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 Status
Claims 1-13 and 15-17 are pending and examined herein.
Claims 1-13 and 15-17 are rejected.
Priority
Claims 1-13 and 15-17 are not granted the claim to the benefit of priority to the foreign application EP19204932.8 filed 23 October 2019 because there is no disclosure of “a machine learning scheme configured to learn, based on an initial model of a cellular signal transduction pathway that is affected or targeted by the drug to determine an optimized drug concentration trajectory…”. Thus, the effective filling date of claims 1-13 and 15-17 is 22 October 2020.
Information Disclosure Statement
The information disclosure statement (IDS) was received on 19 April 2022. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings received 19 April 2022 are objected to. The drawings are objected to because Figure 2, 2a, 2b, 10 (with partial views A and B), 13 (with partial views (A-F)) and 14 (with partial views (A-G)) contain partial views that are labeled incorrectly. The MPEP sets out the standards for drawings which shows partial views intended to form one complete view, on one or several sheets, must be identified by the same number followed by a capital letter (37 C.F.R. 1.84(u)(1) in MPEP 608.02(V)). To overcome this objection these figures must be labeled Fig. 2A, Fig. 2B, Fig. 10A, Fig. 10B, Fig. 13A, Fig. 13B, Fig. 13C, Fig. 13D, Fig. 13E, Fig. 13F, Fig. 14A, Fig. 14B, Fig. 14C, Fig. 14D, Fig. 14E, Fig. 14F, and Fig. 14G. Further, the labels which encompass these partial views (Fig. 2, Fig. 10, Fig. 13, and Fig. 14) should be deleted.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
Claim 8 recites “preferably classified as living or dead”. Claim 11 recites “preferably a live-cell imaging device”, “preferably comprises a cover plate”, “preferably is at least partly transparent”, and “preferably comprises a metallic bottom plate”. Claim 12 recites “preferably comprises a perfusion device…”, “preferably comprises a light source”, and “preferably an LED”. The BRI of claims 8, 11, and 12 do not require the limitations that are preferred and these limitations are interpreted as being optional.
112/f Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “the probe manipulation device” and “the processing module” in claims 1, 9, 13, and 16 and “the live cell imaging system” in claims 13 and 16.
The structure of “the probe manipulation device” is shown on page 23 of the instant disclosure and provides that the probe manipulation device comprises a perfusion device and the perfusion device may comprise a fluid pump, fluid conduits and/or fluid connectors for controlling and guiding the flow of the experimental fluid. The structure of “the processing module” is shown on page 7 of the instant disclosure and shows processors. The structure of “the live cell imaging system” is shown on pages 27-28 and figure 1 of the instant disclosure and provides that the live cell imaging device comprises an imaging device, a probe manipulation device, and a control unit implemented on an internal processing unit.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 7, 8, 11-13, and 15-17 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 7 recites “the policy” in line 1 of the claim. There is insufficient antecedent basis for this limitation in the claim. The indefiniteness arises because the claim does not make clear what “the policy” is. This rejection could be overcome by amending claim 7 to depend from claim 6 which provides proper antecedent basis for “the policy”. For the sake of furthering examination, claim 7 will be interpreted as depending from claim 6 which gives proper antecedent basis for “the policy”.
Claim 8 recites “the one or more biological probes” in line 7 of the claim, claim 11 recites “the one or more biological probes” in lines 14-15, and claim 12 recites “the biological probes” and “the one or more biological probes” in lines 2-3, line 4, line 7, and line 9. There is insufficient antecedent basis for these limitations in the claims. The indefiniteness arises because the claim does not make clear what “the one or more biological probes” or “the biological probes” are. Claim 1 recites “a biological probe” but does not give antecedent basis for multiple probes. This rejection could be overcome by amendment of these limitations to “the biological probe”. For the sake of furthering examination, these limitations will be interpreted as “the biological probe”.
Claim 13 recites “the drug” in line 4 of the claim. There is insufficient antecedent basis for this limitation in the claim. The indefiniteness arises because the claim does not make clear what “the drug” is. This rejection could be overcome by amendment of this limitation to “a drug”. For the sake of furthering examination, this limitation will be interpreted as “a drug”. Claim 15 is further rejected by virtue of its dependency on a rejected claim without alleviating the indefiniteness.
Claim 16 recites “wherein the processing module is further configured for controlling the live-cell imaging system so as to implement the method defined in claim 1” which renders the metes and bounds of the claim indefinite. The indefiniteness arises because it is unclear what particular steps the processing module takes for controlling the live-cell imaging system so as to implement the method defined in claim 1 and it is further unclear if the limitation of “so as to implement the method defined in claim 1” is an intended use of the system. Dependent claim 17 is further rejected by virtue of its dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination, this limitation will be interpreted as an intended use of the system.
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 1-13 and 15-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1)
Claims 1-8 fall under the statutory category of a process and claims 9-13 and 15-17 fall under the statutory category of a machine.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. The instant claims further recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Independent claims 1, 9, 13, and 16 recites a mathematical concept of executing a machine learning scheme configured to learn, based on an initial model of a cellular signal transduction pathway that is affected or targeted by the drug, to determine an optimized drug concentration trajectory such that at least one predefined cellular parameter of a biological probe is improved when the drug is applied and fitting based on the at least one measurement value of the at least one predefined cellular parameter of the biological probe, the initial model to obtain a first refined model, and repeating execution of the machine learning scheme based on the first refined model.
Independent claims 1, 9, 13, and 16 recite a mental process of determining at least one measurement value of the at least one predefined cellular parameter of the biological probe form the optical measurement.
Dependent claim 5 recites mathematical concepts of apply a time series of actions A(t) on an environment resulting in observations O(t) and rewards R(t), wherein the environment is defined by the model of a cellular signal transduction pathway that is affected or targeted by the drug. Dependent claim 6 recites a mental process of select a drug concentration trajectory according to a policy. Dependent claim 7 recites a mathematical concept of wherein the policy is iteratively updated in order to maximize the rewards R(t), wherein the rewards R(t) are defined based on an improvement of the at least one predefined cellular parameter of the biological probe effected by applying a drug concentration trajectory selected by the agent to the biological probe. Dependent claim 8 recites a mental process of classifying, counting and/or identifying cells in the corresponding biological probe with respect to the cellular parameter, wherein the cells are preferably classified as living or dead, wherein the cellular parameter comprises one or more of cell number, living cell number, living cell fraction, dead cell number, dead cell fraction, cell proliferation rate, cell death rate, cell division rate, cell differentiation rate, cell exocytosis rate, cell size, cell dimensions, cell adherence area, beating frequency, cell depolarization rate, and drug concentration.
The claims recite process of analyzing/evaluating data as determining at least one measurement value of the at least one predefined cellular parameter of the biological probe form the optical measurement, select a drug based on a policy, classifying, counting and/or identifying cells in the biological probe. The human mind is capable of analyzing/evaluating data. The claims recite mathematical concepts of a machine learning scheme configured to learn an optimized drug concentration trajectory based on a model, fitting an initial model to based on measurements to generate a refined model, apply a time series of actions A(t) on an environment resulting in observations O(t) and rewards R(t), iteratively update the policy to maximize rewards. The instant disclosure provides the machine learning scheme uses numerical data such as drug injection rates as input into the environment which may be described as ordinary differential equations giving a numerical output of cell parameters and a numerical reward for optimization on pages 5 and page 42 and shows that fitting the model to get a refined model can be achieved through least sequence fitting on page 12 which is a series of mathematical calculations. Dependent claims 2, 3, and 4 further limit the mental process/mathematical concept recited in the independent claim but do not change their nature as a mental process/mathematical concept.
(Step 2A Prong 2)
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional elements in claims 1, 9, 13, and 16 of experimentally applying utilizing a probe manipulation device the drug to the biological probe according to the optimized drug concentration trajectory determined by the machine learning scheme and obtaining, by an imaging device, optical measurements of the biological probe, the additional element in claims 10, 13, and 16 of a functional connection for transmitting/receiving information between an image device and a computer, and the additional elements in claims 11 and 12 which further limits the system which require the structure of one or more of a microscopy, a digital camera, a CCD, one or more mirrors, one or more deflectors and/or one or more focusing lenses, a reflective element for directing illumination light to and/or through a biological probe, an illumination light, and a probe manipulation device of perfusion pumps do not integrate the judicial exceptions into a practical application because this is adding insufficient extra solution activity of data gathering. These additional elements amount to insignificant extra solution activity of data gathering because they only interact with the judicial exceptions in a manner that gathers data to be processed by the judicial exceptions.
The additional elements in claims 1, 9, 13, 15-17 of using a generic computer (such as a processor and non-transitory computer readable medium to cause a processor to execute instructions) to perform judicial exceptions and the additional element in claims 6 and 8 of use of neural networks to perform judicial exceptions (which amounts to using a computer to perform abstract ideas and is generally linking the abstract idea to the technological environment of neural networks (MPEP 2106.05(h))) do not integrate the judicial exceptions into a practical application because this is applying the judicial exceptions to a generic computer without an improvement to computer technology. This additional element only interacts with the judicial exceptions by using a generic computer as a tool to perform the judicial exceptions and generally links the abstract idea to the technological environment of neural networks.
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 1-13 and 15-17 are directed to the abstract idea.
(Step 2B)
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The additional elements in claims 1, 9, 13, 15-17 of using a generic computer (such as a processor and non-transitory computer readable medium to cause a processor to execute instructions) to perform judicial exceptions and the additional element in claims 6 and 8 of use of neural networks to perform judicial exceptions (which amounts to using a computer to perform abstract ideas) is conventional as shown by MPEP 2106.05(b) and MPEP 2106.05(d)(II).
The additional elements in claims 1, 9, 13, and 16 of experimentally applying utilizing a probe manipulation device the drug to the biological probe according to the optimized drug concentration trajectory determined by the machine learning scheme and obtaining, by an imaging device, optical measurements of the biological probe, the additional element in claims 10, 13, and 16 of a functional connection for transmitting/receiving information between an image device and a computer, and the additional elements in claims 11 and 12 which further limits the system which require the structure of one or more of a microscopy, a digital camera, a CCD, one or more mirrors, one or more deflectors and/or one or more focusing lenses, a reflective element for directing illumination light to and/or through a biological probe, an illumination light, and a probe manipulation device of perfusion pumps is conventional as shown in col. 7, col. 12, col. 17, and col. 20-21 of Callahan et al. (US 8,548,745 B2; newly cited) and shown in page 59 figure 1 and page 60 left col-right col. of Song et al. (Biosens Bioelectron. 2018 May 1;104:58-64; newly cited).
Thus, the additional elements are not sufficient to amount to significantly more than the judicial exception because they are conventional.
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 1-4, 8-13, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Callahan et al. (US 8,548,745 B2; newly cited) in view of Rehm et al. (Methods. 2013 Jun 1;61(2):165-73; newly cited).
Claim 1 (a method), claim 9 (a system), claim 13 (a system), and claim 16 (a system) are directed to executing, by a processing module (30), a machine learning scheme configured to learn, based on an initial model of a cellular signal transduction pathway that is affected or targeted by the drug, to determine an optimized drug concentration trajectory such that at least one predefined cellular parameter of a biological probe (20) is improved when the drug is applied to the biological probe (20) according to the optimized drug concentration trajectory;
Callahan et al. shows visual-servoing optical microscopy (VSOM) processes and a VSOM system that searches for improved recipes and protocols, that produce cell specific biological consequences, which can be used to achieve a variety of objectives (Callahan et al. col 11). Callahan shows an objective of discovering chemotherapeutic regimes (which includes time-dependent drug dosing/concentration) designed for an individual patient tumor, especially those which may exhibit multidrug resistance (Callahan et al. col 11). Callahan et al. shows learning techniques for optimization of cell specific differences and shows the basis of the learning technique involves a policy, rewards, values, and a model of environment (Callahan et al. col. 39).
experimentally applying, by a probe manipulation device (16), the drug to the biological probe (20) according to the optimized drug concentration trajectory determined by the machine learning scheme; obtaining, by an imaging device (12), optical measurements of the biological probe (20); determining, by the processing module (30), at least one measurement value of the at least one predefined cellular parameter of the biological probe (20) from the optical measurements;
Callahan et al. shows utilizing a stimulating device to provide a stimulus to cells/subcellular components (Callahan et al. col. 7). Callahan et al. shows utilizing a detection device to receiving cellular image data (such as information regarding the physiology or morphology of a cell or cells) (Callahan et al. col. 7). Callahan et al. shows analyzing the cellular image data (Callahan et al. col. 7).
and fitting, by the processing module (30) based on the at least one measurement value of the at least one predefined cellular parameter of the biological probe (20), the initial model to obtain a first refined model, and repeating execution of the machine learning scheme based on the first refined model.
Callahan et al. shows that a history of the applied stimuli and a history of the multiple correlated individual cell responses of hundreds or thousands of cells observed is stored in a database (Callahan et al. col. 11). Callahan et al. shows as this database grows, VSOM assays and protocols gain greater predictive power and become more useful for optimization (Callahan et al. col. 11). Callahan et al. shows learning from cell responses for optimizing difference in cell type with an approach that involves a policy, rewards, values, and a model of environment. Callahan et al. further shows that the database content provides a basis for evaluation and refinement (Callahan et al. col. 39). Callahan et al. shows performing many cycles of test/store/analyze/learn/re-design/re-test cycles which shows an iterative process (Callahan et al. col. 5).
Callahan et al. does not show the machine learning scheme being based on an initial model of a cellular signal transduction pathway.
Like Callahan et al., Rehm et al. show computationally modeling a biological system to simulate the effects of stimuli or perturbations. Rehm et al. shows a mathematical model of an apoptosis signaling network and conducting mathematical simulations of perturbations utilizing this mathematical model (Rehm et al. page 168 and page 169 figure 2).
Claim 2 is directed to wherein an improvement of the at least one predefined cellular parameter of the biological probe (20) comprises maximizing the number of dead cells contained in the biological probe (20) and/or minimizing the number of dividing cells contained in the biological probe (20).
Callahan et al. shows optimizing differences between different cell types. Callahan et al. shows cell type is a classification achieved by any observation that can separate a group of cells into multiple groups of cells and this classification is typically based on repeated observation of relatively rapid physiological responses at the individual single cell level such as cell death (Callahan et al. col. 4-5). Therefore, optimizing difference of a cell type classified based on cell death would be an optimization of cell death for this cell type.
Claim 3 is directed to wherein the step of experimentally applying the drug to the biological probe (20) according to the optimized drug concentration trajectory determined by the machine learning scheme is performed when a first convergence criterion is fulfilled, wherein the first convergence criterion may be defined by a predefined number of learning cycles of the machine learning scheme.
Callahan et al. shows performing an on-line learning technique for dynamic control and estimation of a policy (i.e., a protocol or recipe) to elicit direct responses from two or more different cell types which are then utilized in the experiment (Callahan et al. col. 28). It is noted that the BRI of the claim does not require the first convergence criterion to be a predefined number of learning cycles of the machine learning scheme. The claim recites that the first criterion “may be defined” in that manner which is interpreted as being optional.
Claim 4 is directed to wherein the step of repeating execution of the machine learning scheme based on a refined model is performed until a second convergence criterion is fulfilled, wherein the second convergence criterion may be defined by a predefined number of experimental cycles, or wherein the second convergence criterion may be defined by the determination that a measurement value of the at least one predefined cellular parameter corresponds to a target value of the at least one predefined cellular parameter within a predefined tolerance.
Callahan et al. shows performing many cycles of test/store/analyze/learn/re-design/re-test cycles which shows an iterative process (Callahan et al. col. 5). Callahan et al. further shows these experimental cycles stop after a given time-lapse assay of living cells experiment (Callahan et al. col. 48-49). It is noted that the BRI of the claim does not require the second convergence criterion to be a predefined number of experimental cycles or the determination that a measurement value of a predefined cellular parameter corresponds to a target value within a predefined tolerance. The claim recites that these second criteria “may be defined” in that manner which is interpreted as being optional.
Claim 8 is directed to wherein determining the at least one measurement value of the at least one predefined cellular parameter comprises classifying, counting and/or identifying cells in the corresponding biological probe (20) with respect to the cellular parameter, wherein the cells are preferably classified as living or dead; and/or wherein the cells are classified, counted and/or identified by a neural network algorithm trained for classifying, counting and/or identifying cells of the one or more biological probes (20) based on one or more optical measurements with respect to the cellular parameter; and/or wherein the cellular parameter comprises one or more of cell number, living cell number, living cell fraction, dead cell number, dead cell fraction, cell proliferation rate, cell death rate, cell division rate, cell differentiation rate, cell exocytosis rate, cell endocytosis rate, cell size, cell dimensions, cell adherence area, beating frequency, cell depolarization rate, and drug concentration.
Callahan et al. shows a classification of cell type which is based on repeated observations of relatively rapid physiological responses at the individual single cell level (Callahan et al. col. 4-5). The limitations of the identification of the cells being identified by a neural network and the particular cellular parameters are recited in the alternative form and there exists an embodiment that does not utilize a neural network or use one or more of these cellular parameters.
Claim 10 is directed to further comprising a control unit (18) configured for controlling the operation of the imaging device (12) and the probe manipulation device (16) based on control instructions received over a functional connection (40) from the processing module (30).
Callahan et al. shows a computer for controlling the operation of the imaging device and probe manipulation device (computer-controlled pumps) over a functional connection (Callahan et al. col. 20-21).
Claim 11 is directed to wherein the imaging device (12) is preferably a live-cell imaging device (12) and comprises an optical device (126), in particular one or more of a microscope, a digital camera, a CCD, one or more mirrors, one or more deflectors and/or one or more focusing lenses; and/or wherein the imaging device (12) or the optical device (126) is movable for scanning the one or more probes, and wherein the control unit (18) is further configured for controlling a movement of the imaging device (12) or the optical device (126); and/or wherein the imaging device (12) comprises a housing (121) enclosing at least some of the remaining components of the imaging device (12); wherein the housing (121) preferably comprises a cover plate (122), a bottom plate (132) and at least a lateral wall (124) extending between the cover plate (122) and the bottom plate (132), wherein the cover plate (122) preferably is at least partly transparent and is configured for supporting the one or more biological probes (20) and/or one or more probe carriers containing the one or more biological probes (20), and/or wherein the housing (121) preferably comprises a metallic bottom plate (132).
The claim recite that the imaging device is preferably a live-cell imaging device which is interpreted as an alternate embodiment of the claim and is not required. Further, the claim recites that the imaging device is moveable and comprise a housing in an alternative form and therefore does not require the imaging device to be moveable or comprise a housing. Therefore, there exists an embodiment where the imaging device is not moveable or comprise a housing device. It is interpreted that the claim requires that the imaging device to comprise one or more of a microscope, a digital camera, a CCD, one or more mirrors, one or more deflectors and/or one or more focusing lenses. Callahan et al. shows that the VSOM optical platform is an inverted optical microscope with a digital camera (Callahan et al. col. 20).
Claim 12 is directed to further comprising a reflective element (50) for directing illumination light to and/or through the biological probes (20) and an illumination light source for generating the illumination light for illuminating the one or more biological probes (20) for obtaining the at least one optical measurement by the imaging device (12), wherein the probe manipulation device (16) preferably comprises a perfusion device for perfusing the one or more biological probes (20) with an experimental fluid; and/or wherein the probe manipulation device (16) preferably comprises a light source, preferably an LED, for emitting experimental light on the one or more biological probes (20).
Callahan et al. shows a system where a digital camera is mounted on the microscope and the cells are illuminated using white light and standard transmitted light techniques such as bright field microscopy is used which contains a condenser lens which is a reflective element which directs illumination light to and through a sample (Callahan et al. col. 12). Callahan et al. shows a system computer-controlled perfusion pumps (Callahan et al. col. 17).
Claim 15 is directed to a non-transitory computer readable medium comprising processor executable instruction that, when executed by one or more processors, causes the one or more processors to operate as a processing module (30) according to claim 13. Claim 17 is directed to A non-transitory computer readable medium comprising processor executable instructions that, when executed by one or more processors, causes the one or more processors to operate as a processing module (30) according to claim 16.
Callahan et al. shows computer software for the control of the VSOM system and analysis of the data produced by the system (Callahan et al. col. 28).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date before the effective filling date of the invention to have modified the model of environment in learning time-dependent chemical stimulation processes for optimizing cell specific differences of Callahan et al. with the mathematical model of an apoptosis signaling network of Rehm et al. because this would allow for learning time-dependent chemical stimulation processes based on apoptosis signaling network models which allows for investigation of a system to a pharmacological perturbation (Rehm et al. page 169 right col.). One would have a reasonable expectation of succus because the learning method of Callahan et al. utilizes a model that characterizes changes to stimulations for optimizing protocols while Rehm et al. shows a model of apoptotic signaling in the presence of stimulations.
Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Callahan et al. in view of Rehm et al. as applied to claim 1-4, 8-13, and 15-17 above, and further in view of Arulkumaran et al. (IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 26-38, Nov. 2017; newly cited).
Claim 5 wherein the machine learning scheme includes a reinforcement learning framework including an agent configured to apply a time series of actions A (t) on an environment resulting in observations O(t) and rewards R(t), wherein the environment is defined by the model of a cellular signal transduction pathway that is affected or targeted by the drug. Claim 6 is directed to wherein the agent of the reinforcement learning framework is configured to select drug concentration trajectories according to a policy associated with a neural network. Claim 7 is directed to wherein the policy is iteratively updated in order to maximize the rewards R(t), wherein the rewards R(t) are defined based on an improvement of the at least one predefined cellular parameter of the biological probe (20) effected by applying a drug concentration trajectory selected by the agent to the biological probe (20).
Callahan et al. in view of Rehm et al. as applied to claim 1-4, 8-13, and 15-17 does not explicitly show the particular reinforcement learning framework of time series actions resulting observations as a function of time and rewards as a function of time, or where a policy is associated with a neural network, or where the policy is iteratively updated in order to maximize the rewards R(t).
Like Callahan et al. in view of Rehm et al., Arulkumaran et al. shows a learning framework which includes a policy, rewards, observation values, and a model of an environment. Arulkumaran et al. shows a reinforcement learning framework which implements a time series of actions resulting in a time series of states (or observations) and a time series of rewards (Arulkumaran et al. page 28 figure 2). Arulkumaran et al. shows that the policy is associated with a neural network (Arulkumaran et al. page 28 figure 2). Arulkumaran et al. shows that the policy is iteratively updated to maximize rewards (Arulkumaran et al. page 30 right col.).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have substituted the learning framework of Callahan et al. in view of Rehm et al. with the reinforcement learning framework of Arulkumaran et al. because both these learning frameworks utilize policy, rewards, observation values, and a model of an environment for optimization problems (Arulkumaran et al. page 28 figure 2) and would result in a predictable result of optimizing cell type differences through time-dependent chemical stimulations.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN EDWARD HAYES whose telephone number is (571)272-6165. The examiner can normally be reached M-F 9am-5pm.
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/J.E.H./Examiner, Art Unit 1685
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685