DETAILED ACTION
Claims 1-10 are presented for examination.
This office action is in response to submission of application on 05/14/2024.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/14/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 12/27/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1, 2, 4, and 10 are objected to because of the following informalities:
In claims 1 and 10, “ first extracting a time and a sensor at which the characteristic waveform information appears in the waveform data, based on the probability information, wherein the calculating includes: second extracting a feature map…” should be “ extracting a time and a sensor at which the characteristic waveform information appears in the waveform data, based on the probability information, wherein the calculating includes: extracting a feature map…”. In claim 2, “wherein in the first extracting, …” should be “wherein in extracting the time and the sensor,…”. In claim 4, “wherein in the second extracting, …” should be “wherein in extracting the feature map,…”.
Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities:
In paragraph 0014, “described in details” should be “described in detail”.
In paragraph 0099, “the shape are not limited the embodiments” should be “the shape are not limited to the embodiments”.
Appropriate correction is required.
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 1-10 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.
Claim 1 recites the limitation “identifying the probability information by inputting information obtained by multiplying the feature map by the attention map". It is unclear whether the obtained information is input to the deep learning model recited in claim 1 or some other structure. There is insufficient antecedent basis for this limitation. Claim 10 is substantially similar and is rejected on the same basis. Dependent claims 2-9 inherit the deficiency and therefore are rejected on the same basis.
Claim 3 recites the limitation “information obtained by multiplying the feature map by the attention map and furthermore adding the feature map is input to identify the probability information". It is unclear whether the obtained information is input to the deep learning model recited in claim 1 or some other structure.
Claim 6 recites “wherein in the calculating, the model trained using a manually modified version of the attention map generated in the generating is used”. There is insufficient antecedent basis for this model trained using a manually modified attention map. For examination purposes, the examiner is interpreting “wherein in the calculating, the model trained using a manually modified version of the attention map generated in the generating is used” to be “wherein in the calculating, a model trained using a manually modified version of the attention map generated in the generating is used”.
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.
The claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 1 includes the steps of:
A waveform generation identifying method including: acquiring waveform data of biological signals measured by a plurality of sensors;
calculating probability information of appearance of IEDs (interictal epileptiform discharges) from a deep learning model trained using the waveform data with labels indicating whether characteristic waveform information appears or not; and
first extracting a time and a sensor at which the characteristic waveform information appears in the waveform data, based on the probability information,
wherein the calculating includes: second extracting a feature map indicating waveform data characteristics from the waveform data;
generating an attention map indicating an important region in IED recognition, from the feature map; and
identifying the probability information by inputting information obtained by multiplying the feature map by the attention map.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind through the use of a physical aid, like a pen and paper. A human can:
calculate probability information of appearance of IEDs (interictal epileptiform discharges) from using labeled waveform data,
based on a probability information, extract a time and a sensor at which characteristic waveform information appears in a waveform data,
extracting a feature map indicating waveform data characteristics from waveform data,
generating an attention map indicating an important region in IED recognition from a feature map, and
identifying probability information by inputting information obtained by multiplying the feature map by the attention map.
The broadest reasonable interpretation of the following limitations also encompasses an abstract idea of mathematical concepts and calculations. See MPEP 2106.04(a)(2), subsection I.
calculate probability information of appearance of IEDs (interictal epileptiform discharges) from using labeled waveform data,
multiplying the feature map by the attention map.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
As drafted and under their broadest reasonable interpretation, the following limitations recite additional elements which amount to generic computer components recited at a high level of generality, with merely the words “apply it” or an equivalent with the judicial exception, merely including instructions to implement an abstract idea on the additional elements, or merely using the additional elements as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
a deep learning model trained…
sensor
As drafted and under their broadest reasonable interpretation, the following limitations recite additional elements which amount to mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
acquiring waveform data of biological signals measured by a plurality of sensors.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application. Therefore, no meaningful claim limits are imposed practicing the abstract idea. Accordingly, at Step 2A, prong two, the additional elements do not integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim limitation(s) reciting generic computer elements amounts to no more than mere instructions to apply the exception using a generic computer.
The claim reciting the additional element(s) of “acquiring” amount to necessary data gathering and output.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. Thus, the claim does not provide an inventive concept.
The claim is ineligible.
Claims 2-9 further recite limitations that encompass mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components (such as attention branch network and Deep U-Net). The claims do not integrate the judicial exception into practical application. The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 2-9 are ineligible.
Claim 10 is substantially similar to claim 1, and is rejected on the same basis as claim 1. These claims recite additional elements that amount to generic computer components recited at a high level of generality, with merely the words “apply it” or an equivalent with the judicial exception, merely including instructions to implement an abstract idea on the additional elements, or merely using the additional elements as a tool to perform an abstract idea.
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.
Claims 1-7 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Desai et al. (Pub. No.: US 2022/0314002 A1), hereafter Desai, in view of Sun et al. ("Personalized Deep Learning based Source Imaging Framework Improves the Imaging of Epileptic Sources from MEG Interictal Spikes"), hereafter Sun, in further view of Peng et al. (CN115381407 A), hereafter Peng.
Regarding claim 1, Desai discloses:
A waveform generation identifying method including: acquiring waveform data of biological signals measured by a plurality of sensors (¶[0058] and ¶[0226] teaches acquiring EEG waveform data of biological signals measured by a plurality of sensors),
calculating probability information of appearance of IEDs (interictal epileptiform discharges) from a deep learning model trained using the waveform data with labels indicating whether characteristic waveform information appears or not (¶[0059], ¶[0067] and ¶[0187] teaches calculating the seizure probability as probability information of appearance of IEDs from a deep learning model trained using the waveform data with labels indicating appearance of characteristic waveform information),
first extracting a time … at which the characteristic waveform information appears in the waveform data, based on the probability information ([0188] teaches extracting a time at which the characteristic waveform information appears in the waveform data, based on the probability information),
wherein the calculating includes: second extracting a feature map indicating waveform data characteristics from the waveform data (Fig. 3, ¶[0090] and ¶[0093] teaches extracting feature maps during feature selection and data processing which indicate waveform data characteristics from the waveform data),
generating an attention map indicating an important region in IED recognition, from the feature map (¶[0286] teaches generating a saliency map as attention maps indicating important regions in IED recognition from the feature map).
identifying the probability information by inputting information obtained by … the feature map... (Fig. 3 and Fig. 16 teaches identifying the probability information by inputting information obtained by the feature map).
While Desai discloses first extracting a time … at which the characteristic waveform information appears in the waveform data, based on the probability information, they do not disclose extracting a sensor as well.
Sun discloses:
extracting …a sensor at which the characteristic waveform information appears (Figure 1 and page 17, paragraph 2, lines 5-6 “GDeepSIF already learned the mapping relationship between the source and sensor space” teaches extracting the sensor at which the at which the characteristic waveform information appears).
Desai and Sun are analogous art because they are from the same field of endeavor, epilepsy and machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Desai to include extracting …a sensor at which the characteristic waveform information appears, based on the teachings of Sun. One of ordinary skill in the art would have been motivated to make this modification in order to provide robust capability of imaging brain activity, as suggested by Sun (page 17, final paragraph, lines 1-2).
While Desai teaches identifying the probability information by inputting information obtained by … the feature map..., they do not disclose doing so by multiplying the feature map by the attention map.
Peng discloses:
…multiplying the feature map by the attention map (¶[0048] and Figure 6 teaches multiplying the feature map by the attention map through dot product).
Desai, Sun, and Peng are analogous art because they are from the same field of endeavor, epilepsy and machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Desai, in view of Sun, to include multiplying the feature map by the attention map, based on the teachings of Peng. One of ordinary skill in the art would have been motivated to make this modification in order to develop models with higher accuracy and fewer parameters in epileptic seizure prediction technology, as suggested by Peng (¶[0004-0006]).
Regarding claim 2, Desai, in view of Sun, in further view of Peng, discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Desai further discloses:
wherein in the first extracting, the time … corresponding to the important region in IED recognition indicated in the attention map generated in the generating are extracted based on the probability information (¶[0286] and Fig. 9A-9D teaches time corresponding to the important region in IED recognition indicated in the attention map generated in the generating are extracted based on the probability information).
While Desai discloses wherein in the first extracting, the time … corresponding to the important region in IED recognition indicated in the attention map generated in the generating are extracted based on the probability information, they do not disclose the sensor corresponding to the important region in IED recognition indicated.
Sun discloses:
the sensor corresponding to the important region in IED recognition indicated (Figure 1).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Desai to include the sensor corresponding to the important region in IED recognition indicated, based on the teachings of Sun. One of ordinary skill in the art would have been motivated to make this modification in order to provide robust capability of imaging brain activity, as suggested by Sun (page 17, final paragraph, lines 1-2).
Regarding claim 3, Desai, in view of Sun, in further view of Peng, discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Peng further discloses:
wherein in the identifying, information obtained by multiplying the feature map by the attention map and furthermore adding the feature map is input to identify the probability information (¶[0048] and Figure 6 teaches multiplying the feature map by the attention map and furthermore adding the feature map through dot product).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Desai, in view of Sun, to include information obtained by multiplying the feature map by the attention map and furthermore adding the feature map is input to identify the probability information, based on the teachings of Peng. One of ordinary skill in the art would have been motivated to make this modification in order to develop models with higher accuracy and fewer parameters in epileptic seizure prediction technology, as suggested by Peng (¶[0004-0006]).
Regarding claim 4, Desai, in view of Sun, in further view of Peng, discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Desai further discloses:
determining whether the probability information calculated in the calculating is equal to or greater than a predetermined threshold value (¶[0188] teaches determining whether the probability information calculated in the calculating is equal to or greater than a predetermined threshold value),
wherein in the first extracting, the time … at which the characteristic waveform information appears in the waveform data are extracted based on the probability information determined as equal to or greater than the predetermined threshold value in the determining (¶[0188] teaches that the time an at which the characteristic waveform information appears in the waveform data are extracted based on the probability information determined as equal to or greater than the predetermined threshold value).
While Desai teaches wherein in the first extracting, the time … at which the characteristic waveform information appears in the waveform data are extracted based on the probability information determined as equal to or greater than the predetermined threshold value in the determining, they do not disclose the sensor.
Sun teaches:
the sensor at which the characteristic waveform information appears in the waveform data are extracted (Figure 1).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Desai to include the sensor at which the characteristic waveform information appears in the waveform data are extracted, based on the teachings of Sun. One of ordinary skill in the art would have been motivated to make this modification in order to provide robust capability of imaging brain activity, as suggested by Sun (page 17, final paragraph, lines 1-2).
Regarding claim 5, Desai, in view of Sun, in further view of Peng, discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Desai further discloses:
wherein in the calculating, the deep learning model trained using master information to reduce a difference between the master information and the attention map generated in the generating is used, the master information being prepared in advance as information on the time … at which the characteristic waveform information appears (¶[0019], ¶[0056], an ¶[0286] teaches using labeled datasets with timestamps and seizure labels as master information that trains the model to reduce the difference between the master information and the output saliency map).
While Desai teaches wherein in the calculating, the deep learning model trained using master information to reduce a difference between the master information and the attention map generated in the generating is used, the master information being prepared in advance as information on the time … at which the characteristic waveform information appears, they do not disclose the sensor.
Sun teaches:
information on the …the sensor at which the characteristic waveform information appears (Figure 1).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Desai to include information on the …the sensor at which the characteristic waveform information appears, based on the teachings of Sun. One of ordinary skill in the art would have been motivated to make this modification in order to provide robust capability of imaging brain activity, as suggested by Sun (page 17, final paragraph, lines 1-2).
Regarding claim 6, Desai, in view of Sun, in further view of Peng, discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Desai further discloses:
wherein in the calculating, the model trained using a manually modified version of the attention map generated in the generating is used (Fig. 5A and ¶[0097] teaches training models with transformed data that is the manually modified version of the attention map generated).
Regarding claim 7, Desai, in view of Sun, in further view of Peng, discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Desai further discloses:
performing, on the waveform data acquired at the acquiring, pre-processing including processing of clipping out waveform data at least at a predetermined interval, wherein at the calculating, the probability information is calculated from the waveform data subjected to the pre-processing, using the model (¶[0093] teaches pre-processing raw data, including clipping out waveform data at least at a predetermined interval before applying the model).
Claims 10 is substantially similar to claim 1, and thus is rejected on the same basis as claim 1.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Desai et al. (Pub. No.: US 2022/0314002 A1), hereafter Desai, in view of Sun et al. ("Personalized Deep Learning based Source Imaging Framework Improves the Imaging of Epileptic Sources from MEG Interictal Spikes"), hereafter Sun, in further view of Peng et al. (CN115381407 A), hereafter Peng, in further view of Song et al. ("Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map"), hereafter Song.
Regarding claim 8, Desai, in view of Sun, in further view of Peng, discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Desai further discloses:
the model based on an attention … network … is used (¶[0287] and ¶[0245] teaches models to be based on attention networks).
While Desai discloses the model based on an attention … network … is used, they do not teach using an attention branch network (ABN)… processing is performed by a function of an attention branch of the ABN … processing is performed by a function of a perception branch of the ABN.
Song discloses:
an attention branch network (ABN)… processing is performed by a function of an attention branch of the ABN … processing is performed by a function of a perception branch of the ABN (Figure 1 discloses using perception and attention branch to carry out separate processing tasks).
Desai, Sun, Peng, and Song are analogous art because they are from the same field of endeavor, medical diagnosis and machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Desai, in view of Sun, in further view of Peng, to include an attention branch network (ABN)… processing is performed by a function of an attention branch of the ABN … processing is performed by a function of a perception branch of the ABN, based on the teachings of Song. One of ordinary skill in the art would have been motivated to make this modification in order to improve recognition performance, as suggested by Song (page 11, paragraph 3, line 2)
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Desai et al. (Pub. No.: US 2022/0314002 A1), hereafter Desai, in view of Sun et al. ("Personalized Deep Learning based Source Imaging Framework Improves the Imaging of Epileptic Sources from MEG Interictal Spikes"), hereafter Sun, in further view of Peng et al. (CN115381407 A), hereafter Peng, in further view of Chatzichristos et al. ("Epileptic Seizure Detection in EEG via Fusion of Multi-View Attention-Gated U-net Deep Neural Networks"), hereafter Chatzichristos.
Regarding claim 9, Desai, in view of Sun, in further view of Peng, discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Desai further discloses:
wherein in the calculating, the model based on Deep …Net is used (¶[0287] and ¶[0245] teaches models to be based on deep transformer networks).
While Desai discloses wherein in the calculating, the model based on Deep …Net is used, they do not teach using Deep U-Net … processing is performed by a function of an encoder of the Deep U-Net, and … processing is performed by a function of a decoder of the Deep U-Net.
Chatzichristos discloses:
model based on Deep U-Net is used, … processing is performed by a function of an encoder of the Deep U-Net, and … processing is performed by a function of a decoder of the Deep U-Net (Figure 1 and Figure 2 discloses using encoder and decoder of a Deep U-Net to carry out separate processing tasks).
Desai, Sun, Peng, and Chatzichristos are analogous art because they are from the same field of endeavor, epilepsy and machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Desai, in view of Sun, in further view of Peng, to include model based on Deep U-Net is used, … processing is performed by a function of an encoder of the Deep U-Net, and … processing is performed by a function of a decoder of the Deep U-Net, based on the teachings of Chatzichristos. One of ordinary skill in the art would have been motivated to make this modification in order to improve classification outcome, as suggested by Chatzichristos (page 1, right column, paragraph 3, line 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
U.S. Pub No. 20230172529 A1: Desai et al. teaches epilepsy detection and machine learning.
Dash et al. (“MEG Sensor Selection for Neural Speech Decoding”) teaches sensor selection and neural waveforms.
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/H.Z.M./Examiner, Art Unit 2141
/ANDREW L TANK/Primary Examiner, Art Unit 2141
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