DETAILED ACTION
Applicant' s arguments, filed 01/05/2026 have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 03/23/2023, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1, 3, and 6-10 are the current claims hereby under examination.
All references to Applicant’s specification are made using the paragraph numbers assigned in the US publication of the present application US 2023/0225649 A1.
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 Objections
Claim 3 is objected to because of the following informalities:
Claim 3 line 6 it appears that “dMRI or fMRI” should read “diffusion magnetic resonance imaging (dMRI) or functional magnetic resonance imaging (fMRI)”
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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, 3, and 6-10 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 “recording cognitive function states on time frames in the functional brain imaging data set” but it is unclear what “cognitive function states” entail. It is further unclear on what basis the “cognitive function states” are recorded in the time frames of the data set. It is unclear if there is any correlation between the recorded states and associated time frames or if the recorded states may be randomly assigned. For the purposes of this examination, this limitation will be interpreted as labeling each time frame with a cognitive state through any means.
Claim 1 recites “registering functional brain imaging data in the functional brain imaging data set to an image template based on structural morphology information” but it is unclear what “registering” data entails. It is further unclear what the “image template based on structural morphology information” entails and if such an image template is limited to being a structure of the brain or if it may be any image template based on morphology information of any physiological or non-physiological structure. It is further unclear if the registered information is being registered based on the structural morphology information or if the “based on structural morphology information” is meant to define the “image template”. For the purposes of this examination, the limitation will be interpreted as any correlation between functional brain imaging data and a brain model.
Claim 1 recites “obtaining a brain graph model” but it is unclear what “a brain graph model” entails. It is unclear how or if this recitation is related to the previous steps and registered functional brain imaging data or if the obtained model is entirely separate from the previously recited steps. It is unclear what this model entails and from where it is obtained. For the purposes of this examination, the limitation will be interpreted as any brain model.
Claim 1 recites “converting the functional brain imaging data into a two-dimensional time-series matrix using the brain graph model” but it is unclear how this conversion is performed using the brain graph model. It is unclear how the produced “two-dimensional time-series matrix” is derived from the functional brain imaging data. For the purposes of this examination, the limitation will be interpreted as converting a feature of the acquired data into a two dimensional matrix using any type of model.
Claim 1 recites “incorporating the two-dimensional time-series matrix as a graph signal into the brain graph model for representing brain functional activity signals on each brain region” but it is unclear what “incorporating” a matrix as a graph signal into a model entails. It is unclear how such a signal is incorporated into the “brain graph model” and what such an incorporation entails. It is further unclear how the brain graph model is considered “for representing brain functional activity signals on each brain region” as the brain graph model has not been described as relating to brain functional activity signals on each brain region. For the purposes of this examination, the limitation will be interpreted as any form of incorporating the derived matrix into a model.
Claim 1 recites “applying a graph convolution operation and spectral analysis on the brain functional activity signals thereby creating a graph convolutional network model” but it is unclear what spectral analysis and graph convolution operation is being applied, and in what manner. It is unclear how the graph convolution network model is created. The wording of “thereby” appears to imply that the model is created as a result of the step of applying a graph convolution operation and spectral analysis but it is unclear how these operations would result in a “graph convolutional network model”. It is further unclear what such a model entails. For the purposes of this examination, the limitation will be interpreted as the generation and training of a machine learning model.
Claim 1 recites “using a meta-analysis method to obtain priori knowledge under a brain functional activation paradigm, and generating a brain activation distribution priori graph” but it is unclear what “a meta-analysis method” comprises. It is unclear what data or information is undergoing the “meta-analysis”. It is unclear what “priori knowledge under a brain functional activation paradigm” comprises. It is unclear what “a brain activation distribution priori graph” comprises or how it is generated. It is unclear if there is any relationship between the obtained priori knowledge and the generated brain activation distribution priori graph. For the purposes of this examination, the limitation will be interpreted a determining any type of information that may be considered “priori” information and using said information to generate a graph.
Claim 1 recites “adding a term representing correspondence of brain activation in each brain region and the brain activation distribution priori graph into a target function of the graph convolutional network model based on the priori knowledge”. It is unclear how “a term representing correspondence of brain activation in each brain region and the brain activation distribution priori graph” is determined “based on the priori knowledge” and what such a determination would entail. It is unclear if the “term” is meant to represent just the correspondence degree or is also meant to represent the brain activation distribution priori graph. For the purposes of this examination, this limitation will be interpreted as the generation of a loss function.
Claim 1 recites “training the graph convolutional network model based, extracting feature information of a last convolutional layer of the graph convolutional network model as graph representation information of the brain functional activity signals …” but it is unclear if this limitation is meant to convey that the “graph convolutional network model” is trained using the following recitations of “extracting feature information …”. If so, it is unclear how the model is trained on its own output. If not, then it is unclear what the “training” of the model entails. For the purposes of this examination, these limitations will be interpreted as the same steps.
Claim 1 recites “extracting feature information of a last convolutional layer of the graph convolutional network model as graph representation information of the brain functional activity signals the graph representation information mapping the functional brain imaging data to a same representation space” but it is unclear if the graph representation information is meant to be comprised by the mapping to a representation space or if the recitation of mapping to a representation space is meant to convey a step that is performed using the graph representation information. It is unclear what the “graph representation information” entails. For the purposes of this examination, the limitation will be interpreted as the graph representation information comprising the mapping of the functional imaging data to a single representation space.
Claim 1 recites “generating a brain function activation graph” but it is unclear what information is utilized to generate the brain function activation graph. It is unclear how such a generation is performed. It is unclear if this generation is related to the representation space or any other parameters generated in step (8). It is unclear what this graph entails. For the purposes of this examination, the limitation will be interpreted as any type of generation of any type of brain function activation graph.
Claims 3 and 6-10 are rejected by virtue of their dependence on claim 1.
Claim 3 recites “wherein in the step (3) obtaining the brain graph model comprises: parcellating … calculating …” but it is unclear how the output of the parcellation and/or calculation steps are combined, manipulated, transformed, or otherwise processed to result in the obtained brain graph model. For the purposes of this examination, the limitations will be interpreted as combining the outputs of the two recited steps to produce the model.
Claim 6 recites “applying the spectral analysis on the brain functional activity signals comprises: calculating the graph Laplacian matrix of the brain graph model …” which appears to indicate that the spectral analysis of the brain graph signals includes the steps of “calculating a graph Laplacian matrix of the brain graph model, obtaining eigenvalues and eigen vectors” of claim 1 step (5). However claim 1 step 5 recites these as separate steps. Specifically, claim 1 step (5) indicates that the calculation of the graph Laplacian matrix of the brain graph model and associated eigenvalues and eigen vectors is a separate calculated from “applying a graph convolution operation and spectral analysis on the brain function activity signals”. It is unclear if these Laplacian calculation are being performed twice or if the spectral analysis recited in claim 1 is meant to entail the Laplacian calculations. It is unclear what relationship is present between the Laplacian calculations of claim 1 step (5) and the Laplacian calculations of claim 6. For the purposes of this examination, the limitations are interpreted as referring to the same calculation.
Claim 6 recites “calculating the graph Laplacian matrix of the brain graph model … wherein I represents a unit matrix, A represents a connection relationship between nodes and D represents connectivity on each of the nodes” but it is unclear how each of the recited variables (the nodes and connection relationships” are related to the brain graph model and from where these variable originate. The brain graph model was “obtained” in claim 1 and has “a graph signal” incorporated therein but the brain graph model has not been described as incorporating nodes or connectivity relationships. For the purposes of this examination, the brain graph model will be interpreted as including a model of brain connectivity including nodes and connectivity measures therebetween.
Claim 7 recites “wherein in the step (6), generating the brain activation distribution priori graph comprises: extracting peak coordinates of activated brain regions under a cognitive task, generating a Gaussian smoothed brain activation map on each of the peaks” but it is unclear what “a cognitive task” entails and what the extraction of “peak coordinates” during such a task includes. It is unclear what elements are being evaluated to determine a “peak coordinate” and from where such coordinates are extracted. It is further unclear what “generating a Gaussian smoothed brain activation map on each of the peaks” entails. For the purposes of this examination the limitation will be interpreted as identifying areas of the brain involved in particular cognitive tasks.
Claim 8 recites “the target function of the graph convolutional network model comprises a term representing a cross entropy loss … and a term representing a masked mean square error” but it is unclear if these terms of the target function are in addition to or in place of “a term representing correspondence of brain activation …” of claim 1 step (7). For the purposes of this examination, the terms will be considered in addition to the term of claim 1.
Claim 9 describes the loss function and defines the various parameters therein but it is unclear which of the parameters are associated with each of the terms set forth in claims 1 and 8. It is unclear which of these elements should be associated with each term
Claim 9 recites “wherein Yik represents a kth cognitive function state label corresponding to an ith sample, Pik is a probability of belonging to the kth cognitive function state as predicated by the graph convolutional network model, Zk is the brain activation distribution priori graph, Zik is brain activation degree values obtained by the graph convolutional network model, Wik is a brain mask containing the activated brain regions and α is a weight coefficient”. The recitation “the activated brain regions” lacks sufficient antecedent basis. It is further unclear what “sample” is being referred to and if the same as, related to, or different from the “functional brain imaging data” of claim 1. It is unclear what “the probability” is meant to refer to as none of the prior method steps are directed towards a probability calculation. It is unclear from where each of the defined parameters are derived and how they relate to the previously claimed limitations. Several of the parameters seemingly require that the defined parameter be provided by the graph convolutional network but it is unclear how the network generates these parameters in order to implement them into the target function. It is unclear from where and how the parameters Yik,, Zik, and Wik are generated or derived. It is unclear what the intended output of the target function is meant to be. For the purposes of this examination, the limitation will be interpreted as further defining the two items of the loss function and the various component will be considered as any component which comprises a loss function.
Claim 10 recites “training the graph convolutional network model comprises: randomly dividing the functional brain imaging data set into a training set, a validation set and a test set, and taking the brain graph model and the brain functional activity signals as input, taking the cognitive function states as labels to serve as output of the graph convolutional network model” but it is unclear how the recited input and output data is user to train the model. It is unclear what training methods would be utilized. It is further unclear how the model seemingly trained to output cognitive function states relates to the method of claim 1. Claim 1 appears to indicate that cognitive states are assigned to functional brain imaging data but does not utilize the cognitive states further and the cognitive states are not the intended output of the method of claim 1. Rather the method of claim 1 is directed towards displaying a brain function activation graph. It is unclear how a model trained to determine cognitive states is related to the method of claim 1.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3, and 9 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “recording cognitive function states on time frames in the functional brain imaging data set” but the specification does not appear to describe what a “cognitive function state” entails. The specification further does not appear to support the claimed scope of recording a cognitive state on a time frame through any method. In particular, the specification appears directed towards assigning cognitive states using a model designed for a plurality of cognitive experimental paradigms. In particular, paragraphs 0013, 0018, 0029, 0044, table 1, and 0049-0050 reference the cognitive state and/or the cognitive experimental paradigm. Paragraph 0044 mentions 8 different cognitive states but does not appear to describe what these states are. Additionally, table 1 illustrates 6 different cognitive experimental paradigms. The specification does not appear to describe how these paradigms are used to identify the cognitive states and/or how the cognitive states themselves are identified from the received data.
Claim 1 recites “obtaining a brain graph model “ but the specification does not appear to describe what such a model entails or how it is obtained. In particular, paragraphs 0015 and 0046 appear to describe this step in purely functional language. Paragraphs 0023 and 0046 appear to describe the “specific method” utilized in this process but merely provide more broad statements of functionality as the particular method steps such as “the connection mode between different brain regions is calculated, including a brain anatomical connection based on diffusion magnetic resonance, a brain functional connection based on resting state functional magnetic resonance and a brain structural connection based on structural magnetic resonance and morphological feature covariation” which merely recites that a highly complex parameter is “calculated” using various generic inputs. The particular calculation processes does not appear to be disclosed. Similarly, paragraph 0046 then states “finally, a brain graph model is constructed, wherein a node set V is formed by a brain region extracted by a brain atlas, and an edge set E is defined by brain connectome obtained through calculation” which does not describe how the particular input parameters are transformed to create the brain graph model. The specification merely provides a generic statement of functionality that the step is performed and do not provide sufficient support for “obtaining” such a model or describe what such a model entails.
Claim 1 recites “converting the functional brain imaging data into a two-dimensional time-series matrix using the brain graph model” but the specification does not appear to describe how this conversion process is carried out. In particular, paragraphs 0012, 0016, 0032, and 0047 each describe the step in purely functional language and do not appear to detail how the brain graph model is used to convert the functional brain imaging data into a two-dimensional time-series matrix. The particular steps taken by the model are not disclosed nor is the particular structure or training regimen of the model.
Claim 1 recites “incorporating the two-dimensional time-series matrix as a graph signal into the brain graph model for representing brain functional activity signals on each brain region” but the specification does not appear to describe what this “incorporating” operation entails or how the time-series matrix is incorporated into the brain graph model as a graph signal. In particular paragraphs 0016 and 0047 appear to describe this operation in purely functional language and do not appear to describe the particular steps taken to perform the recited operation.
Claim 1 recites “applying a graph convolution operation and spectral analysis on the brain functional activity signals” but the specification does not appear to describe what “spectral analysis” is being performed during this step. In particular, paragraphs 0026 seems to indicate that the calculation of the graph Laplacian matrix, Fourier transforms, and convolutional operations are the spectral analysis being performed. This appears to contradict the claim language which appears to indicate that the spectral analysis is a separate operation. Paragraphs 0026 and 0048 define the convolutional operation being performed but not the “spectral analysis”.
Claim 1 recites “thereby creating a graph convolutional model” but the specification does not appear to specifically describe what this step entails. The specification describes the generation of a model in functional language and recites that is may be trained using supervised learning in paragraphs 0011, 0041, and 0051, but these paragraphs do not appear to describe what the “graph convolutional model” is, or how they are generated. In particular, paragraphs 0028- 0030, 0041, 0048, 0050-0051 each describe how the model is constructed based on a plurality of inputs, and paragraphs 0026 and 0048 describe a convolutional operation that is performed, but none of these paragraphs appear to describe how the model is constructed from the recited inputs or the resultant model’s structure or composition. The specification does not appear to describe what the “model” is or what architectures of known neural network or machine learning models would be acceptable to generate it. Thus it would seem that the specification does not fully support the generation of any known or as of yet unknown model types from the recited inputs.
Claim 1 recites “using a meta-analysis method to obtain priori knowledge under a brain functional activation paradigm, and generating a brain activation distribution priori graph” but the specification does not appear to describe the “meta-analysis” utilized, describe what the “priori knowledge” entails, or describe how the “brain activation distribution priori graph” is generated. In particular, paragraphs 0027, 0041, and 0049 state that meta-analysis is performed and paragraph 0049 states that common software can be used for the analysis but does not appear to describe the particular inputs and outputs of the analysis. Furthermore, paragraph 0049 states that the brain activation distribution priori graph is generated by “a statistical test method” but the particular test method utilized is not disclosed. The specification does not appear to define what “priori knowledge” is obtained.
Claim 1 recites “adding a term representing correspondence of brain activation in each brain region and the brain activation distribution priori graph into a target function of the graph convolutional network model based on the priori knowledge”. The specification paragraphs 0028-0029 and 0050 define what the loss function is and state that an additional loss function is added to a target function. Thus it would seem that the specification supports the addition of a mean square loss function to a cross entropy loss function as defined in paragraph 0050. However, paragraph 0050 does not describe what “the priori knowledge” entails or how it is used to generate the term and add it to the target function. Paragraph 0050 provides a mere statement of functionality that the priori knowledge is combined to predict the brain functional state.
Claim 1 recites “training the graph convolutional network model based” but the specification does not support any and all types of training of an algorithm based on graph convolutional networks. In particular, paragraph 0051 provides a generic description of what the training entails by describing how a dataset is divided into training, validation, and testing sets and what the inputs and outputs of the model are. This description is not considered sufficient to support the claimed training the model because the particular training methods are not described. The recitations of dividing a dataset into various groups as part of training is a generic recitation of data manipulation and not a description of a training method.
Claim 1 recites “extracting feature information of a last convolutional layer of the graph convolutional network model as graph representation information of the brain functional activity signals, the graph representation information mapping the functional brain imaging data to a same representation space” but the specification does not detail the particular structure of the model. In particular, paragraphs 0020, 0030, and 0051 recite that such information is extracted from “the last convolutional layer” but do not particularly describe the structure of the model and its various layers and their functions. The specification does not describe how the model generates the extracted information from the recited inputs,
Claim 1 recites “generating a brain function activation graph” but the specification does not appear to describe how such a graph is generated and from what information it is generated from. In particular paragraphs 0020, 0030, and 0051 describe this step with mere statements of functionality and do not appear to describe the particular steps taken to generate such a graph including what information is input and how it is transformed into the output.
Claim 3 recites “obtaining the brain graph model comprises: parcellating a whole cerebral cortex and subcutaneous substructure into the brain regions; calculating a connectivity pattern between the brain regions using dMRI or fMRI”. Paragraphs 0023 and 0046 appear to describe the “specific method” utilized in this process but merely provide more broad statements of functionality as the particular method steps such as “the connection mode between different brain regions is calculated, including a brain anatomical connection based on diffusion magnetic resonance, a brain functional connection based on resting state functional magnetic resonance and a brain structural connection based on structural magnetic resonance and morphological feature covariation” which merely recites that a highly complex parameter is “calculated” using various generic inputs. The particular calculation process does not appear to be disclosed. Similarly, paragraph 0046 then states “finally, a brain graph model is constructed” which does not describe how the particular input parameters are transformed to create the brain graph model. The specification merely provides a generic statement of functionality that the step is performed. The specification does not appear to detail how each of the input elements are combined to generate the recited output.
Claim 9 recites “Claim 9 recites “wherein Yik represents a kth cognitive function state label corresponding to an ith sample, Pik is a probability of belonging to the kth cognitive function state as predicated by the graph convolutional network model, Zk is the brain activation distribution priori graph, Zik is brain activation degree values obtained by the graph convolutional network model, Wik is a brain mask containing the activated brain regions, and α is a weight coefficient” but the specification does not describe how each of these parameters are generated, calculated, or otherwise acquired. In particular, paragraph 0050 does not describe how the probability of a sample belonging to the cognitive function state is predicted by the model. The specification does not appear to describe how the weighting coefficient is determined. The particular outputs of the “model” and how they are generated are not fully described.
Prior Art
A prior art mapping is unable to be performed at this time given the above presented clarity issues. As best understood in light of the above presented clarity rejections, the closest prior art of record is presently considered to be:
Greicius US Patent Application Publication Number US 2011/0301431 A1 hereinafter Greicius teaches improved brain imaging and decoding methods that test subjects under authentic, natural conditions that allow for regular patterns of free-flowing thought and perception, as they occur in everyday life, while taking into account brain activities that were measured over spatially diverse regions of the whole-brain (whole-brain connectivity signatures). From such whole-brain connectivity signatures, specific cognitive traits and states are decoded and classified in a whole-brain connectivity analysis which takes into account the full pattern of brain activity. Such methods find applications in clinical diagnosis and monitoring of neuropsychiatric diseases and in nonclinical areas such as neuromarketing and neuroeconomics (Abstract). Greicius teaches a method of parceling a subject’s brain into a large number of functional regions of interest to provide a vast functional connectivity matrix where distinct cognitive states and traits can be isolated (Paragraph 0057). A classification algorithm is used to identify specific patterns of whole-brain connectivity to identify cognitive states (Paragraph 0062).
Sughrue US Patent Number US 11055849 B2 hereinafter Sughrue teaches a method including: determining a registration function for the particular brain in a coordinate space, determining a registered atlas from the registration function and an HCP-MMP1 Atlas containing a standard parcellation scheme, performing diffusion tractography to determine a set of brain tractography images of the particular brain, for a voxel in a particular parcellation in the registered atlas, determining voxel level tractography vectors showing connectivity of the voxel with voxels in other parcellations, classifying the voxel based on the probability of the voxel being part of the particular parcellation, and repeating the determining of the voxel level tractography vectors and the classifying of the voxels for parcellations of the HCP-MMP1 Atlas to form a personalized brain atlas containing an adjusted parcellation scheme reflecting the particular brain (Abstract). Sughrue teaches a method of determining a registration function of an individual to a standard brain data image (Col 2 line 43 – Col 3 lines 5).
Etkin US Patent Application Publication Number US 2020/0401938 A1 hereinafter Etkin teaches a method may include applying, to a corpus of data, a first machine learning technique to identify candidate domains of an ontology mapping brain structure to mental function. The corpus of data may include textual data describing a plurality of mental functions and spatial data corresponding to a plurality of brain structures. A second machine technique may be applied to optimize a quantity of domains included in the ontology and/or a quantity of mental function terms included in each domain. The ontology may be applied to phenotype an electronic medical record and predict a clinical outcome for a patient associated with the electronic medical record (Abstract). Etkin teaches a method which maps brain structures to mental functions via a machine learning algorithm (Paragraphs 0054-0055; Fig. 2A). Etkin further teaches that mental function term lists for a plurality of domains may be mapped onto brain circuits (Paragraph 0060; Figs. 3A and 4A-C)
Williams US Patent Number US 10034645 B1 hereinafter Williams teaches systems and methods for detecting complex networks in MRI image data. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device (Abstract). Williams teaches a method involving obtaining magnetic resonance imaging data which is pre-processed to identify structures within the images and measure connectivity between the structures (Col 1 lines 39-67). The magnetic resonance imaging data may be time stamped with provided stimuli and the pre-processing may further include the generation of a neurological model. The model includes at least one network within the time series data. The model assigns a biotype to the patient and provides a graphical user interface containing the biotype (Col 2 lines 1-28). Williams further teaches method of performing connectivity analysis and generating voxel wise resting models (Col 12 line 59 – Col 13 line 2; Fig. 7)
Response to Arguments
Applicant’s amendments overcome some of the previously presented rejections under 35 USC 112 but do not address the basic thrust of many of the rejections. In particular the composition and method of generating brain graph model and the graph convolutional network model are unclear and not seemingly disclosed. Moreover, it is unclear what the output of each of these models is intended to be and how the model transforms the recited inputs into the recited outputs.
The previously presented rejection under 35 USC 101 has been withdrawn. In particular, the presentation of a brain function activation graph is considered a presentation of a particular display. Additionally the recitations drawn towards the modifications made to the machine learning algorithms such as the addition of terms into a target function of the model are not considered to be practically performed in the human mind and are directed towards particular modifications to the machine learning model itself which is not considered an abstract idea.
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.
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/MATTHEW ERIC OGLES/ Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791