Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 26 is objected to because of the following informalities:
Claim 26 recites limitations regarding the passaging of cells. The Examiner notes that the triturating step (i.e., “triturating the incubated subset of iPSCs after a media is added to the incubated subset of iPSCs”) and the suspending of a cell pellet (i.e., “suspending the aspirated subset of iPSCs into the media”) both refer to the cell culture media used to culture the cells. The Examiner notes that while the same media is being used in each step, they are different samples of the same media (i.e., the same volume of media which is used to triturate the cells is not then reused again to resuspend the cells – instead a fresh sample of the same media is used to resuspend the pellet). The Examiner suggests, if appropriate, that the two volumes be explicitly defined (e.g., “triturating the incubated subset of iPSCs after a first volume of cell culture media is added to the incubated subset of iPSCs” and “suspending the aspirated subset of iPSCs into a second volume of the cell culture media”.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 33 and 36-37 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 33, the usage of “about” when defining different ranges of confluence scores (e.g., “20% to about 90%”, “about 25% to about 85%”) is indefinite because it is unclear how much variance (i.e., ±3%, ±5%, etc.) should be assigned to the term “about”.
Regarding Claims 36-37, there is no antecedent basis for the term “the second machine learning model”. Claims 1, 2, and 34 (which are the claims which claims 36-37 depend on) only explicitly recite a first machine learning model used for determining a quality score – there is no clear indication for how a “second machine learning model” should be interpreted. The Examiner notes that in the prior art rejections made for claims 36-37, the Examiner assumes that a “second machine learning model” is interpreted as any machine learning model which is utilized in the processing and analysis of cells.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 15, 17, 27, and 38-39 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Elanzew et al. (“The StemCellFactory: A Modular System Integration for Automated Generation and Expansion of Human Induced Pluripotent Stem Cells”, DOI: 10.3389/fbioe.2020.580352, Publication Year: 2020; hereinafter “Elanzew”).
Regarding Claim 1, Elanzew discloses a system for autonomous selection and maintenance of induced pluripotent stem cells (iPSCs), the system comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for (see Figs. 2-3, Elanzew):
capturing a first plurality of images depicting a plurality of iPSCs stored within a first plurality of sample containers using an imaging system (Technical Set-Up of the Automated hiPSC Cultivation Platform “StemCellFactory”, Elanzew discloses an autonomous system StemCellFactory which captures images of human iPSCs placed in multi-well (i.e., 6-well and 24-well) plates.);
determining, using one or more machine learning models, a confluence score for each sample container of the first plurality of sample containers based on the first plurality of images, the confluence score representing a confluence of the iPSCs of the plurality of iPSCs distributed within each sample container (Fig. 1, Automated Confluence-Based Passaging of hiPSCs, Automated High-Speed, Deep-Learning Microscopy, Elanzew discloses analyzing images with a trained deep learning algorithm to obtain confluence values for each well in a multi-well plate.);
autonomously selecting at least one sample container from the first plurality of sample containers based on the confluence score of each of the first plurality of sample containers, wherein the at least one selected sample container comprises a subset of iPSCs of the plurality of iPSCs; and autonomously performing one or more maintenance operations on the subset of iPSCs stored within the at least one selected sample container (Automated Confluence-Based Passaging of hiPSCs, Module 3: Parallel Expansion of Primary hiPSC Clones and Establishment of Transgene-Free hiPSC Lines, Figs. 7A-7B, Elanzew discloses an autonomous passaging process (i.e., a maintenance operation) based on the confluence value obtained from imaging the well containing the hiPSC.).
Regarding Claim 15, Elanzew discloses the system of claim 1, wherein autonomously performing the one or more maintenance operations on the subset of iPSCs stored within the at least one selected sample container comprises: performing passaging on the subset of iPSCs stored within the at least one selected sample container (Automated Confluence-Based Passaging of hiPSCs, Module 3: Parallel Expansion of Primary hiPSC Clones and Establishment of Transgene-Free hiPSC Lines, Figs. 7A-7B, Elanzew discloses an autonomous passaging process (i.e., a maintenance operation) based on the confluence value obtained from imaging the well containing the hiPSC.), adding one or more reagents to the subset of iPSCs stored within the at least one selected sample container, banking the subset of iPSCs stored within the at least one selected sample container, performing a quality control (QC) check of the subset of iPSCs stored within the at least one selected sample container, performing a pluripotency status check of the subset of iPSCs stored within the at least one selected sample container, or discarding at least the subset of iPSCs stored within the at least one selected sample container.
Regarding Claim 17, Elanzew discloses the system of claim 1, wherein autonomously performing the one or more maintenance operations on the subset of iPSCs stored within the at least one selected sample container comprises: performing passaging of the subset of iPSCs stored within the at least one selected sample container (Automated Confluence-Based Passaging of hiPSCs, Module 3: Parallel Expansion of Primary hiPSC Clones and Establishment of Transgene-Free hiPSC Lines, Figs. 7A-7B, Elanzew discloses an autonomous passaging process (i.e., a maintenance operation) based on the confluence value obtained from imaging the well containing the hiPSC.).
Regarding Claim 27, Elanzew discloses the system of claim 1, wherein the one or more programs further include instructions for: performing one or more feedings to the plurality of iPSCs stored within the first plurality of sample containers prior to the first plurality of images being captured (Human Pluripotent Stem Cell Culture, Elanzew discloses feeding the human iPSCs using cell culture media).
Regarding Claim 38, Elanzew discloses the system of claim 1, wherein the imaging system comprises a digital microscopy imaging system, and wherein the imaging system captures bright-field, phase contrast, or fluorescent images (Fig. 2, Elanzew discloses an imaging system within the StemCellFactory which includes a microscope capable of obtaining bright-field and phase contrast images.).
Regarding Claim 39, Elanzew discloses the system of claim 1, wherein the first plurality of sample containers is disposed on a slide plate, and the slide plate is a multi-well plate comprising a plurality of sample wells (Technical Set-Up of the Automated hiPSC Cultivation Platform “StemCellFactory”, Elanzew discloses an autonomous system StemCellFactory which captures images of human iPSCs placed in multi-well (i.e., 6-well and 24-well) plates.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2, 5-10, 16, and 29-36 are rejected as being unpatentable over Elanzew in view of Wagner et al. (US 2022/0282203; hereinafter “Wagner”).
Regarding Claim 2, Elanzew discloses the system of claim 1, wherein the one or more programs further include instructions for:
determining, (Figs. 7C-7D, Quality Control of Newly Generated hiPSC Clones, Elanzew discloses performing a quality control check on generated hiPSC clones (i.e., a measurement of hiPSC health).),
wherein autonomously selecting the at least one sample container comprises: autonomously selecting the at least one sample container from the first plurality of sample containers based on the confluence score and the quality score of each of the first plurality of sample containers (Automated Confluence-Based Passaging of hiPSCs, Module 3: Parallel Expansion of Primary hiPSC Clones and Establishment of Transgene-Free hiPSC Lines, Figs. 7A-7B, Elanzew discloses an autonomous passaging process (i.e., a maintenance operation) based on the confluence value obtained from imaging the well containing the hiPSC. The Examiner notes that the process disclosed by Elanzew ensures that the generated hiPSC’s meet a certain quality benchmark (note the Abstract and Conclusion with regards to quality control), and therefore the autonomous selection is “based on” the combination of confluence score and the quality score.).
Elanzew does not explicitly disclose determining, using the one or more machine learning models, a quality score for each sample container of the first plurality of sample containers based on the first plurality of images, the quality score representing a quality of the iPSCs of the plurality of iPSCs distributed within each sample container (italicized for context).
Wagner discloses using the one or more machine learning models, a quality score for each sample container of the first plurality of sample containers based on the first plurality of images, the quality score representing a quality of the iPSCs of the plurality of iPSCs distributed within each sample container (italicized for context) ([0024], [0440-0441], Wagner discloses predicting clonal quality using a predictive model, which is used in the process of selecting an iPSC colony for passaging.).
Elanzew and Wagner are considered to be analogous to the claimed invention as they are in the same field of utilizing image processing techniques for the automatic culture and growth of iPSCs. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elanzew such that the quality control and metrics performed by Elanzew were replaced by the automated predictive model disclosed by Wagner, and consequently utilizing that information in the determinization of which cells should be passaged. The motivation for this combination being the ability to automate the quality control process instead of manually performing experimentation to determine cell quality.
Regarding Claim 5, Elanzew in view of Wagner teaches the system of claim 2, wherein the one or more programs further include instructions for:
determining a growth metric for each sample container of the first plurality of sample containers based on the first plurality of images, the growth metric representing a growth status of the iPSCs of the plurality of iPSCs distributed within each sample container (Fig. 4B, Fig. 7A, Elanzew discloses plotting the growth of cells in the cell culture over time (i.e., the changes in cell number over time). The Examiner further notes how the cell culture number can be directly associated with confluence as shown in Fig. 7A. Additionally, Elanzew begins his experiments using human fibroblasts, but those same cells are then further differentiated and reprogrammed into iPSCs.),
wherein autonomously selecting the at least one sample container comprises: autonomously selecting the at least one sample container from the first plurality of sample containers based on the confluence score, the quality score, and the growth metric of each of the first plurality of sample containers (Automated Confluence-Based Passaging of hiPSCs, Module 3: Parallel Expansion of Primary hiPSC Clones and Establishment of Transgene-Free hiPSC Lines, Figs. 7A-7B, Elanzew discloses an autonomous passaging process (i.e., a maintenance operation) based on the confluence value obtained from imaging the well containing the hiPSC. The Examiner notes that a confluence value is related to a growth metric, and consequently that a confluence value is based on a growth metric. Also note [0159], wherein Wagner discloses determining the proliferation rate of iPSCs to make predictions related to success, quality, or functionality of the iPSCs.).
Regarding Claim 6, Elanzew in view of Wagner teaches the system of claim 5, wherein the growth metric is indicative of whether the iPSCs of the plurality of iPSCs distributed within each sample container is in a growth phase (Fig. 4B, Fig. 7A, Elanzew discloses plotting the growth of cells over time. The Examiner notes that regions of positive growth (i.e., a positive slope/trend) correspond to a growth phase.).
Regarding Claim 7, Elanzew in view of Wagner teaches the system of claim 5, wherein the growth metric is indicative of a growth rate of the iPSCs of the plurality of iPSCs distributed within each sample container (Fig. 4B, Elanzew discloses plotting the growth of cells over time.).
Regarding Claim 8, Elanzew in view of Wagner teaches the system of claim 6, wherein the growth metric is indicative of whether the growth rate of the iPSCs of the plurality of iPSCs distributed within each sample container is positive (Fig. 4B, Fig. 7A, Elanzew discloses plotting the growth of cells over time. The Examiner notes that regions of positive growth (i.e., a positive slope/trend) correspond to a growth phase.).
Regarding Claim 9, Elanzew in view of Wagner teaches the system of claim 5, wherein the growth metric is determined based on a first confluence score and a second confluence score of the plurality of iPSCs distributed within each sample container, wherein the first confluence score is associated with a first time point, and wherein the second confluence score is associated with a second time point later than the first time point (Fig. 7A, Fig. 4B, Elanzew discloses determining the growth rate of cells based on plotting the change in cell count number over time. The Examiner notes how cell count number and confluence are related, and additionally that a growth rate is based on a rate of change between two time points.).
Regarding Claim 10, Elanzew in view of Wagner teaches the system of claim 9, wherein the growth metric indicates positive growth if the second confluence score is higher than the first confluence score (Fig. 4B, Fig. 7A, Elanzew discloses a positive growth rate then there is a positive increase in cell number (and consequently in cell confluence) between two time points.).
Regarding Claim 161, Elanzew discloses the system of claim 15.
Elanzew does not explicitly disclose wherein discarding at least the subset of iPSCs stored within the at least one selected sample container comprises: discarding the subset of iPSCs stored within the at least one selected sample container; or discarding the plurality of iPSCs stored within the first plurality of sample containers.
Wagner discloses wherein discarding at least the subset of iPSCs stored within the at least one selected sample container comprises: discarding the subset of iPSCs stored within the at least one selected sample container; or discarding the plurality of iPSCs stored within the first plurality of sample containers ([0288], [0290-0292], Wagner discloses a cell removal mechanism for removing/discarding certain cells.).
Elanzew and Wagner are considered to be analogous to the claimed invention as they are in the same field of utilizing image processing techniques for the automatic culture and growth of iPSCs. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elanzew such that it further incorporates the ability to remove and discard cells based on the disclosure of Wagner. The motivation for this combination being the ability to remove cells which may not be relevant to the analysis.
Regarding Claim 29, Elanzew in view of Wanger teaches the system of claim 10, wherein the one or more programs further include instructions for: discarding one or more sample containers from the first plurality of sample containers based on at least one of the quality score, the confluence score, and/or the growth metric of the one or more sample containers ([0424-0425], Wagner discloses determining a colony quality to determine viable colonies and other colonies which should be removed.).
Regarding Claim 30, Elanzew in view of Wagner teaches the system of claim 10, wherein the one or more programs further include instructions for:
identifying one or more sample containers, wherein the one or more identified sample containers have at least one of: a quality score that is (i) less than a first threshold quality score and (ii) greater than or equal to a second threshold quality score,
a confluence score outside of a predefined range of confluence scores2, or
a growth metric indicating a growth rate that is negative or being less than a growth metric threshold ([0424-0425], Wanger discloses utilizing proliferation rate as a way to determine if a cell colony is viable and whether the cell should be removed.).
Regarding Claim 31, Elanzew in view of Wagner teaches the system of claim 30, wherein the one or more programs further include instructions for: feeding iPSCs stored within the one or more identified sample containers; capturing a second plurality of images depicting the iPSCs stored within the one or more identified sample containers using the imaging system; and determining at least one of an updated quality score, an updated confluence score, or an updated growth metric for each of the one or more identified sample containers using the one or more machine learning models (The Examiner notes Fig. 4B and Fig. 7A from Elanzew, which show the growth over time of the cultured cells. This growth is due to the continuous feeding and maintenance of the cells, and additionally the updated values each day are obtained based on images captured of the cultured cells.).
Regarding Claim 32, Elanzew in view of Wagner teaches the system of claim 31, wherein the one or more programs further include instructions for: selecting at least one of the one or more identified sample containers based on the at least one of the updated quality score, the updated confluence score, or the updated growth metric of the one or more sample containers (The Examiner notes that the selection of cells for passaging (as disclosed in Automated Confluence-Based Passaging of hiPSCs, Module 3: Parallel Expansion of Primary hiPSC Clones and Establishment of Transgene-Free hiPSC Lines, Figs. 7A-7B, Elanzew) is based on an updated cell confluence value (i.e., the selection is not done on day 0 based on an initial confluence value, but rather as the cells grow and the confluence value is updated).).
Regarding Claim 34, Elanzew in view of Wagner teaches the system of claim 2, wherein the one or more machine learning models comprise a first machine learning model trained to determine a quality score representing a quality of the iPSCs stored within each of the first plurality of sample containers ([0024], [0440-0441], Wagner discloses predicting clonal quality using a predictive model, which is used in the process of selecting an iPSC colony for passaging.).
Regarding Claim 36, Elanzew in view of Wagner teaches system of claim 34, wherein at least one of the first machine learning model or the second machine learning model comprise a convolutional neural network ([0419], Wagner discloses utilizing a Mask R-CNN as a predictive model.).
Claims 3-4 are rejected as being unpatentable over Elanzew in view of Wagner in view of Joutsijoki et al. (“Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images”, DOI: 10.1155/2016/3091039, Publication Year: 2016; hereinafter “Joutsijoki”).
Regarding Claim 3, Elanzew in view of Wagner teaches the system of claim 2.
Elanzew in view of Wagner does not explicitly teach wherein the quality score comprises a binary value, a numeric value, a classification, or any combination thereof.
Joutsijoki discloses wherein the quality score comprises a binary value, a numeric value, a classification (Figs. 1-7, Table 2, Joutsijoki discloses using different machine learning classifiers to classify images of iPSCs into a “good quality”, “bad quality”, or “semigood quality”.), or any combination thereof.
Elanzew, Wagner, and Joutsijoki are considered to be analogous to the claimed invention as they are in the same field of utilizing image processing techniques for the culture and growth of iPSCs. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elanzew in view of Wagner such that the quality score determination, as disclosed by Elanzew in view of Wagner, is based on the machine learning models classifying images into different quality categories as disclosed by Joutsijoki. The motivation for this combination being the ability to specifically train a model to output defined categories related to the quality of the cells.
Regarding Claim 4, Elanzew in view of Wagner in view of Joutsijoki teaches the system of claim 3, wherein the quality score comprises one of: a low-quality score, a medium-quality score, or a high-quality score (Figs. 1-7, Table 2, Joutsijoki discloses using different machine learning classifiers to classify images of iPSCs into a “good quality”, “bad quality”, or “semigood quality”.)
Claim 11-13 are rejected in view of Elanzew in view of Wagner in view of Bittner et al. (US 2020/0124626; hereinafter “Bittner”).
Regarding Claim 11, Elanzew in view of Wagner teaches the system of claim 5.
Elanzew in view of Wagner does not explicitly teach wherein autonomously selecting the at least one sample container from the first plurality of sample containers comprises: obtaining a ranking of a plurality of predefined confluence score ranges, wherein the plurality of predefined confluence score ranges comprises a first predefined confluence score range ranked higher than a second predefined confluence score range; and prioritizing selection of a sample having a confluence score in the first predefined confluence score range over a sample having a confluence score in the second predefined confluence score range.
Bittner discloses wherein autonomously selecting the at least one sample container from the first plurality of sample containers comprises:
obtaining a ranking of a plurality of predefined confluence score ranges, wherein the plurality of predefined confluence score ranges comprises a first predefined confluence score range ranked higher than a second predefined confluence score range ([0135], Bittner discloses a log-phase confluence range of 50-80% (i.e., a first predefined confluence range), and any cells which are less than that range (i.e., 0%-49% confluence – a second predefined confluence score range) are not in log-phase.); and
prioritizing selection of a sample having a confluence score in the first predefined confluence score range over a sample having a confluence score in the second predefined confluence score range ([0135], Bittner discloses splitting/passaging cells which are in a confluence range of 50%-80%.).
Elanzew, Wagner, and Bittner are considered to be analogous to the claimed invention as they are in the same field of culture and growth of iPSCs by utilizing image processing techniques. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elanzew in view of Wagner to include the confluence score ranges used to determine when cells should be split/passaged as disclosed by Bittner. The motivation for this combination being the ability to explicitly define specific categories when splitting/passaging should occur, which aide in the automation of the system.
Regarding Claim 12, Elanzew in view of Wagner in view of Bittner teaches the system of claim 11, wherein autonomously selecting the at least one sample container from the first plurality of sample containers comprises: prioritizing selection of a sample having a higher quality score over a sample having a lower quality score ([0024], [0424-0425], [0437], [0445], Wagner discloses utilizing cell proliferation rate (i.e., growth metric) as indicative of cell quality, and consequently keeping cells with a higher cell quality score.).
Regarding Claim 13, Elanzew in view of Wagner in view of Bittner teaches the system of claim 11, wherein autonomously selecting the at least one sample container from the first plurality of sample containers comprises: prioritizing selection of a sample having a higher or positive growth metric over a sample having a lower or negative growth metric ([0024], [0424-0425], [0437], [0445], Wagner discloses utilizing cell proliferation rate (i.e., growth metric) as indicative of cell quality, and consequently keeping cells with a higher cell quality score.).
Claims 18-253 are rejected as being unpatentable over Elanzew in view of Conway et al. (“Scalable 96-well Plate Based iPSC Culture and Production Using a Robotic Liquid Handling System”, DOI: 10.3791/52755, Publication Year: 2015; hereinafter “Conway”).
Regarding Claim 18, Elanzew discloses the system of claim 17.
Elanzew does not explicitly disclose wherein the one or more programs further include instructions for: distributing, subsequent to the subset of iPSCs stored within the at least one selected sample container being passaged, the subset of iPSCs across a second plurality of sample containers; and optionally subjecting the distributed subset of iPSCs stored in the second plurality of sample containers to one or more cell differentiation steps.
Conway discloses wherein the one or more programs further include instructions for: distributing, subsequent to the subset of iPSCs stored within the at least one selected sample container being passaged, the subset of iPSCs across a second plurality of sample containers (Fig. 8, Conway discloses distributing cells from one plate and passaging the cells to multiple additional plates.); and optionally subjecting the distributed subset of iPSCs stored in the second plurality of sample containers to one or more cell differentiation steps.
Elanzew, Wagner, and Conway are considered to be analogous to the claimed invention as they are in the same field of automated culture and growth of iPSCs. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elanzew to further include the additional distribution after passaging step disclosed by Conway. The motivation for this combination being the ability to further distribute cultured cells which extend the time which cells can be cultured.
Claim 26 is rejected as being unpatentable over Elanzew in view of Truong et al. (“Automating Human Induced Pluripotent Stem Cell Culture and Differentiation of iPSC-derived Retinal Pigment Epithelium for Personalized Drug Testing”, DOI: 10.1177/2472630320972110, Publication Year: 2022; hereinafter “Truong”).
Regarding Claim 26, Elanzew discloses the system of claim 17, wherein passaging comprises:
washing the subset of iPSCs stored within the at least one selected sample container (Automated Confluence-Based Passaging of hiPSCs, Elanzew discloses detaching cells by first washing cells with PBS),
incubating the washed subset of iPSCs with a dissociation reagent (Automated Confluence-Based Passaging of hiPSCs, Elanzew discloses adding EDTA to the washed cells.),
triturating the incubated subset of iPSCs after a media is added to the incubated subset of iPSCs (Automated Confluence-Based Passaging of hiPSCs, Elanzew discloses shaking the cells to promote detatchment.),
transferring the triturated subset of iPSCs to a sample container block (Automated Confluence-Based Passaging of hiPSCs, Elanzew discloses transferring the detached cells to a 50mL tube.),
Elanzew does not explicitly disclose centrifuging the transferred subset of iPSCs in the sample container block to pellet the centrifuged subset of iPSCs, performing a buffering exchange to the pelleted subset of iPSCs by aspirating the pelleted subset of iPSCs, and suspending the aspirated subset of iPSCs into the media.
Truong discloses centrifuging the transferred subset of iPSCs in the sample container block to pellet the centrifuged subset of iPSCs (Automated hiPSC culture on University of Minnesota Fluent 780 workstations, Truong discloses centrifuging in suspension.),
performing a buffering exchange to the pelleted subset of iPSCs by aspirating the pelleted subset of iPSCs, and suspending the aspirated subset of iPSCs into the media (Automated hiPSC culture on University of Minnesota Fluent 780 workstations, Truong discloses aspirating the supernatant after pelleting the cells, and consequently disrupting and resuspending the pellet in media.).
Elanzew and Truong are considered to be analogous to the claimed invention as they are in the same field of automated culture and growth of iPSCs. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elanzew to further include specific method of passaging disclosed by Truong which involves centrifuging the cells in suspension and consequently resuspending the pelleted cells in media. The motivation for this combination being the ability to adjust the concentration of cells by changing the volume of media the cells are suspended in.
Claim 28 is rejected as being unpatentable over Elanzew in view of Noggle et al. (US 2022/0073884; hereinafter “Noggle”).
Regarding Claim 28, Elanzew discloses the system of claim 1.
Elanzew does not explicitly disclose wherein the one or more programs further include instructions for: autonomously removing the plurality of iPSCs from cell storage using a cell handling system; and thawing the plurality of iPSCs using a cell thawing system, wherein the first plurality of images is captured after the thawing.
Noggle discloses wherein the one or more programs further include instructions for: autonomously removing the plurality of iPSCs from cell storage using a cell handling system; and thawing the plurality of iPSCs using a cell thawing system, wherein the first plurality of images is captured after the thawing ([0129-0130], [0265], [0279], Noggle discloses an automated system of obtaining frozen samples from a -80°C freezer and consequently thawing the samples.).
Elanzew and Noggle are considered to be analogous to the claimed invention as they are in the same field of culture and growth of iPSCs. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elanzew to further include the ability to thaw samples based on the disclosure provided by Noggle. The motivation for this combination being the ability to utilize previously-stored cells, increasing the range of possible samples which can be used with the autonomous system.
Claim 37 is rejected as being unpatentable over Elanzew in view of Wagner in view of Zhu et al. (“Deep learning-based predictive identification of neural stem cell differentiation”, DOI: 10.1038/s41467-021-22758-0, Publication Year: 2021; hereinafter “Zhu”) in view of Joy et al. (“Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis”, DOI: 10.1016/j.stemcr.2021.04.008, Publication Year: 2021; hereinafter “Joy”)
Regarding Claim 37, Elanzew in view of Wagner teaches system of claim 34.
Elanzew in view of Wagner does not explicitly teach wherein the first machine learning model is built on a ResNet architecture and the second machine learning model is built on a U-Net architecture.
Zhu discloses wherein the first machine learning model is built on a ResNet architecture (Fig. 1, Fig. 5, Zhu discloses utilizing a ResNet model to analyze images of stem cells. The Examiner notes that the classification of stem cells into different differentiations is a method of determining quality as it determines the quality of the differentiation technique (i.e., how successful the differentiation process is).)
Elanzew, Wagner, and Zhu are considered to be analogous to the claimed invention as they are in the same field of culture and growth of iPSCs by utilizing image processing techniques. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elanzew in view of Wagner such that it included the specific usage of a ResNet model to determine quality of a cell culture. The motivation being the ability to utilize a specific well-established machine learning model.
Elanzew in view of Wagner in view of Zhu does not explicitly teach wherein the second machine learning model is built on a U-Net architecture.
Joy discloses wherein the second machine learning model4 is built on a U-Net architecture (Ensemble deep neural network segmentation of dense hiPSC colonies, Joy discloses analyzing images of hiPSCs using models utilizing a U-net structure).
Elanzew, Wagner, Zhu, and Joy are considered to be analogous to the claimed invention as they are in the same field of culture and growth of iPSCs by utilizing image processing techniques. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elanzew in view of Wagner in view of Zhu such that it included the specific usage of a U-net model. The motivation being the ability to utilize a specific well-established machine learning model which can provide information regarding the location of cells in an image.
Allowable Subject Matter
Claim 14 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Schenk et al. (“Metrology-based quality and process control in automated stem cell production”, DOI: 10.1515/teme-2015-0036, Publication Year: 2015)
Ochs et al. (“Fully Automated Cultivation of Adipose-Derived Stem Cells in the StemCellDiscovery—A Robotic Laboratory for Small-Scale, High-Throughput Cell Production Including Deep Learning-Based Confluence Estimation”, DOI: 10.3390/pr9040575, Publication Year: 2021)
Sirenko et al. (“Automation of iPSC culture, passaging, and expansion with the CellXpress.ai Automated Cell Culture System”, https://www.moleculardevices.com/sites/default/files/en/assets/app-note/dd/3d-biology/automation-of-ipsc-culture-passaging-and-expansion-with-cellxpress-ai-automated-cell-culture-system.pdf, Publication Year: 2024)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PROMOTTO TAJRIAN ISLAM whose telephone number is (703)756-5584. The examiner can normally be reached Monday - Friday 8:30 am - 5:00 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at (571) 272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PROMOTTO TAJRIAN ISLAM/ Examiner, Art Unit 2669
/CHAN S PARK/ Supervisory Patent Examiner, Art Unit 2669
1 The Examiner notes that claim 16 inherits the claim limitations from claim 15, wherein the “discarding” of a subset of iPSCs is listed as one potential “maintenance mechanism” is a list of alternatives (i.e., the “discarding” limitation is not explicitly required to be one of the potential “maintenance mechanisms”). As such, claim 16 is consequently not explicitly required as it further expands on the optional “discarding” limitation – however for the clarity of the record the Examiner notes how the Wagner reference discloses the “discarding” limitation.
2 The Examiner notes that claims 33 and 35 inherit all of the limitations of the dependent claim 30, and furthermore expand on the usage of “confluence values” which is listed as one of the alternatives in claim 30. As claim 30 is rejected based on the growth metric limitation, and additionally the limitation of a “confluence score outside of a predefined range” is not explicitly required by claim 30, the limitations of claims 33 and 35 and also not explicitly required.
3 The Examiner notes the claim 19 (and consequently claims 20-25 which all depend on claim 19) inherits the limitations of claim 18, and specifically that claim 19 further expands on the optional step of further differentiating the distributed iPSCs. As the limitation in claim 18 is optional, the claim limitations of claims 19-25 are also optional and are rejected by the combination of Elanzew in view of Conway.
4 As noted in the 112(b) rejection made, the claimed “second machine learning model” is interpreted as any machine learning model which is utilized in the processing and analysis of cells.
Prosecution Insights