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 .
This communication is in response to the Application No. 18/813,652 filed 08/23/2024. Claims 1-23 are pending.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Japan on 2023-08-30. It is noted, however, that applicant has not filed a certified copy of the JP 2023139638 application as required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 08/23/2024 have been entered and considered. Initialed copies of the PTO-1449 by the examiner are attached.
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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.
Claims 1-9 and 12-19, recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claims 1 and 9; recites the limitation, “an image acquisition unit configured to …”.
Claims 1 and 9; recites the limitation, “a region extraction unit configured to …”.
Claims 1 and 9; recites the limitation, “a feature value extraction unit configured to …”.
Claims 1-6, 8-9, 12-14, and 16-17; recites the limitation, “a feature value estimation unit configured to …”.
Claims 1, 7-9, and 18-19; recites the limitation, “an evaluation unit configured to …”.
Claims 2 and 12; recites the limitation, “an instruction acquisition unit configured to …”.
Claims 13 and 15; recites the limitation, “a time point estimation unit configured to …”.
Claim 17; recites the limitation, “a regression model creation unit configured to …”.
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.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1-9 and 12-19:
“image acquisition unit” (Fig. 1, #110. Paragraphs [0036], [0027], [0028]- “the image acquisition unit 110 receives a request for processing from the user, and acquires time-series images of a cell colony stored in the data server 30 and picked up by the imaging device 20” wherein the imaging device “is, for example, a phase contrast microscope having an image pickup function and an incubation function for cells, and has a function of generating time-series images of a cell colony by performing image pickup, for example, every 24 hours during culture of the cells” thus, have sufficient structure or material wherein a phase contrast microscope).
“region extraction unit” (Fig. 1, #120. Paragraphs [0040], [0030]- “the region extraction unit 120 extracts, over respective image frames, a region of the same cell colony included in each of the time-series images of the cell colony acquired in Step S300 by the image acquisition unit 110. After that, identification of the same colony over respective image frames is performed for each extracted colony region by tracking processing or the like. In this case, the colony to be subjected to extraction of a region is not limited to one type of colony, and a region can be extracted from a plurality of types of colonies as the requirement arises. Specifically, the colony to be subjected to the extraction may be selected based on the instruction information input by the user and acquired by the instruction acquisition unit 160, or, for example, processing for automatically extracting all colonies included in a well may be performed” wherein “The processing performed by the CPU 201 may include extraction of a region of a cell colony, estimation of an image feature value, and evaluation of the cell colony or a culture condition” thus, have sufficient structure or material wherein is any kind of processor).
“feature value extraction unit” (Fig. 1, #130. Paragraph2 [0046], [0048]- “, the feature value extraction unit 130 extracts the image feature value from the region of the cell colony extracted by the region extraction unit 120”, “the feature value extraction unit 130 may extract the image feature value from an image that uses a mask to show only the colony region from which the image feature value is to be extracted... In the extraction of the image feature value performed by the feature value extraction unit 130, an appropriate extraction method can be selected depending on a type of the image feature value to be extracted” thus, does not have sufficient structure or material associated with it).
“feature value estimation unit” (Fig. 1, #140. Paragraphs [0049], [0051], [0030]- “the feature value estimation unit 140 uses the image feature value extracted by the feature value extraction unit 130 to estimate the image feature value regarding the cell colony assumed to be obtained at a given time point at which no time-series image is acquired. The image feature value (image feature value extracted by the feature value extraction unit 130) to be used for the estimation by the feature value estimation unit 140 is the image feature value extracted for the same cell colony as the cell colony for which the image feature value is to be estimated”, “When the above-described given time point at which no time-series image is acquired falls within a range of a time period including time points at which the time-series images were acquired on the time series during the cell culture, the feature value estimation unit 140 performs the estimation through interpolation processing” wherein “The processing performed by the CPU 201 may include extraction of a region of a cell colony, estimation of an image feature value, and evaluation of the cell colony or a culture condition” thus, have sufficient structure or material wherein is any kind of processor).
“evaluation unit” (Fig. 1, #150. Paragraphs [0069], [0030]- “the evaluation unit 150 uses the image feature value obtained through the estimation by the feature value estimation unit 140 to carry out evaluation on at least any one selected from the group consisting of a cell colony and a condition for cell culture. The evaluation unit 150 can perform, based on an evaluation criterion, the evaluation on the image feature value to be subjected to the evaluation, and may determine, for example, whether or not the image feature value regarding the cell colony estimated by the feature value estimation unit 140 exceeds a predetermined reference value included in the evaluation criterion. Examples of details of the evaluation include determination of whether a colony is currently differentiated or non-differentiated and evaluation of a colony property such as a differentiation ability or a growth rate of a subject colony” wherein “The processing performed by the CPU 201 may include extraction of a region of a cell colony, estimation of an image feature value, and evaluation of the cell colony or a culture condition” thus, have sufficient structure or material wherein is any kind of processor).
“instruction acquisition unit” (Fig. 1, #160. Paragraphs [0038]- “The cell image analysis apparatus 10 includes the instruction acquisition unit 160 capable of receiving instruction information input from the user, and the image acquisition unit 110 may acquire the time-series images from the data server 30 based on the instruction information acquired by the instruction acquisition unit 160” thus, does not have sufficient structure or material associated with it).
“time point estimation unit” (Fig. 6, #600. Paragraphs [0088]- “the time point estimation unit 600 estimates a target time point that is a time point at which the first image feature value of the cell colony has a given value. Specifically, the time point estimation unit 600 estimates the above-described target time point from values of the first image feature value extracted by the feature value extraction unit 130 and the time stamps associated with the time-series images including the regions of the cell colony from which the respective values of the first image feature value were extracted. The time point estimation unit 600 is not always required to estimate a specific time point as the target time point, and may only estimate a relative positional relationship on the time series between the target time point and a time point at which each time-series image was acquired” thus, does not have sufficient structure or material associated with it).
“regression model creation unit” (Fig. 9, #900. Paragraphs [0103], [0105]- “the regression model creation unit 900 creates a regression model through use of the first image feature value and the second image feature value extracted by the feature value extraction unit 130. After that, in Step S1010, which is a feature value estimation step, the feature value estimation unit 140 uses the regression model created in Step S1000 to estimate the second feature value corresponding to the above-described given value regarding the first image feature value”, “The regression model creation unit 900 may have a function of evaluating validity or likelihood of the created regression model. In this case, the created regression model may be used for the estimation processing by the feature value estimation unit 140 only when the created regression model has accuracy equal to or more than a predetermined degree” thus, does not have sufficient structure or material associated with it).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1-2, 9, 12-13, 15 and 17 along with their dependent claim(s) 3-8, 10-11, 14, 16 and 18-21 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1-2, 9, 12-13, 15 and 17 recites limitations:
Claims 1 and 9; recites the limitation, “a feature value extraction unit configured to…”.
Claims 2 and 12; recites the limitation, “an instruction acquisition unit configured to…”.
Claims 13 and 15; recites the limitation, “a time point estimation unit configured to…”.
Claim 17; recites the limitation, “a regression model creation unit configured to…”.
Claims 1-2, 9, 12-13, 15 and 17 respectively invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed functions. The specification does not provide sufficient details such that one of the ordinary skill in the art would understand which structure performed(s) the claimed function.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 1 recites, in part, “a feature value estimation unit configured to estimate, through use of the image feature value extracted by the feature value extraction unit, the image feature value regarding the same cell colony assumed to be obtained at a given time point at which none of the time-series images is acquired”. It is unclear what the word “assumed” is with regard to the image feature value in relation to a time point. That is, the claim uses the word “assumed” but fails to explicitly state the metes and bounds of the claim since the word is under an undefined assumption, thus is not explicit and therefore indefinite.
Claims 2, 3, 5 and 20 recite similar limitations with the word “assumed” as described above in claim 1 with similar deficiencies.
Claim 1 recites, in part, “an evaluation unit configured to evaluate, through use of the image feature value obtained through the estimation by the feature value estimation unit, at least any one selected from the group consisting of the same cell colony and a condition for the cell culture”. There is insufficient antecedent basis for the limitations “the group” as there is no mentioning of any group or groups prior to this recitation and there is no explicit list or definition. Similarly, the limitations of “evaluate... at least any one selected from” is awkwardly constructed as the limitations state a selection consisting of a list or group, but is unclear as to what is being selected from a list or group.
Claims 7-9, 11, 18-19 and 21 recite similar limitations with the words “the group” and a selection as described above in claim 1 with similar deficiencies. The office respectfully requests the Applicant to amend claims in order to clarify the claimed invention.
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 1-2, 9, 12-13, 15 and 17 along with their dependent claims 3-8, 10-11, 14, 16 and 18-19, 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function in the recited limitation Claims 5-7 and 9 recites limitations:
Claims 1 and 9; recites the limitation, “a feature value extraction unit configured to…”.
Claims 2 and 12; recites the limitation, “an instruction acquisition unit configured to…”.
Claims 13 and 15; recites the limitation, “a time point estimation unit configured to…”.
Claim 17; recites the limitation, “a regression model creation unit configured to…”.
The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed 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.
Claim(s) 1-2, 5-12 and 15-23 are rejected under 35 U.S.C. 103 as being unpatentable over Matsubara et al. (US 20160163049 A1, hereinafter referred to as “Matsubara”) in view of SJÖGREN et al. (WO 2024094674 A1, hereinafter referred to as “SJÖGREN”).
Regarding claim 1, Matsubara teaches a cell image analysis apparatus comprising (“A cell image evaluation device” Matsubara, abstract):
an image acquisition unit (“The imaging device 2 captures the image of the cell in the culture container placed on the stage 10. The imaging device 2 includes an optical system 20 which captures the image of the cell” Matsubara, [0051]) configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture (“a cell image of an observation region including the stem cell in the culture container is captured by the phase contrast microscope or the differential interference microscope of the imaging device 2 (S12). Specifically, 40 shots×40 shots of images of a rectangular observation region... the maturity information acquisition unit 33 acquires, for example, the culture period at the time when the cell image is captured as the information related to the maturity (S16)” Matsubara, [0117], [0119]; “in the initial stage of seeding and the stage after a few days have elapsed since the seeding, the number of exposures may be two or more and a plurality of cell images may be added. In the stage after a week has elapsed since the seeding, the number of exposures may be one and a cell image may be acquired. A change in the number of exposures substantially corresponds to a change in the exposure time” Matsubara, [0133]), and which include the same cell colony (“a culture period which is measured by a timer can be acquired as the information related to the maturity. In addition, the information related to the maturity is not limited to the culture period. For example, the following information may be acquired as the information related to the maturity: the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony, the number of cells in the cell colony, or the number of cells in a unit area smaller than the cell colony and maturity increases as the measured number of cells increases” Matsubara, [0061]);
a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images (“the feature amount acquisition unit 32 extracts the outer circumferential shape and internal defect of the stem cell colony. However, in this stage, the colony is not clearly formed, as described above. Therefore, the feature amount acquisition unit 32 specifies the region in which the stem cell colony is estimated to be formed from the distribution state of the stem cells and extracts the outer circumferential shape and internal defect of the specified region” Matsubara, [0101]; “the following information may be acquired as the information related to the maturity: the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony” Matsubara, [0061]);
a feature value extraction unit configured to extract an image feature value from the region extracted by the region extraction unit (“the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony, the number of cells in the cell colony, or the number of cells in a unit area smaller than the cell colony and maturity increases as the measured number of cells increases. For example, the area, peripheral length, and maximum diameter of the cell colony can be acquired as the size of the cell colony” Matsubara, [0061]).
Matsubara fails to explicitly teach a feature value estimation unit configured to estimate, through use of the image feature value extracted by the feature value extraction unit, the image feature value regarding the same cell colony assumed to be obtained at a given time point at which none of the time-series images is acquired; and an evaluation unit configured to evaluate, through use of the image feature value obtained through the estimation by the feature value estimation unit, at least any one selected from the group consisting of the same cell colony and a condition for the cell culture.
However, SJÖGREN teaches a feature value estimation unit configured to estimate, through use of the image feature value extracted by the feature value extraction unit (“The statistical model may be adapted to predict the values of one or more metrics indicative of the progress or outcome of the maturation process using inputs comprising the plurality of image-derived features obtained at a plurality of time points during the maturation process” SJÖGREN, bottom of pg. 4 ¶6), the image feature value regarding the same cell colony assumed to be obtained at a given time point at which none of the time-series images is acquired (“when predicting end point metrics at a time point earlier than the end of the time trajectory with which the model was fitted, where imputation may be used to impute the missing time points in the time trajectory. Thus, it is possible to use imputation by regression during the experiment to predict what the future trajectory of image-derived features is likely to look like based on the measurements collected up to the current point” SJÖGREN, bottom of pg. 6 last ¶); and
an evaluation unit configured to evaluate, through use of the image feature value obtained through the estimation by the feature value estimation unit (“the statistical model comprise a model that predicts end point metrics (metrics indicative of the outcome of the maturation process) from a fitted batch-level model (a model that models the maturation time as a function of a set of variables comprising the image- derived features)... The model may take as input a set of variables comprising the image-derived features quantified for one or more time points, and a set of variables imputed for one or more further time points” SJÖGREN, bottom of pg. 6), at least any one selected from the group consisting of the same cell colony and a condition for the cell culture (wherein the “[i]mage-derived features are values that are quantified for an image or set of images” in which “[t]he scalar or vector of image derived features may comprise one or more values quantifying an expert-defined visual feature in an image” SJÖGREN , pg. 12 ¶3; wherein “[t]he expert-defined visual features may be features associated with the local chemical or physiological state in the cell culture. The expert-defined visual features may further comprise one or more features selected from: features associated with localised cells, clusters of cells or macrostructures, features associated with the morphology of cells, clusters of cells or macrostructures, features associated with the spectral characteristics of a cell, cell cluster or macrostructure, where the spectral characteristics are indicative of a phenotype of the cells. Each image-derived feature may comprise one or more values quantifying an expert-defined visual feature in an image, or a summarised value derived therefrom” SJÖGREN, pg. 7 ¶4; additionally, see last ¶ of pg. 12; wherein “Using these metrics we can define expert-defined descriptors of the samples. In the printed bone tissue, osteoblasts successfully differentiated into osteocytes display filamentous morphologies” SJÖGREN, pg. 20 second to last ¶).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract an image feature value from the region extracted by the region extraction unit with the teachings of SJÖGREN of having a feature value estimation unit configured to estimate, through use of the image feature value extracted by the feature value extraction unit, the image feature value regarding the same cell colony assumed to be obtained at a given time point at which none of the time-series images is acquired; and an evaluation unit configured to evaluate, through use of the image feature value obtained through the estimation by the feature value estimation unit, at least any one selected from the group consisting of the same cell colony and a condition for the cell culture.
Wherein having Matsubara’s cell image evaluation device wherein a feature value estimation unit configured to estimate, through use of the image feature value extracted by the feature value extraction unit, the image feature value regarding the same cell colony assumed to be obtained at a given time point at which none of the time-series images is acquired; and an evaluation unit configured to evaluate, through use of the image feature value obtained through the estimation by the feature value estimation unit, at least any one selected from the group consisting of the same cell colony and a condition for the cell culture.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 2, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 1, Matsubara fails to explicitly teach further comprising an instruction acquisition unit configured to acquire instruction information, wherein the instruction information includes information specifying the given time point at which none of the time-series images is acquired, and
wherein the feature value estimation unit is configured to estimate the image feature value regarding the same cell colony assumed to be obtained at a time point specified by the instruction information.
However, SJÖGREN teaches further comprising an instruction acquisition unit configured to acquire instruction information (“A metric of interest may instead or in addition be selected from the latent variables of a statistical model that models the maturation time as a function of a multivariate profile, where the multivariate profile comprises one or more of the image-derived features obtained for a plurality of maturation processes that have been deemed to progress normally... In other words, metrics of interest that characterises the progress of a maturation process along a trajectory defined using a batch evolution modelling approach may be used as input to a predictive statistical model that predicts the outcome of the maturation process” SJÖGREN, pg. 14 ¶1),
wherein the instruction information includes information specifying the given time point at which none of the time-series images is acquired (“when predicting end point metrics at a time point earlier than the end of the time trajectory with which the model was fitted, where imputation may be used to impute the missing time points in the time trajectory. Thus, it is possible to use imputation by regression during the experiment to predict what the future trajectory of image-derived features is likely to look like based on the measurements collected up to the current point” SJÖGREN, bottom of pg. 6 last ¶), and
wherein the feature value estimation unit is configured to estimate the image feature value regarding the same cell colony assumed to be obtained at a time point specified by the instruction information (“the predictions made by the statistical models as described herein may be predictions strictly speaking in that they relate to the expected value of a metric at a future point in time, or they may be predictions in the sense that they determine the value of a metric that is not directly measured and is instead “predicted” on the basis of the values of other metrics (image-derived features). The prediction of metrics indicative of the progress or outcome of a maturation process according to the methods described herein uses a statistical model that takes as an input the image-derived feature(s) and provides as an output the one or more metrics indicative of the progress or outcome of the maturation process” SJÖGREN, pg. 14 ¶3).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract an image feature value from the region extracted by the region extraction unit with the teachings of SJÖGREN of having an instruction acquisition unit configured to acquire instruction information, wherein the instruction information includes information specifying the given time point at which none of the time-series images is acquired, and wherein the feature value estimation unit is configured to estimate the image feature value regarding the same cell colony assumed to be obtained at a time point specified by the instruction information.
Wherein having Matsubara’s cell image evaluation device wherein an instruction acquisition unit configured to acquire instruction information, wherein the instruction information includes information specifying the given time point at which none of the time-series images is acquired, and wherein the feature value estimation unit is configured to estimate the image feature value regarding the same cell colony assumed to be obtained at a time point specified by the instruction information.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 5, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 1, Matsubara fails to explicitly teach wherein the feature value estimation unit is configured to estimate, through extrapolation processing, the image feature value regarding the same cell colony assumed to be obtained at the given time point at which none of the time-series images is acquired.
However, SJÖGREN teaches wherein the feature value estimation unit is configured to estimate, through extrapolation processing (“The statistical model may be adapted to predict the values of one or more metrics indicative of the progress or outcome of the maturation process using inputs comprising the plurality of image-derived features obtained at a plurality of time points during the maturation process” SJÖGREN, bottom of pg. 4 ¶6), the image feature value regarding the same cell colony assumed to be obtained at the given time point at which none of the time-series images is acquired (“when predicting end point metrics at a time point earlier than the end of the time trajectory with which the model was fitted, where imputation may be used to impute the missing time points in the time trajectory. Thus, it is possible to use imputation by regression during the experiment to predict what the future trajectory of image-derived features is likely to look like based on the measurements collected up to the current point” SJÖGREN, bottom of pg. 6 last ¶).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract an image feature value from the region extracted by the region extraction unit with the teachings of SJÖGREN of having wherein the feature value estimation unit is configured to estimate, through extrapolation processing, the image feature value regarding the same cell colony assumed to be obtained at the given time point at which none of the time-series images is acquired.
Wherein having Matsubara’s cell image evaluation device wherein, the feature value estimation unit is configured to estimate, through extrapolation processing, the image feature value regarding the same cell colony assumed to be obtained at the given time point at which none of the time-series images is acquired.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 6, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 5, Matsubara fails to explicitly teach wherein the feature value estimation unit is configured to perform the extrapolation processing through use of the time-series images acquired at two or more consecutive time points on the time series that are closest to the given time point at which none of the time-series images is acquired.
However, SJÖGREN teaches wherein the feature value estimation unit is configured to perform the extrapolation processing through use of the time-series images acquired at two or more consecutive time points on the time series that are closest to the given time point at which none of the time-series images is acquired (“the statistical model is trained to predict one or more metrics of interest at a future time (k+1), based on input values comprising the values of one or more image-derived features obtained from images acquired at one or more time points k, k- 1, etc.” SJÖGREN, pg. 16 third to last ¶).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract an image feature value from the region extracted by the region extraction unit with the teachings of SJÖGREN of having wherein the feature value estimation unit is configured to perform the extrapolation processing through use of the time-series images acquired at two or more consecutive time points on the time series that are closest to the given time point at which none of the time-series images is acquired.
Wherein having Matsubara’s cell image evaluation device, wherein the feature value estimation unit is configured to perform the extrapolation processing through use of the time-series images acquired at two or more consecutive time points on the time series that are closest to the given time point at which none of the time-series images is acquired.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 7, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 1, Matsubara fails to explicitly teach wherein the evaluation unit is configured to evaluate, based on a predetermined evaluation criterion, at least any one selected from the group consisting of a state of the same cell colony and the condition for the cell culture.
However, SJÖGREN teaches wherein the evaluation unit is configured to evaluate, based on a predetermined evaluation criterion, at least any one selected from the group consisting of a state of the same cell colony and the condition for the cell culture (“Using these metrics we can define expert-defined descriptors of the samples. In the printed bone tissue, osteoblasts successfully differentiated into osteocytes display filamentous morphologies” SJÖGREN, pg. 20 second to last ¶; wherein “metrics may also be used to characterise the progress of the maturation process, for example by comparison to a corresponding target metric” SJÖGREN, pg. 13 last ¶; “A maturation process may be deemed to progress normally if it leads to a product that meets predetermined quality criteria” SJÖGREN, top of pg. 14).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract an image feature value from the region extracted by the region extraction unit with the teachings of SJÖGREN of having wherein the evaluation unit is configured to evaluate, based on a predetermined evaluation criterion, at least any one selected from the group consisting of a state of the same cell colony and the condition for the cell culture.
Wherein having Matsubara’s cell image evaluation device wherein, the evaluation unit is configured to evaluate, based on a predetermined evaluation criterion, at least any one selected from the group consisting of a state of the same cell colony and the condition for the cell culture.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 8, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 1, Matsubara fails to explicitly teach wherein the evaluation unit is configured to evaluate a degree of correlation between the image feature value obtained through the estimation by the feature value estimation unit and at least any one selected from the group consisting of a predetermined feature of the same cell colony and the condition for the cell culture.
However, SJÖGREN teaches wherein the evaluation unit is configured to evaluate a degree of correlation between the image feature value obtained through the estimation by the feature value estimation unit and at least any one selected from the group consisting of a predetermined feature of the same cell colony and the condition for the cell culture (“The statistical model may be a latent variable model that models the maturation time as a function of a set of process-related variables comprising the image-derived features... The latent variable model may capture how the covariance structure of the process-related variables varies over time during operation of a process. This may be referred to as a “batch evolution model”... latent variables that describe the aspects of the process related variables that are most correlated with maturity.” SJÖGREN, top of pg. 15; “This may be used for example to predict or otherwise relate localised metrics of interest derived from histology with localised expert defined features derived from imaging of the sample during culture. For example, this may be used to identify whether cells differentiate slower than expected in a local region of the sample during on-line measurement, by directly correlating imaging data at that location with the resulting local phenotype as described by histology” SJÖGREN, top of pg. 18).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract an image feature value from the region extracted by the region extraction unit with the teachings of SJÖGREN of having wherein the evaluation unit is configured to evaluate a degree of correlation between the image feature value obtained through the estimation by the feature value estimation unit and at least any one selected from the group consisting of a predetermined feature of the same cell colony and the condition for the cell culture.
Wherein having Matsubara’s cell image evaluation device wherein, the evaluation unit is configured to evaluate a degree of correlation between the image feature value obtained through the estimation by the feature value estimation unit and at least any one selected from the group consisting of a predetermined feature of the same cell colony and the condition for the cell culture.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 9, Matsubara teaches cell image analysis apparatus comprising (“A cell image evaluation device” Matsubara, abstract):
an image acquisition unit (“The imaging device 2 captures the image of the cell in the culture container placed on the stage 10. The imaging device 2 includes an optical system 20 which captures the image of the cell” Matsubara, [0051]) configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture (“a cell image of an observation region including the stem cell in the culture container is captured by the phase contrast microscope or the differential interference microscope of the imaging device 2 (S12). Specifically, 40 shots×40 shots of images of a rectangular observation region... the maturity information acquisition unit 33 acquires, for example, the culture period at the time when the cell image is captured as the information related to the maturity (S16)” Matsubara, [0117], [0119]; “in the initial stage of seeding and the stage after a few days have elapsed since the seeding, the number of exposures may be two or more and a plurality of cell images may be added. In the stage after a week has elapsed since the seeding, the number of exposures may be one and a cell image may be acquired. A change in the number of exposures substantially corresponds to a change in the exposure time” Matsubara, [0133]), and which include the same cell colony (“a culture period which is measured by a timer can be acquired as the information related to the maturity. In addition, the information related to the maturity is not limited to the culture period. For example, the following information may be acquired as the information related to the maturity: the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony, the number of cells in the cell colony, or the number of cells in a unit area smaller than the cell colony and maturity increases as the measured number of cells increases” Matsubara, [0061]);
a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images (“the feature amount acquisition unit 32 extracts the outer circumferential shape and internal defect of the stem cell colony. However, in this stage, the colony is not clearly formed, as described above. Therefore, the feature amount acquisition unit 32 specifies the region in which the stem cell colony is estimated to be formed from the distribution state of the stem cells and extracts the outer circumferential shape and internal defect of the specified region” Matsubara, [0101]; “the following information may be acquired as the information related to the maturity: the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony” Matsubara, [0061]);
a feature value extraction unit configured to extract, from the region extracted by the region extraction unit, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation (“a culture period which is measured by a timer can be acquired as the information related to the maturity. In addition, the information related to the maturity is not limited to the culture period. For example, the following information may be acquired as the information related to the maturity: the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony, the number of cells in the cell colony, or the number of cells in a unit area smaller than the cell colony and maturity increases as the measured number of cells increases. For example, the area, peripheral length, and maximum diameter of the cell colony can be acquired as the size of the cell colony” Matsubara, [0061]; wherein the first feature is that of the size of the cell colony and the second feature is that of a texture, “[f]or example, the brightness of the image of the cell colony region or texture, such as uniformity or roughness, may be acquired as the information related to the maturity” Matsubara, [0062]).
Matsubara fails to explicitly teach a feature value estimation unit configured to estimate the second image feature value corresponding to a given value regarding the first image feature value; and
an evaluation unit configured to evaluate, through use of the second image feature value estimated by the feature value estimation unit, at least any one selected from the group consisting of the same cell colony and a condition for the cell culture.
However, SJÖGREN teaches a feature value estimation unit configured to estimate the second image feature value corresponding to a given value regarding the first image feature value (“The statistical model may be adapted to predict the values of one or more metrics indicative of the progress or outcome of the maturation process using inputs comprising the plurality of image-derived features obtained at a plurality of time points during the maturation process” SJÖGREN, bottom of pg. 4 ¶6; wherein the image-derived features are that of the second image feature value, “geometric properties of cells, clusters or macrostructures may be quantified and used as image-derived features. Geometric properties of cells, clusters or macrostructures that may be quantified include their size (quantified as the length of a minor and/or major axis, the characteristic dimension of an equivalent shape such as a circle or sphere, the Feret diameter, etc.), shape (quantified as the similarity to a predetermined shape such as e.g. sphericity, or any parameter that captures features of the shape such as a ratio of the length of a major and minor axis, the number of branches in a vascular network, the eccentricity, roundness, circularity, solidity, aspect ratio of an object, etc.), area, volume, or circumference” SJÖGREN, last ¶ of pg. 12); and
an evaluation unit configured to evaluate, through use of the second image feature value estimated by the feature value estimation unit (“the statistical model comprise a model that predicts end point metrics (metrics indicative of the outcome of the maturation process) from a fitted batch-level model (a model that models the maturation time as a function of a set of variables comprising the image- derived features)... The model may take as input a set of variables comprising the image-derived features quantified for one or more time points, and a set of variables imputed for one or more further time points” SJÖGREN, bottom of pg. 6 and last ¶ of pg. 12 for the second image feature value(s) as noted above), at least any one selected from the group consisting of the same cell colony and a condition for the cell culture (wherein the “[i]mage-derived features are values that are quantified for an image or set of images” in which “[t]he scalar or vector of image derived features may comprise one or more values quantifying an expert-defined visual feature in an image” SJÖGREN , pg. 12 ¶3; wherein “[t]he expert-defined visual features may be features associated with the local chemical or physiological state in the cell culture. The expert-defined visual features may further comprise one or more features selected from: features associated with localised cells, clusters of cells or macrostructures, features associated with the morphology of cells, clusters of cells or macrostructures, features associated with the spectral characteristics of a cell, cell cluster or macrostructure, where the spectral characteristics are indicative of a phenotype of the cells. Each image-derived feature may comprise one or more values quantifying an expert-defined visual feature in an image, or a summarised value derived therefrom” SJÖGREN, pg. 7 ¶4; additionally, see last ¶ of pg. 12; wherein “Using these metrics we can define expert-defined descriptors of the samples. In the printed bone tissue, osteoblasts successfully differentiated into osteocytes display filamentous morphologies” SJÖGREN, pg. 20 second to last ¶).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract, from the region extracted by the region extraction unit, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation, with the teachings of SJÖGREN of having a feature value estimation unit configured to estimate the second image feature value corresponding to a given value regarding the first image feature value; and an evaluation unit configured to evaluate, through use of the second image feature value estimated by the feature value estimation unit, at least any one selected from the group consisting of the same cell colony and a condition for the cell culture.
Wherein having Matsubara’s cell image evaluation device wherein, a feature value estimation unit configured to estimate the second image feature value corresponding to a given value regarding the first image feature value; and an evaluation unit configured to evaluate, through use of the second image feature value estimated by the feature value estimation unit, at least any one selected from the group consisting of the same cell colony and a condition for the cell culture.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 10, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 9, Matsubara further teaches wherein the first image feature value comprises the image feature value relating to a size of a colony (“a culture period which is measured by a timer can be acquired as the information related to the maturity. In addition, the information related to the maturity is not limited to the culture period. For example, the following information may be acquired as the information related to the maturity: the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony, the number of cells in the cell colony, or the number of cells in a unit area smaller than the cell colony and maturity increases as the measured number of cells increases. For example, the area, peripheral length, and maximum diameter of the cell colony can be acquired as the size of the cell colony” Matsubara, [0061]).
Regarding claim 11, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 10, Matsubara further teaches wherein the first image feature value comprises at least any one selected from the group consisting of a colony area, a colony volume, a colony contour length, and an average cell area (“a culture period which is measured by a timer can be acquired as the information related to the maturity. In addition, the information related to the maturity is not limited to the culture period. For example, the following information may be acquired as the information related to the maturity: the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony, the number of cells in the cell colony, or the number of cells in a unit area smaller than the cell colony and maturity increases as the measured number of cells increases. For example, the area, peripheral length, and maximum diameter of the cell colony can be acquired as the size of the cell colony” Matsubara, [0061].
Regarding claim 12, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 9, Matsubara fails to explicitly teach further comprising an instruction acquisition unit configured to acquire instruction information, wherein the instruction information includes information specifying the given value regarding the first image feature value, and wherein the feature value estimation unit is configured to estimate the second image feature value based on the given value regarding the first image feature value specified by the instruction information.
However, SJÖGREN teaches further comprising an instruction acquisition unit configured to acquire instruction information, wherein the instruction information includes information specifying the given value regarding the first image feature value (“A metric of interest may instead or in addition be selected from the latent variables of a statistical model that models the maturation time as a function of a multivariate profile, where the multivariate profile comprises one or more of the image-derived features obtained for a plurality of maturation processes that have been deemed to progress normally... In other words, metrics of interest that characterises the progress of a maturation process along a trajectory defined using a batch evolution modelling approach may be used as input to a predictive statistical model that predicts the outcome of the maturation process” SJÖGREN, pg. 14 ¶1), and
wherein the feature value estimation unit is configured to estimate the second image feature value based on the given value regarding the first image feature value specified by the instruction information (“the predictions made by the statistical models as described herein may be predictions strictly speaking in that they relate to the expected value of a metric at a future point in time, or they may be predictions in the sense that they determine the value of a metric that is not directly measured and is instead “predicted” on the basis of the values of other metrics (image-derived features). The prediction of metrics indicative of the progress or outcome of a maturation process according to the methods described herein uses a statistical model that takes as an input the image-derived feature(s) and provides as an output the one or more metrics indicative of the progress or outcome of the maturation process” SJÖGREN, pg. 14 ¶3).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract, from the region extracted by the region extraction unit, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation, with the teachings of SJÖGREN of having an instruction acquisition unit configured to acquire instruction information, wherein the instruction information includes information specifying the given value regarding the first image feature value, and wherein the feature value estimation unit is configured to estimate the second image feature value based on the given value regarding the first image feature value specified by the instruction information.
Wherein having Matsubara’s cell image evaluation device wherein, an instruction acquisition unit configured to acquire instruction information, wherein the instruction information includes information specifying the given value regarding the first image feature value, and wherein the feature value estimation unit is configured to estimate the second image feature value based on the given value regarding the first image feature value specified by the instruction information.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 15, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 9, Matsubara fails to explicitly teach further comprising a time point estimation unit configured to estimate a target time point that is a time point at which the first image feature value of the same cell colony has the given value, wherein the feature value estimation unit is configured to estimate, through extrapolation processing, the second image feature value corresponding to a given value regarding the first image feature value when the target time point falls out of a range of a time period including the two or more time points at which the time-series images were acquired on the time series.
However, SJÖGREN teaches further comprising a time point estimation unit configured to estimate a target time point that is a time point at which the first image feature value of the same cell colony has the given value (“The statistical model may be adapted to predict the values of one or more metrics indicative of the progress or outcome of the maturation process using inputs comprising the plurality of image-derived features obtained at a plurality of time points during the maturation process” SJÖGREN, bottom of pg. 4 ¶6; wherein the “[i]mage-derived features are values that are quantified for an image or set of images” in which “[t]he scalar or vector of image derived features may comprise one or more values quantifying an expert-defined visual feature in an image” SJÖGREN, pg. 12 ¶3;),
wherein the feature value estimation unit is configured to estimate, through extrapolation processing (“The statistical model may be adapted to predict the values of one or more metrics indicative of the progress or outcome of the maturation process using inputs comprising the plurality of image-derived features obtained at a plurality of time points during the maturation process” SJÖGREN, bottom of pg. 4 ¶6), the second image feature value corresponding to a given value regarding the first image feature value when the target time point falls out of a range of a time period including the two or more time points at which the time-series images were acquired on the time series (“the statistical model is trained to predict one or more metrics of interest at a future time (k+1), based on input values comprising the values of one or more image-derived features obtained from images acquired at one or more time points k, k- 1, etc.” SJÖGREN, pg. 16 third to last ¶).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract, from the region extracted by the region extraction unit, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation, with the teachings of SJÖGREN of having a time point estimation unit configured to estimate a target time point that is a time point at which the first image feature value of the same cell colony has the given value, wherein the feature value estimation unit is configured to estimate, through extrapolation processing, the second image feature value corresponding to a given value regarding the first image feature value when the target time point falls out of a range of a time period including the two or more time points at which the time-series images were acquired on the time series.
Wherein having Matsubara’s cell image evaluation device wherein, a time point estimation unit configured to estimate a target time point that is a time point at which the first image feature value of the same cell colony has the given value, wherein the feature value estimation unit is configured to estimate, through extrapolation processing, the second image feature value corresponding to a given value regarding the first image feature value when the target time point falls out of a range of a time period including the two or more time points at which the time-series images were acquired on the time series.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 16, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 15, Matsubara fails to explicitly teach wherein the feature value estimation unit is configured to perform the extrapolation processing through use of the second image feature value included in the time-series images acquired at two or more consecutive time points on the time series that are closest to the target time point.
However, SJÖGREN teaches wherein the feature value estimation unit is configured to perform the extrapolation processing through use of the second image feature value included in the time-series images acquired at two or more consecutive time points on the time series that are closest to the target time point (“the statistical model is trained to predict one or more metrics of interest at a future time (k+1), based on input values comprising the values of one or more image-derived features obtained from images acquired at one or more time points k, k- 1, etc.” SJÖGREN, pg. 16 third to last ¶).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract, from the region extracted by the region extraction unit, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation, with the teachings of SJÖGREN of having wherein the feature value estimation unit is configured to perform the extrapolation processing through use of the second image feature value included in the time-series images acquired at two or more consecutive time points on the time series that are closest to the target time point.
Wherein having Matsubara’s cell image evaluation device, wherein the feature value estimation unit is configured to perform the extrapolation processing through use of the second image feature value included in the time-series images acquired at two or more consecutive time points on the time series that are closest to the target time point.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 17, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 9, Matsubara fails to explicitly teach further comprising a regression model creation unit configured to create a regression model through use of the first image feature value and the second image feature value, wherein the feature value estimation unit is configured to estimate the second image feature value corresponding to a given value regarding the first image feature value based on the regression model created by the regression model creation unit.
However, SJÖGREN teaches further comprising a regression model creation unit configured to create a regression model through use of the first image feature value and the second image feature value (“The statistical model may be a regression model” SJÖGREN, pg. 14, ¶4),
wherein the feature value estimation unit is configured to estimate the second image feature value corresponding to a given value regarding the first image feature value based on the regression model created by the regression model creation unit (“Suitable regression models for used in the context of the present invention may depend on the type and number of the predictive and predicted variables. For example, when the predictive variable (including the image-derived features) that is a scalar numerical value and the predicted variable is also a single scalar variable (metrics indicative of a cell state transition), a single linear regression (also referred to as simple linear regression) may be used” SJÖGREN, pg. 14, ¶4; wherein the “[i]mage-derived features are values that are quantified for an image or set of images” in which “[t]he scalar or vector of image derived features may comprise one or more values quantifying an expert-defined visual feature in an image” SJÖGREN , pg. 12 ¶3).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract, from the region extracted by the region extraction unit, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation, with the teachings of SJÖGREN of having a regression model creation unit configured to create a regression model through use of the first image feature value and the second image feature value, wherein the feature value estimation unit is configured to estimate the second image feature value corresponding to a given value regarding the first image feature value based on the regression model created by the regression model creation unit.
Wherein having Matsubara’s cell image evaluation device wherein, a regression model creation unit configured to create a regression model through use of the first image feature value and the second image feature value, wherein the feature value estimation unit is configured to estimate the second image feature value corresponding to a given value regarding the first image feature value based on the regression model created by the regression model creation unit.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 18, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 9, Matsubara fails to explicitly teach wherein the evaluation unit is configured to evaluate, based on a predetermined evaluation criterion, at least any one selected from the group consisting of a state of the same cell colony and the condition for the cell culture.
However, SJÖGREN teaches wherein the evaluation unit is configured to evaluate, based on a predetermined evaluation criterion, at least any one selected from the group consisting of a state of the same cell colony and the condition for the cell culture (“Using these metrics we can define expert-defined descriptors of the samples. In the printed bone tissue, osteoblasts successfully differentiated into osteocytes display filamentous morphologies” SJÖGREN, pg. 20 second to last ¶; wherein “metrics may also be used to characterise the progress of the maturation process, for example by comparison to a corresponding target metric” SJÖGREN, pg. 13 last ¶; “A maturation process may be deemed to progress normally if it leads to a product that meets predetermined quality criteria” SJÖGREN, top of pg. 14).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract, from the region extracted by the region extraction unit, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation, with the teachings of SJÖGREN of having wherein the evaluation unit is configured to evaluate, based on a predetermined evaluation criterion, at least any one selected from the group consisting of a state of the same cell colony and the condition for the cell culture.
Wherein having Matsubara’s cell image evaluation device, wherein the evaluation unit is configured to evaluate, based on a predetermined evaluation criterion, at least any one selected from the group consisting of a state of the same cell colony and the condition for the cell culture.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 19, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 9, Matsubara fails to explicitly teach wherein the evaluation unit is configured to evaluate a degree of correlation between the image feature value obtained through the estimation by the feature value estimation unit and at least any one selected from the group consisting of a predetermined feature of the same cell colony and the condition for the cell culture.
However, SJÖGREN teaches wherein the evaluation unit is configured to evaluate a degree of correlation between the image feature value obtained through the estimation by the feature value estimation unit and at least any one selected from the group consisting of a predetermined feature of the same cell colony and the condition for the cell culture (“The statistical model may be a latent variable model that models the maturation time as a function of a set of process-related variables comprising the image-derived features... The latent variable model may capture how the covariance structure of the process-related variables varies over time during operation of a process. This may be referred to as a “batch evolution model”... latent variables that describe the aspects of the process related variables that are most correlated with maturity.” SJÖGREN, top of pg. 15; “This may be used for example to predict or otherwise relate localised metrics of interest derived from histology with localised expert defined features derived from imaging of the sample during culture. For example, this may be used to identify whether cells differentiate slower than expected in a local region of the sample during on-line measurement, by directly correlating imaging data at that location with the resulting local phenotype as described by histology” SJÖGREN, top of pg. 18).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract, from the region extracted by the region extraction unit, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation, with the teachings of SJÖGREN of having wherein the evaluation unit is configured to evaluate a degree of correlation between the image feature value obtained through the estimation by the feature value estimation unit and at least any one selected from the group consisting of a predetermined feature of the same cell colony and the condition for the cell culture.
Wherein having Matsubara’s cell image evaluation device, wherein the evaluation unit is configured to evaluate a degree of correlation between the image feature value obtained through the estimation by the feature value estimation unit and at least any one selected from the group consisting of a predetermined feature of the same cell colony and the condition for the cell culture.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 20, Matsubara teaches a cell image analysis method comprising:
an image acquisition step of acquiring(“The imaging device 2 captures the image of the cell in the culture container placed on the stage 10. The imaging device 2 includes an optical system 20 which captures the image of the cell” Matsubara, [0051]) time-series images, which were acquired at two or more time points different from each other on a time series during cell culture (“a cell image of an observation region including the stem cell in the culture container is captured by the phase contrast microscope or the differential interference microscope of the imaging device 2 (S12). Specifically, 40 shots×40 shots of images of a rectangular observation region... the maturity information acquisition unit 33 acquires, for example, the culture period at the time when the cell image is captured as the information related to the maturity (S16)” Matsubara, [0117], [0119]; “in the initial stage of seeding and the stage after a few days have elapsed since the seeding, the number of exposures may be two or more and a plurality of cell images may be added. In the stage after a week has elapsed since the seeding, the number of exposures may be one and a cell image may be acquired. A change in the number of exposures substantially corresponds to a change in the exposure time” Matsubara, [0133]), and which include the same cell colony (“a culture period which is measured by a timer can be acquired as the information related to the maturity. In addition, the information related to the maturity is not limited to the culture period. For example, the following information may be acquired as the information related to the maturity: the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony, the number of cells in the cell colony, or the number of cells in a unit area smaller than the cell colony and maturity increases as the measured number of cells increases” Matsubara, [0061]);
a region extraction step of extracting a region of the same cell colony included in each of the time-series images (“the feature amount acquisition unit 32 extracts the outer circumferential shape and internal defect of the stem cell colony. However, in this stage, the colony is not clearly formed, as described above. Therefore, the feature amount acquisition unit 32 specifies the region in which the stem cell colony is estimated to be formed from the distribution state of the stem cells and extracts the outer circumferential shape and internal defect of the specified region” Matsubara, [0101]; “the following information may be acquired as the information related to the maturity: the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony” Matsubara, [0061]);
a feature value extraction step of extracting an image feature value from the region of the same cell colony in a plurality of images included in the time-series images (“the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony, the number of cells in the cell colony, or the number of cells in a unit area smaller than the cell colony and maturity increases as the measured number of cells increases. For example, the area, peripheral length, and maximum diameter of the cell colony can be acquired as the size of the cell colony” Matsubara, [0061]; “in the initial stage of seeding and the stage after a few days have elapsed since the seeding, the number of exposures may be two or more and a plurality of cell images may be added. In the stage after a week has elapsed since the seeding, the number of exposures may be one and a cell image may be acquired. A change in the number of exposures substantially corresponds to a change in the exposure time” Matsubara, [0133]);
Matsubara fails to explicitly teach a feature value estimation step of estimating, through use of the image feature value extracted in the feature value extraction step, the image feature value regarding the same cell colony assumed to be obtained at a given time point at which none of the time-series images is acquired; and an evaluation step of evaluating, through use of the image feature value obtained through the estimation in the feature value estimation step, one of the same cell colony or a condition for the cell culture.
However, SJÖGREN teaches a feature value estimation step of estimating, through use of the image feature value extracted in the feature value extraction step (“The statistical model may be adapted to predict the values of one or more metrics indicative of the progress or outcome of the maturation process using inputs comprising the plurality of image-derived features obtained at a plurality of time points during the maturation process” SJÖGREN, bottom of pg. 4 ¶6), the image feature value regarding the same cell colony assumed to be obtained at a given time point at which none of the time-series images is acquired (“when predicting end point metrics at a time point earlier than the end of the time trajectory with which the model was fitted, where imputation may be used to impute the missing time points in the time trajectory. Thus, it is possible to use imputation by regression during the experiment to predict what the future trajectory of image-derived features is likely to look like based on the measurements collected up to the current point” SJÖGREN, bottom of pg. 6 last ¶); and
an evaluation step of evaluating, through use of the image feature value obtained through the estimation in the feature value estimation step (“the statistical model comprise a model that predicts end point metrics (metrics indicative of the outcome of the maturation process) from a fitted batch-level model (a model that models the maturation time as a function of a set of variables comprising the image- derived features)... The model may take as input a set of variables comprising the image-derived features quantified for one or more time points, and a set of variables imputed for one or more further time points” SJÖGREN, bottom of pg. 6), one of the same cell colony or a condition for the cell culture (wherein the “[i]mage-derived features are values that are quantified for an image or set of images” in which “[t]he scalar or vector of image derived features may comprise one or more values quantifying an expert-defined visual feature in an image” SJÖGREN , pg. 12 ¶3; wherein “[t]he expert-defined visual features may be features associated with the local chemical or physiological state in the cell culture. The expert-defined visual features may further comprise one or more features selected from: features associated with localised cells, clusters of cells or macrostructures, features associated with the morphology of cells, clusters of cells or macrostructures, features associated with the spectral characteristics of a cell, cell cluster or macrostructure, where the spectral characteristics are indicative of a phenotype of the cells. Each image-derived feature may comprise one or more values quantifying an expert-defined visual feature in an image, or a summarised value derived therefrom” SJÖGREN, pg. 7 ¶4; additionally, see last ¶ of pg. 12; wherein “Using these metrics we can define expert-defined descriptors of the samples. In the printed bone tissue, osteoblasts successfully differentiated into osteocytes display filamentous morphologies” SJÖGREN, pg. 20 second to last ¶).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having a cell image analysis method comprising: an image acquisition step of acquiring time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction step of extracting a region of the same cell colony included in each of the time-series images; a feature value extraction step of extracting an image feature value from the region of the same cell colony in a plurality of images included in the time-series images with the teachings of SJÖGREN of having a feature value estimation step of estimating, through use of the image feature value extracted in the feature value extraction step, the image feature value regarding the same cell colony assumed to be obtained at a given time point at which none of the time-series images is acquired; and an evaluation step of evaluating, through use of the image feature value obtained through the estimation in the feature value estimation step, one of the same cell colony or a condition for the cell culture.
Wherein having Matsubara’s cell image evaluation device wherein a feature value estimation step of estimating, through use of the image feature value extracted in the feature value extraction step, the image feature value regarding the same cell colony assumed to be obtained at a given time point at which none of the time-series images is acquired; and an evaluation step of evaluating, through use of the image feature value obtained through the estimation in the feature value estimation step, one of the same cell colony or a condition for the cell culture.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 21, Matsubara teaches a cell image analysis method comprising:
an image acquisition step of acquiring (“The imaging device 2 captures the image of the cell in the culture container placed on the stage 10. The imaging device 2 includes an optical system 20 which captures the image of the cell” Matsubara, [0051]) time-series images, which were acquired at two or more time points different from each other on a time series during cell culture (“a cell image of an observation region including the stem cell in the culture container is captured by the phase contrast microscope or the differential interference microscope of the imaging device 2 (S12). Specifically, 40 shots×40 shots of images of a rectangular observation region... the maturity information acquisition unit 33 acquires, for example, the culture period at the time when the cell image is captured as the information related to the maturity (S16)” Matsubara, [0117], [0119]; “in the initial stage of seeding and the stage after a few days have elapsed since the seeding, the number of exposures may be two or more and a plurality of cell images may be added. In the stage after a week has elapsed since the seeding, the number of exposures may be one and a cell image may be acquired. A change in the number of exposures substantially corresponds to a change in the exposure time” Matsubara, [0133]), and which include the same cell colony (“a culture period which is measured by a timer can be acquired as the information related to the maturity. In addition, the information related to the maturity is not limited to the culture period. For example, the following information may be acquired as the information related to the maturity: the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony, the number of cells in the cell colony, or the number of cells in a unit area smaller than the cell colony and maturity increases as the measured number of cells increases” Matsubara, [0061]);
a region extraction step of extracting a region of the same cell colony included in each of the time-series images (“the feature amount acquisition unit 32 extracts the outer circumferential shape and internal defect of the stem cell colony. However, in this stage, the colony is not clearly formed, as described above. Therefore, the feature amount acquisition unit 32 specifies the region in which the stem cell colony is estimated to be formed from the distribution state of the stem cells and extracts the outer circumferential shape and internal defect of the specified region” Matsubara, [0101]; “the following information may be acquired as the information related to the maturity: the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony” Matsubara, [0061]);
a feature value extraction step of extracting, from the region extracted in the region extraction step, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation (“a culture period which is measured by a timer can be acquired as the information related to the maturity. In addition, the information related to the maturity is not limited to the culture period. For example, the following information may be acquired as the information related to the maturity: the image information of a cell colony region in the cell image is analyzed to measure the size of the cell colony, the number of cells in the cell colony, or the number of cells in a unit area smaller than the cell colony and maturity increases as the measured number of cells increases. For example, the area, peripheral length, and maximum diameter of the cell colony can be acquired as the size of the cell colony” Matsubara, [0061]; wherein the first feature is that of the size of the cell colony and the second feature is that of a texture, “[f]or example, the brightness of the image of the cell colony region or texture, such as uniformity or roughness, may be acquired as the information related to the maturity” Matsubara, [0062]);
Matsubara fails to explicitly teach a feature value estimation step of estimating the second image feature value corresponding to a given value regarding the first image feature value; and
an evaluation step of evaluating, through use of the second image feature value obtained through the estimation in the feature value estimation step, at least any one selected from the group consisting of the same cell colony and a condition for the cell culture.
However, SJÖGREN teaches a feature value estimation step of estimating the second image feature value corresponding to a given value regarding the first image feature value (“The statistical model may be adapted to predict the values of one or more metrics indicative of the progress or outcome of the maturation process using inputs comprising the plurality of image-derived features obtained at a plurality of time points during the maturation process” SJÖGREN, bottom of pg. 4 ¶6; wherein the image-derived features are that of the second image feature value, “geometric properties of cells, clusters or macrostructures may be quantified and used as image-derived features. Geometric properties of cells, clusters or macrostructures that may be quantified include their size (quantified as the length of a minor and/or major axis, the characteristic dimension of an equivalent shape such as a circle or sphere, the Feret diameter, etc.), shape (quantified as the similarity to a predetermined shape such as e.g. sphericity, or any parameter that captures features of the shape such as a ratio of the length of a major and minor axis, the number of branches in a vascular network, the eccentricity, roundness, circularity, solidity, aspect ratio of an object, etc.), area, volume, or circumference” SJÖGREN, last ¶ of pg. 12); and
an evaluation step of evaluating, through use of the second image feature value obtained through the estimation in the feature value estimation step (“the statistical model comprise a model that predicts end point metrics (metrics indicative of the outcome of the maturation process) from a fitted batch-level model (a model that models the maturation time as a function of a set of variables comprising the image- derived features)... The model may take as input a set of variables comprising the image-derived features quantified for one or more time points, and a set of variables imputed for one or more further time points” SJÖGREN, bottom of pg. 6 and last ¶ of pg. 12 for the second image feature value(s) as noted above), at least any one selected from the group consisting of the same cell colony and a condition for the cell culture (wherein the “[i]mage-derived features are values that are quantified for an image or set of images” in which “[t]he scalar or vector of image derived features may comprise one or more values quantifying an expert-defined visual feature in an image” SJÖGREN , pg. 12 ¶3; wherein “[t]he expert-defined visual features may be features associated with the local chemical or physiological state in the cell culture. The expert-defined visual features may further comprise one or more features selected from: features associated with localised cells, clusters of cells or macrostructures, features associated with the morphology of cells, clusters of cells or macrostructures, features associated with the spectral characteristics of a cell, cell cluster or macrostructure, where the spectral characteristics are indicative of a phenotype of the cells. Each image-derived feature may comprise one or more values quantifying an expert-defined visual feature in an image, or a summarised value derived therefrom” SJÖGREN, pg. 7 ¶4; additionally, see last ¶ of pg. 12; wherein “Using these metrics we can define expert-defined descriptors of the samples. In the printed bone tissue, osteoblasts successfully differentiated into osteocytes display filamentous morphologies” SJÖGREN, pg. 20 second to last ¶).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara of having a cell image analysis method comprising: an image acquisition step of acquiring time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction step of extracting a region of the same cell colony included in each of the time-series images; a feature value extraction step of extracting, from the region extracted in the region extraction step, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation with the teachings of SJÖGREN of having a feature value estimation step of estimating the second image feature value corresponding to a given value regarding the first image feature value; and an evaluation step of evaluating, through use of the second image feature value obtained through the estimation in the feature value estimation step, at least any one selected from the group consisting of the same cell colony and a condition for the cell culture.
Wherein having Matsubara’s cell image evaluation device wherein, a feature value estimation step of estimating the second image feature value corresponding to a given value regarding the first image feature value; and an evaluation step of evaluating, through use of the second image feature value obtained through the estimation in the feature value estimation step, at least any one selected from the group consisting of the same cell colony and a condition for the cell culture.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and SJÖGREN are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while SJÖGREN quantifies features that are informative of the cells and cellular structural morphology, function and identity, and/or cell environment to integrate these into a statistical model that captures a relationship between these features and metrics that characterize the progress and quality of the maturation progress. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and SJÖGREN et al. (WO 2024094674 A1), page 4 paragraph 4).
Regarding claim 22, Matsubara in view of SJÖGREN teach the cell analysis method of claim 20, Matsubara further teaches a non-transitory storage medium having stored thereon a program for causing a computer to execute (“a non-transitory computer readable recording medium”, see Matsubara, claim 19) the cell image analysis method of claim 20.
Regarding claim 23, Matsubara in view of SJÖGREN teach the cell analysis method of claim 21, Matsubara further teaches a non-transitory storage medium having stored thereon a program for causing a computer to execute (“a non-transitory computer readable recording medium”, see Matsubara, claim 19) the cell image analysis method of claim 21.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Matsubara et al. in view of SJÖGREN et al. and in further view of Erhard et al. (US 20220306979 A1, hereinafter referred to as “Erhard”).
Regarding claim 3, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 1, Matsubara in view of SJÖGREN fail to explicitly teach wherein the feature value estimation unit is configured to estimate, through interpolation processing, the image feature value regarding the same cell colony assumed to be obtained at the given time point at which none of the time-series images is acquired.
However, Erhard teaches wherein the feature value estimation unit is configured to estimate, through interpolation processing, the image feature value regarding the same cell colony assumed to be obtained at the given time point at which none of the time-series images is acquired (“The data, i.e. the on-line parameter set, associated with the entire cultivation process, and the associated date and time stamp were used for each cultivation... to ensure that the runs can be compared with one another, the corresponding on-line parameters were interpolated for all missing time stamps” Erhard, [0174]; “Since the data were available with different data densities, they had to be interpolated accordingly. A linear interpolation and an interpolation using the moving average method were used for this purpose.... This ensured that the interpolated values were always between two raw measured values. The interpolation is therefore always within the natural fluctuation range of the measurement signal of the process variables” Erhard, [0319]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara in view of SJÖGREN of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract an image feature value from the region extracted by the region extraction unit with the teachings of Erhard of having wherein the feature value estimation unit is configured to estimate, through interpolation processing, the image feature value regarding the same cell colony assumed to be obtained at the given time point at which none of the time-series images is acquired.
Wherein having Matsubara’s cell image evaluation device, wherein the feature value estimation unit is configured to estimate, through interpolation processing, the image feature value regarding the same cell colony assumed to be obtained at the given time point at which none of the time-series images is acquired.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and Erhard are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while Erhard of having a monitoring cultivation of cells to assess cultivation conditions and analyze the measured parameters and their relationships using mathematical models of machine learning. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and Erhard et al. (US 20220306979 A1), paragraphs [0004]-[0005].
Claim(s) 4 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Matsubara et al. in view of SJÖGREN et al. in view of Erhard et al. and in further view of Akiyoshi et al. (US 20220101568 A1, hereinafter referred to as “Akiyoshi”).
Regarding claim 4, Matsubara in view of SJÖGREN and in further view of Erhard teach the cell image analysis apparatus according to claim 3, Matsubara in view of SJÖGREN fail to explicitly teach wherein the feature value estimation unit is configured to perform the interpolation processing through use of the image feature value included in the time-series images.
However, Erhard further teaches wherein the feature value estimation unit is configured to perform the interpolation processing through use of the image feature value included in the time-series images (“The data, i.e. the on-line parameter set, associated with the entire cultivation process, and the associated date and time stamp were used for each cultivation... to ensure that the runs can be compared with one another, the corresponding on-line parameters were interpolated for all missing time stamps” Erhard, [0174]; “Since the data were available with different data densities, they had to be interpolated accordingly. A linear interpolation and an interpolation using the moving average method were used for this purpose.... This ensured that the interpolated values were always between two raw measured values. The interpolation is therefore always within the natural fluctuation range of the measurement signal of the process variables” Erhard, [0319]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara in view of SJÖGREN of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract an image feature value from the region extracted by the region extraction unit with the teachings of Erhard of having wherein the feature value estimation unit is configured to perform the interpolation processing through use of the image feature value included in the time-series images.
Wherein having Matsubara’s cell image evaluation device, wherein the feature value estimation unit is configured to perform the interpolation processing through use of the image feature value included in the time-series images.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and Erhard are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while Erhard of having a monitoring cultivation of cells to assess cultivation conditions and analyze the measured parameters and their relationships using mathematical models of machine learning. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and Erhard et al. (US 20220306979 A1), paragraphs [0004]-[0005].
Matsubara in view of SJÖGREN and in further view of Erhard fails to explicitly teach acquired at two or more consecutive time points including the time points immediately before and after the given time point at which none of the time-series images is acquired.
However, Akiyoshi teaches acquired at two or more consecutive time points including the time points immediately before and after the given time point at which none of the time-series images is acquired (“FIG. 5 is a schematic diagram showing different examples of the time-lapse image input to the image generator 2 and the output growth prediction image. The input designated feature D is the culture elapsed time T2.5, which is longer than the culture elapsed time T2 and shorter than the culture elapsed time T3. As shown in FIG. 5, the image generation system 100 generates a growth prediction image B2.5 of the observed colony X corresponding to the culture elapsed time T2.5 (designated feature D) of the observed colony X” Akiyoshi, [0056]; Fig. 5).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara in view of SJÖGREN and in further view of Erhard of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract an image feature value from the region extracted by the region extraction unit with the teachings of Akiyoshi of having acquired at two or more consecutive time points including the time points immediately before and after the given time point at which none of the time-series images is acquired.
Wherein having Matsubara’s cell image evaluation device wherein acquired at two or more consecutive time points including the time points immediately before and after the given time point at which none of the time-series images is acquired.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and Akiyoshi are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while Akiyoshi provides generated growth prediction images of cell colonies at an arbitrary designated culture elapsed time which has learned a relationship between the time-series images of cell culture and its feature(s). Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and Akiyoshi et al. (US 20220101568 A1), paragraphs [0005]-[0007].
Regarding claim 13, Matsubara in view of SJÖGREN teach the cell image analysis apparatus according to claim 9, Matsubara in view of SJÖGREN teach fails to explicitly teach further comprising a time point estimation unit configured to estimate a target time point that is a time point at which the first image feature value of the same cell colony has the given value, the second image feature value corresponding to a given value regarding the first image feature value when the target time point falls within a range of a time period including the two or more time points at which the time-series images were acquired on the time series.
However, Akiyoshi teaches further comprising a time point estimation unit configured to estimate a target time point that is a time point at which the first image feature value of the same cell colony has the given value (“FIG. 5 is a schematic diagram showing different examples of the time-lapse image input to the image generator 2 and the output growth prediction image” Akiyoshi, [0056]; Fig. 5), the second image feature value corresponding to a given value regarding the first image feature value when the target time point falls within a range of a time period including the two or more time points at which the time-series images were acquired on the time series (“FIG. 5 is a schematic diagram showing different examples of the time-lapse image input to the image generator 2 and the output growth prediction image. The input designated feature D is the culture elapsed time T2.5, which is longer than the culture elapsed time T2 and shorter than the culture elapsed time T3. As shown in FIG. 5, the image generation system 100 generates a growth prediction image B2.5 of the observed colony X corresponding to the culture elapsed time T2.5 (designated feature D) of the observed colony X” Akiyoshi, [0056]; Fig. 5; wherein the features correspond to their given values, since feature D in Akiyoshi is that of “the size of the observed cell O, the color of the observed cell O, the thickness of the observed cell O, the transmittance of the observed cell O, the fluorescence intensity of the observed cell O, or the luminescence intensity of observed cell O” or a combination of these, see Akiyoshi, [0100]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara in view of SJÖGREN of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract, from the region extracted by the region extraction unit, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation with the teachings of Akiyoshi of having a time point estimation unit configured to estimate a target time point that is a time point at which the first image feature value of the same cell colony has the given value, the second image feature value corresponding to a given value regarding the first image feature value when the target time point falls within a range of a time period including the two or more time points at which the time-series images were acquired on the time series.
Wherein having Matsubara’s cell image evaluation device wherein a time point estimation unit configured to estimate a target time point that is a time point at which the first image feature value of the same cell colony has the given value, the second image feature value corresponding to a given value regarding the first image feature value when the target time point falls within a range of a time period including the two or more time points at which the time-series images were acquired on the time series.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and Akiyoshi are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while Akiyoshi provides generated growth prediction images of cell colonies at an arbitrary designated culture elapsed time which has learned a relationship between the time-series images of cell culture and its feature(s). Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and Akiyoshi et al. (US 20220101568 A1), paragraphs [0005]-[0007].
Matsubara in view of SJÖGREN and in further view of Akiyoshi fails to explicitly teach wherein the feature value estimation unit is configured to estimate, through interpolation processing. However, Erhard teaches wherein the feature value estimation unit is configured to estimate, through interpolation processing (“The data, i.e. the on-line parameter set, associated with the entire cultivation process, and the associated date and time stamp were used for each cultivation... to ensure that the runs can be compared with one another, the corresponding on-line parameters were interpolated for all missing time stamps” Erhard, [0174]; “Since the data were available with different data densities, they had to be interpolated accordingly. A linear interpolation and an interpolation using the moving average method were used for this purpose.... This ensured that the interpolated values were always between two raw measured values. The interpolation is therefore always within the natural fluctuation range of the measurement signal of the process variables” Erhard, [0319])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara in view of SJÖGREN and in further view of Akiyoshi of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract, from the region extracted by the region extraction unit, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation with the teachings of Erhard of having wherein the feature value estimation unit is configured to estimate, through interpolation processing.
Wherein having Matsubara’s cell image evaluation device, wherein the feature value estimation unit is configured to estimate, through interpolation processing.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and Erhard are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while Erhard of having a monitoring cultivation of cells to assess cultivation conditions and analyze the measured parameters and their relationships using mathematical models of machine learning. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and Erhard et al. (US 20220306979 A1), paragraphs [0004]-[0005].
Regarding claim 14, Matsubara in view of SJÖGREN in view of Akiyoshi and in further view of Erhard teach the cell image analysis apparatus according to claim 13, Matsubara in view of SJÖGREN in view of Akiyoshi fail to explicitly teach wherein the feature value estimation unit is configured to perform the interpolation processing through use of the second image feature value included in the time-series images.
However, Erhard teaches wherein the feature value estimation unit is configured to perform the interpolation processing through use of the second image feature value included in the time-series images (“The data, i.e. the on-line parameter set, associated with the entire cultivation process, and the associated date and time stamp were used for each cultivation... to ensure that the runs can be compared with one another, the corresponding on-line parameters were interpolated for all missing time stamps” Erhard, [0174]; “Since the data were available with different data densities, they had to be interpolated accordingly. A linear interpolation and an interpolation using the moving average method were used for this purpose.... This ensured that the interpolated values were always between two raw measured values. The interpolation is therefore always within the natural fluctuation range of the measurement signal of the process variables” Erhard, [0319]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara in view of SJÖGREN and in further view of Akiyoshi of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract, from the region extracted by the region extraction unit, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation with the teachings of Erhard of having wherein the feature value estimation unit is configured to perform the interpolation processing through use of the second image feature value included in the time-series images.
Wherein having Matsubara’s cell image evaluation device, wherein the feature value estimation unit is configured to perform the interpolation processing through use of the second image feature value included in the time-series images.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and Erhard are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while Erhard of having a monitoring cultivation of cells to assess cultivation conditions and analyze the measured parameters and their relationships using mathematical models of machine learning. Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and Erhard et al. (US 20220306979 A1), paragraphs [0004]-[0005].
Matsubara in view of SJÖGREN and in further view of Erhard fails to explicitly teach acquired at two or more consecutive time points including the time points immediately before and after the target time point.
However, Akiyoshi teaches acquired at two or more consecutive time points including the time points immediately before and after the target time point (“FIG. 5 is a schematic diagram showing different examples of the time-lapse image input to the image generator 2 and the output growth prediction image. The input designated feature D is the culture elapsed time T2.5, which is longer than the culture elapsed time T2 and shorter than the culture elapsed time T3. As shown in FIG. 5, the image generation system 100 generates a growth prediction image B2.5 of the observed colony X corresponding to the culture elapsed time T2.5 (designated feature D) of the observed colony X” Akiyoshi, [0056]; Fig. 5).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Matsubara in view of SJÖGREN and in further view of Erhard of having a cell image analysis apparatus comprising: an image acquisition unit configured to acquire time-series images, which were acquired at two or more time points different from each other on a time series during cell culture, and which include the same cell colony; a region extraction unit configured to extract a region of the same cell colony included in each of the time-series images; a feature value extraction unit configured to extract, from the region extracted by the region extraction unit, a first image feature value corresponding to a feature that changes in accordance with a culture time period of the cell culture and a second image feature value corresponding to a feature to be used for evaluation with the teachings of Akiyoshi of having acquired at two or more consecutive time points including the time points immediately before and after the target time point.
Wherein having Matsubara’s cell image evaluation device wherein acquired at two or more consecutive time points including the time points immediately before and after the target time point.
The motivation behind the modification would have been to obtain a cell image evaluation device that evaluates cell images on the basis of feature information, since both Matsubara and Akiyoshi are processes that evaluate cell maturation over a period of time. Wherein Matsubara’s cell evaluation technique evaluates the culture state of a cell colony on the basis of the same criteria for differentiation, while Akiyoshi provides generated growth prediction images of cell colonies at an arbitrary designated culture elapsed time which has learned a relationship between the time-series images of cell culture and its feature(s). Please see Matsubara et al. (US 20160163049 A1), paragraphs [0011]-[0012] and Akiyoshi et al. (US 20220101568 A1), paragraphs [0005]-[0007].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Floto et al. (US 20200131465 A1) – capturing at a first time shortly after plating a series of vertically spaced-apart images of the well showing locations of the cells, capturing at a second, later time images of the same well showing locations of candidate cell colonies.
Wagner et al. (US 20220284574 A1) – automated cell culture analysis based on analysis of images to edit the cell culture produce using machine learning or algorithms trained to evaluate quality of a cell and/or colony based on features determined to be predictive.
Bharti et al. (US 20210117729 A1) – uses machine learning model to identify characteristics for a plurality of cells and evaluates a cell; predicting includes identifying at least one feature of the test cell(s) and the characteristics of the test cell(s) using the at least one feature identified.
Tsujimoto (US 20160364599 A1) – a colony evaluation in which the state of the colony is evaluated, based on time series of cell images, to determine a degree of non-differentiation or degree of differentiation of stem cell colonies.
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/EMMANUEL SILVA-AVINA/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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