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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 21, 2026 has been entered.
Examiner Note
Please submit documents such as the cover page, the claims, the remarks and the abstract as separate documents with the appropriate document codes (separating the document eases the Patent Office’s processing).
Additionally, please use a larger, more legible font for claim amendments. The current font causes difficulties with optical character recognition that the examiner has to manually correct.
Response to Arguments
Applicant’s arguments and amendments have persuasively overcome the: title objection, abstract objection, claim objections, the 112 rejections, and the obviousness rejection. The remaining issues are addressed below.
101
Applicant argues:
Similar to example 47, the pending claims recite …
Examiner responds:
The examiner does not see sufficient similarity between detecting real time malicious network threats (from example 47) and the present image processing system to determine that this is not a mental process.
Applicant argues:
Because the second trained model is different from the first, the system achieves faster overall
operation while improving classification accuracy and reducing computational load.
Examiner responds:
Applicant’s background states:
[0002] For example, an information processing device described in Japanese Patent Application Laid-Open (JP-A) No. 2020-085535 suppresses erroneous operation from occurring in classification work for cases in which re-classification is performed on material component images classified by respective prescribed classification by image capturing in a flow system.
Because the alleged advantages appear to be taught by Applicant’s Admitted Prior Art (i.e., the reclassification), this argument is unpersuasive.
Applicant argues:
That reduces unnecessary processing and increases overall classification speed
Examiner responds:
The examiner is unclear what processing is reduced or how the classification speed is improved (however, the examiner appreciates that the second classification step could improve accuracy).
Applicant argues:
the claims improve the user interface display performance by configuring a user interface including
graphical elements representing the re-classification result which is not taught in the prior art.
Examiner responds:
MPEP 2106.04(a)(2)(III)(A) cites Electric Power Grp. as teaching that displaying results is part of a mental process.
Applicant argues:
Thus, Ramirez employes the second level model which is more complex than the first level model.
Examiner responds:
If Applicant feels that a second model being simpler than the first model is inventive, that should be claimed. However, presently, claim 4 recites that both the first and second model are chosen from the same set and claim 6 recites that the models are different. According to Applicant’s remarks, Ramirez teaches this.
Claim Objections
Claim 7 references claim 1, but does not properly depend from claim 1 because it only incorporates “the image processing device,” but does not incorporate claim 1’s camera (i.e., the rest of claim 1’s measurement system).
MPEP 607(III) states:
Any claim which is in dependent form but which is so worded that it, in fact, is not a proper dependent claim, as for example it does not include every limitation of the claim on which it depends, will be required to be canceled as not being a proper dependent claim; and cancellation of any further claim depending on such a dependent claim will be similarly required. The applicant may thereupon amend the claims to place them in proper dependent form, or may redraft them as independent claims, upon payment of any necessary additional fee.
Claim 7 is such a claim because it only incorporates part of the system in referenced claim 1. MPEP 608.01(n)(III). Therefore, claim 1 must be canceled. Claims 8-12, which reference claim 7, also must be canceled.
In the interest of compact prosecution, the examiner notes that if claims 7-12 were examined for prior art they appear to read on Ramirez (as per the mapped counterpart claims). Claims 7-12 are examined as dependent claims but not separately treated because they must be canceled.
Claim Rejections - 35 USC § 112
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.
Claims 1-12 and 14-21 (all claims) are 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.
Claim 1 recites a variety of elements that are each examples of unlimited functional claiming because they recite a result without specifying the steps that are taken. MPEP 2173.05(g). Specifying which technology is used is likely to overcome this rejection, examples below.
“extract a plurality of material component images … .” Specifying, for example, that this is performed with a convolutional neural network is expected overcome this rejection. See, e.g., specification, [0047].
“classify, using a first trained model, … .” Specifying, for example, that this is performed with a convolutional neural network is expected overcome this rejection. See, e.g., specification, [0047].
“control a reception of … from the external device.” This is outside the configuration of the processor because the reception requires action by the external device, not just the processor.
The corresponding language for all of the above issues in claims 14 and 15 is likewise rejected.
Claim 1 recites “classification precision value,” but this is new matter.
Claim 2 recites “based on a [first/second] machine learning technique,” but does not specify the techniques. MPEP 2173.05(g). Dependent claim 4 overcomes this rejection.
Claim 4 recites “classification performance index,” but this is new matter.
Claim 4 recites “improved classification performance,” but this is new matter.
Claims 17 and 20 are rejected as per claim 4.
Claim 5 recites “select a representative image,” but this is claiming a result rather than a step and extends to all means or methods of resolving this problem. MPEP 2173.05(g).
Dependent claims are likewise rejected. In the interest of clarity, claim 7 (and its dependents) are likewise rejected.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 and 14-21 (all claims) 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 1 recites “classify … to detect.” It is unclear if this means that the plurality of material components should be classified such that they can be detected, or if the detection is a required step.
Claim 1 recites “the classifications configured for classifying,” but this is subjective because different people can have different opinions about what classifications are for. MPEP 2173.05(b)(IV). Specifying an objective standard, such as what the classifications are, is expected to overcome this rejection.
Claim 1 recites “having classification precision values higher than or equal to a threshold and one or more unclassified material component images,” but it is not clear if these “unclassified” images are images that were never classified, or if they are images that were classified as “UNCL” (as per earlier in the claim).
Claim 1 recites “compute a goodness of fit indicating a classification precision value.” While “classification precision value” is definite, it is unclear what a “goodness of fit” would be that “indicates,” but is not simply a classification precision value. Here, “goodness of fit” does specify what the fit is (other than a classification precision value) and “indicating” is relative terminology. MPEP 2173.05(b)(IV).
Claim 1 recites “based on a pattern matching …,” but it is not clear what the “based on” modifies (e.g., is this the classification precision value, the computing a goodness of fit, or something else).
Claim 1 recites “a correct answer image or a feature point for a respective material component type,” but correct answers and feature points are not interchangeable. Specifically, a feature point does not indicate whether something is the right answer or not.
Claim 1 recites “the designated material component images are reclassified,” but earlier claim 1 recited that the designated images include unclassified images. For images that were unclassified to become reclassified, does the external device need to classify them once or twice?
Claim 1 recites “update the designated material component images,” but it is unclear if this means to change which images are designated or if it means to change the designated images’ metadata (or something else).
Claim 1 recites “graphical elements representing the re-classification result,” but “representing” is relative terminology. MPEP 2173.05(b)(IV). One way to overcome this rejection is to change “representing” to “displaying.”
Claims 14 and 15 recite corresponding language to the above rejections of claim 1 and are likewise rejected.
Claim 1 recites “a camera that obtains,” but this is an impermissible method step in an apparatus claim. MPEP 2173.05(p)(II).
Claim 1 recites “obtained by ;” but the missing words create indefiniteness.
Claims 1 and 14 recite “image processing device,” but this is new terminology. MPEP 2173.05(a). Note that claims 1 and 14 recite different meanings of “image processing device.”
Claims 1 and 15 recite “via a network line,” but it is unclear whether the network line is required to be present (e.g., claim 15 is to a storage medium, not a network line).
Claim 2 recites “is produced,” but it is unclear if this is impermissible method steps in an apparatus claim (MPEP 2173.05(p)(II)) or product-by-process. MPEP 2113. If product-by-process, it is unclear what the structural implications are.
Claim 4 recites “based on a classification performance index indicating classification precision,” but this is not a machine learning technique.
Claim 4 recites “classification performance index,” but this is new terminology. MPEP 2173.05(a).
Claim 4 recites “indicates,” but this is relative terminology. MPEP 2173.05(b)(IV). Reciting an objective comparison, such as “shows” is expected to overcome this rejection.
Claims 17 and 20 are rejected as per claim 4.
Dependent claims are likewise rejected. In the interest of clarity, claim 7 (and its dependents) are likewise rejected.
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 1-12 and 14-21 (all claims) are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Step 1:
Claim 1 (and its dependents) recite a device (and a system, see claim 7), and machines are eligible subject matter.
Claim 14 recites a method, and processes are eligible subject matter.
Claim 15 recites a non-transitory storage medium, and manufactures are eligible subject matter.
Step 2A, prong one: All of the elements of claims 1-15 are a mental process because a person classify images by looking at them. Further, the various models are also mental processes, see example 47, claim 2, element (d) (from the July 2024 AI subject matter eligibility examples). MPEP 2106.04(a)(2)(III)(C) explains that use of a generic computer or in a computer environment is still a mental process. In particular, this section begins by citing Gottschalk v. Benson, 409 US 63 (1972). “The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea.” In Benson the Supreme Court did not separately analyze the computer hardware at issue; the specifics of what hardware was claimed is only included in an appendix to the decision.
Because there are no additional elements, no further analysis is required for Step 2A, prong two or Step 2B.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6 and 14-21 (all claims except 7-12) are rejected under 35 U.S.C. 102(a)(1) and/or(a)(2) as being anticipated by U.S. Pat. Pub 20190228527 (“Ramirez”). References are listed in a PTO-892 from the Office Action in which they are first used.
A measurement system comprising:
a camera that obtains plural images of a sample fluid flowing in a flow cell; and (Ramirez, claim 57, “wherein the method uses a digital microscope camera”)
an image processing device comprising:
a memory, (Ramirez, Fig. 1A, memory 140)
a processor coupled to the memory; (Ramirez, Fig. 1A, processor 120)
a camera operatively connected to the processor; and a
wherein the processor is configured to:
extract a plurality of material component images including a plurality of material components contained in the sample fluid from the plural images obtained by; (Ramirez, [0004] “To analyze the cells and/or particles contained within a biological sample, images of the cells or particles may first be collected or acquired.”)
classify, using a first trained model, the plurality of material component images to detect the plurality of material components in the sample fluid based on classifications, (Ramirez, abstract “In a majority of cases, the first level model architecture provides an accurate identification of the cell or particle.”)
the classifications configured for classifying the plurality of material components images based on a material component type of a corresponding material component pictured in a corresponding material component image, (Ramirez, [0041] “Automated particle classification systems may be used to analyze biological samples to determine the composition and/or number of one or more types of cells and/or particles contained in the samples.”)
the classifications including one or more material component types of:
red blood cell (RBC), (Ramirez, [0046] “erythrocytes (RBCs)”)
white blood cell (WBC), (Ramirez, [0046] “leukocytes (WBCs)”)
non-squamous epithelial cell (NSE), (Ramirez, [0046] “non-squamous epithelial cells”)
squamous epithelial cell (SQEC), (Ramirez, [0046] “squamous epithelial cells”)
non-hyaline cast (NHC), (Ramirez, [0046] “casts”)
bacteria (BACT), (Ramirez, [0046] “bacteria”)
crystal (CRYS), (Ramirez, [0046] “crystals”)
yeast (YST), (Ramirez, [0046] “yeast”)
hyaline cast (HYST), (Ramirez, [0046] “casts”)
mucus (MUCS), (Ramirez, [0046] “mucus”)
spermatozoa (SPRM), (Ramirez, [0046] “spermatozoa”)
white blood cell clump (WBCC) and (Ramirez, [0046] “cell clumps”)
unclassified material (UNCL), (Ramirez, [0046] “unclassified cast”)
compute a goodness of fit indicating a classification precision value for each of the classified plurality of material component images based on a pattern matching between a respective classified material component image and a correct answer image or a feature point for a respective material component type and designate material component images including classified material component images having classification precision values higher than or equal to a threshold and one or more unclassified material component images; (Ramirez, [0004] “The system may then use hierarchical or cascaded classification architecture in analysis of the extracted features. According to various embodiments, the cascade classifier architecture used in the determination step may include a two-level analysis. If the outcome of the first level analysis is inconclusive, … .” Ramirez’s conclusiveness teaches the claimed goodness of fit. Ramirez’s inconclusiveness teaches the claimed designating (see the wherein clause of this claim). See also the mapping of claim 13.)
control a transmission of the designated material component images from among the plurality of material component images by the processor via a network line to an external device, and (Ramirez, Fig. 1A, network components 190. See also [0054] “Additionally, the storage medium 180 may be located in a first computer in which the programs may be executed, or may be located in a second different computer which connects to the first computer over a network 190.” Ramirez’s networked computer teaches the claimed data management device.)
a reception of a re-classification result of the designated material component images from the external device, wherein the designated material component images are re-classified by using a second trained model which is different from the first trained model; (Ramirez, abstract “In a minority of cases, the classification of the cell or particle requires a second level step requiring the use of numerical or categorical values from the first level in combination with a second level model.” See also Fig. 1B)
update the designated material component images with the classification result and configure a user interface including graphical elements representing the re-classification result. (Ramirez, Fig. 1A, display 160. See also, Fig. 7, step 790 “Determine a classification.”)
2. The measurement system of claim 1, wherein: the first trained model is produced based on a first machine learning technique applied to training data and the second trained model is produced based on a second machine learning technique applied to the same training data. (Ramirez, [0052] “In some embodiments, the reference images may be used as training data for a neural network implementation of the cascade classifier architecture.” Ramirez’s first and second models are different levels of the cascade architecture, and thus are trained on the one set of references images used as training data.)
3. The measurement system of claim 2, wherein:
wherein the training data is obtained by associating components of the classifications with material component images obtained in the past, and (Ramirez, [0052] “In some embodiments, the reference images may be used as training data for a neural network implementation of the cascade classifier architecture.”)
wherein the first trained model receives the plurality of material component images as an input of the first trained model and outputs the detected plurality of material components of the sample fluid. (Ramirez, abstract “In a majority of cases, the first level model architecture provides an accurate identification of the cell or particle.”)
4. The measurement system of claim 3, wherein the processor receives from the data management device a classification result obtained by classifying the designated material component images as detected components. (Ramirez, abstract “In a majority of cases, the first level model architecture provides an accurate identification of the cell or particle.” See also [0054] “Additionally, the storage medium 180 may be located in a first computer in which the programs may be executed, or may be located in a second different computer which connects to the first computer over a network 190.” Ramirez’s networked computer teaches the claimed data management device.)
wherein the first machine learning technique and the second machine learning technique are selected from the group consisting of: convolutional neural network, linear regression, regularization, decision tree, random forest, k-nearest neighbors algorithm (k-NN), logistic regression, support vector machine (SVM), based on a classification performance index indicating classification precision, and (Ramirez, [0084] “Examples of machine learning models suitable for this architecture [“first level model”] may be Random Forest, multiclass Support Vector Machines (SVMs), Feedforward Neural Networks (FNNs), etc.” [0098] “Examples of machine learning models suitable under this architecture [“level two classifier”] may be Support Vector Machines (SVMs), Feedforward Neural Networks (FNNs), Random Forest, etc.”)
wherein the classification performance index indicates that the second machine learning technique provides an improved classification performance as compared with the first machine learning technique. (Ramirez, [0005] “the classification of the cell or particle requires a further step (a “second level model”) to classify the cell or the particle.”)
5. The measurement system of claim 1 wherein:
the processor is configured to select one or more groups from among a plurality of designated material component images, to select a representative image of each group, and (Ramirez, [0074] “FIG. 4 shows an example of the clustering process for a given ring mask in the HSV color space, where the X 400 in the chart represents the center of the clusters and each color is associated to pixels belonging to a cell category.” Ramirez’s associated pixels teach the claimed images.)
to transmit the selected representative image to the external device. (Ramirez, Fig. 1A, network components 190. See also [0054] “Additionally, the storage medium 180 may be located in a first computer in which the programs may be executed, or may be located in a second different computer which connects to the first computer over a network 190.” Ramirez’s networked computer teaches the claimed external device.)
6. The measurement system of claim 4, wherein the first machine learning technique and the
second machine learning technique are different. (Ramirez, abstract “In a minority of cases, the classification of the cell or particle requires a second level step requiring the use of numerical or categorical values from the first level in combination with a second level model.” Ramirez’s different results teach the claimed different techniques.)
Claims 7-12 are not examined here because they require cancelation.
Claim 13 is canceled.
Claim 14 is rejected as per claim 1.
Claim 15 is rejected as per claim 1. See also, Ramirez, claim 61 “A non-transitory computer-readable storage medium.”
Claims 16-21 are rejected as per their counterpart claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11494905 B2 – Medical Image Recognition
US 20200134287 A1 – Material Component Images
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/DAVID ORANGE/ Primary Examiner, Art Unit 2663