained.
Conclusion
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/ELIZA A LAM/Primary Examiner, Art Unit 3681