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 . All the claims have been examined on the basis of the merit of the claims.
Priority
The present application is a continuation of PCT/CN2023128995 filed 11/01/2023 which claims foreign priority benefits from CN202310171555 filed in China on 02/20/2023. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/27/2025, 12/15/2025 & 12/15/2025 are considered and attached.
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)(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.
Claim(s) 1,10 and 13-14 is/are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Laszlo et al., (US-20190294243-A1, hereinafter as Laszlo).
In regards to claims 1, 13-14, Laszlo discloses a computer device, comprising: a processor; and a memory configured to store computer-readable instructions of the processor, the processor being configured to execute the computer-readable instructions to cause the computer device to perform, a non-transitory computer-readable storage medium, having computer-readable instructions stored therein, the computer-readable instructions, when executed by a processor, implementing (fig.1, para 0091, computer device comprising processing module 120 which includes one or more computer processors programmed to process the EEG signal) a method for processing an electroencephalogram signal executed by a computer device (method according to fig.9 which is executed by the computer device as shown in fig.1 which processes an electroencephalogram signal), comprising: acquiring an electroencephalogram signal, the electroencephalogram signal comprising an electroencephalogram signal data segment of a specified duration (fig. 9, receives EEG signal from a sensor coupled to a user step 910, the EEG signals being acquired over a period, fig. 8, in which the user selects an action from a plurality of possible actions); determining a plurality of time nodes in the electroencephalogram signal data segment according to a preset time interval (step 920 extracts at least one first segment from each of the EEG signals that precedes a moment when the user selects the action. Each segment is understood as corresponding to a time node. The plurality of first segment from each of the EEG signals corresponds to plurality of time nodes (understood as time periods)); predicting an action intention corresponding to each time node based on the electroencephalogram signal data segment to obtain action intention prediction results respectively corresponding to the plurality of time nodes; and determining a target action intention according to the action intention prediction results (step 925, provide at least the first segments from each of the EEG signals…. to predict the selected action).
In regard to claim 10, Laszlo discloses the method for processing an electroencephalogram signal according to claim 1, further comprising: performing filtering processing on the action intention prediction result corresponding to each time node in the electroencephalogram signal data segment to obtain a filtered action intention prediction result corresponding to each time node in the electroencephalogram signal data segment (step 240 of fig. 2, clean the amplified signals in real time using machine learning module, thereby generating filtered signals); the determining a target action intention according to the action intention prediction results respectively corresponding to the plurality of time nodes comprising: determining a target action intention of a target object according to the filtered action intention prediction result corresponding to each time node (fig.11,step 1130, generates an output associated with the predicted action).
Allowable Subject Matter
Claim 2-9, 11-12 and 15-20 are 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.
In regard to claim 2, Laszlo discloses the method for processing an electroencephalogram signal according to claim 1, Laszlo does not disclose wherein the predicting an action intention corresponding to each time node based on the electroencephalogram signal data segment to obtain action intention prediction results respectively corresponding to the plurality of time nodes comprises: performing feature extraction on the electroencephalogram signal data segment to obtain a first electroencephalogram feature; dividing the first electroencephalogram feature into a plurality of first electroencephalogram feature segments according to the plurality of time nodes, and performing feature extraction on the plurality of first electroencephalogram feature segments to obtain a second electroencephalogram feature; and performing action intention prediction according to the second electroencephalogram feature to obtain an action intention prediction result corresponding to each time node in the electroencephalogram signal data segment.
Claims 3-5 depend on claim 2.
In regard to claim 6, Laszlo discloses the method for processing an electroencephalogram signal according to claim 1, Laszlo does not disclose further comprising: acquiring a sample electroencephalogram signal of a known action intention; determining a plurality of sample time nodes in the sample electroencephalogram signal according to the preset time interval, and generating an action intention label corresponding to each sample time node according to the known action intention; performing feature extraction and mapping processing on the sample electroencephalogram signal through a prediction model to obtain an action prediction result corresponding to each sample time node in the sample electroencephalogram signal; and updating model parameters of the prediction model according to the action prediction result and the action intention label to obtain an action intention prediction model, wherein the action intention prediction model is configured to perform action intention prediction on the electroencephalogram signal data segment, or the action intention prediction model is configured to perform time node division and action intention prediction on the electroencephalogram signal data segment.
Claims 7-9 depend on claim 6.
In regard to claim 11, Laszlo discloses the method for processing an electroencephalogram signal according to claim 1, Laszlo does not disclose wherein the determining a target action intention according to the action intention prediction results respectively corresponding to the plurality of time nodes comprises: identifying an action intention prediction result corresponding to a time node closest to a current time of the plurality of time nodes as the target action intention.
Claim 12 depends on claim 11.
In regard to claim 15, Laszlo discloses the computer-readable storage medium according to claim 14, , Laszlo does not disclose wherein the predicting an action intention corresponding to each time node based on the electroencephalogram signal data segment to obtain action intention prediction results respectively corresponding to the plurality of time nodes comprises: performing feature extraction on the electroencephalogram signal data segment to obtain a first electroencephalogram feature; dividing the first electroencephalogram feature into a plurality of first electroencephalogram feature segments according to the plurality of time nodes, and performing feature extraction on the plurality of first electroencephalogram feature segments to obtain a second electroencephalogram feature; and performing action intention prediction according to the second electroencephalogram feature to obtain an action intention prediction result corresponding to each time node in the electroencephalogram signal data segment.
Claims 16-18 depend on claim 15.
In regard to claim 19, Laszlo discloses the computer-readable storage medium according to claim 14, Laszlo does not disclose the method further comprising: acquiring a sample electroencephalogram signal of a known action intention; determining a plurality of sample time nodes in the sample electroencephalogram signal according to the preset time interval, and generating an action intention label corresponding to each sample time node according to the known action intention; performing feature extraction and mapping processing on the sample electroencephalogram signal through a prediction model to obtain an action prediction result corresponding to each sample time node in the sample electroencephalogram signal; and updating model parameters of the prediction model according to the action prediction result and the action intention label to obtain an action intention prediction model, wherein the action intention prediction model is configured to perform action intention prediction on the electroencephalogram signal data segment, or the action intention prediction model is configured to perform time node division and action intention prediction on the electroencephalogram signal data segment.
In regard to claim 20, Laszlo discloses the computer-readable storage medium according to claim 14, Laszlo does not disclose wherein the action intention prediction result corresponding to each time node in the electroencephalogram signal data segment comprises a probability that the action intention corresponding to each time node is a preset action, and the method further comprises: acquiring a maximum probability value in the action intention prediction result corresponding to each time node; using a preset action corresponding to the maximum probability value corresponding to each time node as an action intention label corresponding to each time node when a minimum value of maximum probability values respectively corresponding to the plurality of time nodes is greater than or equal to a preset probability threshold; and adding an electroencephalogram signal with the action intention label to a historical data set, the historical data set being configured for training the action intention prediction model.
Conclusion
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/DEEPROSE SUBEDI/Primary Examiner, Art Unit 2627