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 .
Election/Restrictions
Applicant’s election of group I in the reply filed on 05 January 2026 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)).
Specification
The use of the term cytoNet, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-7 are directed to a method (process) of evaluating a living system.
Step 2A, Prong One
Regarding claim 1, the recited steps are directed to a mental process of performing concepts in a human mind or by a human using a pen and paper. See MPEP § 2106.04(a)(2)(Ill). The limitation(s) of “(a) measuring in vivo physiologic, behavioral, or physiologic and behavioral characteristics of a living subject to obtain non-invasive data; (b) establishing an in vitro cell model of a cellular network, exposing the in vitro cell model to a condition(s) to model a cellular environment in the living subject, and measuring cellular changes to obtain in vitro model data; (c) transforming the non-invasive data to functional graphs; (d) transforming the in vitro model data to topological, functional, or topological and functional graphs” is/are a process that, as drafted, covers performance of the limitation by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard interpretation. For example, these limitations are nothing more than observing the behavior of a person, drawing a model on a piece of paper of cellular structures, and then creating a graph from these previous steps.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, claim 1 provides no structure for implementing the method steps. The additional limitation of the “neural network” generally could be applied to a piece of software rather than a physical structure. “Neural network” here is recited at a high level of generality and is nothing more than a deep learning neural network that is well known as suggested in par. 0076-0078 of the published application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no structures claimed.
Regarding dependent claims 2-7, the limitations of claim 1 further defines the limitations already indicated as being directed to the abstract idea.
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 2-3 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. Claims 2-3 contain the trademark/trade name “cytoNet”. Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe a software or computer program that “characterize[s] relationships between objects within images and video frames” (according to par. 0056 of the Published Application of the instant case) and, accordingly, the identification/description is indefinite.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4-5 and 7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Howard (U.S. 2019/0381314). Howard discloses (par. 0080-0083) (a) measuring in vivo physiologic and behavioral characteristics of a living subject to obtain non-invasive data (“With multimodal, multiscale data and corresponding analysis methods, it is possible to build the structural and functional mapping from peripheral stimulation, to brain activity, and to behavioral output.”); (b) establishing an in vitro cell model of a cellular network, exposing the in vitro cell model to a condition(s) to model a cellular environment in the living subject, and measuring cellular changes to obtain in vitro model data (“data from in vitro and in vivo may be analyzed using intracellular and extracellular recordings to measure plasticity on a cellular/neuronal level and develop anatomical and functional mapping by using cellular imaging data as well as electrode electrophysiological data. Here neural plasticity refers to changes in spiking activity or dendritic spine density.”); (c) transforming the non-invasive data to functional graphs (par. 0204 and 0206, especially “Graph theoretical analysis with peripheral stimulation, brain activity, and behavioral output. Graph theoretical analysis of structural and functional systems: with multimodal brain activity and big behavioral data, we will focus on graph theoretical approaches to the analysis of complex networks that could provide a powerful new way of quantifying the brain's structural and functional systems, thus to enhance cognitive skill learning in healthy adults by using noninvasive peripheral neurostimulation to promote synaptic plasticity in the brain.”); (d) transforming the in vitro model data to functional graphs (see previous citation); and (e) integrating the non-invasive graphs and the in vitro model graph using a neural network (previous citation and par. 0151).
Regarding claim 4, Howard discloses (par. 0204) the non-invasive data comprises at least bio-electrical patterns (“With neural and behavioral data we will analyze the structural and functional systems using graph theory. Closed-loop neurostimulation will be correlated with EEG recording, behavioral measures and cognitive skill training performance to automatically adjust level of peripheral neurostimulation while the training is underway for real time optimization.”).
Regarding claim 5, Howard discloses (par. 0204) bio-electrical patterns comprise electroencephalograms (“With neural and behavioral data we will analyze the structural and functional systems using graph theory. Closed-loop neurostimulation will be correlated with EEG recording, behavioral measures and cognitive skill training performance to automatically adjust level of peripheral neurostimulation while the training is underway for real time optimization.”).
Regarding claim 7, Howard discloses (par. 0074) non-invasive imaging comprises biomarker analysis of a blood sample (“We may use Calcium imaging to measure changes in neural activity and demonstrate that VNS promotes synaptic plasticity.”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Howard (U.S. 2019/0381314) in view of Mahadevan et al (“cytoNet: Network Analysis of Cell Communities”). Howard discloses the claimed invention except for transforming the non-invasive data and in vitro model data to topological and functional graphs utilizes cytoNet software. Howard does disclose (par. 0088) the use of various algorithms and software for analysis and visualization of data. Mahadevan teaches (lines 134-145) that “[t]he cytoNet workflow can be used to quantitatively study biological pathways involved in cell-cell communication. The combination of visualizing dynamic cell behavior through time-lapse movies and quantifying local cell-cell interactions is particularly powerful. This paradigm can be of great benefit in stem cell biology to evaluate environmental effects on cell fate decisions” and “From the image informatics perspective, cytoNet adds crucial information on local cell density to the suite of metrics that are currently used to characterize individual cells. We illustrated how local network metrics can be used to infer independent effects of cell density and chemical perturbations This workflow can be used to more comprehensively characterize the response of cells to chemical perturbations, which can aid in drug discovery… More broadly, the principle behind cytoNet – treating cell communities as complex ecosystems – will help transition from characterizing cells as independent ‘silos’ to a more holistic approach, where due importance is given to the environment surrounding cells.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use cytoNet as the AI platform for analysis and visualization of data as taught by Mahadevan in the method of Howard in order to effectively model and characterize cellular level information.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Howard (U.S. 2019/0381314) in view of Werblin et al (U.S. 5,717,834). Howard discloses the claimed invention except for the non-invasive imaging comprises retinal scans. Werblin, however, discloses (col. 2, lines 34-65) a bionic sensor for generating models of a retina using a neural network. Werblin and Howard both disclose methods of generating models using physiologic and cell changes. Therefore it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Howard’s brain model with Werblin’s retinal imaging scan in order to provide treatment to visual processing disorders ( “These retinas, and other sensory organs, not only respond in specific ways to the sensory input, but they adjust their responsive properties to match the environment and the objective of the animal. All of these functions and the adjustments in function performed by biological sensors are also implementable in a CNN universal machine through its analogic (analog and logical) programming capability.”).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Howard (U.S. 2019/0381314) in view of Koller et al (U.S. 2021/0366577). Howard discloses the claimed invention except for a CNN-LSTM. Koller, however, discloses (par. 0257) generating machine learning models including CNN and LSTM models. Koller and Howard both disclose systems of gathering and analyzing data using neural network models. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Howard’s topological and/or functional graphs with Koller’s CNN-LSTM models in order to differentiate between artifact differences and intrinsic data for more accurate reporting (par. 0580 of Koller).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEBORAH L MALAMUD whose telephone number is (571)272-2106. The examiner can normally be reached Mon - Fri 1:00-9:30 Eastern.
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/DEBORAH L MALAMUD/Primary Examiner, Art Unit 3792