CTNF 18/895,444 CTNF 99673 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION The United States Patent & Trademark Office appreciates the application that is submitted by the inventor/assignee. The United States Patent & Trademark Office reviewed the following application and has made the following comments below. Information Disclosure Statement The information disclosure statements (IDS) submitted on 09/25/2024 and 11/06/2024 are considered and attached. Priority This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of: JP2023-168648 , filed in Japan on 09/28/2023 . Copies of certified papers required by 37 CFR 1.55 have been retrieved. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA 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. 07-30-05 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. Such claim limitation(s) is/are: Claim 15, “ an image acquisition unit ”. The corresponding structure is disclosed in the description, ¶ [0167]. The interpretation of the “an image acquisition unit” is a camera system and equivalent thereof. Claim 15, “ a cell region extraction unit ”. The corresponding structure is disclosed in the description, ¶ [0167]. The interpretation of the “an image acquisition unit” is a computer system and equivalent thereof. Claim 15, “ a first feature value distribution acquisition unit ”. The corresponding structure is disclosed in the description, ¶ [0167]. The interpretation of the “an image acquisition unit” is a computer system and equivalent thereof. Claim 15, “ a region division unit ”. The corresponding structure is disclosed in the description, ¶ [0167]. The interpretation of the “an image acquisition unit” is a computer system and equivalent thereof. Claim 15, “ a second feature value calculation unit ”. The corresponding structure is disclosed in the description, ¶ [0167]. The interpretation of the “an image acquisition unit” is a computer system and equivalent thereof. Claim 15, “ a cell information acquisition unit ”. The corresponding structure is disclosed in the description, ¶ [0167]. The interpretation of the “an image acquisition unit” is a computer system and equivalent thereof. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. 07-30-03-h AIA Claim Interpretation Under MPEP 2143.03, "All words in a claim must be considered in judging the patentability of that claim against the prior art." In re Wilson, 424 F.2d 1382, 1385, 165 USPQ 494, 496 (CCPA 1970). As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. Under SuperGuide Corp. v. DirecTV Enters., Inc., 358 F.3d 870 (Fed. Cir. 2004), “the phrase ‘at least one of’ precedes a series of categories of criteria, and the patentee used the term ‘and’ to separate the categories of criteria, which connotes a conjunctive list. The district court correctly interpreted this phrase as requiring that the user select at least one value for each category ; that is, at least one of a desired program start time, a desired program end time, a desired program service, and a desired program type.”, SuperGuide, 358 F.3d at 886. Regarding Claim 9 , applicant has presented the following categorical list, all of which must be present in order to reject the claim : viability, proliferation, a degree of undifferentiation, a residual state of a foreign gene, presence or absence of a genomic abnormality, production efficiency of a useful substance, presence or absence of cancerous transformation, and information about whether transition to a subsequent step in cell manufacturing is appropriate or inappropriate. (emphasis added) Regarding Claim 12 , applicant has presented the following categorical list, all of which must be present in order to reject the claim : sorting of a cell, removal of a cell, and transition to a subsequent step in cell manufacturing, based on the information about the state of the cell acquired in the cell state information acquisition step. (emphasis added) Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-4, 8, 10 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akazawa et al. (WO-2019175959-A1, a translated copy is attached, hereinafter Akazawa) in view of Takahashi et al. (US-20210133963-A1, hereinafter Takahashi) and Yang et al. (US-20200202514-A1, hereinafter Yang) . CLAIM 1 In regards to Claim 1, Akazawa teaches A cell information acquisition method ( Akazawa, ¶ [0001-0003]: “The present invention relates to a cell state determination method and a cell state determination device for non-invasively determining the state of cells during the process of culturing pluripotent stem cells” ) comprising: an image acquisition step of acquiring a cell image ( Akazawa , ¶ [0021-0022]: “observation images may be those obtained using a general optical microscope … an image created by processing hologram data acquired by a digital holographic microscope as the observation image” ) including an image of a cell aggregation ( Akazawa, ¶ [0012-0013]: “an observation image of a cell or a colony” ); a cell region extraction step of extracting at least one cell region from the cell image data ( Akazawa, ¶ [0018 and 0043]: “0043: the cell region extraction unit extracts regions where cells or colonies are estimated to exist based on the texture feature image, and obtains data indicating the contour of those cell regions or colony regions (step S3).”; ), where each cell region corresponds to a cell aggregate ( Akazawa, ¶ [0018 and 0043]: “0018: the entire colony may be considered a second sub-region, or multiple second sub-regions may be defined , each having a size that contains one or more appropriate numbers of cells. In other words , the second region can be a region corresponding to a cell colony or one or more cells ” ); a first feature value distribution acquisition step of calculating a texture feature value serving as a first feature value for pixels within the cell region ( Akazawa, ¶ [0012-0013]: “a texture feature image creation unit that calculates texture features by performing texture analysis … ”, ¶ [0017]: “Statistical texture analysis can be performed using various methods, including the concentration histogram method, the concentration level difference method, the spatial concentration level dependence method, and the concentration co-occurrence matrix ”, ¶ [0035]: “ the texture feature calculation unit acquires the data that constitutes the phase image created by the phase image creation unit 23, that is, the intensity value data for each pixel of the phase image” Akazawa teaches calculating texture feature using co-occurrence matrix method ), and acquiring a distribution of the first feature value in the cell region ( Akazawa, ¶ [0035]: “the texture feature image creation unit creates a texture feature image that shows a two-dimensional distribution of the texture feature values for each type of texture feature ” ); Akazawa does not explicitly disclose a region division step of dividing the cell region into two or more divided regions . Takahashi is in the same field of art of machine learning based cell identification. Further, Takahashi teaches a region division step of dividing the cell region into two or more divided regions. ( Takahashi, ¶ [0072 and 0089]: “the IHM phase image which is an input image, the undifferentiated cell region , the deviated cell region , the background region , the foreign substance region , and the like are identified on a pixel-by-pixel basis , and the respective regions are segmented with different display colors , that is, semantic segmented colors”; see annotated Fig. 8c below. ) PNG media_image1.png 834 1369 media_image1.png Greyscale 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 Akazawa by incorporating the machine learning model and method to prepare training data that is taught by Takahashi, to make a machine learning model to identify multiple classes of regions of a cell image; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve accuracy of machine learning system to identify cell region ( Takahashi, ¶ [0022]: “The inventors of the present invention conceived to use a machine learning technique typified by a fully convolution neural network to identify a region, such as, e.g., an undifferentiated cell and a deviated cell in an observation image of a cell. However, to improve the accuracy of the discrimination by using a machine learning method … not only simply using machine learning for performing segmentation of an observation image of a cell but also adding a technique in which prepared training data is appropriately expanded to increase the data amount” ). The combination of Akazawa and Takahashi then teaches a second feature value calculation step of calculating a statistical value of the first feature value distribution for each divided region. ( Akazawa, ¶ [0044]: “ based on the texture feature values for each sub-region included in the colony region in each texture feature image, several index values, which are statistical quantities, are calculated (step S4). Generally, statistical measures such as the mean, standard deviation, variance, maximum value, minimum value, median, and mode are known, but in the device of this embodiment, four types of indicator values are used: the mean, standard deviation, maximum value, and minimum value …” ) The combination of Akazawa and Takahashi does not explicitly disclose calculating a second feature value indicating a relationship of the statistical values of the first feature value distribution among the divided regions . Yang is in the same field of art of machine learning based cell image analysis. Further, Yang teaches calculating a second feature value indicating a relationship of the statistical values of the first feature value distribution among the divided regions. ( Yang, ¶ [0017]: “Next, a nuclear area ratio, an average nuclear brightness, an average cytoplasmic brightness, and a nuclear and cytoplasmic brightness ratio are obtained … the average nuclear brightness refers to the average of the gray levels of all nuclei; the average cytoplasmic brightness refers to the average of the gray levels of all cytoplasm; the nuclear and cytoplasmic brightness ratio refers to the ratio of average nuclear brightness to average cytoplasmic brightness ”) 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 Akazawa and Takahashi by incorporating method of acquiring average brightness ratio between different regions that is taught by Yang, to make a machine learning system to identify cell regions based on global information; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to overcome hardware limitation of machine learning model while considering both global and local information in images ( Yang, ¶ [0018]: “ Note that these ratios and brightness are calculated according to all cells of the digital image 210, and therefore they are “global information” compared to some conventional art only using single cell (i.e. local information) to calculate the ratio or brightness”; ¶ [0022]: “ thus both of global information and local information is considered . In addition, since the digital image 120 is divided into several regions 221-229, and size of each region is more likely to meet the hardware limitation of the convolutional neural networks . For example, the memory of a convolutional neural network circuit may have a memory limit (e.g. 1 G bytes), and the size of a typical medical image is far beyond the memory limit . Based on the disclosed method, each region can meet the memory limit of the convolutional neural network circuit ” ). The combination of Akazawa, Takahashi and Yang then teaches a cell information acquisition step of acquiring one of information about a cell type or (*** The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) a state of cells based on the second feature value. ( Akazawa, ¶ [0014 and 0019-0020]: “, the determination of the cell state performed in the above determination step typically involves determining whether a single cell or a cell in a colony is an undifferentiated cell, an undifferentiated escaped cell, or a differentiated cell ” ) ( Yang, ¶ [0020-0022]: “an image analyzing process is performed according to the feature vector … The image analyzing process is used to calculate a cancer index … feature vector is extracted according to global information of a digital image” Yang teaches the brightness ratio between regions are used to determine a cancerous state of the cells ) Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention. CLAIM 2 In regards to Claim 2, the combination of Akazawa, Takahashi and Yang teaches the method of Claim 1. In addition, the combination of Akazawa, Takahashi and Yang teaches dividing the cell region ( Takahashi, ¶ [0072 and 0089]: “the IHM phase image which is an input image, the undifferentiated cell region , the deviated cell region , the background region , the foreign substance region , and the like are identified on a pixel-by-pixel basis , and the respective regions are segmented with different display colors , that is, semantic segmented colors”; see annotated Fig. 8c above ) based on one of an area of the cell region, a distance from a center-of-gravity of the cell region, or (*** The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) the distribution of the first feature value. ( Akazawa, ¶ [0018]: “when determining the state of cells within a colony , the entire colony may be considered a second sub-region, or multiple second sub-regions may be defined, each having a size that contains one or more appropriate numbers of cells. In other words, the second region can be a region corresponding to a cell colony or one or more cells. Furthermore, statistical measures such as the mean, standard deviation, variance, maximum value, minimum value, median, and mode of multiple features within the second sub-region can be used as indicator values ”,¶ [0035]: “the texture feature image creation unit creates a texture feature image that shows a two-dimensional distribution of the texture feature values for each type of texture feature ” Akazawa teaches using statistical measure of texture value distribution to determine cell state, undifferentiated state or deviated state ) CLAIM 3 In regards to Claim 3, the combination of Akazawa, Takahashi and Yang teaches the method of Claim 1. In addition, the combination of Akazawa, Takahashi and Yang teaches calculating one of a difference or (*** The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) a ratio of the statistical value of the first feature value distribution among the divided regions. ( Yang, ¶ [0017]: “Next, a nuclear area ratio, an average nuclear brightness, an average cytoplasmic brightness, and a nuclear and cytoplasmic brightness ratio are obtained … the average nuclear brightness refers to the average of the gray levels of all nuclei; the average cytoplasmic brightness refers to the average of the gray levels of all cytoplasm; the nuclear and cytoplasmic brightness ratio refers to the ratio of average nuclear brightness to average cytoplasmic brightness ”) CLAIM 4 In regards to Claim 4, the combination of Akazawa, Takahashi and Yang teaches the method of Claim 1. In addition, the combination of Akazawa, Takahashi and Yang teaches calculating one of a variance, a standard deviation, or (*** The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) a coefficient of variation (CV value) of the statistical value of the first feature value distribution among the divided regions. ( Akazawa, ¶ [0018 and 0044]: “when determining the state of cells within a colony, the entire colony may be considered a second sub-region, or multiple second sub-regions may be defined, each having a size that contains one or more appropriate numbers of cells. In other words, the second region can be a region corresponding to a cell colony or one or more cells. Furthermore, statistical measures such as the mean, standard deviation, variance , maximum value, minimum value, median, and mode of multiple features within the second sub-region can be used as indicator values” ) CLAIM 8 In regards to Claim 8, the combination of Akazawa, Takahashi and Yang teaches the method of Claim 1. In addition, the combination of Akazawa, Takahashi and Yang teaches a trained model acquisition step of acquiring a trained model ( Akazawa, ¶ [0046]: “the apparatus uses supervised machine learning for the determination, specifically, the Support Vector Machine (SVM), a representative two-class classification method in machine learning (step S5). ... The model then outputs a classification result into either an undifferentiated colony or an undifferentiated deviant colony as a result of inputting index value data acquired based on unknown phase images” Akazawa teaches machine learning model to determine cell state ) ( Yang, ¶ [0018-0020]: “a convolutional neural network” ) subjected to machine learning through use of the second feature value ( Yang, ¶ [0017-0018]. The convolutional neural network is trained to extract brightness ratio or “global information” as feature vector ) and one of the information about the cell type or (*** The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) the information about the state of the cell ( Akazawa, ¶ [0046]: “a learning model is constructed using index value data acquired based on pre-prepared phase images for each of the undifferentiated colonies and undifferentiated deviant colonies as training data .” ), wherein the cell information acquisition step includes using the trained model. ( Akazawa, ¶ [0046]: “The model then outputs a classification result into either an undifferentiated colony or an undifferentiated deviant colony as a result of inputting index value data acquired based on unknown phase images” Akazawa teaches machine learning model to determine cell state ) CLAIM 10 In regards to Claim 10, the combination of Akazawa, Takahashi and Yang teaches the method of Claim 1. In addition, the combination of Akazawa, Takahashi and Yang teaches cells that form the cell aggregation comprise pluripotent stem cells. ( Akazawa, ¶ [0001]: “The present invention relates to a cell state determination method and a cell state determination device for non-invasively determining the state of cells during the process of culturing pluripotent stem cells (ES cells and iPS cells) , …” ) ( Takahashi, ¶ [0040-0041]: “The type, etc., of a cell to be observed and evaluated in the present invention is not particularly limited. However, in particular, the present invention is suitable for observing and evaluating a pluripotent stem cell including human iPS cells ”, claim 6: “wherein a cell included in a sample is a pluripotent stem cell including a human iPS cell” ) CLAIM 16 In regards to Claim 16, the combination of Akazawa, Takahashi and Yang teaches the method of Claim 1. In addition, the combination of Akazawa, Takahashi and Yang teaches a non-transitory recording medium having recorded thereon a program for causing a computer to execute the cell information acquisition method of claim 1. ( Akazawa, ¶ [0027-0028]: “The cell state determination device of this embodiment comprises a microscopic observation unit 1, a control and processing unit 2, and an input unit 3 and a display unit 4 which are user interfaces … The control and processing unit 2 is actually a personal computer or a more powerful workstation, and the functions of each of the above-mentioned functional blocks can be realized by running dedicated control and processing software installed on such a computer on that computer ” ) ( Yang, ¶ [0023]: “a computer program product is provided. The computer program product may be written by any programming language and on any platform. The computer program product is loaded and executed by the electrical device to perform the aforementioned image analyzing method.” ) CLAIM 15 In regards to Claim 15, Akazawa teaches a cell information acquisition apparatus ( Akazawa, ¶ [0001-0003]: “The present invention relates to a cell state determination method and a cell state determination device for non-invasively determining the state of cells during the process of culturing pluripotent stem cells” ) comprising: an image acquisition unit configured to acquire a cell image ( Akazawa , ¶ [0021-0022]: “observation images may be those obtained using a general optical microscope … an image created by processing hologram data acquired by a digital holographic microscope as the observation image” ) including an image of a cell aggregation ( Akazawa, ¶ [0012-0013]: “an observation image of a cell or a colony” ); a cell region extraction unit configured to extract at least one cell region from the cell image data ( Akazawa, ¶ [0018 and 0043]: “0043: the cell region extraction unit extracts regions where cells or colonies are estimated to exist based on the texture feature image, and obtains data indicating the contour of those cell regions or colony regions (step S3).” ), where each cell region corresponds to a cell aggregate ( Akazawa, ¶ [0018 and 0043]: “0018: the entire colony may be considered a second sub-region, or multiple second sub-regions may be defined , each having a size that contains one or more appropriate numbers of cells. In other words , the second region can be a region corresponding to a cell colony or one or more cells ” ); a first feature value distribution acquisition unit configured to calculate a texture feature value serving as a first feature value for pixels within the cell region ( Akazawa, ¶ [0012-0013]: “a texture feature image creation unit that calculates texture features by performing texture analysis … ”, ¶ [0017]: “Statistical texture analysis can be performed using various methods, including the concentration histogram method, the concentration level difference method, the spatial concentration level dependence method, and the concentration co-occurrence matrix ”, ¶ [0035]: “ the texture feature calculation unit acquires the data that constitutes the phase image created by the phase image creation unit 23, that is, the intensity value data for each pixel of the phase image” Akazawa teaches calculating texture feature using co-occurrence matrix method ), and acquire a distribution of the first feature value in the cell region ( Akazawa, ¶ [0035]: “the texture feature image creation unit creates a texture feature image that shows a two-dimensional distribution of the texture feature values for each type of texture feature ” ); Akazawa does not explicitly disclose a region division unit configured to divide the cell region into two or more divided regions . PNG media_image1.png 834 1369 media_image1.png Greyscale Takahashi is in the same field of art of machine learning based cell identification. Further, Takahashi teaches a region division unit configured to divide the cell region into two or more divided regions. ( Takahashi, ¶ [0072 and 0089]: “a fully convolution neural network … the IHM phase image which is an input image, the undifferentiated cell region , the deviated cell region , the background region , the foreign substance region , and the like are identified on a pixel-by-pixel basis , and the respective regions are segmented with different display colors , that is, semantic segmented colors”; see annotated Fig. 8c below. ) 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 Akazawa by incorporating the machine learning model and method to prepare training data that is taught by Takahashi, to make a machine learning model to identify multiple classes of regions of a cell image; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve accuracy of machine learning system to identify cell region ( Takahashi, ¶ [0022]: “The inventors of the present invention conceived to use a machine learning technique typified by a fully convolution neural network to identify a region, such as, e.g., an undifferentiated cell and a deviated cell in an observation image of a cell. However, to improve the accuracy of the discrimination by using a machine learning method … not only simply using machine learning for performing segmentation of an observation image of a cell but also adding a technique in which prepared training data is appropriately expanded to increase the data amount” ). The combination of Akazawa and Takahashi then teaches a second feature value calculation unit configured to calculate a statistical value of the first feature value distribution for each divided region. ( Akazawa, ¶ [0044]: “The index value calculation unit … based on the texture feature values for each sub-region included in the colony region in each texture feature image, several index values, which are statistical quantities, are calculated (step S4). Generally, statistical measures such as the mean, standard deviation, variance, maximum value, minimum value, median, and mode are known, but in the device of this embodiment, four types of indicator values are used: the mean, standard deviation, maximum value, and minimum value …” ) The combination of Akazawa and Takahashi does not explicitly disclose calculating a second feature value indicating a relationship of the statistical values of the first feature value distribution among the divided regions . Yang is in the same field of art of machine learning based cell image analysis. Further, Yang teaches calculating a second feature value indicating a relationship of the statistical values of the first feature value distribution among the divided regions. ( Yang, ¶ [0017]: “Next, a nuclear area ratio, an average nuclear brightness, an average cytoplasmic brightness, and a nuclear and cytoplasmic brightness ratio are obtained … the average nuclear brightness refers to the average of the gray levels of all nuclei; the average cytoplasmic brightness refers to the average of the gray levels of all cytoplasm; the nuclear and cytoplasmic brightness ratio refers to the ratio of average nuclear brightness to average cytoplasmic brightness ”) 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 Akazawa and Takahashi by incorporating method of acquiring average brightness ratio between different regions that is taught by Yang, to make a machine learning system to identify cell regions based on global information; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to overcome hardware limitation of machine learning model while considering both global and local information in images ( Yang, ¶ [0018]: “ Note that these ratios and brightness are calculated according to all cells of the digital image 210, and therefore they are “global information” compared to some conventional art only using single cell (i.e. local information) to calculate the ratio or brightness”; ¶ [0022]: “ thus both of global information and local information is considered . In addition, since the digital image 120 is divided into several regions 221-229, and size of each region is more likely to meet the hardware limitation of the convolutional neural networks . For example, the memory of a convolutional neural network circuit may have a memory limit (e.g. 1 G bytes), and the size of a typical medical image is far beyond the memory limit . Based on the disclosed method, each region can meet the memory limit of the convolutional neural network circuit ” ). The combination of Akazawa, Takahashi and Yang then teaches a cell information acquisition step of acquiring one of information about a cell type or (*** The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) a state of cells based on the second feature value. ( Akazawa, ¶ [0014 and 0019-0020]: “, the determination of the cell state performed in the above determination step typically involves determining whether a single cell or a cell in a colony is an undifferentiated cell, an undifferentiated escaped cell, or a differentiated cell ” ) ( Yang, ¶ [0020-0022]: “an image analyzing process is performed according to the feature vector … The image analyzing process is used to calculate a cancer index … feature vector is extracted according to global information of a digital image” Yang teaches the brightness ratio between regions are used to determine a cancerous state of the cells ) Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention . 07-21-aia AIA Claim (s) 5, 7 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akazawa in view of Takahashi in view of Yang, and further in view of Wagner et al. (US-20230065504-A1, hereinafter Wagner) . CLAIM 5 In regards to Claim 5, the combination of Akazawa, Takahashi and Yang teaches the method of Claim 1. In addition, the combination of Akazawa, Takahashi and Yang teaches number of cell regions to be extracted. ( Akazawa, ¶ [0018]: “the entire colony may be considered a second sub-region, or multiple second sub-regions may be defined, each having a size that contains one or more appropriate numbers of cells” ) The combination of Akazawa, Takahashi and Yang does not explicitly disclose the number of cell regions to be extracted in the cell region extraction step is three or more, and wherein the cell information acquisition step includes acquiring a clustering result of clustering the cell regions into two or more clusters. PNG media_image2.png 1221 2546 media_image2.png Greyscale Wagner is in the same field of art of machine learning based cell image analysis. Further, Wagner teaches the number of cell regions to be extracted in the cell region extraction ( Wagner, ¶ [0027]: “analyze the images to determine a location and one or more characteristics of a set of cell colonies in the cell culture; identify at least one of the set of cell colonies ”; ¶ [0463]: “geometric features of the colony may be calculated from the cell locations and/or outline polygon.” Wagner teaches extraction of cell colonies ) step is three or more ( Wagner, ¶ [0333]: “FIG. 26A shows an example where there are three clonal iPS-like colonies”; see FIG. 26-27 below, three target cell colonies are identified ), and wherein the cell information acquisition step includes acquiring a clustering result of clustering ( Wagner, ¶ [0414 and 0500]: “a computing subsystem that processes the images of the cell culture and classifies cells, cell regions or cell colonies , … The unsupervised clustering module 6008 may be configured to apply unsupervised clustering (e.g., k-means) to the encoded features of the lower dimensional image patch data generated by the autoencoder network training module 6006 to identify visual categories. In other words, the unsupervised clustering module 6008 may identify similar visual features across image data and classify those features into the same category ” Wagner teaches classifying identified cell regions based on visual features using k-mean clustering) the cell regions into two or more clusters. ( Wagner, see FIG. 61 for multiple visual categories ) 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 Akazawa, Takahashi and Yang by incorporating the system for identify and classify cell regions that is taught by Wagner, to make a system to classify cell regions using k-means clustering algorithm; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve the cell region identification of the system by learning visual features from a large dataset ( Wagner, ¶ [0500]: “the unsupervised clustering module may identify similar visual features across image data and classify those features into the same category. By using an unsupervised approach, a large number of visual patch classes may be learned …” K-means clustering offers exceptional computational speed, straightforward implementation, and high scalability for large datasets ). Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention. CLAIM 7 In regards to Claim 7, the combination of Akazawa, Takahashi and Yang teaches the method of Claim 1. The combination of Akazawa, Takahashi and Yang does not explicitly disclose the image acquisition step includes acquiring two or more cell images different from one another, and wherein the cell information acquisition step includes statistically analyzing differences among the second feature values calculated from the respective two or more cell images . Wagner is in the same field of art of machine learning based cell image analysis. Further, Wagner teaches the image acquisition step includes acquiring two or more cell images different from one another ( Wagner, ¶ [0573]: “more imaging modules 7410 that are configured to capture time series images of biological samples” Cell images are captured at different times ), and wherein the cell information acquisition step includes statistically analyzing differences among the second feature values calculated from the respective two or more cell images. ( Wagner, ¶ [0463-0464]: “Each colony record is then stored in an instant colony features database 5714A. Additional colony properties may be calculated using colony feature calculator(s) 5716A. For example, various statistics regarding the cells contained in the colony may be determined or estimated , including count, density, mean virtual fluorescence predictions, and other measures. In addition, geometric features of the colony may be calculated from the cell locations and/or outline polygon … Each colony record is then stored in an instant colony features database 5714A. Additional colony properties may be calculated using colony feature calculator(s) 5716A. For example, various statistics regarding the cells contained in the colony may be determined or estimated, including count, density, mean virtual fluorescence predictions, and other measures. In addition, geometric features of the colony may be calculated from the cell locations and/or outline polygon” ) 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 Akazawa, Takahashi and Yang by incorporating the system for identify and classify cell regions that is taught by Wagner, to make a system to classify cell regions using learned features from a large database of cell images; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve the cell region identification of the system by learning visual features from a large dataset ( Wagner, ¶ [0500]: “the unsupervised clustering module may identify similar visual features across image data and classify those features into the same category. By using an unsupervised approach, a large number of visual patch classes may be learned …” K-means clustering offers exceptional computational speed, straightforward implementation, and high scalability for large datasets ). Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention. CLAIM 12 In regards to Claim 12, the combination of Akazawa, Takahashi and Yang teaches the method of Claim 1. In addition, the combination of Akazawa, Takahashi and Yang teaches determining states of a cell. ( Akazawa, ¶ [0014 and 0019-0020]: “, the determination of the cell state performed in the above determination step typically involves determining whether a single cell or a cell in a colony is an undifferentiated cell, an undifferentiated escaped cell, or a differentiated cell ” ) ( Yang, ¶ [0020-0022]: “an image analyzing process is performed according to the feature vector … The image analyzing process is used to calculate a cancer index … feature vector is extracted according to global information of a digital image” Yang teaches the brightness ratio between regions are used to determine a cancerous state of the cells ) The combination of Akazawa, Takahashi and Yang does not explicitly disclose a cell state information acquisition step of acquiring the information about a state of a cell through use of the cell information acquisition method of claim 1; and a cell processing step of carrying out at least any one selected from the group consisting of sorting of a cell, removal of a cell, and transition to a subsequent step in cell manufacturing, based on the information about the state of the cell acquired in the cell state information acquisition step . Wagner is in the same field of art of machine learning based cell image analysis. Further, Wagner teaches a cell state information acquisition step of acquiring the information about a state of a cell through use of the cell information acquisition method of claim 1 ( Wagner, ¶ [0560]: “Bioprocessing systems should ideally collect detailed, fine-grained information about the progression of the process, the state of cells and cell colonies, and potential problems with purity or yield far in advance of final quality control assays ”; ¶ [0620-0621]: “Such a system would enable a wide range of cell biomanufacturing processes … the elimination of any cells or material which lowers the quality of the final product (including but not limited to unwanted cells or undifferentiated cells)” Wagner teaches collecting information of cells and determine a wanted or unwanted state for cells ); and a cell processing ( Wagner, ¶ [0007-0008]. Wagner teaches a cell culture system that perform cell imaging, cell analysis and cell editing to output desired cell products ) step of carrying out at least any one selected from the group consisting of sorting of a cell ( Wagner, ¶ [0191 and 0194]: “The cell culture 104 may be used for a number of cell processes performed and monitored by the cell culture system 100, including but not limited to: cell reprogramming (into pluripotent or multipotent forms), cell differentiation, cell trans-differentiation, cell expansion, cell sorting , clonal isolation, cell gene editing, cell-based protein production, cell-based viral production …” ), removal of a cell ( Wagner, ¶ [0194, 0196 and 0204]: “ The cell editing subsystem 114 may edit the cell culture 104 at a regional, colony-specific, and/or cell-specific level. Editing, in this context, may include selective destruction and/or removal of cells or cell regions , and non-destructive operations on cells (including intracellular delivery of compounds into cells or extraction of compounds from cells)”; ¶ [0620-0621]: “Such a system would enable a wide range of cell biomanufacturing processes … the elimination of any cells or material which lowers the quality of the final product (including but not limited to unwanted cells or undifferentiated cells ” ), and transition to a subsequent step in cell manufacturing based on the information about the state of the cell acquired in the cell state information acquisition step. ( Wagner, ¶ [0200-0201 and 0210]: “The computing subsystem may be configured to control the other components of the cell culture system to perform the specified cell culture process on the cell culture to produce output cell products. The output cell products may include both cells and cell-derived products , and may be harvested from the cell culture …This approach is also easily scalable to enable large scale biological manufacturing ”. Wagner teaches analyzing cells, perform cell editing and output desired cell products ) 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 Akazawa, Takahashi and Yang by incorporating the cell processing system that is taught by Wagner, to make a system that not only can analyze cell images but perform cell manufacturing based on analysis result; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for a fast, accurate, automated, and scalable system for biological manufacturing (Wagner, ¶ [0006-0007]: “one or more combinations of the subsystems can be integrated within an overall platform or cell culture system to achieve greater synergy in providing a fast, accurate, automated, and scalable system for biological manufacturing .” ). In addition, Akazawa suggests combining his invention with a cell culture system. ( Akazawa, ¶ [0024]: “This makes it easier to control the quality of cells during culture, thereby improving productivity in cell culture” ) Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention. CLAIM 13 In regards to Claim 13, the combination of Akazawa, Takahashi and Yang teaches the method of Claim 1. In addition, the combination of Akazawa, Takahashi and Yang teaches determining states of a cell. ( Akazawa, ¶ [0014 and 0019-0020]: “, the determination of the cell state performed in the above determination step typically involves determining whether a single cell or a cell in a colony is an undifferentiated cell, an undifferentiated escaped cell, or a differentiated cell ” ) ( Yang, ¶ [0020-0022]: “an image analyzing process is performed according to the feature vector … The image analyzing process is used to calculate a cancer index … feature vector is extracted according to global information of a digital image” Yang teaches the brightness ratio between regions are used to determine a cancerous state of the cells ) The combination of Akazawa, Takahashi and Yang does not explicitly disclose a cell state information acquisition step of acquiring the information about a state of a cell through use of the cell information acquisition method of claim 1; and a cell processing step of carrying out at least any one selected from the group consisting of sorting of a cell, removal of a cell, and transition to a subsequent step in cell manufacturing, based on the information about the state of the cell acquired in the cell state information acquisition step . Wagner is in the same field of art of machine learning based cell image analysis. Further, Wagner teaches a cell state information acquisition step of acquiring the information about a state of a cell through use of the cell information acquisition method of claim 1 ( Wagner, ¶ [0560]: “Bioprocessing systems should ideally collect detailed, fine-grained information about the progression of the process, the state of cells and cell colonies, and potential problems with purity or yield far in advance of final quality control assays ”; ¶ [0620-0621]: “Such a system would enable a wide range of cell biomanufacturing processes … the elimination of any cells or material which lowers the quality of the final product (including but not limited to unwanted cells or undifferentiated cells)” Wagner teaches collecting information of cells and determine a wanted or unwanted state for cells ); and a cell inspection step of carrying out inspection of the cell based on the information about the state of the cell acquired in the cell state information acquisition step. ( Wagner, ¶ [0621]: “ generation of enough viable cells for a therapy (estimated ˜10 5 to 10 8 cells per dose over approximately 1-10 injections), the elimination of any cells or material which lowers the quality of the final product (including but not limited to unwanted cells or undifferentiated cells) ,” Wagner teaches modifying unwanted or wanted cells; ¶ [0201]: “The output cell products may be measured by output cell product assays in order to determine critical product parameters … Output cell product assays may include, but not be limited to, viability assays , cell counting, flow cytometry, immunostained imaging assays, PCR assays …”. The applicant does not specify the definition of the limitation “inspection of the cell”, the Examiner interprets the limitation reads on determine the viability of a target cell ) 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 Akazawa, Takahashi and Yang by incorporating the cell processing system that is taught by Wagner, to make a system that not only can analyze cell images but perform cell manufacturing based on analysis result; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for a fast, accurate, automated, and scalable system for biological manufacturing (Wagner, ¶ [0006-0007]: “one or more combinations of the subsystems can be integrated within an overall platform or cell culture system to achieve greater synergy in providing a fast, accurate, automated, and scalable system for biological manufacturing .” ) In addition, Akazawa suggests combining his invention with a cell culture system. ( Akazawa, ¶ [0024]: “This makes it easier to control the quality of cells during culture, thereby improving productivity in cell culture” ) Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention. CLAIM 6 07-21-aia AIA Claim (s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akazawa in view of Takahashi in view of Yang, and further in view of Tandon et al. (US-20180211380-A1, hereinafter Tandon) . In regards to Claim 6, the combination of Akazawa, Takahashi and Yang teaches the method of claim 1. In addition, the combination of Akazawa, Takahashi and Yang teaches the cell information acquisition step includes acquiring a result of analyzing a two- or (*** The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) more-dimensional feature value vector including the second feature value. ( Yang, ¶ [0017]: “ the nuclear and cytoplasmic brightness ratio refers to the ratio of average nuclear brightness to average cytoplasmic brightness. Note that these ratios and brightness are calculated according to all cells of the digital image 210, and therefore they are “global information” , ¶ [0020-0022]: “a first feature vector is extracted according to global information of a digital image … an image analyzing process is performed according to the third feature vector ” Yang teaches using a convolutional neural network to generate 2D feature vector based on brightness ratio between different regions of an image; the feature vector then be analyzed. See modified FIG. 2 below ) PNG media_image3.png 739 1328 media_image3.png Greyscale The combination of Akazawa, Takahashi and Yang does not explicitly disclose acquiring a result of analyzing , through principal component analysis , a two- or more-dimensional feature value vector including the second feature value . (emphasis added) Tandon is in the same field of art of machine learning based cell image analysis. Further, Tandon teaches acquiring a result of analyzing, through principal component analysis, a two- or more-dimensional feature value vector including the second feature value. ( Tandon, ¶ [0029-0030]: “applying, by the one or more processors, a principal component analysis to the plurality of images of cellular artifacts to obtain a plurality of feature vectors for the plurality of cellular artifacts; and applying, by the one or more processors, a random forest classifier to the plurality of feature vectors for the plurality of cellular artifacts to classify the cellular artifacts ” Tandon teaches using a randomized PCA method to obtain feature vectors in an image, and detecting abnormality in the image by analyzing obtained feature vectors ) 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 Akazawa, Takahashi and Yang by incorporating the randomized PCA method that is taught by Tandon, to make a system to analyze image using PCA instead of convolutional neural network; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to prioritize time and computational power over accuracy ( Tandon, ¶ [0294]: “ two primary implementations of machine learning model will be presented: a convolutional neural network and a randomized Principal Component Analysis (PCA) random forests model . However, other forms machine learning model may be employed in the context of this disclosure. A random forests model has is relatively easy to generate from training set, and may employ relatively fewer training set members. A convolutional neural network may be more time-consuming and computationally expensive to generate from training set, but it tends to good at accurately classifying cellular artifacts.” ). Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention. CLAIM 11 07-21-aia AIA Claim (s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akazawa in view of Takahashi in view of Yang, and further in view of Arakagi (US-20150111291-A1, hereinafter Arakagi) . In regards to Claim 11, the combination of Akazawa, Takahashi and Yang teaches the apparatus of Claim 1. The combination of Akazawa, Takahashi and Yang does not explicitly disclose the information about the cell type includes information indicating a cell line type of cells that form the cell aggregation. Arakagi is in the same field of art of cell image analysis. Further, Arakagi teaches disclose the information about the cell type includes information indicating a cell line type of cells that form the cell aggregation. ( Arakagi, ¶ [0010-0011 and 0067-0069]: “0010: neighboring cell regions that are likely to be daughter cells are extracted on the basis of information (e.g., information regarding the distance between the centroids of the regions; hereinafter referred to as relative position information) indicating the positional relation between a cell region targeted for tracking (hereinafter referred to as a tracking target cell region) and cell regions therearound; 0011: A cell division tracking apparatus … a mother cell detection unit which detects a mother cell region … a daughter cell judgment unit which judges whether the cell regions are the daughter cell regions ” Arakagi teaches determining lineage relationship (mother-daughter) between two cell regions ) 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 Akazawa, Takahashi and Yang by incorporating the method to detect lineage relationship between cells that is taught by Arakagi, to make a system that can determine origin of a cell; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for an automated method to track cell lineage in cell analysis ( Arakagi, ¶ [0008]: “For the analysis of the process of cell division, it is necessary to correctly detect the phenomenon of cell division in the cell image, and correctly recognize the relationship between a cell before division and two daughter cells that emerge as a result of the division” ). Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention . Allowable Subject Matter 12-151-07 AIA 07-97 12-51-07 Claim s 14 and 17 are allowed. The closest prior arts for Claim 9 and Claim 14 are: Akazawa (WO-2019175959-A1). Akazawa teaches method and device to determine a state of cells in a cell image. Specifically, Akazawa teaches extracting region of cell or cell colony; calculate texture feature value of cell regions and obtain a distribution of calculated texture feature value; based on a statistic of the obtained distribution, determine a state of extracted cell/cell colony region. Akazawa fails to discloses dividing the extracted cell region into multiple regions. Takahashi (US-20210133963-A1). Takahashi teaches using a fully convolution neural network to identify an undifferentiated cell region, a deviated cell region, a foreign substance region in a cell image. In addition, the cell image is divided into different colored regions corresponding to identification result. Takahashi fails to discloses calculating a brightness ratio among divided regions. Yang (US-20200202514-A1). Yang teaches an image analyzing method that calculate a brightness ratio between cell regions as “global information”, specifically, a ratio of average nuclear brightness to average cytoplasmic brightness is calculated. In addition, feature vector of the input image based on the global information is extracted, the feature vector then be analyzed to calculate a cancer index, which can be used to detect cancer cells. None teaches : “ A cell abnormality detection method comprising: an image acquisition step of acquiring a cell image including an image of a cell aggregation; a cell region extraction step of extracting at least one cell region from the cell image data, where each cell region corresponds to a cell aggregate; a first feature value distribution acquisition step of calculating a texture feature value serving as a first feature value for pixels within the cell region, and acquiring a distribution of the first feature value in the cell region; a region division step of dividing the cell region into two or more divided regions; a second feature value calculation step of calculating a statistical value of the first feature value distribution for each divided region, and calculating a second feature value indicating a relationship of the statistical values of the first feature value distribution among the divided regions; a trained model acquisition step of acquiring a trained model that has machine-learned a probability of occurrence of the second feature value through use of a data group of the second feature values obtained from a plurality of the cell images prepared for machine learning; an abnormality score calculation step of inputting, to the trained model, the second feature value obtained from the cell image to be evaluated, and calculating an abnormality score based on an output probability; and a detection step of detecting an abnormality of the cell aggregation based on the abnormality score. ” (emphasis added) Thus, claim 14 is allowed. Claim 17 is allowed due to its dependence on allowed independent claim 14. 12-151-08 AIA 07-43 12-51-08 Claim 9 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 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHUT HUY (JEREMY) PHAM whose telephone number is (703)756-5797. The examiner can normally be reached Mo - Fr. 8:30am - 6pm ET. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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NHUT HUY (JEREMY) PHAM ExaminerArt Unit 2674 /ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674 Application/Control Number: 18/895,444 Page 2 Art Unit: 2674 Application/Control Number: 18/895,444 Page 3 Art Unit: 2674 Application/Control Number: 18/895,444 Page 4 Art Unit: 2674 Application/Control Number: 18/895,444 Page 5 Art Unit: 2674 Application/Control Number: 18/895,444 Page 6 Art Unit: 2674 Application/Control Number: 18/895,444 Page 7 Art Unit: 2674 Application/Control Number: 18/895,444 Page 8 Art Unit: 2674 Application/Control Number: 18/895,444 Page 9 Art Unit: 2674 Application/Control Number: 18/895,444 Page 10 Art Unit: 2674 Application/Control Number: 18/895,444 Page 11 Art Unit: 2674 Application/Control Number: 18/895,444 Page 12 Art Unit: 2674 Application/Control Number: 18/895,444 Page 13 Art Unit: 2674 Application/Control Number: 18/895,444 Page 14 Art Unit: 2674 Application/Control Number: 18/895,444 Page 15 Art Unit: 2674 Application/Control Number: 18/895,444 Page 16 Art Unit: 2674 Application/Control Number: 18/895,444 Page 17 Art Unit: 2674 Application/Control Number: 18/895,444 Page 18 Art Unit: 2674 Application/Control Number: 18/895,444 Page 19 Art Unit: 2674 Application/Control Number: 18/895,444 Page 20 Art Unit: 2674 Application/Control Number: 18/895,444 Page 21 Art Unit: 2674 Application/Control Number: 18/895,444 Page 22 Art Unit: 2674 Application/Control Number: 18/895,444 Page 23 Art Unit: 2674 Application/Control Number: 18/895,444 Page 24 Art Unit: 2674 Application/Control Number: 18/895,444 Page 25 Art Unit: 2674 Application/Control Number: 18/895,444 Page 26 Art Unit: 2674 Application/Control Number: 18/895,444 Page 27 Art Unit: 2674 Application/Control Number: 18/895,444 Page 28 Art Unit: 2674 Application/Control Number: 18/895,444 Page 29 Art Unit: 2674 Application/Control Number: 18/895,444 Page 30 Art Unit: 2674 Application/Control Number: 18/895,444 Page 31 Art Unit: 2674 Application/Control Number: 18/895,444 Page 32 Art Unit: 2674 Application/Control Number: 18/895,444 Page 33 Art Unit: 2674 Application/Control Number: 18/895,444 Page 34 Art Unit: 2674 Application/Control Number: 18/895,444 Page 35 Art Unit: 2674 Application/Control Number: 18/895,444 Page 36 Art Unit: 2674 Application/Control Number: 18/895,444 Page 37 Art Unit: 2674 Application/Control Number: 18/895,444 Page 38 Art Unit: 2674 Application/Control Number: 18/895,444 Page 39 Art Unit: 2674 Application/Control Number: 18/895,444 Page 40 Art Unit: 2674
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