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 .
Response to Amendments
The preliminary amendments to claims 1-15 are accepted and entered.
New claim 16 is accepted and entered.
The preliminary amendments to the specification are accepted and entered.
The preliminary amendments to the abstract are accepted and entered.
Priority
The present application claims foreign priority benefits from EP22171672.3 filed on
05/04/2022. The certified copies of the priority documents were electronically retrieved on 05/24/2022.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/09/2025 and 05/22/2025 are
considered and attached.
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.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because data per se does not fall into one of the four categories of statutory invention (machine, process, manufacture, composition). More specifically, claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability (i.e., laws of nature, natural phenomena, and abstract ideas). Alice Corp. v. CLS Bank Int'l, 573 U. S. 208 (2014)
Regarding claim 14, the claim is drawn towards “data per se”. As described in MPEP § 2106, “Non-limiting examples of claims that are not directed to any of the statutory categories include: Products that do not have a physical or tangible form, such as information (often referred to as "data per se") [] when claimed as a product without any structural recitation”. Claim 14 recites “A dataset for a machine learning model, the dataset comprising a plurality of microscopy images, or references thereto, obtained using the method of claim 1”. Here, the “dataset” is interpreted as data per se, as it represents information that does not have a physical or tangible form. Furthermore, the dataset as claimed in claim 14 is not paired with any structural recitation. While the method of claim 1 is referenced in this claim, it is solely relied on to produce said dataset. No functional subject matter is recited that either links the dataset to the functional method steps of claim 1, or recites an additional function or structure attached to the dataset within claim 14. Therefore, since claim 14 is drawn towards data per se, the claim is not eligible for patent protection.
Claim Rejections - 35 USC § 112(a), written description
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 5 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Background
35 U.S.C. § 112(a) requires that the “specification shall contain a written description of the invention”. To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See, e.g., Moba, B.V. v. Diamond Automation, Inc., 325 F.3d 1306, 1319, 66 USPQ2d 1429, 1438 (Fed. Cir. 2003); Vas-Cath, Inc. v. Mahurkar, 935 F.2d at 1563, 19 USPQ2d at 1116. An applicant shows possession of the claimed invention by describing the claimed invention with all of its limitations using such descriptive means as words, structures, figures, diagrams, and formulas that fully set forth the claimed invention. Lockwood v. Amer. Airlines, Inc., 107 F.3d 1565, 1572, 41 USPQ2d 1961, 1966 (Fed. Cir. 1997). Possession may be shown in a variety of ways including description of an actual reduction to practice, or by showing that the invention was “ready for patenting” such as by the disclosure of drawings or structural chemical formulas that show that the invention was complete, or by describing distinguishing identifying characteristics sufficient to show that the applicant was in possession of the claimed invention. See, e.g., Pfaff v. Wells Elecs., Inc., 525 U.S. 55, 68, 119 S.Ct. 304, 312, 48 USPQ2d 1641, 1647 (1998); Eli Lilly, 119 F.3d at 1568, 43 USPQ2d at 1406; Amgen, Inc. v. Chugai Pharm., 927 F.2d 1200, 1206, 18 USPQ2d 1016, 1021 (Fed. Cir. 1991).
There is a presumption that an adequate written description of the claimed invention is present when the application is filed. In re Wertheim, 541 F.2d 257, 263, 191 USPQ 90, 97 (CCPA 1976) (“we are of the opinion that the PTO has the initial burden of presenting evidence or reasons why persons skilled in the art would not recognize in the disclosure a description of the invention defined by the claims”). However, as discussed in subsection I., supra, the issue of a lack of adequate written description may arise even for an original claim when an aspect of the claimed invention has not been described with sufficient particularity such that one skilled in the art would recognize that the applicant had possession of the claimed invention. The claimed invention as a whole may not be adequately described if the claims require an essential or critical feature which is not adequately described in the specification and which is not conventional in the art or known to one of ordinary skill in the art. While it is not necessary for the examiner to present factual evidence, to make a prima facie case it is necessary to point out the claim limitations that are not adequately supported and explain any other reasons that the claim is not fully supported by the disclosure to show that the inventor had possession of the invention. See for example, Hyatt v. Dudas, 492 F.3d 1365, 1371, 83 USPQ2d 1373, 1376-1377 (Fed. Cir. 2007).
The courts have described the essential question to be addressed in a description requirement issue in a variety of ways. An objective standard for determining compliance with the written description requirement is, “does the description clearly allow persons of ordinary skill in the art to recognize that he or she invented what is claimed.” In re Gosteli, 872 F.2d 1008, 1012, 10 USPQ2d 1614, 1618 (Fed. Cir. 1989). Under Vas-Cath, Inc.v. Mahurkar, 935 F.2d 1555, 1563-64, 19 USPQ2d 1111, 1117 (Fed. Cir. 1991), to satisfy the written description requirement, an applicant must convey with reasonable clarity to those skilled in the art that, as of the filing date sought, he or she was in possession of the invention, and that the invention, in that context, is whatever is now claimed. The test for sufficiency of support in a parent application is whether the disclosure of the application relied upon “reasonably conveys to the artisan that the inventor had possession at that time of the later claimed subject matter.” Ralston Purina Co.v.Far-Mar-Co., Inc., 772 F.2d 1570, 1575, 227 USPQ 177, 179 (Fed. Cir. 1985) (quoting In reKaslow, 707 F.2d 1366, 1375, 217 USPQ 1089, 1096 (Fed. Cir. 1983)). See MPEP§ 2163 - https://www.uspto.gov/web/offices/pac/mpep/s2163.html
Regarding claim 5, applicant claims “the method of claim 1, wherein the target signal-to-noise ratio applies to a selected one of a plurality of channels”. However, applicant’s specification never describes what “channel” is being referenced in the claim. Furthermore, the limitation presents further confusion as it is unclear how a target signal-to-noise ratio can be applied to a “channel”, wherein the specific channel is never defined. The only portion of the specification that discusses the above limitation is in para. [0021]. This section merely recites language similar to the claim language, and further states “This provides for an efficient implementation of use cases such as "I want ROI no. 1 to have an SNR of 5 in channel 3 and ROI no. 2 to have an SNR of 7 in channel 2"”. However, the section remains silent as to what the recitation of “channel 3” or “channel 2” refers. Furthermore, one of ordinary skill in the art would not understand how, based on applicant’s disclosure, the target signal-to-noise ratio can be applied to a selected one of a plurality of channels. For example, the channels may be color channels, brightness channels, transparency channels, etc…. Therefore, one of ordinary skill in the art would not have recognized that the inventor was in possession of the invention as claimed in view of the disclosure of the application as filed.
Claim Rejections - 35 USC § 112(a), enablement
Claim 5 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Regarding claim 5, applicant claims “wherein the target signal-to-noise ratio applies to a selected one of a plurality of channels”. It is the Examiner’s position that the subject matter described above is not described in the specification in such a way as to enable one skilled in the art to which it pertains, to make and use the invention, without undue experimentation.
In accordance with MPEP § 2164, the examiner has the initial burden of establishing a prima facie case of lack of enablement. The question posed when making a lack of enablement rejection is: Is the experimentation needed to practice the invention undue or unreasonable? See Mineral Separation v. Hyde, 242 U.S. 261, 270 (1916). The test for lack of enablement was established in In re Wands, 858 F.2d 731, 737, 8 USPQ2d 1400, 1404 (Fed. Cir. 1988) and set forth several factors which must be considered by the examiner when making a determination of lack of enablement. These factors can be found in MPEP § 2164.01(a). Furthermore, the examiner need not discuss every factor. The examiner need only to focus on those factors, reasons, and evidence that lead the examiner to conclude that the specification fails to teach how to make and use the claimed invention without undue experimentation.
In Re Wands Factors
B) The nature of the invention
The invention of the aforementioned claim is directed towards a method of encoding projection information of two-dimensional projections. The limitation in question is drawn towards “wherein the target signal-to-noise ratio applies to a selected one of a plurality of channels”. Applicant’s disclosure generally recites “this may be done by simply selecting the channels to be imaged and the desired quality, i.e., without having to set up the acquisition parameters individually for each region” in para. [0021]. However, no clarification as to what type of channels are utilized is recited in the specification.
C) The state of the prior art
After a thorough prior art search, regarding the claims, a determination has been made that it is known in the art to determine a target signal-to-noise ratio . However, no prior art has been found that is capable of applying “the target signal-to-noise ratio [] to a selected one of a plurality of channels”, wherein the specific “channel” in question, is unknown.
D) The level of one of ordinary skill in the art
The examiner is of the opinion that it is well known in the art to determine a target signal-to-noise ratio. However, the Examiner's position on the claims is that it is not well known in the art to apply “the target signal-to-noise ratio [] to a selected one of a plurality of channels”, when the specific type of channel is unknown. Applicant has not described the invention is sufficient detail for one of ordinary skill in the art to ascertain how applicant’s invention is carried out; and one of ordinary skill in the art would have trouble understanding applicant’s invention based on their disclosure.
F) The amount of direction provided by the inventor
As discussed above applicant has not provided any details on the aforementioned claim limitations. Regarding the claims, applicant has not described how the applying “the target signal-to-noise ratio [] to a selected one of a plurality of channels” – is carried out. Applicant appears to only generally discuss the term “channel” in para. [0021], wherein no description or explanation regarding what type of channels are utilized is provided anywhere within the disclosure. Applicant’s specification does not describe anything capable of applying “the target signal-to-noise ratio [] to a selected one of a plurality of channels”. Additionally, it is unclear how this process is even possible given the fact that the type of channel utilized in the process is unknown. One of ordinary skill in the art would recognize that much more information would be needed in order to use the device to apply “the target signal-to-noise ratio [] to a selected one of a plurality of channels”.
G) The existence of working examples
There is neither mention of a working example, nor any example in any of the prior art.
H) The quantity of experimentation needed based on the disclosure
Since the invention as claimed is not described in detail in the specification, the amount of experimentation would be great in order to make/use the invention. As mentioned previously, applicant has not provided any direction on the above-mentioned claim limitation. Applicant merely discloses this limitation generally in para. [0020]-[0021] of the specification and has not provided any disclosure on these limitations. In particular, applicant has not described anything capable of applying “the target signal-to-noise ratio [] to a selected one of a plurality of channels”, specifically in a situation where the specific type of channel utilized in the above process is unknown. Thus, one of ordinary skill in the art would have to engage in undue experimentation in order to figure out how to create the claimed invention. Therefore, since the specification provides no detail on how these claim limitations are made and used, the disclosure is non-enabling. See MPEP § 2164.06.
When considering all of the pertinent In re Wands factors, 858 F.2d 731, 737, 8 USPQ2d 1400, 1404 (Fed. Cir. 1988), the Examiner has reached the conclusion that one of ordinary skill in the art would not be enabled to make and/or use the claimed invention without undue experimentation, particularly since the amount of direction provided by the applicant is minimal.
**NOTE: as a result of the above 112(a) rejection(s) of claim 5, a proper prior art search regarding claim 5 was unable to be conducted. Therefore, no prior art rejection of claim 5 is included in the art rejections below.
Claim Rejections - 35 USC § 112(b)
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 10 and 14 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 10 recites “wherein the plurality of tags is associated, in the generated dataset, with a subset of the microscopy images that are associated with a respective related region of interest of the plurality of regions of interest”. However, the terms “respective” and “related” in the above context present a lack of clarity regarding to what the region of interest is respective or related. Applicant discusses the above subject matter in para. [0023] of applicant’s specification. However, the applicant solely repeats the claim language as recited in claim 10, and does not provide any further clarity regarding to what the respective related region of interest is respective or related.
Claim 14 recites “a dataset” in line 1. However, “a dataset” is already introduced in claim 1, which is incorporated in its entirety by claim 14. As such, it is unclear whether the dataset as recited in claim 14 is equivalent to or distinct from the dataset as recited in claim 14. Applicant’s specification discusses a dataset in para. [0015], [0023], and [0026]. However, none of these sections clarify whether the dataset as recited in claim 14 is equivalent to or distinct from the dataset as recited in claim 14. As such, claim 14 is rejected for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
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, 6-10, 12, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Schumann et al. (U.S. Publication No. 2023/0324662 A1), hereinafter Schumann in view of Fomitchov et al. (U.S. Publication No. 2014/0152800 A1), hereinafter Fomitchov and Hing et al. (U.S. Publication No. 2012/0044342 A1), hereinafter Hing.
Regarding claim 1, Schumann teaches a computer-implemented image acquisition method (Fomitchov teaches “the system control unit 121 acts as a typical computer” in para. [0039]. See also para. [0040] and FIG. 1), comprising:
receiving a user input for indicating at least one quality condition, the quality condition comprising a target signal-to-noise ratio (Schumann teaches “the selection of the signal-to-noise ratio may be made directly by the user” in para. [0063], wherein the input relates to a quality of the image to be captured as shown in para. [0062]-[0064]. See also that “the GUI embodied as a Reuleaux triangle thus allows a user to intuitively input a desired setpoint of the image quality, a setpoint for the signal-to-noise ratio being derived from the input value 2” as shown in para. [0074]) associated with (Schumann teaches “the explanations regarding the method apply analogously to the fluorescence microscope” in para. [0060], wherein “the user has a certain choice of “image quality,” while the load on the sample, which varies dynamically during illumination of the sample” as shown in para. [0064]);
causing the fluorescence microscope to automatically acquire, (Schumann teaches that “the illumination brightnesses Pk to be adjusted are then automatically ascertained by processing unit 150 in such a way that a predefined setpoint of a signal-to-noise ratio is set for each distinguishable fluorophore” in para. [0072], wherein “the main image acquisition is started in step S 9 , using the ascertained and set illumination brightnesses Pk of light sources 120 k” as shown in para. [0082] and FIG. 3).
Schumann fails to teach regions of interest within a sample arrangement and generating a dataset for generating, training, validating and/or testing a machine-learning model, the dataset comprising the acquired microscopy images or references thereto.
However, Fomitchov teaches regions of interest (Fomitchov teaches “the biological object selection means may further be arranged to let the user select the one or more BRO's by marking one or more Regions of Interest (ROI) in the displayed image of the biological sample” in para. [0026]. See also para. [0060] and [0064], wherein “the image quality evaluation means 142 is arranged to count pixels with intensities within defined range of the BRO as Object pixels. Default object intensity values may be Max=100%, Min=90% of brightest pixel within BRO. These values may be user configurable to allow the user to set appropriate values for each specific imaging situation”. Here, the regions of interest as taught by Fomitchov can be combined with the teachings of Schumann as shown above to teach acquiring images of regions of interest and determining image quality conditions for regions of interest). Fomitchov additionally teaches generating a dataset for generating, training, validating and/or testing a machine-learning model, the dataset comprising the acquired microscopy images or references thereto (Fomitchov teaches generating a set of training images wherein the training images may include images wherein the quality parameters may be selected by a user in para. [0096]-[0100], quality parameters which may include signal-to-noise ratio as shown in para. [0071] and [0121]).
Schumann and Fomitchov are both considered to be analogous to the claimed invention because they are in the same field of using user input to capture images using a fluorescence microscope. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Schumann to incorporate the teachings of Fomitchov and include “regions of interest and generating a dataset for generating, training, validating and/or testing a machine-learning model, the dataset comprising the acquired microscopy images or references thereto”. The motivation for doing so would have been to accurately ensure the persons on the vehicle meet the criteria, as suggested by Fomitchov in para. [0045]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Schumann with Fomitchov to obtain the invention specified in the above claim limitations.
Schumann and Fomitchov fail to specifically teach regions of interest within a sample arrangement.
However, Hing teaches regions of interest within a sample arrangement (Hing teaches “a low-resolution overview image of all inserted slides and of their labels is then taken” wherein each slide contains a distinct sample in para. [0070], and wherein “a user may select a particular slide or slides based on the displayed overview images” in para. [0075]. Here, the particular slide or slides is interpreted as equivalent to the regions of interest within a sample arrangement).
Schumann, Fomitchov, and Hing are all considered to be analogous to the claimed invention because they are in the same field of using user input to capture images using a fluorescence microscope. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Schumann (as modified by Fomitchov to incorporate the teachings of Hing and include “regions of interest within a sample arrangement”. The motivation for doing so would have been to “allow[] a user to put an annotation on an image and save the location of the annotation”, as suggested by Hing in para. [0058]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Schumann and Fomitchov with Hing to obtain the invention specified in claim 1.
Regarding claim 2, Schumann, Fomitchov, and Hing teach the method of claim 1, wherein the target signal-to-noise ratio applies to an entirety of the plurality of regions of interest (Fomitchov teaches “the biological object selection means 140 is arranged to let the user select the one or more BRO's by marking a Region of Interest (ROI) 141 in the displayed image of the biological sample” in para. [0058], and determining image quality parameters (including SNR as shown in para. [0057]) for the BROs, wherein “the “best”, “acceptable”, and “low” ranges for each parameter may further be user-configurable” as shown in para. [0069] and “the system is arranged to automatically set one or more image acquisition parameters to achieve optimal imaging for the selected optimization mode based on at least one image quality parameter derived from one or more Biological Reference Objects (BRO) in the image of the biological sample selected by the user” as shown in para. [0074]. Here, it is assumed that Fomitchov’s teaching of acquiring a new image of the sample includes the plurality of BROs).
Regarding claim 3, Schumann, Fomitchov, and Hing teach the method of claim 1, wherein the target signal-to-noise ratio applies to a selected one of the plurality of regions of interest (Fomitchov teaches “the biological object selection means 140 is arranged to let the user select the one or more BRO's by marking a Region of Interest (ROI) 141 in the displayed image of the biological sample” in para. [0058], and determining image quality parameters (including SNR as shown in para. [0057]) for the BROs, wherein “the “best”, “acceptable”, and “low” ranges for each parameter may further be user-configurable” as shown in para. [0069] and “the system is arranged to automatically set one or more image acquisition parameters to achieve optimal imaging for the selected optimization mode based on at least one image quality parameter derived from one or more Biological Reference Objects (BRO) in the image of the biological sample selected by the user” as shown in para. [0074]. Here, it is assumed that Fomitchov’s teaching of acquiring a new image of the sample includes the plurality of BROs. While Fomitchov also teaches determining the target SNR based on a specific class of BROs (see para. [0068], since the claim broadly recites applying the target SNR to a selected ROI, and Fomitchov teaches applying the target SNR to multiple selected ROIs, it can be broadly said that Fomitchov also teaches applying the target SNR to at least a selected ROI).
Regarding claim 4, Schumann, Fomitchov, and Hing teach the method of claim 1, wherein the target signal-to-noise ratio is a specific signal-to-noise-ratio value or a minimum signal-to-noise ratio value (Schumann teaches that “the selection of the signal-to-noise ratio may be made directly by the user. However, it is also possible for the user to define the signal-to-noise ratio in a predetermined range” as shown in para. [0063]).
Regarding claim 6, Schumann, Fomitchov, and Hing teach the method of claim 1, wherein the at least one quality condition further comprises at least one of:
a first threshold condition based on a light dose;
a second threshold condition based on fluorophore quality; or
a third threshold condition based on an image quality (Schumann teaches a threshold condition based on image quality (SNR ratio) wherein there may exist a threshold range or a specific threshold number that the image must adhere to as shown in para. [0063]. See also that Schumann teaches a threshold value condition related to the exposure value in para. [0064]).
Regarding claim 7, Schumann, Fomitchov, and Hing teach the method of claim 1, wherein the sample arrangement comprises a plurality of distinct samples (Hing teaches “a low-resolution overview image of all inserted slides and of their labels is then taken” wherein each slide contains a distinct sample in para. [0070], and wherein “a user may select a particular slide or slides based on the displayed overview images” in para. [0075]. Here, the particular slide or slides is interpreted as equivalent to the regions of interest within a sample arrangement).
Similar motivations as applied to claim 1 can be applied here to claim 7.
Regarding claim 8, Schumann, Fomitchov, and Hing teach the method of claim 7, further comprising receiving a second user input indicating a selection of the plurality of regions of interest, wherein the selection indicates:
all samples of the sample arrangement;
one or more individual samples of the sample arrangement (Fomitchov specifically teaches a user selecting multiple ROIs of a biological samples in para. [0058]. Additionally, Hing further teaches “a user may select a particular slide or slides based on the displayed overview images” in para. [0075], wherein each slide contains an individual sample);
one or more rows and/or columns of samples of the sample arrangement; or
a shape enclosing one or more samples of the sample arrangement.
Similar motivations as applied to claim 1 may be applied here to claim 8.
Regarding claim 9, Schumann, Fomitchov, and Hing teach the method of claim 1, further comprising receiving a second user input indicating a plurality of tags (Fomitchov teaches that types of ranges (such as low, best, acceptable) may be determined for a plurality of image parameters for image acquisition of a sample based on user input in para. [0069]);
wherein the generated dataset associates the acquired microscopy images with the plurality of tags (Since Fomitchov teaches that the training images may include the images generated by the user optimizing the image acquisition parameters as shown in para. [0096] and [0098], it is inherent that the training image dataset includes acquired microscopy images associated with the plurality of tags (image parameter ranges) as defined in the above citation(s)).
Similar motivations as applied to claim 1 may be applied here to claim 9.
Regarding claim 10, Schumann, Fomitchov, and Hing teach the method of claim 9, wherein the second user input indicates the plurality of tags in relation to the plurality of regions of interest (Fomitchov teaches that types of ranges (such as low, best, acceptable) may be determined for a plurality of image parameters for image acquisition of a sample based on user input in para. [0069]); and
wherein the plurality of tags is associated, in the generated dataset, with a subset of the microscopy images that are associated with a respective related region of interest of the plurality of regions of interest (Since Fomitchov teaches that the training images may include the images generated by the user optimizing the image acquisition parameters as shown in para. [0096] and [0098], it is inherent that the training image dataset includes acquired microscopy images associated with the plurality of tags (image parameter ranges) as defined in the above citation(s). These images may be a subset of the overall training images as there are many other images that may be included in the training images as shown in para. [0096]-[0100]. Furthermore, Hing teaches generating a dataset of images of a specific region of interest in para. [0052]. Please also see the 112(b) rejection above wherein the definition of the “respective related region of interest” was unclear. As such, regarding prior art searching, the above limitation is interpreted as though the microscopy images are associated with a region of interest).
Similar motivations as applied to claim 1 may be applied here to claim 10.
Regarding claim 12, Schumann, Fomitchov, and Hing teach a data processing apparatus, comprising a computer device for carrying out the method of claim 1 (Schumann teaches “some or all of the method steps may be executed by (or using) a hardware apparatus, such as, for example, a processor, a microprocessor, a programmable computer, or an electronic circuit” in para. [0088]).
Regarding claim 13, Schumann, Fomitchov, and Hing teach a non-transitory computer-readable medium having a program code stored thereon, the program code, when executed by one or more computer processors, causing performance of the method of claim 1 (Schumann teaches “the implementation can be performed using a non-volatile storage medium like a digital storage medium” … “having electronically readable control signals stored thereon, which cooperate (or are capable of cooperating) with a programmable computer system such that the respective method is performed” as shown in para. [0089]).
Regarding claim 15, Schumann, Fomitchov, and Hing teach a microscope configured for use in the method of claim 1 (Schumann teaches “a method for automatically ascertaining illumination brightnesses to be adjusted of at least two light sources for exciting at least one respective fluorophore in a sample to be imaged in a fluorescence microscope” in para. [0013]).
Claims 11 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Schumann et al. (U.S. Publication No. 2023/0324662 A1), hereinafter Schumann in view of Fomitchov et al. (U.S. Publication No. 2014/0152800 A1), hereinafter Fomitchov, Hing et al. (U.S. Publication No. 2012/0044342 A1), hereinafter Hing, and Plesch et al. (U.S. Publication No. 2021/0264595 A1), hereinafter Plesch.
Regarding claim 11, Schumann, Fomitchov, and Hing teach the method of claim 9.
Schumann, Fomitchov, and Hing fail to teach wherein the plurality of tags comprises at least one tag that qualifies an associated microscopy image for a specific machine-learning purpose, as one or more of ground truth, training data, validation data, or test data.
However, Plesch teaches wherein the plurality of tags comprises at least one tag that qualifies an associated microscopy image for a specific machine-learning purpose, as one or more of
ground truth,
training data (Plesch teaches that “the classified acquired targeted images are suitable for use as training images for the supervised machine learning model” in para. [0008]),
validation data, or
test data.
Schumann, Fomitchov, Hing, and Plesch are all considered to be analogous to the claimed invention because they are in the same field of analyzing images of a sample. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Schumann (as modified by Fomitchov and Hing) to incorporate the teachings of Plesch and include “wherein the plurality of tags comprises at least one tag that qualifies an associated microscopy image for a specific machine-learning purpose, as one or more of ground truth, training data, validation data, or test data”. The motivation for doing so would have been to “make[] the training of CNNs more efficient by providing computer-assisted image acquisition and pre-classification of training images” and “generating training images from microscope slides for use in optimizing a supervised machine learning model”, as suggested by Plesch in para. [0006] and para. [0008], respectively. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Schumann, Fomitchov, and Hing with Plesch to obtain the invention specified in claim 11.
Regarding claim 14, Schumann, Fomitchov, and Hing teach the method of claim 1.
While Fomitchov teaches a dataset for training (see claim 1), Schumann, Fomitchov, and Hing fail to teach a dataset for a machine-learning model, the dataset comprising a plurality of microscopy images, or references thereto, obtained using the method of claim 1.
However, Plesch teaches a dataset for a machine-learning model, the dataset comprising a plurality of microscopy images, or references thereto, obtained using the method of claim 1 (Plesch teaches “high quality images of the OOI are required for review or for using them as training data for supervised machine learning approaches” in para. [0043], wherein the high quality images are captured using improved image acquisition parameters based on a region of interest. Plesch’s teaching of the training dataset can be combined with Schumann, Fomitchov, and Hing’s teaching of the images generated using the method of claim 1 as shown in claim 1).
Schumann, Fomitchov, Hing, and Plesch are all considered to be analogous to the claimed invention because they are in the same field of analyzing images of a sample. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Schumann (as modified by Fomitchov and Hing) to incorporate the teachings of Plesch and include “a dataset for a machine-learning model, the dataset comprising a plurality of microscopy images, or references thereto, obtained using the method of claim 1”. The motivation for doing so would have been to “make[] the training of CNNs more efficient by providing computer-assisted image acquisition and pre-classification of training images” and “generating training images from microscope slides for use in optimizing a supervised machine learning model”, as suggested by Plesch in para. [0006] and para. [0008], respectively. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Schumann, Fomitchov, and Hing with Plesch to obtain the invention specified in claim 14.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Schumann et al. (U.S. Publication No. 2023/0324662 A1), hereinafter Schumann in view of Fomitchov et al. (U.S. Publication No. 2014/0152800 A1), hereinafter Fomitchov, Hing et al. (U.S. Publication No. 2012/0044342 A1), hereinafter Hing, and Lai et al. (U.S. Publication No. 2023/0029710 A1), hereinafter Lai.
Regarding claim 16, Schumann, Fomitchov, and Hing teach the method of claim 7.
Schumann, Fomitchov, and Hing fail to teach wherein the sample arrangement is a well plate.
However, Lai teaches wherein the sample arrangement is a well plate (Lai teaches that the plurality of samples are located in a well plate, e.g., a 96-well plate” as shown in para. [0044]).
Schumann, Fomitchov, Hing, and Lai are all considered to be analogous to the claimed invention because they are in the same field of analyzing images of a sample. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Schumann (as modified by Fomitchov and Hing) to incorporate the teachings of Lai and include “wherein the sample arrangement is a well plate”. The motivation for doing so would have been “for improving image quality and reducing artifacts for analyzing samples using light sheet imaging”, as suggested by Lai in para. [0027]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Schumann, Fomitchov, and Hing with Lai to obtain the invention specified in claim 16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bhattacharyya et al. (U.S. Publication No. 2020/0184628 A1) teaches using images acquired with adjusted image parameters to train a deep learning network.
Georgescu et al. (U.S. Publication No. 2022/0076410 A1) teaches increasing signal-to-noise ratios in images of samples and training a convolutional neural network.
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/Kyla Guan-Ping Tiao Allen/
Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661
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