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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-4 and 6-7, are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 3-4, 6-7 refer to “a first group” and “a first group” is unclear if these are a new grouping or the same “first group” and “second group” from claim 1.
Claim Rejections - 35 USC § 102
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 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)(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.
Claim(s) 1-4, 6-7, 10-17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ogino et al. (US 20240164691).
Regarding claim 1, Ogino discloses comprising at least one processor 11, the at least one processor carrying out: an acquisition process 100 of acquiring a plurality of unit waveforms which have been obtained by dividing a waveform indicated by electrocardiogram data (Fig. 4, section 0086, the CPU 11 reads a pair of waveform data first training waveform data and peak information from the first training waveform data database); an extraction process, of extracting 102, using a first machine learning model 104 which has been trained with use of a first group derived from the plurality of unit waveforms acquired in the acquisition process, one or more unit waveforms from a second group derived from the plurality of unit waveforms (Fig. 9, section 0086-0087, selects and extracts the first training divided waveform data from the read first training waveform data as described above. The CPU 11 performs machine learning on the peak estimation model 13C employing the selected and extracted first training divided waveform data as input information and employing the read peak information as output information, correct information); and a training data generation process 106 of generating training data including the one or more unit waveforms which have been extracted in the extraction process (Fig. 9, section 0087, the CPU 11 determines whether or not the machine learning of step 104 has been completed for all waveform data stored in the first training waveform data database 13E, with processing returning to step 100 when negative determination is made, and processing transitioning to step 108 when affirmative determination is made), wherein a second machine learning model 112 is made by re-training of the first machine learning model using a third group 110 extracted from the first group by using the first machine learning model in the extraction process, and the one or more unit waveforms are extracted by using the second machine learning model (Section 0089).
Regarding claim 2, Ogino discloses wherein, in the acquisition process, the at least one processor carries out: an electrocardiogram data acquisition process of acquiring the electrocardiogram data (section 0043, the peak information not only includes information indicating whether or not the predetermined types of peak related to electrocardiogram analysis are present in the waveform data, but if such a peak is present then also includes information indicating the predetermined type when such as peak is present); and a division process 102 of dividing the waveform indicated by the electrocardiogram data into unit waveforms which are waveforms in predetermined cycles (section 0068, The estimation unit 11H according to the present exemplary embodiment estimates a condition indicted by the electrocardiogram for analysis by synthesizing the peak information derived by the derivation unit 11E using the peak estimation model 13C input with the first analysis divided waveform data, together with the segment type information derived by the second derivation unit 11G using the segmentation estimation model 13D input with the second analysis divided waveform data).
Regarding claim 3, Ogino discloses the plurality of unit waveforms acquired by the at least one processor in the acquisition process are provided with labels (Fig. 7-8, section 0077-0078, the first training waveform data database 13E according to the present exemplary embodiment is stored with each information of a waveform identification (ID), waveform data, and peak information. The waveform ID is employed to discriminate between corresponding waveform data, and is information pre-assigned so as to be different for each item of the waveform data); and in the extraction process, the at least one processor carries out a training process of training the first machine learning model 104 with use of a first group derived from the plurality of unit waveforms and labels which are provided to unit waveforms included in the first group (section 0084-0085, electrocardiogram analysis assistance device 10 when training the peak estimation model 13C and the segmentation estimation model 13D, the CPU 11 of the electrocardiogram analysis assistance device 10 executing the training program 13A. The training processing illustrated in FIG. 9 is executed when an instruction input to start execution of the training program 13A is performed by a user through the input section).
Regarding claim 4, Ogino discloses in the training process, the at least one processor trains a plurality of models using a plurality of groups (Fig. 9), respectively, which are derived from the plurality of unit waveforms acquired in the acquisition process and in the extraction process, the at least one processor extracts, using each of the plurality of models, one or more unit waveforms from a second group different from a group which has been used to train the first machine learning model, wherein the second group is not included in the first group (sections 0047, 0087-0089, a convolutional neural network (CNN) is employed as the peak estimation model 13C and the segmentation estimation model 13D, there is no limitation thereto. For example, a configuration may be adopted in which a recurrent neural network (RNN) is employed for at least one out of the peak estimation model 13C or the segmentation estimation model 13D).
Regarding claim 6, Ogino discloses in the extraction process, the at least one processor updates any of the plurality of models using a first group derived from the plurality of unit waveforms which have been extracted using each of the plurality of models (Fig. 9, sections 0086-0087, a pair of waveform data (first training waveform data) and peak information from the first training waveform data database 13E. At step 102, the CPU 11 selects and extracts the first training divided waveform data from the read first training waveform data as described above. The CPU 11 performs machine learning on the peak estimation model 13C employing the selected and extracted first training divided waveform data as input information and employing the read peak information as output information (correct information). At step 106, the CPU 11 determines whether or not the machine learning of step 104 has been completed for all waveform data stored in the first training waveform data database 13E, with processing returning to step 100 when negative determination is made); and in the extraction process, the at least one processor extracts, using a model which has been updated 112, one or more unit waveforms from a second group 108 derived from the plurality of unit waveforms which have been extracted using each of the plurality of models, wherein the second group is not included in the first group (Fig. 9, Section 0088-0089, At step 108, the CPU 11 reads a pair of the waveform data (second training waveform data) and segment type information from the second training waveform data database 13F. At step 110, the CPU 11 selects and extracts the second training divided waveform data from the read second training waveform data as described above. At step 112, the CPU 11 performs machine learning on the segmentation estimation model 13D employing the selected and extracted second training divided waveform data as input information and employing the read segment type information as output information (correct information). At step 114, the CPU 11 determines whether or not the machine learning of step 112 has been completed for all waveform data stored in the second training waveform data database 13F, with processing returning to step 108 when negative determination is made).
Regarding claim 7, Ogino discloses in the extraction process, the at least one processor carries out updating of a model 112 and extraction using the model two or more times 114 while altering a certain group derived from the plurality of unit waveforms which have been extracted using each of the plurality of models (Section 0088/-0089, At step 108, the CPU 11 reads a pair of the waveform data (second training waveform data) and segment type information from the second training waveform data database 13F. At step 110, the CPU 11 selects and extracts the second training divided waveform data from the read second training waveform data as described above. At step 112, the CPU 11 performs machine learning on the segmentation estimation model 13D employing the selected and extracted second training divided waveform data as input information and employing the read segment type information as output information (correct information). At step 114, the CPU 11 determines whether or not the machine learning of step 112 has been completed for all waveform data stored in the second training waveform data database 13F, with processing returning to step 108 when negative determination is made).
Regarding claim 10, Ogino discloses in the training data generation process, the at least one processor generates an added unit waveform by adding up a plurality of unit waveforms extracted in the extraction process, and generates the training data including the added unit waveform which has been generated (Section 0071, a combination of two types or three types from out of these four types may be included in the above waveform types, or another fourth type may be added for inclusion in the above waveform types).
Regarding claim 11, Ogino discloses the at least one processor further carries out an evaluation model training process 11J of training an evaluation model using the training data which has been generated in the training data generation process, the evaluation model including the model 13C or being different from the model (Fig. 4, section 0069, 0070, By inputting the first analysis divided waveform data selected and extracted by the analysis waveform select and extract unit 11D into the peak estimation model 13C, the intermediate information acquisition unit 11I according to the present exemplary embodiment acquires intermediate information generated in a middle layer of the peak estimation model. the classification unit 11J according to the present exemplary embodiment classifies a type of the waveform of the electrocardiogram for analysis using the intermediate information acquired by the intermediate information acquisition unit).
Regarding claim 12, Ogino discloses the at least one processor further carries out an evaluation process of evaluating, using the evaluation model which has been trained in the evaluation model training process, a plurality of unit waveforms that are obtained from evaluation electrocardiogram data for evaluation (Section 0070, he classification unit 11J according to the present exemplary embodiment classifies a type of the waveform of the electrocardiogram for analysis using the intermediate information acquired by the intermediate information acquisition unit); and the at least one processor further carries out an evaluation integration process of evaluating the evaluation electrocardiogram data with reference to evaluation in the evaluation process with respect to the plurality of unit waveforms (section 0095, 0127, executing the processing of step 202 to step 210 as described above, as illustrated in the example in FIG. 11, the first analysis divided waveform data having a width of the first period (two seconds in the present exemplary embodiment) is input into the peak estimation model 13C at a step width of the shift period (0.1 seconds in the present exemplary embodiment), and the peak information output from the peak estimation model 13C in response thereto is stored in the storage section. The peak estimation model 13C is a model trained by machine learning employing the training divided waveform data selected and extracted by the training waveform select and extract unit 11B as input information and employing the peak information corresponding to the training divided waveform data as output information. This thereby enables a contribution toward determining whether or not a particular type of arrhythmia has occurred with higher accuracy than cases in which waveform data selected and extracted at a different period to the waveform data during operation of the peak estimation model 13C is employed for machine learning).
Regarding claim 13, Ogino discloses in the evaluation integration process, the at least one processor evaluates the electrocardiogram data as a disease in a case where one or more unit waveforms which have been evaluated as a disease in the evaluation process include a unit waveform for which an evaluation value obtained in the evaluation process is equal to or greater than a predetermined value, or in a case where the number of unit waveforms which have been evaluated as a disease in the evaluation process is equal to or greater than a predetermined value (Section 0126-0127, The peak information obtained by the peak estimation model 13C is accordingly employed, enabling a contribution toward determining whether or not a particular type of. The peak estimation model 13C is a model trained by machine learning employing the training divided waveform data selected and extracted by the training waveform select and extract unit 11B as input information and employing the peak information corresponding to the training divided waveform data as output information. This thereby enables a contribution toward determining whether or not a particular type of arrhythmia has occurred with higher accuracy than cases in which waveform data selected and extracted at a different period to the waveform data during operation of the peak estimation model 13C is employed for machine learning).
Regarding claim 14, Ogino discloses the at least one processor further carries out a first display process of displaying an evaluation result obtained in the evaluation integration process (Fig. 17, Section 0123, the CPU 11 controls the display section 15 using the electrocardiographic condition information and the classification result information derived by the processing described above so as to display a result screen of a predetermined configuration, and at step 244 the CPU 11 stands by for input of specific information).
Regarding claim 15, Ogino discloses the at least one processor further carries out a second display 15 process of displaying at least one of the plurality of unit waveforms which have been obtained by dividing the waveform indicated by the electrocardiogram data or at least one of the one or more unit waveforms which have been extracted in the extraction process (Fig. 17, Section 0123, the CPU 11 controls the display section 15 using the electrocardiographic condition information and the classification result information derived by the processing described above so as to display a result screen of a predetermined configuration, and at step 244 the CPU 11 stands by for input of specific information).
Regarding claim 16, Ogino discloses an information processing method, comprising: acquiring, by at least one processor 11, a plurality of unit waveforms 100 which have been obtained by dividing a waveform indicated by electrocardiogram data (Fig. 4, section 0086, the CPU 11 reads a pair of waveform data first training waveform data and peak information from the first training waveform data database); extracting 102, by the at least one processor, using a first machine learning model 104 which has been trained with use of a first group derived from the plurality of unit waveforms, one or more unit waveforms from a second group derived from the plurality of unit waveforms (Fig. 9, section 0086-0087, selects and extracts the first training divided waveform data from the read first training waveform data as described above. The CPU 11 performs machine learning on the peak estimation model 13C employing the selected and extracted first training divided waveform data as input information and employing the read peak information as output information, correct information); and generating 106, by the at least one processor, training data including the one or more unit waveforms which have been extracted (Fig. 9, section 0087, the CPU 11 determines whether or not the machine learning of step 104 has been completed for all waveform data stored in the first training waveform data database 13E, with processing returning to step 100 when negative determination is made, and processing transitioning to step 108 when affirmative determination is made), wherein a second machine learning model 112 is made by re-training of the first machine learning model using a third group 110 extracted from the first group by using the first machine learning model in the extraction process, and the one or more unit waveforms are extracted by using the second machine learning model (Section 0089).
Regarding claim 17, Ogino discloses a computer-readable non-transitory storage medium storing a program for causing a computer to function as an information processing apparatus recited in claim 1, the program causing the computer to carry out the acquisition process, the extraction process, and the training data generation process (section 0145-0148, using a non-transitory storage medium stored with a program employing a peak estimation model and executing processing including: acquiring waveform data in an electrocardiogram for analysis; selecting and extracting waveform data of a predetermined first period from the acquired waveform data as analysis divided waveform data; acquiring intermediate information generated in a middle layer of a peak estimation model and indicating a shape feature of a peak contained in the analysis divided waveform data by inputting the selected and extracted analysis divided waveform data into the peak estimation model that employs waveform data of partial segments of waveform data in the electrocardiogram as input information and employs peak information indicating whether or not a predetermined type of peak related to analysis of the electrocardiogram is present in the waveform data as output information).
Allowable Subject Matter
Claim 5 is allowed.
The following is a statement of reasons for the indication of allowable subject matter: Regarding independent claim 5, for a An information processing apparatus, comprising at least one processor, the at least one processor carrying out: an acquisition process of acquiring a plurality of unit waveforms which have been obtained by dividing a waveform indicated by electrocardiogram data; and the at least one processor divides the plurality of unit waveforms acquired in the acquisition process into N sets, and trains each of N models using each of N groups each of which excludes one set in turn from the N sets; and in the extraction process, the at least one processor extracts, using each of the N models, one or more unit waveforms from one set which has not been used to train that model has not been suggested or disclosed in the prior art when combined with the rest of the claim limitations of independent claim 5.
Claims 8-9 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 7/1/2026 have been fully considered but they are not persuasive. Examiner finds that Ogino discloses an extraction process, of extracting 102, using a first machine learning model 104 which has been trained with use of a first group derived from the plurality of unit waveforms acquired in the acquisition process, one or more unit waveforms from a second group derived from the plurality of unit waveforms (Fig. 9, section 0086-0087, selects and extracts the first training divided waveform data from the read first training waveform data as described above. The CPU 11 performs machine learning on the peak estimation model 13C employing the selected and extracted first training divided waveform data as input information and employing the read peak information as output information, correct information); and a training data generation process 106 of generating training data including the one or more unit waveforms which have been extracted in the extraction process (Fig. 9, section 0087, the CPU 11 determines whether or not the machine learning of step 104 has been completed for all waveform data stored in the first training waveform data database 13E, with processing returning to step 100 when negative determination is made, and processing transitioning to step 108 when affirmative determination is made), wherein a second machine learning model 112 is made by re-training of the first machine learning model using a third group 110 extracted from the first group by using the first machine learning model in the extraction process, and the one or more unit waveforms are extracted by using the second machine learning model (Section 0089).
Examiner also finds that amendments to claims 3-4, 6-7 refer to “a first group” and “a first group” are unclear if these are a new grouping or the same “first group” and “second group” from claim 1.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JON ERIC C MORALES whose telephone number is (571)272-3107. The examiner can normally be reached Monday-Friday 830AM-530PM CST.
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/JON ERIC C MORALES/Primary Examiner, Art Unit 3796
/J.C.M/Primary Examiner, Art Unit 3796
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