DETAILED ACTION
This office action is responsive to the above identified application filed 10/24/2023. The application contains claims 1-20, all examined and rejected.
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 with references submitted 3/6/2024, 7/19/2024, 10/28/2024, 1/24/2025, 5/29/2025, 7/28/2025, 8/6/2025, 9/11/2025, 9/30/2025,10/29/2025, and 5/1/2026, have been considered and entered into the file.
Claim Objections
Claim 12 objected to because of the following informalities: claim 12 recites “an electrode comprising 1,000 or more electrodes” instead of “an electrode array comprising 1,000 or more electrodes”. 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 10 and 20 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.
The term “small” in claims 10 and 20 is a relative term which renders the claim indefinite. The term “small” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claims 1 and 11 are each directed to a statutory category, it recites a series of steps which appears to be directed to an abstract idea (mental process, mathematical concept).
Claims 1-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claims are determined to include process, and machine as in independent Claim 1 and 11, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
“aggregating calibration data across a user population to define a global dataset”, “identifying similar data segments across the global dataset to define a task-independent training dataset” (Mental process, observation, evaluation and judgment).
The claim recites additional elements as
“the calibration data comprising at least one of neural data recorded from users calibrating neural devices, neural device data from users calibrating the neural devices, or external sensor data associated with the neural devices from users calibrating the neural devices” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h));
“ training a feature extraction model based on the task-independent training dataset to define a trained, task-independent feature extraction model” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h));
“receiving the calibration data from a user calibrating the neural device” (insignificant extra-solution activity, MPEP 2106.05(g));
“calibrating a user-specific feature extraction model using the trained, task-independent feature extraction model and the calibration data” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)).
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
“the calibration data comprising at least one of neural data recorded from users calibrating neural devices, neural device data from users calibrating the neural devices, or external sensor data associated with the neural devices from users calibrating the neural devices” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h));
“ training a feature extraction model based on the task-independent training dataset to define a trained, task-independent feature extraction model” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h));
“receiving the calibration data from a user calibrating the neural device” (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i));
“calibrating a user-specific feature extraction model using the trained, task-independent feature extraction model and the calibration data” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)).
In the instant case, Claim 1 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically
recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
Claim 11 recites a system comprising “neural device; and a computer system communicably coupled to the neural device, the computer system comprising: a processor, and a memory coupled to the processor, the memory storing instructions” configured to perform the same method as set forth in claim 1, the added element of “neural device; and a computer system communicably coupled to the neural device, the computer system comprising: a processor, and a memory coupled to the processor, the memory storing instructions” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Claim 11 is therefore rejected according to the same findings and rationale as provided above.
Independent claim 11 are the same analogy and rejected using similar analysis as claim 1.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claims 2 disclose “neural device comprises an electrode array comprising 1,000 or more electrodes” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 3 disclose “training the feature extraction model comprises contrastive pre-training” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 4 disclose “identifying similar data segments across the global dataset comprises utilizing data from external sensors configured to sense a characteristic or an action associated with a user.” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 5 disclose “the external sensors comprise at least one of an inertial sensor, a camera, a tactile sensor, and a microphone” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 6 disclose receiving data from the neural device (insignificant extra-solution activity, MPEP 2106.05(g) that is a well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i))); decoding the received data using the user-specific feature extraction model to define decoded data; and determining a task associated with the decoded data (mental process); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 7 disclose “wherein the task is selected from the group consisting of a motor decoding task, an auditory decoding task, a sensory decoding task, and a visual decoding task.” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 8 disclose “wherein identifying the similar data segments comprises: identifying segments of the calibration data generated from replicates of a same training task performed by a plurality of individuals” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 9 disclose “identifying the similar data segments comprises: identifying segments of the calibration data generated during periods of time in which the users are not performing a decoding-relevant task” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 10 disclose “identifying the similar data segments comprises: performing small translational perturbations of an electrode array input of the neural devices” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea.
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims 12-20 for the other statutory classes are similarly analyzed and rejected under similar rationale.
For at least these reasons, the claimed inventions of each of dependent claims 2-10, and 12-20,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
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.
(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, 4-9, 11, and 14-19 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by ANISIMOV et al . [US 2020/0364539 A1, hereinafter D1].
With regard to Claim 1,
D1 teach a computer-implemented method for calibrating a neural device (¶23, Claim 1, “training a calibrating machine learning model on the calibrating features”), the method comprising:
aggregating calibration data across a user population to define a global dataset (Abstract, ¶135, “the gaze tracks may be collected from a number of different sources, such as a source 1, a source 2 and a source 3, which all provide different data sets that were recorded via multiple eye-trackers 702 with multiple training users”, ¶136, “a group of 1000 training users has consumed in a controlled environment a given number of texts and images … All the data from each training session was normalized and averaged, and then fed into the training machine learning model”), the calibration data comprising at least one of neural data recorded from users calibrating neural devices, neural device data from users calibrating the neural devices, or external sensor data associated with the neural devices from users calibrating the neural devices (¶¶24-25, “training data set includes a neural signals, bio-electric signals, captures of facial expressions or any biometrical measurements of the training user”, ¶208, “averaging the EEG features over all of the training user data set “, ¶120, “ sensors 711 of the bioresponse measurement module 704 may record bioresponses of the user … Session data recorded by the bioresponse measurement module 704 may include measurements of pupillary dilation, user's reaction time, heart rate, blood pressure, electrical signals that form brain waves patterns, … heart's electrical signals, etc.”, ¶34, “raw data from an analogue or a digital signal generated by a measurement device, such as an eye tracking device, an electroencephalography device or any other bioelectric signal measurement device within the system, is transformed into a frame-level feature representation”, claim 2, claim 4);
identifying similar data segments across the global dataset to define a task-independent training dataset (¶21, “ to measure and map the similarities and patterns between users' measurable biological reactions to various visual stimuli in order to train a machine learning algorithm that estimates some other user's reactions to some other visual stimuli”, ¶135, “the data sets may also be aggregated by type, the features may be extracted and then they may be normalized to have normalized feature values”, ¶216, “a single individual bioresponses may have a signature like pattern across all types of measurements and bioresponse measurement modules 704. In case of EEG, measured EEG signals may be specific to each user across various elicitation protocols. The user-specific signatures may be identified in EEG signals measurements irrespective of tasks or state of the brain”, ¶217, “training machine learning model may be trained to suppress user-specific components of the data … domain adaptation problem and may be solved with domain-adversarial training of neural networks”);
training a feature extraction model based on the task-independent training dataset to define a trained, task-independent feature extraction model (¶132, “training machine learning model may be constructed designed to extract features (e.g. time domain such as amplitude, frequency domain such as power, additional external spatial patterns or components such as via geoinformation systems or life cycle assessments) from one or more time intervals and render a likelihood output (continuous value from 0 to 1)“, ¶198, “To extract EEG features a convolutional network is used to generate a segment-level representation of bioelectrical signals from a time window centered around a corresponding timestep of a gaze track data”, ¶217, “training machine learning model is trained jointly with the use of gradient reversal layer (GRL). As was described previously, the DNN architecture processes the input first at frame level and then at segment level after accumulating statistics”);
receiving the calibration data from a user calibrating the neural device (¶24, “recording neural signals, bio-electric signals, facial expressions or any biometrical measurements of the user, in response to the user consuming either the calibration visual information or the target visual information or both … When this information is recorded during the calibrating session, this information may be used for training the calibrating machine learning model”, ¶147, “the calibrating data gathered from via the bioresponse measurement module 704, for example, the electroencephalography signal data, is transformed into tensors (frame-level feature representations) using a Fast Fourier Transform”, ¶221, “a resting state EEG data is measured and collected in a form of multi-channel time series, which is then stored in the system 700”); and
calibrating a user-specific feature extraction model using the trained, task-independent feature extraction model and the calibration data (¶150, “to save in the data storage module 716 calibration parameters of the calibration machine learning model, for example, kernel ridge regression parameters, or the support vector regression parameters that are personalized to the user's recording session”, ¶196, “applies to the gaze tracks a hybrid approach based on bidirectional LSTMs using word-level representations generated by the calibrating machine learning model or the algorithms of processing data of the training machine learning model”, ¶220, “the activations from one of the hidden layers of the DNN give a lower dimensional embedding of the user-specific information. LDA may be computed over these embeddings, and a one-versus-all SVM classifier is built using a cosine kernel to estimate the user's cognitive or emotional levels”).
With regard to Claim 4,
D1 teach the method of claim 1, wherein identifying similar data segments across the global dataset comprises utilizing data from external sensors configured to sense a characteristic or an action associated with a user (¶21, “to measure and map the similarities and patterns between users' measurable biological reactions to various visual stimuli”).
With regard to Claim 5,
D1 teach the method of claim 4, wherein the external sensors comprise at least one of an inertial sensor, a camera, a tactile sensor, and a microphone (¶90, “ bioresponse data may be collected from a variety of devices and sensors. These include desktop, laptop, and tablet computers frequently include microphones and high-resolution cameras capable of monitoring a person's facial expressions, eye motion patterns, or verbal responses while consuming visual information stimuli. Smartphones or tablets, for example, may include high-resolution cameras, proximity sensors, accelerometers, and touch-sensitive screens, in addition to microphones and buttons, and additional sensors. Wearable augmented reality glasses may contain sensors for tracking a user's eye positions with respect to the user's field of view”).
With regard to Claim 6,
D1 teach the method of claim 1, further comprises:
receiving data from the neural device (¶34, “ raw data from an analogue or a digital signal generated by a measurement device, such as an eye tracking device, an electroencephalography device or any other bioelectric signal measurement device within the system”, ¶123, “send the raw data generated by the EEG signals to the data gathering module 713”);
decoding the received data using the user-specific feature extraction model to define decoded data (¶34, “ raw data from an analogue or a digital signal generated by a measurement device, such as an eye tracking device, an electroencephalography device or any other bioelectric signal measurement device within the system, is transformed into a frame-level feature representation, by applying any suitable algorithm, including a Fast Fourier transform”, ¶223, “convolutional neural network of the training machine learning model, producing a sequence of the activation tensors “, ¶224, “RNN produces a tensor of activations that are designed to describe a frame-level cognitive and emotional state of a user”, ¶220, “the activations from one of the hidden layers of the DNN give a lower dimensional embedding of the user-specific information”); and
determining a task associated with the decoded data (¶152, “ determining the engagement level (or the reading type)”, ¶171, “wherein the discrete labels in an engagement type classification problem may be as: i) during “reading” the saccades are short and progressive over the context, ii) during “skimming” the saccades are longer compared to the reading pattern, and iii) during scanning the saccades are less structured compared to skimming”, ¶172, “predict a sequence-level class”, ¶169, “assigns the value 1 for the act of gazing at a word and a value 0 for the act of non-gazing at a word”).
With regard to Claim 7,
D1 teach the method of claim 6, wherein the task is selected from the group consisting of a motor decoding task, an auditory decoding task, a sensory decoding task, and a visual decoding task (¶152, “ determining the engagement level (or the reading type)”, ¶171, “wherein the discrete labels in an engagement type classification problem may be as: i) during “reading” the saccades are short and progressive over the context, ii) during “skimming” the saccades are longer compared to the reading pattern, and iii) during scanning the saccades are less structured compared to skimming”, ¶172, “predict a sequence-level class”, ¶169, “assigns the value 1 for the act of gazing at a word and a value 0 for the act of non-gazing at a word”).
With regard to Claim 8,
D1 teach the method of claim 1, wherein identifying the similar data segments comprises: identifying segments of the calibration data generated from replicates of a same training task performed by a plurality of individuals (¶136, “a group of 1000 training users has consumed in a controlled environment a given number of texts and images … All the data from each training session was normalized and averaged, and then fed into the training machine learning model”).
With regard to Claim 9,
D1 teach the method of claim 1, wherein identifying the similar data segments comprises:
identifying segments of the calibration data generated during periods of time in which the users are not performing a decoding-relevant task (¶120, “sensors 711 of the bioresponse measurement module 704 may record bioresponses of the user during the entire recording session, i.e. prior to consuming visual information 717, during consumption of the visual information 717 and after consuming the visual information 717”, ¶136, “a group of 1000 training users has consumed in a controlled environment a given number of texts and images … All the data from each training session was normalized and averaged, and then fed into the training machine learning model”, ¶221, “a resting state EEG data is measured and collected in a form of multi-channel time series, which is then stored in the system 700”, “the user is asked to maintain a relaxed position, avoiding any movements and other activities, so as to maintain his/her body and mind in a resting state for a period of time of no less than 30 seconds. During this resting state time interval, a resting state EEG data is measured and collected in a form of multi-channel time series, which is then stored in the system “, ¶141, “ templates are created for users to create a baseline for measuring the differentials in data in pre and post recording sessions”).
With regard to Claim 11,
Claim 11 is similar in scope to claim 1; therefore it is rejected under similar rationale. D1 further teach a system comprising: a neural device; and a computer system communicably coupled to the neural device, the computer system comprising: a processor, and a memory coupled to the processor, the memory storing instructions that, when executed by the processor (¶34, “raw data from an analogue or a digital signal generated by a measurement device, such as an eye tracking device, an electroencephalography device or any other bioelectric signal measurement device within the system”, “a processor may be connected to memory, but it will be appreciated that a variety of bridges and controllers may reside between the processor and memory”, ¶104, “Any of the modules within the system 700 may use any hardware (not shown) to operate, and such hardware may include a processor, a memory …”, ¶140).
With regard to Claim 14,
Claim 14 is similar in scope to claim 4; therefore it is rejected under similar rationale.
With regard to Claim 15,
Claim 15 is similar in scope to claim 5; therefore it is rejected under similar rationale.
With regard to Claim 16,
Claim 16 is similar in scope to claim 6; therefore it is rejected under similar rationale.
With regard to Claim 17,
Claim 17 is similar in scope to claim 7; therefore it is rejected under similar rationale.
With regard to Claim 18,
Claim 18 is similar in scope to claim 8; therefore it is rejected under similar rationale.
With regard to Claim 19,
Claim 19 is similar in scope to claim 9; therefore it is rejected under similar rationale.
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 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over ANISIMOV et al . [US 2020/0364539 A1, hereinafter D1] in view of Moses et al . [US 2024/0366157 A1, hereinafter Moses]
With regard to Claim 2,
D1 teach the D1 teach the method of claim 1.
D1 does not teach the neural device comprises an electrode array comprising 1,000 or more electrodes.
Moses teach neural device comprises an electrode array comprising 1,000 or more electrodes (¶149, “a high-density ECoG electrode array is used to record electrical signals from neural activity associated with attempted speech and/or spelling by a subject. For example, a high-density ECoG electrode array may comprise at least 100 electrodes, at least 128 electrodes, at least 196 electrodes, at least 256 electrodes, at least 294 electrodes, at least 500 electrodes, or at least 1000 electrodes, or more”).
D1 and Moses are analogous art to the claimed invention because they are from a similar field of endeavor of recording, analyzing and processing brain electrical signal data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by Moses with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to improves the ability to mathematically isolate individual cells (spike sorting) and reduces sorting errors and enhancing decoding accuracy as for neural prosthetics and Brain-computer interfaces, capturing thousands of units translates to higher fidelity decoding. This enables algorithms to map complex, nuanced motor movements (e.g., individual fingers and limbs) or reconstruct speech-related brain activity with higher accuracy. This is simply combining prior art elements according to known methods to yield predictable results and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 12,
Claim 12 is similar in scope to claim 2; therefore it is rejected under similar rationale.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over ANISIMOV et al . [US 2020/0364539 A1, hereinafter D1] in view of Zhang et al . [US 2024/0104352 A1, hereinafter Zhang]
With regard to Claim 3,
D1 teach the D1 teach the method of claim 1.
D1 does not teach the training the feature extraction model comprises contrastive pre-training.
Zhang teach training the feature extraction model comprises contrastive pre-training (claim 1, “evaluating, by the computing system, a loss function comprising a contrastive loss term and a masked modeling term, wherein the contrastive loss term evaluates a contrastive pre-training output”, ¶40, “ contrastive loss term 38 can evaluate a contrastive pre-training output generated “, ¶45, “ for each of one or more masked positions: the contrastive pre-training output”).
D1 and Zhang are analogous art to the claimed invention because they are from a similar field of endeavor of training machine learning models. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by Zhang with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to minimize data label dependency and expensive manual data annotation to learn highly discriminative features. Also, generate models that can easily adapt to completely new, unseen classes or tasks without requiring massive fine-tuning datasets and robust domain transfer as features learned through contrastive approaches generalize better to new environments, reducing model bias and improving performance on complex tasks like semantic segmentation or medical imaging. This is simply combining prior art elements according to known methods to yield predictable results and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 13,
Claim 13 is similar in scope to claim 3; therefore it is rejected under similar rationale.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over ANISIMOV et al . [US 2020/0364539 A1, hereinafter D1] in view of “Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning“ Published 2019 Ulysse Cote-Allard et al . [hereinafter D2]
With regard to Claim 10,
D1 teach the method of claim 1, wherein identifying the similar data segments comprises:
an electrode array input of the neural devices (¶123, “multiple electrodes are placed on the scalp of a user as is well known in the art. The measurement is performed over the time of a recording session … EEG measured voltage fluctuations may be interpreted in several ways, all of which are within the scope of the present technology”, ¶199).
D1 does not disclose performing small translational perturbations.
D3 teach performing small translational perturbations of an electrode array input of the neural devices (P. 13, APP A, “When adding noise to the data, it is important to ensure that the noise does not change the label of the examples “, “The third data augmentation technique employed aims at emulating electrode displacement on the skin … The data augmentation technique consists of shifting part of the power spectrum magnitude from one channel to the next. In other words, part of the signal energy from each channel is sent to an adjacent channel emulating electrode displacement … Electrode Displacement augmentation”).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of processing multi-channel electrode array neural signal. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to induce a robustness to noise into the learned model as this has been shown to lead to better generalization (D2, P. 13, APP A ¶1). This is simply combining prior art elements according to known methods to yield predictable results and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 20,
Claim 20 is similar in scope to claim 10; therefore it is rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 20150279031 filed by Cavusoglu et al. that disclose translational perturbation See at least ¶55
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
Prosecution Insights