DETAILED ACTION
This action is responsive to the claims filed on May 30, 2025. Claims 1-22 are under examination.
Claims 12-19 are being interpreted under 35 USC 112(f) as means-plus-function claims.
Claim 11 is rejected under 35 USC 112(d) as failing to further limit claim 1, from which claim 11 depends.
Claims 1-22 are rejected under 35 USC 101 as ineligible.
Claims 1-4 and 6-22 are rejected under 35 USC 103 as obvious over Mela and Sinko.
Claim 5 is rejected under 35 USC 103 as obvious over Mela, Sinko, and Solas.
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 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 limitation(s) is/are:
Claim 12: optical observation system, determination device, simulation device, evaluation device
Claim 13: optical observation system, evaluation device
Claim 14: optical observation system, simulation device
Claim 15: optical observation system, simulation device
Claim 16: optical observation system, evaluation device
Claim 17: optical observation system, optimization unit
Claim 18: optical observation system, control unit, optimization unit
Claim 19: optical observation system, compensation factor determination 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(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 11 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 11 recites the method of claim 1 and no other elements with patentable weight. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed subject matter is directed to an abstract idea without significantly more. The claims recite mental processes that are capable of being performed in the mind and/or with the aid of pen and paper, abstract ideas.
Independent Claims
Claim 12 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite mental processes, which are abstract ideas.
[…] observing a fluorescence intensity of fluorescence radiation of a fluorescent dye in an observation object, the observation object including object regions that differ from one another in terms of depth and/or orientation, the optical observation system being configured to observe the fluorescence intensity, provided that the fluorescence radiation has a minimum intensity (Mental Process – The observation/determination of fluorescence and/or its intensity is practically performable in the mind or with the aid of pen and paper, so it is an evaluation, a mental process, an abstract idea.)
[…] determine a parameter value of at least one parameter which influences the observation of the fluorescence intensity; (Mental Process – Determining a parameter value of a parameter that influences the observation of fluorescence intensity is practically performable in the mind or with the aid of pen and paper, so it is an evaluation, a mental process, an abstract idea.)
[…] simulate the fluorescence intensity expected for each of the object regions based on the determined parameter value of the at least one parameter and a model of an influence of the at least one parameter on the fluorescence intensity; and (Mental Process – Simulation of fluorescence intensity, using a model with parameter values, is practically performable in the mind or with the aid of pen and paper, so it is an evaluation, a mental process, an abstract idea.)
[…] for a minimum concentration of the fluorescent dye that is predefined within a scope of the simulation, determine the fluorescence intensity expected with the minimum concentration for each object region based on the simulation. (Mental Process – Determining fluorescence intensity of a specified dye concentration based on a model is practically performable in the mind or with the aid of pen and paper, so it is an evaluation, a mental process, an abstract idea.)
Regarding claim 1, claim 1 is a process that recites the method steps of claim 12, so it recites an abstract idea.
Claims 20-22 (a process, machine, and machine, respectively) also recite similar method features to claim 12, aside from substituting the determine step with a receive step to receive the same data that is determined in claim 12. The simulate and second determine steps of claims 20-22, as well as the observation operation in the preamble, that are analogous to those of claim 12, recite abstract ideas for the same reasons as the analogous elements of claim 12.
Claims 1, 12, and 20-22 recite mental processes, which are abstract ideas.
Claims 1, 12, and 20-22 recite an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Application?
No.
Additional limitations:
Claim 1
[…] computer implemented […]
Claim 12
[…] optical observation system […]
a determination device configured to […]
a simulation device configured to […]
an evaluation device configured to […]
Claim 20
[…] computer-implemented […]
[…] optical observation system […]
Claim 21
A non-transitory computer-readable storage medium on which a computer program for preparing […]
[…] optical observation system […]
Claim 22
A data processing unit for […]
[…] optical observation system […]
a processor; and a memory on which a computer program including instructions is stored which, when executed by the processor, cause the processor to:
These are generic computing elements recited at a high level, which, under MPEP 2106.05(f), fail to integrate the abstract idea into a practical application.
Claims 20-22
receive/ing or retrieve/ing a parameter value of at least one parameter which influences the observation of the fluorescence intensity;
This is mere data gathering akin to the MPEP 2106.05(g) examples: “i. Performing clinical tests on individuals to obtain input for an equation” “v. Consulting and updating an activity log, Ultramercial,” “i. Limiting a database index to XML tags” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.” Accordingly, this is extra-solution activity and fails to integrate the abstract ideas into a practical application.
Also, the nature of the data and the context merely limit the abstract idea to a technological environment, which, under MPEP 2106.05(h), fail to integrate the abstract idea into a practical application.
Claims 1, 12, and 20-22 fail to recite any additional limitations that integrate the abstract idea into a practical application.
Claims 1, 12, and 20-22 are directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
Additional limitations:
Claim 1
[…] computer implemented […]
Claim 12
[…] optical observation system […]
a determination device configured to […]
a simulation device configured to […]
an evaluation device configured to […]
Claim 20
[…] computer-implemented […]
[…] optical observation system […]
Claim 21
A non-transitory computer-readable storage medium on which a computer program for preparing […]
[…] optical observation system […]
Claim 22
A data processing unit for […]
[…] optical observation system […]
a processor; and a memory on which a computer program including instructions is stored which, when executed by the processor, cause the processor to:
These are generic computing elements recited at a high level, which, under MPEP 2106.05(f), fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
Claims 20-22
receive/ing or retrieve/ing a parameter value of at least one parameter which influences the observation of the fluorescence intensity;
This is well-understood, routine, and conventional (WURC) activity akin to the MPEP 2106.05(d) examples: “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “v. Electronically scanning or extracting data from a physical document” “i. Determining the level of a biomarker in blood by any means “ “v. Analyzing DNA to provide sequence information or detect allelic variants” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price.” Because this limitation is WURC and insignificant extra-solution activity, under MPEP 2106.05(d) and 2106.05(g), the limitation fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
Also, the nature of the data and the context merely limit the abstract idea to a technological environment, which, under MPEP 2106.05(h), fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
The additional limitations fail to combine with the other elements of claims 1, 12, 20-22 to provide significantly more than the abstract idea that would confer an inventive concept.
Claims 1, 12, and 20-22 are ineligible.
Dependent Claims
Dependent claims 2-11 and 13-19 are also ineligible for at least the following reasons.
Claims 2 and 13
wherein the evaluation device is configured to
This is a generic computing element recited at a high level, so it fails to confer eligibility under MPEP 2106.05(f).
carry out a check, for each object region, as to whether the fluorescence intensity is sufficient to be able to be detected by the optical observation system with a given sensitivity thereof.
Checking intensity readings against a threshold is practically performable in the mind or with the aid of pen and paper, so it is an evaluation, a mental process, an abstract idea.
Claims 2 and 13 fail to provide any additional limitations that confer eligibility.
Claims 2 and 13 are ineligible.
Claims 3 and 14
wherein information about a depth distribution of the object regions and/or information about an orientation of the object regions is used within the simulation.
This is an element of the simulation determination from the model, so it is part of the abstract idea, conferring no additional limitations.
Should it be found otherwise, this is mere data gathering and WURC for at least the same reasons as the receiving steps.
Should it be found otherwise, this merely describes the nature of what the data represents, which merely limits the abstract idea to a technological environment and fails to confer eligibility under MPEP 2106.05(h).
Claims 3 and 14 fail to provide any additional limitations that confer eligibility.
Claims 3 and 14 are ineligible.
Claims 4 and 15
wherein at least the parameter value of one of the following parameters is determined and taken into consideration in the simulation: a distance of an optical observation device of the observation system from the object regions, an orientation of the optical observation device in relation to the object regions, a zoom setting of the optical observation device, a front focal distance of the optical observation device, a stop setting of the optical observation device, a gain of an image sensor provided in the optical observation device, an exposure duration of the image sensor provided in the optical observation device, nonlinearities of the image sensor provided in the optical observation device, a distance of an illumination system from the object regions, an orientation of the illumination system of the observation system in relation to the object regions, an intensity of an illumination light source of the illumination system, a spectral intensity distribution of an the illumination light source, a zoom setting of an illumination zoom, and a position of an illumination stop.
This is an element of the simulation determination from the model, so it is part of the abstract idea, conferring no additional limitations.
Should it be found otherwise, this is mere data gathering and WURC for at least the same reasons as the receiving steps.
Should it be found otherwise, this merely describes the nature of what the data represents, which merely limits the abstract idea to a technological environment and fails to confer eligibility under MPEP 2106.05(h).
Claims 4 and 15 fail to provide any additional limitations that confer eligibility.
Claims 4 and 15 are ineligible.
Claim 5
wherein the spectral intensity distribution of the illumination light source is determined based on a value of a service life counter of the illumination source, and wherein a nominally set intensity of the spectral intensity distribution is based on a degradation model of the illumination source.
This is an element of the simulation determination from the model, so it is part of the abstract idea, conferring no additional limitations.
Should it be found otherwise, this is mere data gathering and WURC for at least the same reasons as the receiving steps.
Should it be found otherwise, this merely describes the nature of what the data represents, which merely limits the abstract idea to a technological environment and fails to confer eligibility under MPEP 2106.05(h).
Claim 5 fails to provide any additional limitations that confer eligibility.
Claim 5 is ineligible.
Claim 6
outputting an alert when a check reveals that an expected fluorescence intensity determined for the minimum concentration of the fluorescent dye is not sufficient, in each object region, to be able to be detected by the optical observation system with a given sensitivity the optical observation system.
The determination to set off the alarm is practically performable in the mind or with the aid of pen and paper, so it is an evaluation, a mental process, an abstract idea.
The outputting is insignificant extra-solution activity (e.g., similar to MPEP 2106.05(g) examples:” a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent” “v. Consulting and updating an activity log” “ii. Printing or downloading generated menus”) and WURC (e.g., similar to MPEP 2106.05(d) examples: “i. Receiving or transmitting data over a network” “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory”), so it fails to confer eligibility under MPEP 2106.05(g) and MPEP 2106.05(d).
Further, outputting is a generic computing operation (e.g., similar to MPEP 2106.05(f) examples: “The claims were found to be directed to the abstract idea of "collecting, displaying, and manipulating data." “ “Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished”), so it fails to confer eligibility under MPEP 2106.05(f).
Claim 6 fails to provide any additional limitations that confer eligibility.
Claim 6 is ineligible.
Claim 7
generating a graphical display, which displays the object regions in which an expected fluorescence intensity is sufficient to be detected by the optical observation system with a given sensitivity of the optical observation system, and the object regions in which the expected fluorescence intensity is not sufficient to be detected by the optical observation system with the given sensitivity.
The display generation is insignificant extra-solution activity (e.g., similar to MPEP 2106.05(g) examples:” a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent” “v. Consulting and updating an activity log” “ii. Printing or downloading generated menus”) and WURC (e.g., similar to MPEP 2106.05(d) examples: “i. Receiving or transmitting data over a network” “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory”), so it fails to confer eligibility under MPEP 2106.05(g) and MPEP 2106.05(d).
Further, display generation is a generic computing operation (e.g., similar to the MPEP 2106.05(f) example: “The claims were found to be directed to the abstract idea of "collecting, displaying, and manipulating data." 850 F.3d at 1340,” so it fails to confer eligibility under MPEP 2106.05(f).
Claim 7 fails to provide any additional limitations that confer eligibility.
Claim 7 is ineligible.
Claims 8 and 17
based on the simulation, determining an improved parameter value for the at least one parameter is determined such that an expected fluorescence intensity simulated with the improved parameter value is sufficient, in as many object regions as possible, to be detected by the optical observation system with a given sensitivity of the optical observation system.
Determining parameter values based on known data is practically performable in the mind or with the aid of pen and paper, so it is an evaluation, a mental process, an abstract idea.
Claim 8 fails to provide any additional limitations that confer eligibility.
Claim 16 recites features similar to claim 8 and fails to confer eligibility for at least the same reasons.
Claims 8 and 17 are ineligible.
Claims 9 and 18
wherein a current parameter value of the at least one parameter is set automatically to the improved parameter value.
Switching parameter values is practically performable in the mind or with the aid of pen and paper, so it is an evaluation, a mental process, an abstract idea.
Claim 8 fails to provide any additional limitations that confer eligibility.
Claim 16 recites features similar to claim 8 and fails to confer eligibility for at least the same reasons.
Claims 8 and 17 are ineligible.
Claim 10
carrying out at least one reference measurement with a reference concentration of the fluorescent dye with a reference parameter value for the at least one parameter which influences the observation of the fluorescence intensity, to obtain a reference value for the fluorescence intensity at the reference concentration of the fluorescent dye;
This is mere data gathering (e.g., similar to MPEP 2106.05(g) examples: “) and WURC (e.g., similar to the MPEP 2106.05(d) examples: “i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information” “ii. A general method of screening emails on a generic computer without any limitations that addressed the issues of shrinking the protection gap and mooting the volume problem”), so it fails to confer eligibility under MPEP 2106.05(g) and 2106.05(d).
Claim 10 fails to provide any additional limitations that confer eligibility.
Claim 10 is ineligible.
Claim 11
A method for observing a fluorescence intensity of fluorescence radiation of a fluorescent dye in an observation object with an optical observation system, the observation object including object regions that differ from one another in terms of depth and/or orientation, the optical observation system being configured to observe fluorescence radiation, provided that the fluorescence radiation has a minimum intensity, the method comprising: the method for preparing the observation of a fluorescence intensity of fluorescence radiation of a fluorescent dye in an observation object as claimed in claim 1.
As indicated with respect to the 112(d) rejection, this fails to provide any limitation beyond claim 1, so it is ineligible for at least the same reasons as claim 1.
Claim 11 fails to provide any additional limitations that confer eligibility.
Claim 11 is ineligible.
Claim 16
wherein the evaluation device is configured to
This is a generic computing component recited high at a high level that fails to confer eligibility under MPEP 2106.05(f).
generate a graphical display that displays the object regions in which the fluorescence intensity is sufficient to be detected by the optical observation system with a given sensitivity thereof.
The display generation is insignificant extra-solution activity (e.g., similar to MPEP 2106.05(g) examples:” a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent” “v. Consulting and updating an activity log” “ii. Printing or downloading generated menus”) and WURC (e.g., similar to MPEP 2106.05(d) examples: “i. Receiving or transmitting data over a network” “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory”), so it fails to confer eligibility under MPEP 2106.05(g) and MPEP 2106.05(d).
Further, display generation is a generic computing operation (e.g., similar to the MPEP 2106.05(f) example: “The claims were found to be directed to the abstract idea of "collecting, displaying, and manipulating data." 850 F.3d at 1340,” so it fails to confer eligibility under MPEP 2106.05(f).
Claim 16 fails to provide any additional limitations that confer eligibility.
Claim 16 is ineligible.
Claim 19
further comprising: a compensation factor determination unit configured to […]
This is a generic computer component recited at a high level, so it fails to confer eligibility under MPEP 2106.05(f).
determine a compensation factor by which, in a digital image recorded by an image sensor a change in the fluorescence intensity caused by a deviation of the parameter value of the at least one parameter which influences the observation of the fluorescence intensity from a reference parameter value can be compensated, determine the compensation factor based on at least one reference value for the fluorescence intensity as determined for a reference concentration of the fluorescent dye and for a reference parameter value and of a simulation of the fluorescence intensity expected for each of the object regions, and wherein, in the simulation, a change in the fluorescence intensity in comparison to the reference intensity is determined for a deviation of the parameter value of the at least one parameter which influences the observation of the fluorescence intensity from the reference parameter value.
These are all determinations that are practically performable in the mind or with the aid of pen and paper, so they are evaluations, mental processes, abstract ideas.
Claim 19 fails to provide any additional limitations that confer eligibility.
Claim 19 is ineligible.
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.
Claims 1- : Mela and Sinko
Claim(s) 1- are rejected under 35 U.S.C. 103 as being unpatentable over NPL: “Comprehensive characterization method for a fluorescence imaging system” by Mela et al. (Mela) in view of NPL: “TestSTORM: Simulator for optimizing sample labeling and image acquisition in localization based super-resolution microscopy” by Sinko et al (Sinko).
Claims 1, 12, and 20-22
Regarding claim 12, Mela teaches:
An optical observation system for observing a fluorescence intensity of fluorescence radiation of a fluorescent dye in an observation object, the observation object including object regions that differ from one another in terms of depth and/or orientation, the optical observation system being configured to observe the fluorescence intensity, provided that the fluorescence radiation has a minimum intensity, the optical observation system comprising: (Mela Page 8237, Introduction “Optical imaging characterization techniques, including fluorescence detection sensitivity tests, are standard procedure in fluorescence imaging systems during prototype development and preclinical evaluation. Fluorescence sensitivity, spatial resolution, and contrast tests can help determine the system’s optimal performance parameters. Additionally, fluorescence testing using tissue phantoms conducted under realistic environmental conditions, such as ambient lighting, can provide useful preclinical data on system performance. Fluorescence detection sensitivity tests commonly employ two methods of evaluation. The first is referred to herein as the dark room test, wherein the optimal fluorescence detection limits, including minimum detectable dye concentration in solution and dynamic range of the system, can be determined [1–5].” – An optical observation system for observing fluorescence of fluorophores in tissues with instruments with minimum fluorescence intensity observability. Page 8238, Right Column, Second Paragraph “In this study, a fluorescence imaging system was characterized for fluorescence detection using both dark room studies as well as tissue phantoms. Phantom imaging of fluorescent inclusions was conducted for varying fluorescent node volume and depth in the tissue.” – The tissue under observation has differing depths and orientations.
[…] device [..] (The claimed determination, simulation, and evaluation devices are elements of the computer used in the reference for computation, Mela Page 8238 Computation “The system was outfitted with 32GB 2133 MHz DDR4 RAM and a 500 GB SSD, while the CPU operated an Intel Core i7-6770HQ processor at 2.6 MHz per core with an Intel Iris Pro 580 integrated graphic card. Ubuntu 16.04 LTE was installed as the primary operating system. Camera connections were made via USB 3.0 ports, and a display was connected via HDMI. Our custom code facilitated camera capture and processing of input imaging frames as well as output display to a stand-alone monitor. Control and processing commands were programmed to operate in real-time using the Python language.” – This is the device element of the claimed “determination device,” “simulation device,” and “evaluation device.”)
a determination device configured to determine a parameter value of at least one parameter which influences the observation of the fluorescence intensity; (Mela Page 8239, C. Fluorescence Detection Sensitivity “Within each study, each individual dye dilution was imaged independently, positioning the camera vertically over the target, perpendicular to the table top (Fig. 1). The excitation light source was positioned at a constant 60 cm distance from the fluorescent dilution and at an incident angle of 15° from the vertical (or 75° up from the horizontal plane of the bench top) to avoid light obstruction by the camera. A range of excitation intensities (4, 2, 1, 0.5, and 0.25 mW∕cm2) was used to quantify the effect of illumination power on fluorescence detection sensitivity. Excitation intensity was measured at the fluorescent target site using a sensitive photodiode (PM16-120 ThorLabs, NJ, USA), which was zeroed at ambient room light prior to testing. Additionally, each experiment was conducted over three working distances (20, 40, and 60 cm), measured from the end of the camera lens to the top of the fluorescent target.” – Parameters including excitation intensities, working distances, and predefined concentrations are determined and used in the tests. These parameters influence the observations of the fluorescence.)
a device configured to [determine] the fluorescence intensity expected for each of the object regions based on the determined parameter value of the at least one parameter and a model of an influence of the at least one parameter on the fluorescence intensity; and (Mela Page 8241, FIG. 1 (shown below) – The charts show a model that is based on excitation intensity, dye concentration, and tissue depth to yield a signal to background ratio (SBR) of 2.0. Page 8240, Right Column, First Paragraph “Intensity readings of each fluorescent inclusion were recorded and averaged over five separately prepared series, and SBRs were calculated, using the detected intensity of a 50 nM inclusion as the background reference.” – Intensities were determined based on these factors.)
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an evaluation device configured to, for a minimum concentration of the fluorescent dye that is predefined within a scope of the simulation, determine the fluorescence intensity expected with the minimum concentration for each object region based on the simulation. (Mela Page 8243, FIG. 4 (shown below) – The charts show a model that is based on excitation intensity, dye concentration, and tissue depth to yield a signal to background ratio (SBR) of 2.0. Page 8240, Right Column, First Paragraph “Intensity readings of each fluorescent inclusion were recorded and averaged over five separately prepared series, and SBRs were calculated, using the detected intensity of a 50 nM inclusion as the background reference.” – Intensities were represented as SBRs, which the models set to a fixed value of 2 for the models presented.)
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Mela teaches the creation of a model to predict intensities of fluorescence based on different settings of a particular microscopic system while varying parameters for measurement, but does not appear to teach, but Mela in view of Sinko teaches:
a simulation device configured to simulate the fluorescence intensity expected for each of the object regions based on the determined parameter value of the at least one parameter and a model of an influence of the at least one parameter on the fluorescence intensity; and (Sinko Abstract “Localization-based super-resolution microscopy image quality depends on several factors such as dye choice and labeling strategy, microscope quality and user-defined parameters such as frame rate and number as well as the image processing algorithm. Experimental optimization of these parameters can be time-consuming and expensive so we present TestSTORM, a simulator that can be used to optimize these steps. TestSTORM users can select from among four different structures with specific patterns, dye and acquisition parameters. Example results are shown and the results of the vesicle pattern are compared with experimental data. Moreover, image stacks can be generated for further evaluation using localization algorithms, offering a tool for further software developments.” – This is a simulator that simulates real world fluorescence determinations using various algorithms, motivating the use of the models presented in Mela for the specific system in Mela)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the experimental models of Mela by the simulator of Sinko because the person of ordinary skill in the art would be motivated by the aim of Mela to standardize the modeling of fluorescence imaging systems for fluorescence characterization to look to Sinko, which provides a standardizing simulator that optimizes parameters of a fluorescence imaging system. (Mela Abstract “Fluorescence imaging systems are regularly characterized by their ability to distinguish varying concentrations of fluorophores in a solution or tissue phantom. However, there is inadequate standardization in the field for fluorescence characterization. In this study, we characterize a fluorescence imaging system developed for pathogen detection, regarding its ability to detect a near-infrared dye. During this process, we vary a number of key factors involved in fluorescence imaging, such as the excitation intensity, background level, working distance, volume of fluorescent solution, and type of container used to hold the fluorescent solution. We then analyze the results, with statistical rigor, to determine which factors result in significant changes in fluorescence detection. Notably, we found that using different types of containers to hold the dye solution can have a significant impact on fluorescence detection, while the effects of working distance and excitation intensity can vary. Based on our findings, greater standardization, or at least more thorough reporting of the experimental setup, is recommended to researchers when publishing characterization results of new imaging systems.”; Sinko Abstract “Localization-based super-resolution microscopy image quality depends on several factors such as dye choice and labeling strategy, microscope quality and user-defined parameters such as frame rate and number as well as the image processing algorithm. Experimental optimization of these parameters can be time-consuming and expensive so we present TestSTORM, a simulator that can be used to optimize these steps. TestSTORM users can select from among four different structures with specific patterns, dye and acquisition parameters. Example results are shown and the results of the vesicle pattern are compared with experimental data. Moreover, image stacks can be generated for further evaluation using localization algorithms, offering a tool for further software developments.”)
Claim 1 recites the method steps of claim 12, so claim 1 is rejected for at least the same reasons as claim 12.
Claims 20-22 (a process, machine, and machine, respectively) also recite similar method features to claim 12, aside from substituting the determine step with a receive step to receive the same data that is determined in claim 12. The simulate and second determine steps of claims 20-22, as well as the observation operation in the preamble, that are analogous to those of claim 12, are rejected for the same reasons as the analogous elements of claim 12.
Claims 2 and 13
Regarding claim 2, Mela in view of Sinko teaches the features of claim 1, and further teaches:
carrying out a check, for each object region to determine whether the fluorescence intensity is sufficient to be detected by the optical observation system with a given sensitivity of the optical observation system. (Mela Page 8237, A. Fluorescence Imaging “Fluorescence detection sensitivity tests commonly employ two methods of evaluation. The first is referred to herein as the dark room test, wherein the optimal fluorescence detection limits, including minimum detectable dye concentration in solution and dynamic range of the system, can be determined [1–5].” – A check is carried out to determine whether the fluorescence intensity is sufficient to be detected by the optical observation system with a given sensitivity of the optical observation system)
Claim 13 recites features similar to those of claim 2, so claim 13 is rejected for at least the same reasons as claim 2.
Claims 3 and 14
Regarding claim 3, Mela in view of Sinko teaches the features of claim 1, and further teaches:
wherein information about a depth distribution of the object regions and/or information about an orientation of the object regions is used within the simulation. (Mela Page 8238, Right Column, Second Paragraph “In this study, a fluorescence imaging system was characterized for fluorescence detection using both dark room studies as well as tissue phantoms. Phantom imaging of fluorescent inclusions was conducted for varying fluorescent node volume and depth in the tissue.” – The tissue has differing depths and orientations for the model that would be used in the Sinko simulation.)
Claim 14 recites features similar to those of claim 3, so claim 14 is rejected for at least the same reasons as claim 3.
Claims 4 and 15
Regarding claim 4, Mela in view of Sinko teaches the features of claim 1, and further teaches:
wherein at least the parameter value of one of the following parameters is determined and taken into consideration in the simulation: a distance of an optical observation device of the observation system from the object regions, an orientation of the optical observation device in relation to the object regions, a zoom setting of the optical observation device, a front focal distance of the optical observation device, a stop setting of the optical observation device, a gain of an image sensor provided in the optical observation device, an exposure duration of the image sensor provided in the optical observation device, nonlinearities of the image sensor provided in the optical observation device, a distance of an illumination system from the object regions, an orientation of the illumination system of the observation system in relation to the object regions, an intensity of an illumination light source of the illumination system, a spectral intensity distribution of an the illumination light source, a zoom setting of an illumination zoom, and a position of an illumination stop. (Mela Page 8241, FIG. 1 (shown below) – Intensities were determined based on these factors, including emission intensity and working distance.)
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Claim 15 recites features similar to those of claim 4, so claim 15 is rejected for at least the same reasons as claim 4.
Claim 6
Regarding claim 6, Mela in view of Sinko teaches the features of claim 1, and further teaches:
outputting an alert when a check reveals that an expected fluorescence intensity determined for the minimum concentration of the fluorescent dye is not sufficient, in each object region, to be able to be detected by the optical observation system with a given sensitivity the optical observation system. (Mela Page 8237, A. Fluorescence Imaging “Fluorescence detection sensitivity tests commonly employ two methods of evaluation. The first is referred to herein as the dark room test, wherein the optimal fluorescence detection limits, including minimum detectable dye concentration in solution and dynamic range of the system, can be determined [1–5].” – The lack of fluorescence alerts the experimenter that the fluorescence is insufficient.)
Claim 7
Regarding claim 7, Mela in view of Sinko teaches the features of claim 1, and further teaches:
further comprising: generating a graphical display, which displays the object regions in which an expected fluorescence intensity is sufficient to be detected by the optical observation system with a given sensitivity of the optical observation system, and the object regions in which the expected fluorescence intensity is not sufficient to be detected by the optical observation system with the given sensitivity. (Sinko Page 2, Fig. 4 (shown below) – This displays object regions regardless of whether their fluorescence is sufficient.)
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Claims 8 and 17
Regarding claim 8, Mela in view of Sinko teaches the features of claim 1, and further teaches:
further comprising: based on the simulation, determining an improved parameter value for the at least one parameter such that an expected fluorescence intensity simulated with the improved parameter value is sufficient, in as many object regions as possible, to be detected by the optical observation system with a given sensitivity of the optical observation system. (Sinko Page 6, Last Paragraph- Page 7, First Paragraph “First we demonstrated the z dependence of resolution in a localization based microscope with the star pattern [Fig. 4]. The defocus of the pattern was changed from 0 nm to 400 nm. The depth of field (DOF = 2λ/NA2) was 549 nm. 500 molecules were located in each arm, randomly attached by 7 nm long linkers. The radius of the pattern, or equivalently the length of the arms, was 3200 nm. The frames were captured at a rate of 20 frames/s.” Also Fig. 4 on Page 7 (shown below) – This shows how varying the defocus parameter effects the image, showing less defocusing is better.)
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Claim 17 recites substantially the same features as claim 8 and is rejected for substantially the same reasons.
Claims 9 and 18
Regarding claim 18, Mela in view of Sinko teaches the features of claim 17, and further teaches:
a control unit configured to control the optical observation system, wherein the control unit is connected to the optimization unit to receive the improved parameter value and is configured to set the at least one parameter to the improved parameter value. (Sinko Page 10, Table 2 (shown below – For defocus issues, the solution is autofocus, a controller.)
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Claim 9 recites substantially the same features as claim 18 and is rejected for substantially the same reasons.
Claims 10 and 19
Regarding claim 19, Mela in view of Sinko teaches the features of claim 12, and further teaches:
a compensation factor determination unit configured to determine a compensation factor by which, in a digital image recorded by an image sensor a change in the fluorescence intensity caused by a deviation of the parameter value of the at least one parameter which influences the observation of the fluorescence intensity from a reference parameter value can be compensated, wherein the compensation factor determination unit is further configured to determine the compensation factor based on at least one reference value for the fluorescence intensity as determined for a reference concentration of the fluorescent dye and for a reference parameter value and of a simulation of the fluorescence intensity expected for each of the object regions, and wherein, in the simulation, a change in the fluorescence intensity in comparison to the reference intensity is determined for a deviation of the parameter value of the at least one parameter which influences the observation of the fluorescence intensity from the reference parameter value. (Sinko Page 10, Table 2 (shown below – For defocus issues, the solution is autofocus. The defocus setting is first set, a resulting unfocused image is produced, then autofocus corrects it.)
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Claim 10 recites substantially the same features as claim 19 and is rejected for substantially the same reasons.
Claim 11
Regarding claim 11, claim 11 recites substantially the same features as claim 1 (see 35 USC 112(d) rejection) and is rejected for the same reasons as claim 1.
Claim 16
Regarding claim 16, Mela in view of Sinko teaches the features of claim 1, and further teaches:
wherein the evaluation device is configured to generate a graphical display that displays the object regions in which the fluorescence intensity is sufficient to be detected by the optical observation system with a given sensitivity thereof. (Sinko Page 2, Fig. 4 (shown below) – This displays object regions regardless of whether their fluorescence is sufficient.)
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Claim 5: Mela, Sinko, and Solas
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over NPL: “Comprehensive characterization method for a fluorescence imaging system” by Mela et al. (Mela) in view of NPL: “TestSTORM: Simulator for optimizing sample labeling and image acquisition in localization based super-resolution microscopy” by Sinko et al (Sinko) and NPL: “Optical degradation impact on the spectral performance of photovoltaic technology” by Fernandez-Solas et al. (Solas).
Claim 5
Regarding claim 5, Mela in view of Sinko teaches the features of claim 4, but does not appear to explicitly teach, but Mela in view of Sinko and Solas teaches:
wherein the spectral intensity distribution of the illumination light source is determined based on a value of a service life counter of the illumination source, and wherein a nominally set intensity of the spectral intensity distribution is based on a degradation model of the illumination source. (Solas Page 4, 3. Degradation mechanisms “Furthermore, in this review, aging has been considered as an independent degradation mechanism itself and it has also been studied. As a consequence, a depth search on current literature has been performed, with a special emphasis on the spectral effects.” – The affects on the light of age is determined. Page 11, Spectral impact analysis “A detailed analysis of the impact of two of the degradation mechanisms mentioned before (soiling and discoloration) on the spectral response of PV modules of six different technologies is presented in this section. The lack of spectral data on the remaining aforementioned degradation mechanisms does not make an in-depth study possible.” – The effect includes the spectral intensity. This will be used in the simulation of Sinko to determine the emission settings for the simulation of Sinko.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the simulation of Sinko by the light source degradation considerations of Solas because the person of ordinary skill in the art would be motivated by the aim of Sinko to simulate real-world microscopy imaging for fluorescence to avoid expensive experimentation, to look to Solas, which presents an innovative procedure to quantify the spectral impact of degradation of light emitting sources. (Sinko Abstract “Experimental optimization of these parameters can be time-consuming and expensive so we present TestSTORM, a simulator that can be used to optimize these steps. TestSTORM users can select from among four different structures with specific patterns, dye and acquisition parameters. Example results are shown and the results of the vesicle pattern are compared with experimental data. Moreover, image stacks can be generated for further evaluation using localization algorithms, offering a tool for further software developments.” Page 10, Conclusion “We developed a program for modeling the whole imaging procedure in an optical fluorescence localization microscope. Four”; Solas Abstract “The impact on the spectral performance of PV modules is evaluated by considering the variations of the short- circuit current since this is the most widely used parameter to study the spectral impact in outdoors. Some of the most common types of optical degradation affecting the performance of PV modules worldwide, such as discoloration, delamination, aging and soiling have been addressed. Due to the widely documented impact of soiling on the spectral response of modules, this mechanism has been specially highlighted in this study. On the other hand, most of the publications analysed in this review report optical degradation in PV modules with polymeric encapsulant materials. Furthermore, an innovative procedure to quantify the spectral impact of degradation on PV devices is presented. This has been used to analyse the impact of two particular cases of degradation due to soiling and discoloration on the spectral response of different PV technologies.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL: “Investigating dye performance and crosstalk in fluorescence enabled bioimaging using a model system” by Arppe et al. (Teaches modeling of bioimaging with fluorophores)
NPL: “Numerical Simulation of the Photobleaching Process in Laser-Induced Fluorescence Photobleaching Anemometer” by Chen et al. (Teaches simulation of photobleaching in fluorescent imaging)
NPL: “Quantification of fluorophore concentration in tissue-simulating media by fluorescence measurements with a single optical fiber” by Diamond et al. (Teaches modeling fluorescent responses to different fluorophore concentrations)
NPL: “The Development of Fluorescence Intensity Standards” by Gaigalas et al. (Teaches procedures for standardizing fluorescent responses to fluorophores)
NPL: “Development and application of advanced single molecule fluorescence methods using PIE-MFD” by Kugel (PhD thesis with extensive information about fluorescence and associated imaging methods)
NPL: “Fluorescence Reference Target Quantitative Analysis Library” by Littler et al. (Teaches standardization efforts for different fluorophores and associated limits of detection, statistical analyses, and visualizations)
NPL: “Evaluating performance in three-dimensional fluorescence Microscopy” by Murray et al. (Teaches procedures for determining the best imaging methods for different fluorescence applications)
NPL: “TestSTORM: Versatile simulator software for multimodal superresolution localization fluorescence microscopy” by Novak et al. (An alternative paper about the TestSTORM simulator to the Sinko reference)
NPL: “A beginner’s guide to improving image acquisition in fluorescence microscopy” by Ogama et al. (Teaches general concepts of image acquisition in fluorescence microscopy)
NPL: “A simple method for quantitating confocal fluorescent images” by Shihan et al. (Teaches quantifying elements presented in fluorescent images)
NPL: “Automated fluorescence intensity and gradient analysis enables detection of rare fluorescent mutant cells deep within the tissue of RaDR mice” by Wadduwage et al. (Teaches automation of fluorescent intensity analysis)
US 6,377,842 B1 to Pogue et al. (Teaches a system for quantitative and qualitative measurement of fluorophores)
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/J.M.W./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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