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 .
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1 recites:
A method of diagnosing a mental illness in a patient comprising:
measuring a plurality of encephalography signals from the patient;
feeding the plurality of encephalography signals into a trained multi-scale recurrent neural network;
classifying, by the trained multi-scale recurrent neural network, the patient as having a selected mental illness of a plurality of mental illnesses; and
communicating the selected mental illness to a user.
Independent Claim 10 recites:
A system comprising:
a computer having a processor and memory;
a user device;
a measurement device;
the computer, the user device and the measurement device configured together to perform a method comprising:
measuring, at the measurement device, a plurality of encephalography signals from a patient;
feeding, at the computer, the plurality of encephalography signals into a trained multi-scale recurrent neural network;
classifying, by the trained multi-scale recurrent neural network, the patient has having a selected mental illness of a plurality of mental illnesses; and
communicating the selected mental illness to the user device.
Step 1:
The examiner finds claim 1 is drawn to a method and claim 10 is drawn to a machine.
Step 2A Prong 1:
The above claim limitations constitute an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
“A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018).
The claimed step of classifying recite a mental process capable of being performed in the human mind.
The steps of “classifying” the patient having a selected mental illness in claims 1 and 10 is a mental process capable of being performed in the human mind. For example, the human mind is capable of classifying a patient’s condition by correlating symptoms to an illness.
The claimed step of classifying can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
“[T]he ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” MPEP 2106.04(a)(2) III. The pending claims merely recite steps for estimation that include observations, evaluations, and judgments.
Examples of ineligible claims that recite mental processes include:
• a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind,
Electric Power Group, LLC v. Alstom, S.A.;
• claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind,
University of Utah Research Foundation v. Ambry Genetics Corp.
• a claim to collecting and comparing known information, which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
Regarding the dependent claims 2-9 and 11-18, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
Step 2A Prong 2:
This judicial exception (abstract idea) in Claims 1-18 is not integrated into a practical application because:
• The abstract idea amounts to simply implementing the abstract idea on a computing device. For example, the recitations regarding the generic computing components for generating, detecting, assigning, receiving, and informing merely invoke a computer as a tool.
• The data-gathering step (measuring, feeding) and the data-output step (communicating) do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
• There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computing device that is used as a tool for generating, detecting, assigning, receiving, and informing.
• The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to determine a relationship among data to estimate bio-information.
• The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computing device for generating, detecting, assigning, receiving, and informing. The claims do not apply the obtained prediction to a particular machine. Rather, the data is merely output in a post-solution step.
Step 2B:
The additional elements are identified as follows: computer, processor, memory, user device, measurement device.
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by
• Applicant’s specification (e.g. par. 0120) which discloses that the processor(s) comprise generic computer components that are configured to perform the generic computer functions (e.g. classifying) that are well-understood, routine, and conventional activities previously known to the pertinent industry.
• Applicant’s specification (e.g. par. 0121) which discloses that the memory comprise generic storage components that are configured to perform the generic storage functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
• Oakley (US 20110015942 A1) discloses the conventional use of caps to acquire scalp EEGs [0083 “In conventional scalp EEGs, the recording is obtained by the EEG test operator manually placing electrodes on the scalp with a conductive gel or paste, usually after manually preparing the scalp area by light abrasion to reduce impedance due to dead skin cells. Many systems typically use electrodes, each of which is attached to an individual wire. Some systems use caps or nets into which electrodes are embedded; this is particularly common when high-density arrays of electrodes are needed”];
• Applicant’s Background in the specification; and
• The non-patent literature of record in the application; and
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3.
Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional generic computer components as those claimed. See option III. A. 2. in the Berkheimer memorandum.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the units associated with the steps do not add meaningful limitation to the abstract idea. A computer, processor, memory, or equivalent hardware is merely used as a tool for executing the abstract idea(s). The process claimed does not reflect an improvement in the functioning of the computer.
When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6 and 10-15 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 12629071 B2) in view of Corrado (US 9652712 B2).
Regarding claim 1, Lee teaches a method of diagnosing a mental illness in a patient comprising:
measuring a plurality of encephalography signals from the patient [col. 9 lns. 50-52 “…the brain wave measuring device 200 to acquire brain wave data and brain activity data of the user…”, see also col. 7 lns. 20-22 “The terminology “brain wave data” used in the present specification may refer to an electroencephalogram (EEG) signal value recorded in a sensor which senses a brain wave”];
classifying the patient as having a selected mental illness of a plurality of mental illnesses [col. 8 lns. 50-54 ”…the classification model may be a model configured to classify at least one of a post-traumatic stress disorder, a major depressive disorder, a post-traumatic stress disorder with a major depressive disorder, and the normal, on the basis of the data”]; and
communicating the selected mental illness to a user [col. 11 lns. 15-17 “…the user may easily acquire information on his/her own mental health without having temporal or spatial restrictions using the mobile device 300”].
Lee teaches acquiring EEG signals and classifying a mental illness based on the received signals, but fails to teach the EEG signals are fed into a trained multi-scale recurrent neural network, wherein the trained multi-scale recurrent neural network performs the classifying.
Corrado teaches the signals are fed into a trained multi-scale recurrent neural network [col. 5 lns. 5-10 “If there are multiple recurrent neural network layers in the recurrent neural network 110, the layers are arranged in a sequence from a lowest layer in the sequence to a highest layer in the sequence and collectively process the health event at the time step to compute the network internal state for the time step”],
wherein the trained multi-scale recurrent neural network performs the classifying [col. 4 lns. 13-15 “…the health-related data can include other health-related data that may be classified as impacting the health of the patient”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Lee and incorporate the teachings of Corrado to include the EEG signals are fed into a trained multi-scale recurrent neural network, wherein the trained multi-scale recurrent neural network performs the classifying. Doing so provides the system an automated means to recognize patterns from the acquired signals so that an accurate and rapid diagnosis of the patient’s condition can be performed.
Regarding claim 2, Lee and Corrado teach the method of claim 1, wherein the plurality of encephalography signals comprises at least one electroencephalography (EEG) signal [Lee col. 7 lns. 23-25 “…the brain wave data may be an EEG signal of a positive potential response that appears after a stimulus of a specific intensity, but it is not limited thereto”] or at least one magnetoencephalography (MEG) signal.
Regarding claim 3, Lee and Corrado teach the method of claim 2, wherein the at least one EEG signal comprises at least one of the bilateral frontal (Fp1 [Lee col. 7 ln. 52], F8 [Lee col. 7 ln. 52]) channels or the parietal-occipital (P7 [Lee col. 7 ln. 53], P3 [Lee col. 7 ln. 53], P8 [Lee col. 7 ln. 53], P04 [Lee col. 7 ln. 57], 01 [Lee col. 7 ln. 53], 02 [Lee col. 7 ln. 53]) channels.
Regarding claim 4, Lee and Corrado teach the method of claim 1, wherein the plurality of mental illnesses comprises at least major depressive disorder [Lee col. 4 lns. 12-13 “…a model configured to classify at least one of a post-traumatic stress disorder, a major depressive disorder…”], bipolar disorder, schizophrenia, and normal control [Lee col. 4 ln. 15].
Regarding claim 5, Lee and Corrado teach the method of claim 1, wherein the combination of Lee and Corrado teach a feature is extracted [Lee col. 14 lns. 16-19 “First Evaluation: Classification Performance Evaluation (Brain Wave Data) of a Device for Extraction of a Feature for Classification of Mental Disorders and Provision of Information on a Mental Disorder”] from the multi-scale recurrent neural network [Corrado col. 5 lns. 5-10 “If there are multiple recurrent neural network layers in the recurrent neural network 110, the layers are arranged in a sequence from a lowest layer in the sequence to a highest layer in the sequence and collectively process the health event at the time step to compute the network internal state for the time step”] and the feature is applied to a second machine learning model [combination of the multiple recurrent neural network as taught by Corrado col. 5 lns. 5-10 and a second evaluation that extracts another feature as taught by Lee col. 15. lns. 12-15 “Second Evaluation: Classification Performance Evaluation (Brain Activity Data) of a Device for Extraction of Feature for Classification of Mental Disorders and Provision of Information on a Mental Disorder”] to classify the feature as a sub-type of the selected mental illness [Lee. col. 11 lns. 5-6 “…a post-traumatic stress disorder having a major depressive disorder…”, the examiner is interpreting this combination of mental illness as a “sub-type”].
Regarding claim 6, Lee and Corrado teach the method of claim 1, further comprising incorporating other patient information as part of an input into the multi-scale recurrent neural network [Corrado col. 5 lns. 32-36 “Examples of per-record inputs can include a genetic sequence of the patient associated with the current temporal sequence or other information characterizing the patient, e.g., demographic information for the patient”].
It would have been obvious before the effective filing date of the claimed invention to further modify the teachings of Lee with the teachings of Corrado to include incorporating other patient information as part of an input into the multi-scale recurrent neural network. Doing so configures the system to provide a more personalized diagnosis with multiple inputs contributing to a more robust and accurate diagnosis.
Regarding claim 10, Lee teaches a system comprising:
a computer [Fig. 1A-B Item 100] having a processor [Fig. 1B Item 150] and memory [Fig. 1B Item 140];
a user device [Fig. 1A Item 300];
a measurement device [Fig. 1A Item 200];
the computer, the user device and the measurement device configured together [see Fig. 1A] to perform a method comprising:
measuring, at the measurement device, a plurality of encephalography signals from a patient [col. 9 lns. 50-52 “…the brain wave measuring device 200 to acquire brain wave data and brain activity data of the user…”];
classifying the patient has having a selected mental illness of a plurality of mental illnesses [col. 8 lns. 50-54 ”…the classification model may be a model configured to classify at least one of a post-traumatic stress disorder, a major depressive disorder, a post-traumatic stress disorder with a major depressive disorder, and the normal, on the basis of the data”]; and
communicating the selected mental illness to the user device [col. 11 lns. 15-17 “…the user may easily acquire information on his/her own mental health without having temporal or spatial restrictions using the mobile device 300”].
Lee teaches acquiring EEG signals and classifying a mental illness based on the received signals, but fails to teach the EEG signals are fed into a trained multi-scale recurrent neural network, wherein the trained multi-scale recurrent neural network performs the classifying.
Corrado teaches the signals are fed into a trained multi-scale recurrent neural network [col. 5 lns. 5-10 “If there are multiple recurrent neural network layers in the recurrent neural network 110, the layers are arranged in a sequence from a lowest layer in the sequence to a highest layer in the sequence and collectively process the health event at the time step to compute the network internal state for the time step”],
wherein the trained multi-scale recurrent neural network performs the classifying [col. 4 lns. 13-15 “…the health-related data can include other health-related data that may be classified as impacting the health of the patient”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Lee and incorporate the teachings of Corrado to include the EEG signals are fed into a trained multi-scale recurrent neural network, wherein the trained multi-scale recurrent neural network performs the classifying. Doing so provides the system an automated means to recognize patterns from the acquired signals so that an accurate and rapid diagnosis of the patient’s condition can be performed.
Regarding claim 11, Lee and Corrado teach the system of claim 10, wherein the plurality of encephalography signals comprises at least one electroencephalography signal [Lee col. 7 lns. 23-25 “…the brain wave data may be an EEG signal of a positive potential response that appears after a stimulus of a specific intensity, but it is not limited thereto”] or at least one magnetoencephalography signal.
Regarding claim 12, Lee and Corrado teach the system of claim 11, wherein the at least one EEG signal comprises at least one of the bilateral frontal (Fp1 [Lee col. 7 ln. 52], F8 [Lee col. 7 ln. 52]) channels or the parietal-occipital (P7 [Lee col. 7 ln. 53], P3 [Lee col. 7 ln. 53], P8 [Lee col. 7 ln. 53], P04 [Lee col. 7 ln. 57], 01 [Lee col. 7 ln. 53], 02 [Lee col. 7 ln. 53]) channels.
Regarding claim 13, Lee and Corrado teach the system of claim 10, wherein the plurality of mental illnesses comprises at least major depressive disorder [Lee col. 4 lns. 12-13 “…a model configured to classify at least one of a post-traumatic stress disorder, a major depressive disorder…”], bipolar disorder, schizophrenia, and normal control [Lee col. 4 ln. 15].
Regarding claim 14, Lee and Corrado teach the system of claim 10, wherein the computer extracts a feature [col. 14 lns. 16-19 “First Evaluation: Classification Performance Evaluation (Brain Wave Data) of a Device for Extraction of a Feature for Classification of Mental Disorders and Provision of Information on a Mental Disorder”] from multi-scale recurrent neural network [Corrado col. 5 lns. 5-10 “If there are multiple recurrent neural network layers in the recurrent neural network 110, the layers are arranged in a sequence from a lowest layer in the sequence to a highest layer in the sequence and collectively process the health event at the time step to compute the network internal state for the time step”] and applies the feature to a second machine learning model [combination of the multiple recurrent neural network as taught by Corrado col. 5 lns. 5-10 and a second evaluation that extracts another feature as taught by Lee col. 15. lns. 12-15 “Second Evaluation: Classification Performance Evaluation (Brain Activity Data) of a Device for Extraction of Feature for Classification of Mental Disorders and Provision of Information on a Mental Disorder”] to classify a sub-type of the selected mental illness [Lee. col. 11 lns. 5-6 “…a post-traumatic stress disorder having a major depressive disorder…”, the examiner is interpreting this combination of mental illness as a “sub-type”].
Regarding claim 15, Lee and Corrado teach the system of claim 10, wherein the computer further incorporates other patient information as part of the input into the multi-scale recurrent neural network [Corrado col. 5 lns. 32-36 “Examples of per-record inputs can include a genetic sequence of the patient associated with the current temporal sequence or other information characterizing the patient, e.g., demographic information for the patient”].
It would have been obvious before the effective filing date of the claimed invention to further modify the teachings of Lee with the teachings of Corrado to include incorporating other patient information as part of an input into the multi-scale recurrent neural network. Doing so configures the system to provide a more personalized diagnosis with multiple inputs contributing to a more robust and accurate diagnosis.
Claims 7-8 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Lee and Corrado as applied to claim 1 and 10 above, and further in view of Etkin (US 20210353224 A1).
Regarding claim 7, Lee and Corrado teach the method of claim 1, further comprising, if the patient is classified as having major depressive disorder:
visualizing the plurality of encephalography signals [col. 10 lns. 25-27 “…the output unit 130 may display brain wave data received by the reception unit 110”];
classifying the patient as belonging to a sub-type of major depressive disorder based on the visualization [Lee. col. 11 lns. 2-6 “…a classification model configured to classify at least one of (…) a post-traumatic stress disorder having a major depressive disorder…”, the examiner is interpreting this combination of mental illness as a “sub-type”]; and
communicating the sub-type to the user [col. 10 lns. 27-29 “…the display unit 130 may display information on a mental disorder which is generated by the processor 150”].
Lee and Corrado teach acquiring EEG signals, but fail to explicitly teach reducing the dimensionality of the plurality of encephalography signals.
Etkin teaches reducing the dimensionality of the plurality of encephalography signals [0276 “…the filter 112 may be configured to reduce the dimensionality of the EEG data including by merging two or more signals to generate a single latent signal”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Lee and Corrado and incorporate the teachings of Etkin to include reducing the dimensionality of the plurality of encephalography signals. Doing so configures the system to help prevent “overfitting at the regression model”, which provides improved generalization, more accurate and stable predictions, and better model interpretability from a less complex model, as recognized by Etkin par. 0276.
Regarding claim 8, Lee, Corrado, and Etkin teach the method of claim 7, wherein the sub-type comprises a major depressive disorder [Lee col. 4 lns. 12-13 “…a model configured to classify at least one of a post-traumatic stress disorder, a major depressive disorder…”] treatable with transcranial magnetic stimulation [Etkin 0099 “…the clinically-defined diagnosis of major depression is itself composed of largely unknown, but critical, neurobiological heterogeneity, which contributes to varying treatment outcomes”, see also Etkin 0099 “We also found that the rsEEG-derived outcome predictions in the third sample indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation (TMS) during EEG, thereby elucidating neurobiological significance”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of Lee and Corrado with the teachings of Etkin to include the sub-type is major depressive disorder treatable with transcranial magnetic stimulation. Doing so configures the system to specialize to a desired illness to improve diagnostic accuracy and sub-type identification.
Regarding claim 16, Lee and Corrado teach the system of claim 10, wherein, if the patient is classified as having major depressive disorder, the computer further performs the steps of:
visualizing the plurality of encephalography signals [col. 10 lns. 25-27 “…the output unit 130 may display brain wave data received by the reception unit 110”];
classifying the patient as belonging to a sub-type of major depressive disorder based on the visualization [Lee. col. 11 lns. 2-6 “…a classification model configured to classify at least one of (…) a post-traumatic stress disorder having a major depressive disorder…”, the examiner is interpreting this combination of mental illness as a “sub-type”]; and
communicating the sub-type to the user [col. 10 lns. 27-29 “…the display unit 130 may display information on a mental disorder which is generated by the processor 150”].
Lee and Corrado teach acquiring EEG signals, but fail to explicitly teach reducing the dimensionality of the plurality of encephalography signals.
Etkin teaches reducing the dimensionality of the plurality of encephalography signals [0276 “…the filter 112 may be configured to reduce the dimensionality of the EEG data including by merging two or more signals to generate a single latent signal”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Lee and Corrado and incorporate the teachings of Etkin to include reducing the dimensionality of the plurality of encephalography signals. Doing so configures the system to help prevent “overfitting at the regression model”, which provides improved generalization, more accurate and stable predictions, and better model interpretability from a less complex model, as recognized by Etkin par. 0276.
Regarding claim 17, Lee, Corrado, and Etkin teach the system of claim 16, wherein the sub-type comprises a major depressive disorder [Lee col. 4 lns. 12-13 “…a model configured to classify at least one of a post-traumatic stress disorder, a major depressive disorder…”] treatable with transcranial magnetic stimulation [Etkin 0099 “…the clinically-defined diagnosis of major depression is itself composed of largely unknown, but critical, neurobiological heterogeneity, which contributes to varying treatment outcomes”, see also Etkin 0099 “We also found that the rsEEG-derived outcome predictions in the third sample indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation (TMS) during EEG, thereby elucidating neurobiological significance”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of Lee and Corrado with the teachings of Etkin to include the sub-type is major depressive disorder treatable with transcranial magnetic stimulation. Doing so configures the system to specialize to a desired illness to improve diagnostic accuracy and sub-type identification.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lee, Corrado, and Etkin as applied to claim 7 and 16 above, and further in view of Golden (US 11551353 B2).
Regarding claim 9, Lee, Corrado, and Etkin teach the method of claim 7, wherein Lee teaches the data visualization [Lee col. 10 lns. 25-27] and Etkin teaches the method of dimensionality reduction [Etkin 0276], but fails to explicitly teach the dimensionality reduction and the method for visualization comprise t-distributed stochastic neighbor embedding (t-SNE).
Golden teaches the dimensionality reduction comprises t-distributed stochastic neighbor embedding (t-SNE) [col. 34 lns. 47-50 “Features may also be combined in a non-linear fashion, e.g., using dimensionality reduction methods such as principal component analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE)”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Lee, Corrado, and Etkin and incorporate the teachings of Golden to include the dimensionality reduction and the method for visualization comprise t-distributed stochastic neighbor embedding (t-SNE). Doing so configures the system to provide a powerful visualization tool that makes complex, high-dimensional data patterns visible in a low-dimensional space, especially for exploring local structures and clusters.
Regarding claim 18, Lee, Corrado, and Etkin teach the system of claim 16, wherein Lee teaches the data visualization [Lee col. 10 lns. 25-27] and Etkin teaches the method of dimensionality reduction [Etkin 0276], but fails to explicitly teach the dimensionality reduction and the method for visualization comprise t-distributed stochastic neighbor embedding (t-SNE).
Golden teaches the dimensionality reduction comprises t-distributed stochastic neighbor embedding (t-SNE) [col. 34 lns. 47-50 “Features may also be combined in a non-linear fashion, e.g., using dimensionality reduction methods such as principal component analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE)”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Lee, Corrado, and Etkin and incorporate the teachings of Golden to include the dimensionality reduction and the method for visualization comprise t-distributed stochastic neighbor embedding (t-SNE). Doing so configures the system to provide a powerful visualization tool that makes complex, high-dimensional data patterns visible in a low-dimensional space, especially for exploring local structures and clusters.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN M HANEY whose telephone number is (571)272-0985. The examiner can normally be reached Monday through Friday, 0730-1630 ET.
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/JONATHAN M HANEY/ Examiner, Art Unit 3791 /ALEX M VALVIS/Supervisory Patent Examiner, Art Unit 3791
