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 .
Information Disclosure Statement
The information disclosure statement filed December 22, 2023 has been considered.
Priority
Provisional application 18/179,583 filed March 7th 2022 is acknowledged; the effective filing date is March 7th 2022.
Status of Claims
Claims 1-20 are pending and examined on the merits.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 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.
With respect to claim 1, the metes and bounds of “…applying the trained model for classifying, predicting, or comparing the substance; and reporting a result of the classifying, predicting, or comparing.” in lines 8-9 are indefinite. It is unclear what element of the claimed invention the active step of “applying the trained model” is applied to. To further prosecution, the examiner interprets the trained model as applied to the “database” on line 8.
With respect to claim 4, the metes and bounds of “…predicting and classifying between high- risk and low-risk in a set of selected side effects of the substance…” are indefinite. The metes and bounds of the terms “high” and “low” are unclear. The specification is silent pertaining to a definition. One skilled in the art would not recognize by what standards to recognize the declared risks. To further prosecution, the examiner interprets “high risk” as a substance or drug requiring a medical prescription and/or diagnosis to obtain, and “low risk” as a substance that can be obtained without a medical prescription and/or diagnosis (such as medications that can be obtained over the counter at a pharmacy or grocery store).
Dependent claims 2-20 are rejected as they depend from claim 1, and fail to remedy the deficiencies of claim 1.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mengxuan Gao et al (Journal of Pharmacological Sciences Volume 133, Issue 2, February 2017, Pages 70-78) and in view of Julia Y.H. Liu et al. (Cell Calcium Volume 80, June 2019, Pages 175-188)
Regarding claim 1, Gao et al. teaches an organ bath study in which brain slices prepared from the ventral part of the hippocampal formation in 3- to 4-week-old ICR male mice and stored in artificial cerebrospinal fluid are treated with 14 drugs that either do or do not induce seizures in humans, and the use of a support-vector machine and a deep learning network to classify drugs based upon their neuronal discharges as measured with local field potential (LFP) measurements conducted with an 8x8 planar multi-electrode array (MED-P515A, electrode size: 50 by 50 micrometers) and Matlab (Abstract, Introduction pg. 70, sec 2.4, pg. 71, Table 1, sec. 2.5, re: clm. 1, …A method of testing effects of one or more substances … on…tissues using a recording platform to determine whether the one or more substances belong to one or more classes…, applying a substance for testing on at least one sub-segment of freshly isolated...tissue from a living organism…, maintaining the tissue in oxygenated medium to maintain a viability of the tissue…, recording electrical signals from a surface of the tissue using the recording platform to create a recorded digital signal…storing the recorded digital signal in a data storage device…)
Gao et al. further teaches recording LFPs from the alveus, detecting seizure-like-events as in LFP traces using an image recognition technique from deep learning network “Caffe”, which was used to extract the features from LFP images and identified SLEs using a linear support vector machine (SVM) in the state space whose dimension was reduced using principal component analysis (Introduction, pg. 70-71, re: clm. 1,… generating a plurality of test results by analyzing the recorded digital signal using a set of machine-readable instructions that allow a computer to extract at least one feature from the recorded digital signal …training one or more machine learning models based on the plurality of test results stored in the database, to create a trained model…)
Gao et al. further teaches the effects of drugs on potentials are measured and recorded using the MEA and the drugs are predicted and classified as either seizure or non-seizure inducing using the features extracted with the machine learning methods (Caffe with SVM and principal components analysis) (Fig. 3, Fig. 4, pg. 74, Fig. 6, pg. 76, re: clm. 1, …applying the trained model for classifying, predicting, or comparing the substance; and reporting a result of the classifying, predicting, or comparing…)
Gao et al. does not teach testing effects using pacemaker activity on gastrointestinal tissues.
Liu et al. teaches recording pacemaker activity using a microelectode array (MEA) in vitro from intact GI tissues freshly isolated from the ICR mouse and Suncus murinus and that the effects of temperature, extracellular calcium and potassium concentrations on pacemaker potentials were quantified using spatiotemporal metrics (Abstract, pg. 175, materials and method, pg. 175).
Liu et al. does not teach machine learning applciations.
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard of obviousness to Gao et al. and Liu et al., the examiner concludes that the classification of drugs applied to hippocampal slices based on local field potential changes using a MED-P515A and machine learning according to Gao et al. with the microelectrode array recordings of pacemaker activity from intact GI tissues freshly isolated from mice as disclosed by Liu et al. represents a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings.
One of ordinary skill in the art of neuroscience and bioinformatics would be motivated to combine the classification method to Gao et al. with the pacemaker activity recording as disclosed by Liu et al. because the combination would lead to a stronger murine gastrointestinal pacemaker potential examination method. In support of this motivation, Liu et al. states on pg. 186: “The MEA has been used to record GI pacemaker activities in previous studies”, and further indicates a desire for future studies examining drug effects, stating on pg. 186:
“Upon activation, signals are transduced via hormones or neuronal connections from the mucosal layer to the muscular layer to induce a final response in gut motility. Therefore, the results generate by isolated layers may not translate to the true and final effects of a drug treatments. In particular, if we aim to use the technique for studying pharmacology, we would like observed a more complete picture, and keep connections, signal transduction, regulatory and compensatory machinery between the mucosal and muscular layers as intact as possible”.
There would have been a reasonable expectation of success because the methods of both arts utilize similar methods to examine, analyze and characterize potentials from freshly isolated murine tissue. Therefore, claim 1 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 2, Gao et al teaches the use of drugs on Table 1 (pg. 71, re: clm. 2, …wherein the substance comprises one or more of drugs, pharmacological agents, chemical compounds, synthesized substances, food, remedies, herbs, extracts, and any combination thereof…).
Applying the KSR standard of obviousness to Gao et al. and Liu et al., the examiner concludes that the classification of drugs applied to hippocampal slices based on local field potential changes using a MED-P515A and machine learning according to Gao et al. with the microelectrode array recordings of pacemaker activity from intact GI tissues freshly isolated from mice as disclosed by Liu et al. represents a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings.
One of ordinary skill in the art of neuroscience and bioinformatics would be motivated to combine the application of drugs to freshly isolated hippocampal tissue as taught by Gao et al. with the pacemaker activity recording of gastrointestinal segments as disclosed by Liu et al. because the combination would lead to a stronger murine gastrointestinal pacemaker potential examination method. There would have been a reasonable expectation of success because the methods of both arts utilize similar methods to examine, analyze and characterize potentials from freshly isolated murine tissue. Therefore, claim 2 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 3, Gao et al. explicitly states the use of a planar multi-electrode array (MED-P515A of Alpha MED Scientific) on pg. 71, section 2.4, which reads on a signal receiver, an amplifier, an internal filter, a grounding electrode, and a microelectrode array chip; the microelectrode array chip comprising a multiplicity of microelectrodes embedded on a rigid substrate (re: clm. 3, …wherein the recording platform comprises a signal receiver, an amplifier, an internal filter, a grounding electrode, and a microelectrode array chip; the microelectrode array chip comprising a multiplicity of microelectrodes embedded on a rigid substrate.)
Applying the KSR standard of obviousness to Gao et al. and Liu et al., the examiner concludes that the combination of the MED-P515A applied to hippocampal slices according to Gao et al. with the microelectrode array recordings as disclosed by Liu et al. represents applying a known technique to a known method with no more than a predictable outcome of a MED-P515A applied to gastrointestinal tissue samples.
One of ordinary skill in the art of neuroscience would have been a reasonable expectation of success of applying the MED-P515A tool to the method of Liu et al. as Gao et al provides all the necessary instructions or elements in the materials and methods on pg. 71. Therefore, claim 3 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 4, the examiner interprets “high risk” as a substance or drug requiring a medical prescription and/or diagnosis to obtain, and “low risk” as a substance that can be obtained without a medical prescription and/or diagnosis (such as medications that can be obtained over the counter at a pharmacy or grocery store).
Regarding claim 4, Gao et al. discloses the inclusion of drugs which are agonists and antagonists as applied to the classification method (sec. 3.3, pg. 73, re: clm. 4., …predicting and classifying between agonist and antagonist actions of the one or more substances, or predicting and classifying between high- risk and low-risk in a set of selected side effects of the substance.
Applying the KSR standard of obviousness to Gao et al. and Liu et al., the examiner concludes that the classification of drugs applied to hippocampal slices based on local field potential changes using a MED-P515A and machine learning according to Gao et al. with the microelectrode array recordings of pacemaker activity from intact GI tissues freshly isolated from mice as disclosed by Liu et al. represents a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings.
One of ordinary skill in the art of neuroscience and bioinformatics would be motivated to combine the classification method to Gao et al. with the pacemaker activity recording as disclosed by Liu et al. because the combination would lead to a stronger murine gastrointestinal pacemaker potential examination method. In support of this motivation, Liu et al. a desire for future studies examining drug effects, stating on pg. 186. There would have been a reasonable expectation of success because the methods of both arts utilize similar methods to examine, analyze and characterize potentials from freshly isolated murine tissue. Therefore, claim 4 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 5, Liu et al. teaches on pg. 186, section 4.6 that pacemaker MEA recordings are superior to single cell microelectrode recordings for studying mechanisms of dysrhythmia as “In our studies, pacemaker potentials with clear waveforms and frequencies matching to the literature were readily recorded…”, which reads on dysthymia side effect examination (re: clm. 5, … the set of selected side effects comprising one or more of vomiting, emesis, nausea, diarrhea, constipation, abdominal discomfort, and dysrhythmia…).
Liu et al. does not teach machine learning.
Applying the KSR standard of obviousness to Gao et al. and Liu et al., the examiner concludes that the classification of drugs applied to hippocampal slices based on local field potential changes using a MED-P515A and machine learning according to Gao et al. with the dysthymia side effect examination as disclosed by Liu et al. represents a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings.
One of ordinary skill in the art of neuroscience and bioinformatics would be motivated to combine the classification method to Gao et al. with the pacemaker activity recording as disclosed by Liu et al. because the combination would lead to a stronger murine gastrointestinal pacemaker potential examination method. In support of this motivation, Liu et al. a desire for future studies examining drug effects on pg. 186. There would have been a reasonable expectation of success because the methods of both arts utilize similar methods to examine, analyze and characterize potentials from freshly isolated murine tissue. Therefore, claim 5 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 6, Liu et al. teaches isolation of a sub-segment of freshly isolated intestinal segments, stating on pg. 176: “…the intestine was cut into six sections of equal length and labeled from proximal to distal with sections 1,4 and 6 considered equivalent to segments from the duodenum, ileum and colon, respectively…” (re: clm. 6, …wherein the sub-segment of freshly isolated gastrointestinal tissue comprises tissue from an esophagus, stomach, duodenum, jejunum, ileum, rectum, caecum, or colon.)
Liu et al. does not teach machine learning.
Applying the KSR standard of obviousness to Gao et al. and Liu et al., the examiner concludes that the combination of the classification method applied to hippocampal slices according to Gao et al. with the intestinal segments as disclosed by Liu et al. represents applying a known technique to a known method with no more than a predictable outcome of a machine learning method applied to pacemaker measurements of gastrointestinal tissue samples.
One of ordinary skill in the art of neuroscience would have been a reasonable expectation of success of applying the classification of Gao et al. to the method of Liu et al. as Liu et al. provides all the necessary instructions or elements for tissue segmentation in the materials and methods on pg. 176 (sec. 2.2-2.5) Therefore, claim 6 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claims 7-9, Gao et al. teaches a method in which hippocampal samples are obtained from healthy mice (Abstract, Materials and Methods, “Animals” section, pg. 71, re: clm. 7, …the living organism is an organism having functional gastrointestinal organs…, clm. 8, … the living organism is human, mammalian, reptilian, or aquatic…, clm. 9, …wherein the living organism is healthy; or is diagnosed with a disease, genetic condition, or alteration; or is pre-treated with the substance prior to the applying the substance for testing.)
Gao et al. does not teach testing pacemaker activity in gastrointestinal tissues.
Applying the KSR standard of obviousness to Gao et al. and Liu et al., the examiner concludes that the combination of the classification method applied to hippocampal slices according to Gao et al. with the intestinal segments as disclosed by Liu et al. represents applying a known technique to a known method with no more than a predictable outcome of a machine learning method applied to pacemaker measurements of gastrointestinal tissue samples from healthy mice.
One of ordinary skill in the art of neuroscience would have been a reasonable expectation of success of applying the classification of Gao et al. to the method of Liu et al. as Liu et al. provides all the necessary instructions or elements for tissue segmentation in the materials and methods on pg. 176 (sec. 2.2-2.5) Therefore, claims 7-9 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claims 10 and 11, Liu et al. discloses a wash on pg. 176: “The entire GI tract was freshly isolated and placed into Krebs’ medium gassed with 95% O2/5% CO2.” The wash reads on preparing intestinal tissue (re: clm. 10, …removing contents from within the freshly isolated gastrointestinal tissue.).
Liu et al. further discloses on pg. 176 temperature control for the tissue segments analysis stating: “Temperature was controlled by a software-controlled heated copper plate underneath the recording chamber and was maintained at 35.0 °C unless stated otherwise. The whole set-up was shielded from environmental noise using a Faraday cage (Fig. 1C). The whole stomach was then placed onto the ˜1mm2 MEA recording field with the muscular side of the corpus and antrum directly facing the electrodes. The intestinal segments were also placed directly onto the electrodes with the lumen aligned horizontally across the electrode field (Fig. 1D).” (re: clm. 11, …maintaining the temperature of the freshly isolated gastrointestinal tissue within a range of twenty to forty degrees Celsius.)
Liu et al. does not teach machine learning.
Applying the KSR standard of obviousness to Gao et al. and Liu et al., the examiner concludes that the combination of the classification method applied to hippocampal slices according to Gao et al. with the intestinal segments as disclosed by Liu et al. represents applying a known technique to a known method with no more than a predictable outcome of a machine learning method applied to pacemaker measurements of gastrointestinal tissue samples prepared for MEA measurements with a stable temperature range.
One of ordinary skill in the art of neuroscience would have been a reasonable expectation of success of applying the classification of Gao et al. to the method of Liu et al. as Liu et al. provides all the necessary instructions or elements for tissue segmentation in the materials and methods on pg. 176 (sec. 2.2-2.5) Therefore, claims 10 and 11 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claims 12-17, Gao et al. discloses on pg. 71, sec. 2.2 and in Table 1 that drugs are used in distilled water and artificial cerebrospinal fluid (aCSF) which reads on a baseline concentration. Gao et al. further discloses on pg. 73, sec. 3.3 that each drug concentration was applied for 10 minutes each, and additionally notes on Fig. 2 a representative local field potential measurement with picrotoxin from 0 to 100 micrometer concentration from 0 min to 10 min, which reads on a baseline concentration applied prior to applying a substance, and applying a substance at a specified time (re: clm. 12, …comprising recording a baseline signal for at least five minutes prior to the applying the substance for testing…, clm. 13, …the applying the substance for testing comprising delivering a specified quantity of the substance onto the sub-segment of freshly isolated… tissue at a specified time after the recording of the baseline signal.).
Gao et al. further discloses on section 2.4 a perfusion system for use with the hippocampal slices and drugs (re: clm. 14, …the delivering comprises either direct delivery using a handheld pipette or machine-controlled delivery using a machine-controlled perfusion system.)
Gao et al. further discloses in the aforementioned representative local field potential measurement with picrotoxin and additionally on Fig. 3 and Fig 4 (pg. 74) local field potential recordings occurring after the delivery of drugs of interest at various concentrations onto the segment of hippocampal tissue, and recordings of a local field potential recording are those from the tissue post-drug delivery (re: clm. 15, …wherein the recording electrical signals occurs after the delivering of the specified quantity of the substance onto the sub-segment of freshly isolated gastrointestinal tissue at the specified time, and wherein the recorded digital signal is a post- substance delivery signal.)
Gao et al. further teaches a comparison of the baseline signal to the post-substance delivery signal on pg. 76 in Fig. 6, and additionally in the discussion on pg. 77 where Gao et al. states: “The distribution of the data points of diphenhydramine in the [principal component] space exhibited a different pattern from those of the other four seizure-inducing drugs.” (re: clm. 16, …further comprising comparing the baseline signal to the post-substance delivery signal…)
Gao et al. further teaches recording a digital signal within less than an hour after applying drug in the representative recording on pg. 73, Fig. 2 (re: clm. 17, …wherein the recorded digital signal is created within less than one hour after the applying one or more substances for testing…)
Gao et al. does not teach the use of gastrointestinal tissue.
Applying the KSR standard of obviousness to Gao et al. and Liu et al., the examiner concludes that the combination of the drug application method applied to hippocampal slices according to Gao et al. with the intestinal segments as disclosed by Liu et al. represents applying a known technique to a known method with no more than a predictable outcome of a machine learning method applied to pacemaker measurements of gastrointestinal tissue samples prepared for MEA measurements with a baseline signal recorded for at least 5 minutes.
One of ordinary skill in the art of neuroscience would have been a reasonable expectation of success of applying the drug application method of Gao et al. to the tissue isolation method of Liu et al. as Liu et al. provides all the necessary instructions or elements for tissue segmentation in the materials and methods on pg. 176 (sec. 2.2-2.5) Therefore, claims 12-17 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 18, Gao et al. teaches amplitude measurements from local field potential measurements on pg. 72, sec. 3.1 and 3.2, and pg. 73, sec. 3.4, which reads on recording dominant propagation patterns using activation times found at each electrode within a baseline and post-substance delivery period. Gao et al. further teaches in the introduction “seizure-like events (SLE), as sustained synchronous neuronal discharges, are considered hallmarks (2) and are detectable in local field potentials (LFPs).” Gao et al. further teaches on Fig. 6 concentration response curves dimensionally reduced into principal component space, “in which each concentration of each drug is indicated in a single point, and different concentrations of the same drug are connected with a line. Red circles indicate doses that induced [seizure-like-events (SLEs)]. (the same as Figs. 4 and 5).” The recording of sustained synchronous neuronal discharges (SLEs) reads on a change in percentage of a first propagation pattern, and Fig. 6 further discloses a comparison between a baseline and post-substance delivery period via the concentration response curve (re: clm. 18, …wherein the at least one feature from the recorded digital signal comprises one or more of: the change in the percentage of a first, second, or third propagation pattern based on a comparison between the baseline period and the post-substance delivery period.)
Gao et al. does not teach gastrointestinal tissue use.
Applying the KSR standard of obviousness to Gao et al. and Liu et al., the examiner concludes that the digital recording of local field potential with a recorded change in percentage of a propagation pattern according to Gao et al. with the gastrointestinal segment tissue samples as disclosed by Liu et al. represents a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings.
One of ordinary skill in the art of neuroscience and bioinformatics would be motivated to combine the classification method of Gao et al. with the pacemaker activity recording as disclosed by Liu et al. because the combination would lead to a stronger murine gastrointestinal pacemaker potential examination method. In support of this motivation, Liu et al. a desire for future studies examining drug effects on pg. 186. There would have been a reasonable expectation of success because the methods of both arts utilize similar methods to examine, analyze and characterize potentials from freshly isolated murine tissue. Therefore, claim 5 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 19, Gao et al. discloses on pg. 75, Fig. 5 different datasets associated with each individual drug applied in the local field potential measurements, stating “Although the horizontal axes are extremely compressed due to the graphical limitation, this figure indicates that different datasets exhibited different values in the vectors, reflecting the features in the corresponding datasets. Datasets containing SLEs are outlined in red (as in Fig. 4).” Individual datasets per drug read on individual datasets per feature (re: clm. 19, …the substance being a first substance and the database comprising (i) a first unique individual database section configured to store the at least one feature from the recorded digital signal for the first substance and (i) a second unique individual database section configured to store at least one feature from a recorded digital signal for a second substance.)
Gao et al. does not teach gastrointestinal tissue use.
Applying the KSR standard of obviousness to Gao et al. and Liu et al., the examiner concludes that the digital recording of local field potential with a recorded change in percentage of a propagation pattern according to Gao et al. with the gastrointestinal segment tissue samples as disclosed by Liu et al. represents a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings.
One of ordinary skill in the art of neuroscience and bioinformatics would be motivated to combine the Individual datasets according to Gao et al. with the pacemaker activity recording of gastrointestinal tissues as disclosed by Liu et al. because the combination would lead to a stronger murine gastrointestinal pacemaker potential examination method. There would have been a reasonable expectation of success because the methods of both arts utilize similar methods to examine, analyze and characterize potentials from freshly isolated murine tissue. Therefore, claim 19 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 20, Gao et al. teaches the integration of the Caffe library on pg. 72 to define local field potential images for machine learning classification with a SVM, which reads on integrating an individual databases of drug-associated local field potential measurement images with SVM for training, and a deep learning model for image definition (re: clm. 20, building a trained machine learning model based on the first unique individual database section and the second unique individual database section; and integrating the first unique individual database section and the second unique individual database section with at least one other database or training model.)
Gao et al. does not teach gastrointestinal tissue use.
Applying the KSR standard of obviousness to Gao et al. and Liu et al., the examiner concludes that the digital recording of local field potential with a recorded change in percentage of a propagation pattern according to Gao et al. with the gastrointestinal segment tissue samples as disclosed by Liu et al. represents a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings.
One of ordinary skill in the art of neuroscience and bioinformatics would be motivated to combine the machine learning classification method according to Gao et al. with the pacemaker activity recording as disclosed by Liu et al. because the combination would lead to a stronger murine gastrointestinal pacemaker potential examination method. There would have been a reasonable expectation of success because the methods of both arts utilize similar methods to examine, analyze and characterize potentials from freshly isolated murine tissue. Therefore, claim 20 of the applicant’s invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN T STUBBS whose telephone number is (571)272-0340. The examiner can normally be reached M-F 8-5 EST.
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/J.T.S./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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