DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Priority
Acknowledgment is made of applicant's claim for domestic priority for CON of PCT/JP2020/011596, filed 03/17/2020. Acknowledgment is also made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d) to Application No. JAPAN 2019-055385, filed on 03/22/2019.
Information Disclosure Statement
The Information Disclosure Statement filed on 09/21/2021 is in compliance with the provisions of 37 CFR 1.97 and have been considered in part because certain references have not been considered and are lined-through, as they do not comply with the requirements set forth in 37 CFR 1.97. The instant citations lack appropriate dates and/or page numbers. The Information Disclosure Statements filed on 01/30/2023, 03/10/2023, 08/08/2023, 09/21/2023, 02/02/2024, 08/06/2024, 08/16/2024 and 09/16/2024 are in compliance with the provisions of 37 CFR 1.97 and have been considered in full. A signed copy of the list of references cited from each IDS is included with this Office Action.
Drawings
The drawings filed 09/21/2021 are accepted.
Claim Status
Claims 1-26 are cancelled. Claims 27-45 are pending. Claims 27-45 are examined below.
Claim Objections
Claims 30, 34-35 and 37 are objected to because of the following informalities:
Claim 30 recites "... a third type of the signal values relating to forward scattered light..." which should be "... a third type of the signal values relating to a forward scattered light..." to indicate a single forward scattered light.
Claim 30 recites "... signal values relating to side scattered light from each of the analysis target cells..." which should be "... signal values relating to a side scattered light from each of the analysis target cells..." to indicate a single side scattered light.
Claim 34 recites "wherein the cell type to be determined includes neutrophil, lymphocyte, monocyte, eosinop-hil, or basophil" which should be "wherein the cell type to be determined includes a neutrophil, a lymphocyte, a monocyte, an eosinophil, or a basophil" to indicate singular form.
Claim 35 recites "wherein the cell type to be determined includes immature granulocyte, tumor cell, lymphoblast, plasma cell, atypical lymphocyte, reactive lymphocyte, nucleated erythrocyte, or megakaryocyte" which should be "wherein the cell type to be determined includes an immature granulocyte, a tumor cell, a lymphoblast, a plasma cell, an atypical lymphocyte, a reactive lymphocyte, a nucleated erythrocyte, or a megakaryocyte" to indicate singular form.
Claim 37 recites "wherein the cell type to be determined includes abnormal cell..." which should be "wherein the cell type to be determined includes an abnormal cell..." to indicate a single abnormal cell.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
1) A cell analyzer configured to determine a cell type of each of analysis target cells contained in a biological sample. (Claim 27)
Spec: Japanese Laid-Open Patent Publication No. S63-180836 discloses a cell analyzer that analyzes the type of a blood cell or the like contained in peripheral blood. In such a cell analyzer, for example, light is applied to each cell in peripheral blood flowing in a flow cell, and signal strengths of scattered light and fluorescence obtained from the cell to which light has been applied are obtained. Peak values of the signal strengths obtained from a plurality of cells are each extracted and plotted on a scattergram. Cluster analysis is performed on the plurality of cells on the scattergram, to identify the type of cells belonging to each cluster. [0003]
In the present case, "cell analyzer", as presently claimed, is coupled with the functional language "configured to determine a cell type of each of analysis target cells contained in a biological sample" The claim and the specification fail to provide or describe any particular structure (see further discussion below under 35 U.S.C. 112(b), the claims lack sufficient structure for performing the claimed function). See this limitation further addressed below under 35 U.S.C. 112(b).
2) processing part is configured to: obtain, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through a predetermined area in a flow path; input, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm, determine the cell type of each of the analysis target cells. (Claim 27)
With reference to FIG. 17, an example of the deep learning process performed by the processing part 1 0A is described. [0111] First, the processing part 10A obtains the training waveform data 70a, 70b, 70c. The training waveform data 70a is waveform data of forward scattered light, the training waveform data 70b is waveform data of side scattered light, and the training waveform data 70c is waveform data of side fluorescence. The training waveform data 70a, 70b, 70c is obtained via the VF part 15 in accordance with an operation by an operator, from the measurement unit 400, 500, from the storage medium 98, or via a network. When the training waveform data 70a, 70b, 70c is obtained, information regarding which kind of cell the training waveform data 70a, 70b, 70c indicates is also obtained. The information regarding which kind of cell is indicated may be associated with the training waveform data 70a, 70b, 70c, or may be inputted by the operator through the input part 16. [0112] In step S 11, the processing part 10A provides: information that indicates which kind of cell is indicated and that is associated with the training waveform data 70a, 70b, 70c; label values associated with the kinds of cells stored in the memory 12 or the storage 13; and a label value 77 that corresponds to the sequence data 76a, 76b, 76c obtained by synchronizing the sequence data 72a, 72b, 72c in terms of the time of obtainment of the waveform data of forward scattered light, side scattered light, and side fluorescence. Accordingly, the processing part 10A generates training data 75. [0113] In step S 12 shown in FIG. 17, the processing part 10A trains the neural network 50 by using the training data 75. The training result of the neural network 50 is accumulated every time training is performed using a plurality of pieces of training data 75. [0114] In the cell type analysis method according to the present embodiment, a convolution neural network is used, and a stochastic gradient descent method is used. Therefore, in step S13, the processing part 10A determines whether or not training results of a previously-set predetermined number of trials have been accumulated. When the training results of the predetermined number of trials have been accumulated (YES), the processing part 10A advances to the process of step S 14, and when the training results of the predetermined number of trials have not been accumulated (NO), the processing part 10A advances to the process of step S15. [0115] Next, when the training results of the predetermined number of trials have been accumulated, the processing part 10A updates, in step S 14, connection weights w of the neural network 50, by using the training results accumulated in step S 12. In the cell type analysis method according to the present embodiment, since the stochastic gradient descent method is used, the connection weights w of the neural network 50 are updated at the stage where the learning results of the predetermined number of trials have been accumulated. Specifically, the process of updating the connection weights w is a process of performing calculation according to the gradient descent method, expressed by Formula 11 and Formula 12 described later. [0116] In step S 15, the processing part 10A determines whether or not the neural network 50 has been trained using a prescribed number of pieces of training data 75. When the training has been performed using the prescribed number of pieces of training data 75 (YES), the deep learning process ends. [0117] When the neural network 50 has not been trained using the prescribed number of pieces of training data 75 (NO), the processing part 10A advances from step S 15 to step S 16, and performs the processes from step S 11 to step S 15 with respect to the next training waveform data 70. [0118] In accordance with the processes described above, the neural network 50 is trained, whereby a deep learning algorithm 60 is obtained.
The above described structure of the specification paragraph [0111] to [0118] is considered to be the corresponding structure described in the specification to obtain, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through a predetermined area in a flow path; input, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm, determine the cell type of each of the analysis target cells.
3) a light source configured to apply a light to each of the analysis target cells passing through the predetermined area in the flow path (Claims 29 and 31)
Spec: FIG. 7 shows a configuration example of an optical system of the nucleated cell detector 411. In FIG. 7, light emitted from a laser diode serving as the light source 4111 is applied via a light application lens system 4112 to each cell passing through the flow cell 4113. [0069]
The above described structure of the specification paragraph [0069] is considered to be the corresponding structure described in the specification to apply a light to each of the analysis target cells passing through the predetermined area in the flow path.
4) a light detector configured to detect a light from each of the analysis target cells. (Claim 31)
A biological sample supplied to the flow cell 4113, 551 is irradiated with light from the light source 4111, 553, and forward scattered light, side scattered light, and side fluorescence emitted from a cell in the biological sample are detected by the light detectors 4116, 4121, 4122, 555, 558, 559. The light detectors 4116, 4121, 4122, 555, 558, 559 transmit signals to the vendor-side apparatus 100 or the user-side apparatus 200. The vendor-side apparatus 100 and the user-side apparatus 200 obtain waveform data of each of the forward scattered light, side scattered light, and side fluorescence detected by the light detectors 4116, 4121, 4122, 555, 558, 559. [0100]
In the present case, "light detector", as presently claimed, is coupled with the functional language "configured to detect a light from each of the analysis target cells" The claim and the specification fail to provide or describe any particular structure (see further discussion below under 35 U.S.C. 112(b), the claims lack sufficient structure for performing the claimed function). See this limitation further addressed below under 35 U.S.C. 112(b).
5) a sample nozzle configured to supply the biological sample to the flow path (Claim 32)
Spec: As shown in FIG. 9A, the red blood cell/platelet detector 412, which is a sheath flow-type electric resistance detector, includes: a chamber wall 412a; an aperture portion 412b for measuring an electric resistance of a cell; a sample nozzle 412c which supplies a sample; and a collection tube 412d which collets cells having passed through the aperture portion 412b. The space around the sample nozzle 412c and the collection tube 412d inside the chamber wall 412a is filled with the sheath liquid. Dashed line arrows indicated by the reference character 412s show the direction in which the sheath liquid flows. A red blood cell 412e and a platelet 412f discharged from the sample nozzle pass through the aperture portion 412b while being enveloped by the flow 412s of the sheath liquid. [0076]
In the present case, "sample nozzle", as presently claimed, is coupled with the functional language "configured to supply the biological sample to the flow path" The claim and the specification fail to provide or describe any particular structure (see further discussion below under 35 U.S.C. 112(b), the claims lack sufficient structure for performing the claimed function). See this limitation further addressed below under 35 U.S.C. 112(b).
6) a collection tube configured to collect the biological sample having passed through the flow path (Claim 32)
As shown in FIG. 9A, the red blood cell/platelet detector 412, which is a sheath flow-type electric resistance detector, includes: a chamber wall 412a; an aperture portion 412b for measuring an electric resistance of a cell; a sample nozzle 412c which supplies a sample; and a collection tube 412d which collets cells having passed through the aperture portion 412b. The space around the sample nozzle 412c and the collection tube 412d inside the chamber wall 412a is filled with the sheath liquid. [0076]
In the present case, "a collection tube", as presently claimed, is coupled with the functional language "configured to collect the biological sample having passed through the flow path." The claim and the specification fail to provide or describe any particular structure as to how to perform or achieve the claimed function (see further discussion below under 35 U.S.C. 112(b), the claims lack sufficient structure for performing the claimed function).
7) processing part is configured to: output information indicating that the abnormal cell is contained in the biological sample if a predetermined number of the analysis target cells have been determined to be the abnormal cell on the basis of the results outputted from the deep learning algorithm (Claim 37)
Spec para. [0101]: The processing part 10 includes: a CPU (Central Processing Unit) 11 which performs data processing described later; a memory 12 to be used as a work area for data processing; a storage 13 which stores a program and processing data described later; a bus 14 which transmits data between parts; an interface part 15 which inputs/outputs data with respect to an external apparatus; and a GPU (Graphics Processing Unit) 19. The input part 16 and the output part 17 are connected to the processing part 10 via the interface part 15. For example, the input part 16 is an input device such as a keyboard or a mouse, and the output part 17 is a display device such as a liquid crystal display. The GPU 19 functions as an accelerator that assists arithmetic processing (e.g., parallel arithmetic processing) performed by the CPU 11. That is, the processing performed by the CPU 11 described below also includes processing performed by the CPU 11 using the GPU 19 as an accelerator. Here, instead of the GPU 19, a chip that is suitable for calculation in a neural network may be installed. Examples of such a chip include FPGA (Field-Programmable Gate Array), ASIC (Application specific integrated circuit), and Myriad X (Intel).
In the present case, the nonce term, "processing part", as presently claimed, is coupled with the functional language "configured to output information indicating that the abnormal cell is contained in the biological sample if a predetermined number of the analysis target cells have been determined to be the abnormal cell on the basis of the results outputted from the deep learning algorithm." The claim and the specification fail to provide or describe any particular structure as the "processing part" configured to perform or achieve the claimed function. The claim and the specification fail to provide or describe any particular structure (see further discussion below under 35 U.S.C. 112(b)), the claims lack sufficient structure for performing the claimed function). See this limitation further addressed below under 35 U.S.C. 112(b).
8) processing part is configured to: obtain the analysis data by applying a predetermined process to the waveform data (Claim 40)
Spec: With reference to FIG. 20, an example of the waveform data analysis process, performed by the processing part 20A, up to generation of an analysis result 83 regarding the cell from the analysis waveform data 80a, 80b, 80c, is described. [0150] First, the processing part 20A obtains analysis waveform data 80a, 80b, 80c. The analysis waveform data 80a, 80b, 80c is obtained via the VF part 25, in accordance with an operation by the user or automatically, from the measurement unit 400, 500, from the storage medium 98, or via a network. [0151] In step S21, from the sequences 82a, 82b, 82c, the processing part 20A generates analysis data in accordance with the procedure described in the analysis data generation method above. [0152] Next, in step S22, the processing part 20A obtains the deep learning algorithm stored in the algorithm database 105. The order of steps S21 and S22 may be reversed. [0153] Next, in step S23, the processing part 20A inputs the analysis data, to the deep learning algorithm. In accordance with the procedure described in the waveform data analysis method above, the processing part 20A outputs a label value of the type of cell to which the analysis target cell from which the analysis waveform data 80a, 80b, 80c has been obtained has been determined to belong, on the basis of the deep learning algorithm. The processing part 20A stores this label value into the memory 22 or the storage 23. [0154] In step S24, the processing part 20A determines whether the identification has been performed on all of the pieces of the analysis waveform data 80a, 80b, 80c obtained first. When the identification of all of the pieces of the analysis waveform data 80a, 80b, 80c has ended (YES), the processing part 20A advances to step S25, and outputs an analysis result including information 83 regarding each cell. When the identification of all of the pieces of the analysis waveform data 80a, 80b, 80c has not ended (NO), the processing part 20A advances to step S26, and performs the processes from step S22 to step S24, on the analysis waveform data 80a, 80b, 80c for which the identification has not yet been performed.
The above described structure of the specification paragraphs [0150]-[154] is considered to be the corresponding structure described in the specification to obtain the analysis data by applying a predetermined process to the waveform data
9) an accelerator configured to assist arithmetic processing performed by the CPU (Claim 42)
The GPU 29 functions as an accelerator that assists arithmetic processing (e.g., parallel arithmetic processing) performed by the CPU 21. That is, the processing performed by the CPU 21 described below also includes processing performed by the CPU 21 using the GPU 29 as an accelerator. [0106]
In the present case, the nonce term, "accelerator", as presently claimed, is coupled with the functional language "configured to assist arithmetic processing performed by the CPU." The claim and the specification fail to provide or describe any particular structure as the "accelerator" configured to perform or achieve the claimed function. The claim and the specification fail to provide or describe any particular structure (see further discussion below under 35 U.S.C. 112(b)), the claims lack sufficient structure for performing the claimed function). See this limitation further addressed below under 35 U.S.C. 112(b).
10) computer program being configured to cause a processing part to execute a process comprising: obtaining, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through a predetermined area in a flow path, inputting, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm, determining the cell type of each of the analysis target cells. (Claim 45).
Spec: Further, a certain embodiment of the present embodiment relates to a program product, such as a storage medium, having stored therein the computer program. That is, the computer program is stored in a storage medium such as a hard disk, a semiconductor memory device such as a flash memory, or an optical disk. The storage form of the program into the storage medium is not limited, as long as the vendor-side apparatus 100 and/or the user-side apparatus 200 can read the program. Preferably, the program is stored in the storage medium in a nonvolatile manner. [0158]
In the present case, the nonce term, "computer program", as presently claimed, is coupled with the functional language "configured to cause a processing part to execute a process." The claim and the specification fail to provide or describe any particular structure as the "computer program" configured to perform or achieve the claimed function. The claim and the specification fail to provide or describe any particular structure (see further discussion below under 35 U.S.C. 112(b)), the claims lack sufficient structure for performing the claimed function). See this limitation further addressed below under 35 U.S.C. 112(b).
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 27-45 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.
See as discussed above (claim interpretation under 35 U.S.C. 112(f)), regarding claim 27, there is no disclosure in the originally filed specification of structure, material or algorithm that is a "cell analyzer" that is "configured to determine a cell type of each of analysis target cells contained in a biological sample" as presently claimed. Because there is no clear structure associated with the claimed terminology "cell analyzer" for performing the recited function, the claim is indefinite.
See as discussed above (claim interpretation under 35 U.S.C. 112(f)), regarding claim 31, there is no disclosure in the originally filed specification of structure, material or algorithm that is a "light detector" that is "configured to detect a light from each of the analysis target cells" as presently claimed. Because there is no clear structure associated with the claimed terminology "light detector" for performing the recited function, the claim is indefinite.
See as discussed above (claim interpretation under 35 U.S.C. 112(f)), regarding claim 32, there is no disclosure in the originally filed specification of structure, material or algorithm that is a "sample nozzle" that is "configured to supply the biological sample to the flow path" as presently claimed. Because there is no clear structure associated with the claimed terminology "a sample nozzle" for performing the recited function, the claim is indefinite.
See as discussed above (claim interpretation under 35 U.S.C. 112(f)), regarding claim 32, there is no disclosure in the originally filed specification of structure, material or algorithm that is a "collection tube" that is "configured to collect the biological sample having passed through the flow path" as presently claimed. Because there is no clear structure associated with the claimed terminology "collection tube" for performing the recited function, the claim is indefinite.
See as discussed above (claim interpretation under 35 U.S.C. 112(f)), regarding claim 37, there is no disclosure in the originally filed specification of structure, material or algorithm that is an "processing part" that is "configured to: output information indicating that the abnormal cell is contained in the biological sample if a predetermined number of the analysis target cells have been determined to be the abnormal cell on the basis of the results outputted from the deep learning algorithm" as presently claimed. Because there is no clear structure associated with the claimed terminology "processing part" for performing the recited function, the claim is indefinite.
See as discussed above (claim interpretation under 35 U.S.C. 112(f)), regarding claim 42, there is no disclosure in the originally filed specification of structure, material or algorithm that is "an accelerator" that is "configured to assist arithmetic processing performed by the CPU" as presently claimed. Because there is no clear structure associated with the claimed terminology "an accelerator" for performing the recited function, the claim is indefinite.
See as discussed above (claim interpretation under 35 U.S.C. 112(f)), regarding claim 42, there is no disclosure in the originally filed specification of structure, material or algorithm that is "computer program" that is "configured to cause a processing part to execute a process" as presently claimed. Because there is no clear structure associated with the claimed terminology "computer program" for performing the recited function, the claim is indefinite.
Regarding the above discussed limitations, each of the non-structural terms referenced are recited as components of the “cell analyzer”; the language suggests a programmed computer system. The corresponding structure, material, or acts in the specification for computer-implemented functions must include both the computer and the algorithm (an algorithm to perform the function would be a step-by step procedure for accomplishing the given result). In the instant case, there is insufficient algorithm for performing the computer-implemented functions as recited. There appears to be no explanation of how the computer performs the claimed functions, rather the specification merely appears to recite the claimed functions. Dependent claims are rejected for depending on rejected claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 27-45 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Mental processes recited include:
Claims 27 and 44-45 recite: "determine the cell type of each of the analysis target cells,"
Claim 34 recites "wherein the cell type to be determined includes neutrophil, lymphocyte, monocyte, eosinophil, or basophil."
Claim 35 recites "wherein the cell type to be determined includes immature granulocyte, tumor cell, lymphoblast, plasma cell, atypical lymphocyte, reactive lymphocyte, nucleated erythrocyte, or megakaryocyte."
Claim 36 recites: "wherein the cell type to be determined includes nucleated erythrocyte."
Claim 37 recites: wherein the cell type to be determined includes abnormal cell"
Claim 39 recites "wherein the signal values in the waveform data include a first signal value at first having reached a predetermined threshold and signal values obtained during a predetermined time after the first signal values"
Claim 40 recites: "applying a predetermined process to the waveform data"
The process of determining and predetermining are acts of analyzing, evaluating, organizing and judging information that could be practically performed in the human mind and/or with pen and paper.
Mathematical concepts recited include:
Claims 27, 37 and 44-45 recites: "deep learning algorithm" which is a mathematical concept and formula.
Claim 41 recites "predetermined process includes noise removal, baseline correction, or normalization."
Claim 42 recites "processing part comprises a CPU and an accelerator configured to assist arithmetic processing"
The processes of claims 27, 34-37, 39-40 and 44-45 include determining and predetermining are acts of analyzing, evaluating and judging information that could be practically performed in the human mind and/or with pen and paper. Acts of evaluating and analyzing data could be practically performed in the human mind and/or with pen and paper because they merely require making observations, evaluations, judgments, and opinions (See MPEP 2106.04(a)(2) subsection III). Therefore, under the broadest reasonable interpretation, claims 27, 34-37, 39-40 and 44-45 can be practically carried out in the human mind or with pen and paper as claimed, which falls under the "Mental processes" grouping of abstract ideas. Claim 27 recites a processing part and claim 45 recites a computer-readable storage medium having stored therein a computer program for determining a cell type. Although claims 27 and 45 recite performing these steps as part of a method executed on a computer there are no additional imitations to indicate that anything other than a generic computer is required. Merely requiring that the steps are carried out with a generic computer does not negate the mental nature of these steps and equates rather to merely using a computer as a tool to perform the mental process.
Claims 27, 37, 41-42 and 44-45 recite mathematical concepts and formulas as discussed above. Claims 27, 37 and 44-45 recites "deep learning algorithm", which are mathematical formulas that requires carrying out a series of mathematical calculations in order to implement the model. Claim 41 recites "predetermined process includes noise removal, baseline correction, or normalization." which are mathematical formulas that requires carrying out a series of mathematical calculations to obtain the normalized value. Claim 42 recites "processing part comprises a CPU and an accelerator configured to assist arithmetic processing." Arithmetic processing are mathematical formulas that requires carrying out a series of mathematical calculations. Therefore, claims 27, 37, 41-42 and 44-45 recite claim elements that falls under the “mathematical concepts” grouping of abstract ideas.
As such, claims 27-45 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The above indicated judicial exceptions are not integrated into a practical application because the claims do not recite an additional elements that apply, rely on or use the judicial exception in such a manner to amount to integration into a practical application. For example, there are no limitations that reflect an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that equate to mere instructions to implement an abstract idea or insignificant extra solution activity. Specifically, the instant claims recite the following additional elements:
Claim 27 recites cell analyzer, processing part is configured to: obtain, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through a predetermined area in a flow path; input, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm
Claim 29 recites a light source configured to apply a light...
Claim 31 recites a flow cytometer including a flow cell having the flow path inside thereof, a light source configured to apply a light to each of the analysis target cells passing through the predetermined area in the flow path, and a light detector configured to detect a light from each of the analysis target cells
Claim 32 recites a sample nozzle configured to supply the biological sample to the flow path, and a collection tube configured to collect the biological sample having passed through the flow path
Claim 37 recites processing part is configured to: output information indicating that the abnormal cell is contained in the biological sample
Claim 40 recites processing part is configured to: obtain the analysis data by applying a predetermined process to the waveform data
Claim 42 recites the processing part comprises a CPU and an accelerator configured to assist arithmetic processing performed by the CPU
Claim 43 recites wherein the accelerator comprises a GPU.
Claim 45 recites computer program.
These elements of claims 27 and 40 equate to mere data gathering activity of obtaining waveform data and analysis data. These limitations equate to methods of obtaining data that serves as input to the recited judicial exception in the claims. The limitations of claim 31 include detecting a light from the analysis target cells which is obtaining more data. Claim 27 includes inputting data and claim 37 includes outputting data, which are necessary data gathering and outputting that amounts to insignificant extra-solution activity (See MPEP 2106.05(g)). Claims 27 and 40 recite processing part, claims 42 and 43 recites CPU and claim 45 recites computer program that equates to generic computer components. Claims 29 and 31 recites flow cytometer, light source, claim 29 also recites a light detector and claim 32 recites sample nozzle and collection tube which are tools to perform an existing process of data gathering and processing. These limitations are mere instructions to apply an exception (See MPEP 2106.05(f)).
Claims 28, 30, 33-36 and 38 are providing information on what the data are and do not require that the particular data generating processes be performed. Therefore, these limitations do not change the character of the obtaining data step beyond mere data gathering activity. Claims 28, 30, 33-36 and 38 do not recite any elements in addition to the judicial exception. As such, as currently recited, the claims do not appear to recite an improvement to technology or apply or use the recited judicial exception in some other meaningful way. Therefore, claims 27-45 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional activities, insignificant extra-solution activity or mere instructions to implement the abstract idea on a generic computer. The instant claims recite the following additional elements:
Claim 27 recites cell analyzer, processing part is configured to: obtain, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through a predetermined area in a flow path; input, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm
Claim 29 recites a light source configured to apply a light...
Claim 31 recites a flow cytometer including a flow cell having the flow path inside thereof, a light source configured to apply a light to each of the analysis target cells passing through the predetermined area in the flow path, and a light detector configured to detect a light from each of the analysis target cells
Claim 32 recites a sample nozzle configured to supply the biological sample to the flow path, and a collection tube configured to collect the biological sample having passed through the flow path
Claim 37 recites processing part is configured to: output information indicating that the abnormal cell is contained in the biological sample
Claim 40 recites processing part is configured to: obtain the analysis data by applying a predetermined process to the waveform data
Claim 42 recites the processing part comprises a CPU and an accelerator configured to assist arithmetic processing performed by the CPU
Claim 43 recites wherein the accelerator comprises a GPU.
Claim 45 recites computer program.
The additional elements indicated above do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Limitations that equate to mere data gathering and outputting via generic computer components, such as receiving data at a computer or outputting data, amount to insignificant extra-solution activity as set forth by the courts in Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and OIP Techs., Inc, v, Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Also, the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more as identified by the courts in Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Additionally, Suzuki (as cited on the attached "Notice of References Cited" form 892) discloses that the use of flow cytometer to measure blood is known (Para. [0063]). Suzuki also discloses a light source and light detector (para. [0018]- [0019]). WO 2018/203568 A1 also discloses that the use of flow cytometry for observing cell phenotypes are known methods (page 3 of 13 of PDF, para. 5-6) and Fukuma discloses that sample analyzers are conventionally known (Col. 1, Para. 2). Overall, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 27-45 are not patent eligible.
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 27-29, 33 and 44-45 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WO 2018/203568 A1 also known as Ugawa (WO 2018/203568 A1, published 11/08/2018; as cited on the 09/21/2021 IDS Document, a machine translated copy is cited in the attached "Notice of References Cited" form 892 ).
Regarding independent claim 27, WO 2018/203568 A1 teaches A cell analyzer configured to determine a cell type of each of analysis target cells contained in a biological sample. WO 2018/203568 A1 teaches "The cell evaluation system to which the present invention is applied is a cell evaluation system that physically measures one or more cells from a cell group and evaluates the cells." (Page 4 of 13 of PDF, Para. 1).
WO 2018/203568 A1 teaches by using a deep learning algorithm having a neural network structure, the cell analyzer comprising a processing part, wherein the processing part is configured to: obtain, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through a predetermined area in a flow path; input, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm, determine the cell type of each of the analysis target cells. WO 2018/203568 A1 teaches "The evaluation unit 6 searches for cell specific information in the cells flowing through the imaging flow cytometer analysis unit 3' based on the time-series waveform obtained in this way. The cell specifying information here is a concept including any information for specifying a cell flowing through the imaging flow cytometer analysis unit 3'. For example, cell phenotype, cell characteristics, cell type, intracellular molecular localization, Information for defining intracellular structures, cell morphology, cell maturity, important cell characterization, and the like. In the following example, a case where the cell type of the cell flowing through the imaging flow cytometer analysis unit 3 ' is searched as an example of the cell specifying information will be described." (page 9 of 13 of PDF, para. 3) and "As shown in FIG. 9, the time-series waveform is arranged on the left side through this association degree, and the cell identification information (cell type) is arranged on the right side through this association degree. The degree of association is the same as in the example of FIG. 4 described above, but in this example, the time-series waveform indicates which of the cell identification information is more relevant." (page 9 of 13 of PDF, para. 5). WO 2018/203568 A1 also teaches "Note that the association degree may be constituted by a model that can be updated through so-called machine learning, or may be constituted by a neural network. In addition, this association degree may be configured by a network on the assumption that deep learning is performed." (page 9 of 13 of PDF, para. 7). The evaluating unit of WO 2018/203568 A1 corresponds to the recited processing unit.
Regarding claim 28, WO 2018/203568 A1 teaches the waveform data includes a plurality of types of waveform data. WO 2018/203568 A1 teaches "Incidentally, this degree of association may be replaced with a neuron of a neural network. In such a case, a learned model is constructed in which one or more cell identification information for a combination of a plurality of time-series waveforms is associated through association. In actual discrimination, one or more pieces of cell specifying information may be selected based on the method described above." (page 9 of 13 of PDF, para. 9).
Regarding claim 29, WO 2018/203568 A1 teaches a light source configured to apply a light to each of the analysis target cells passing through the predetermined area in the flow path, wherein the plurality of types of waveform data includes first waveform data including a first type of the signal values relating to scattered light from each of the analysis target cells and second waveform data including a second type of the signal values relating to fluorescence light from each of the analysis target cells. WO 2018/203568 A1 teaches "The light source 2 emits light necessary for analysis in the imaging flow cytometer analysis unit 3. Based on the imaging flow cytometry method, the imaging flow cytometer analysis unit 3 disperses individual cells in a fluid, finely flows down the fluid, and optically analyzes the cells to obtain cell shape information. Details of the imaging flow cytometer analysis unit 3 will be described later." (page 4 of 13 of PDF, para. 10) and The first objective lens 32 focuses the light supplied from the light source 2 into the flow path 31, collects the return light reflected from the flow path 31, and sends this to the first beam splitter 33 . The first beam splitter 33 plays a role of allowing the light emitted from the light source 2 to pass through as it is and reflecting the return light from the first objective lens 32. (page 5 of 13 of PDF, para. 1). WO 2018/203568 A1 teaches "A flow cytometry method has been proposed as a cell measurement technique in the case of performing analysis using such a single cell (single cell). This flow cytometry method is a technique in which individual cells are dispersed in a fluid, and the fluid is finely flowed down and optically analyzed. An apparatus using this technique is called a flow cytometer. In this flow cytometry method, by irradiating excitation light while flowing fine particles such as cells to be observed in the flow path at high speed, and obtaining the total amount of fluorescence brightness and scattered light emitted from individual cells, The observation object can be evaluated." (page 3 of 13 of PDF, para. 5)
Regarding claim 33, WO 2018/203568 A1 teaches wherein the biological sample is a blood sample. WO 2018/203568 A1 teaches "In the present invention, in addition to supervised learning data based on a learned model created as described above, a semi-teacher based on unlabeled data that is not associated with a positive time-series waveform and a negative time-series waveform Of course, the cell specifying information may be specified based on the learning. For example, it is useful when most of the labeling is known, such as cancer cells in blood, when most do not know how to label." (page 10 of 13 of PDF, para. 5).
Regarding independent claim 44, WO 2018/203568 A1 teaches A cell analysis method for determining a cell type of each of analysis target cells contained in a biological sample, by using a deep learning algorithm having a neural network structure, the cell analysis method comprising: causing each of the analysis target cells to pass through a predetermined area in a flow path; obtaining, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through the predetermined area, inputting, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm, determining the cell type of each of the analysis target cells. WO 2018/203568 A1 teaches "The cell evaluation system to which the present invention is applied is a cell evaluation system that physically measures one or more cells from a cell group and evaluates the cells." (Page 4 of 13 of PDF, Para. 1) and WO 2018/203568 A1 teaches "The evaluation unit 6 searches for cell specific information in the cells flowing through the imaging flow cytometer analysis unit 3' based on the time-series waveform obtained in this way. The cell specifying information here is a concept including any information for specifying a cell flowing through the imaging flow cytometer analysis unit 3'. For example, cell phenotype, cell characteristics, cell type, intracellular molecular localization, Information for defining intracellular structures, cell morphology, cell maturity, important cell characterization, and the like. In the following example, a case where the cell type of the cell flowing through the imaging flow cytometer analysis unit 3 ' is searched as an example of the cell specifying information will be described." (page 9 of 13 of PDF, para. 3) and "As shown in FIG. 9, the time-series waveform is arranged on the left side through this association degree, and the cell identification information (cell type) is arranged on the right side through this association degree. The degree of association is the same as in the example of FIG. 4 described above, but in this example, the time-series waveform indicates which of the cell identification information is more relevant." (page 9 of 13 of PDF, para. 5). WO 2018/203568 A1 also teaches "Note that the association degree may be constituted by a model that can be updated through so-called machine learning, or may be constituted by a neural network. In addition, this association degree may be configured by a network on the assumption that deep learning is performed." (page 9 of 13 of PDF, para. 7). The evaluating unit of WO 2018/203568 A1 corresponds to the recited processing unit.
Regarding independent claim 45, WO 2018/203568 A1 teaches A computer-readable storage medium having stored therein a computer program for determining a cell type of each of analysis target cells contained in a biological sample. WO 2018/203568 A1 teaches "This control part 5 is comprised with a personal computer (PC), a portable terminal, a smart phone, a wearable terminal, a tablet type terminal etc., for example." (page 4 of 13 of PDF, para. 11) and "The computer is caused to evaluate the cell based on the biological measurement information linked to the searched measurement information for reference." (page 10 of 13 of PDF, para. 5).
WO 2018/203568 A1 teaches using a deep learning algorithm having a neural network structure, the computer program being configured to cause a processing part to execute a process comprising: obtaining, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through a predetermined area in a flow path, inputting, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm, determining the cell type of each of the analysis target cells. WO 2018/203568 A1 teaches "The cell evaluation system to which the present invention is applied is a cell evaluation system that physically measures one or more cells from a cell group and evaluates the cells." (Page 4 of 13 of PDF, Para. 1) and WO 2018/203568 A1 teaches "The evaluation unit 6 searches for cell specific information in the cells flowing through the imaging flow cytometer analysis unit 3' based on the time-series waveform obtained in this way. The cell specifying information here is a concept including any information for specifying a cell flowing through the imaging flow cytometer analysis unit 3'. For example, cell phenotype, cell characteristics, cell type, intracellular molecular localization, Information for defining intracellular structures, cell morphology, cell maturity, important cell characterization, and the like. In the following example, a case where the cell type of the cell flowing through the imaging flow cytometer analysis unit 3 ' is searched as an example of the cell specifying information will be described." (page 9 of 13 of PDF, para. 3) and "As shown in FIG. 9, the time-series waveform is arranged on the left side through this association degree, and the cell identification information (cell type) is arranged on the right side through this association degree. The degree of association is the same as in the example of FIG. 4 described above, but in this example, the time-series waveform indicates which of the cell identification information is more relevant." (page 9 of 13 of PDF, para. 5). WO 2018/203568 A1 also teaches "Note that the association degree may be constituted by a model that can be updated through so-called machine learning, or may be constituted by a neural network. In addition, this association degree may be configured by a network on the assumption that deep learning is performed." (page 9 of 13 of PDF, para. 7). The evaluating unit of WO 2018/203568 A1 corresponds to the recited processing unit.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 31-32, 34-37 and 42-43 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2018/203568 A1 (WO 2018/203568 A1, published 11/08/2018; as cited on the 09/21/2021 IDS Document) as applied to claim 27 above, in view of Suzuki (EP 3 073 265 A1, published 28.09.2016; as cited on the 01/30/2023 IDS Document).
Regarding independent claim 27, WO 2018/203568 A1 teaches A cell analyzer configured to determine a cell type of each of analysis target cells contained in a biological sample. WO 2018/203568 A1 teaches "The cell evaluation system to which the present invention is applied is a cell evaluation system that physically measures one or more cells from a cell group and evaluates the cells." (Page 4 of 13 of PDF, Para. 1).
WO 2018/203568 A1 teaches by using a deep learning algorithm having a neural network structure, the cell analyzer comprising a processing part, wherein the processing part is configured to: obtain, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through a predetermined area in a flow path; input, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm, determine the cell type of each of the analysis target cells. WO 2018/203568 A1 teaches "The evaluation unit 6 searches for cell specific information in the cells flowing through the imaging flow cytometer analysis unit 3' based on the time-series waveform obtained in this way. The cell specifying information here is a concept including any information for specifying a cell flowing through the imaging flow cytometer analysis unit 3'. For example, cell phenotype, cell characteristics, cell type, intracellular molecular localization, Information for defining intracellular structures, cell morphology, cell maturity, important cell characterization, and the like. In the following example, a case where the cell type of the cell flowing through the imaging flow cytometer analysis unit 3 ' is searched as an example of the cell specifying information will be described." (page 9 of 13 of PDF, para. 3) and "As shown in FIG. 9, the time-series waveform is arranged on the left side through this association degree, and the cell identification information (cell type) is arranged on the right side through this association degree. The degree of association is the same as in the example of FIG. 4 described above, but in this example, the time-series waveform indicates which of the cell identification information is more relevant." (page 9 of 13 of PDF, para. 5). WO 2018/203568 A1 also teaches "Note that the association degree may be constituted by a model that can be updated through so-called machine learning, or may be constituted by a neural network. In addition, this association degree may be configured by a network on the assumption that deep learning is performed." (page 9 of 13 of PDF, para. 7). The evaluating unit of WO 2018/203568 A1 corresponds to the recited processing unit.
Regarding claim 31, WO 2018/203568 A1 teaches a flow cytometer including a flow cell having the flow path inside thereof, a light source configured to apply a light to each of the analysis target cells passing through the predetermined area in the flow path, and a light detector configured to detect a light from each of the analysis target cells. "The first objective lens 32 focuses the light supplied from the light source 2 into the flow path 31, collects the return light reflected from the flow path 31, and sends this to the first beam splitter 33 . The first beam splitter 33 plays a role of allowing the light emitted from the light source 2 to pass through as it is and reflecting the return light from the first objective lens 32." (page 5 of 13 of PDF, para. 1) and "A flow cytometry method has been proposed as a cell measurement technique in the case of performing analysis using such a single cell (single cell). This flow cytometry method is a technique in which individual cells are dispersed in a fluid, and the fluid is finely flowed down and optically analyzed. An apparatus using this technique is called a flow cytometer. In this flow cytometry method, by irradiating excitation light while flowing fine particles such as cells to be observed in the flow path at high speed, and obtaining the total amount of fluorescence brightness and scattered light emitted from individual cells, The observation object can be evaluated." (page 3 of 13 of PDF, para. 5).
WO 2018/203568 A1 does not teach that the light source is a semiconductor laser light source of claim 31; the first waveform data includes third waveform data including a third type of the signal values relating to forward scattered light from each of the analysis target cells and fourth waveform data including a fourth type of the signal values relating to side scattered light from each of the analysis target cells of claim 30; an electric resistance detector including an aperture portion having the flow path inside thereof, a sample nozzle configured to supply the biological sample to the flow path, and a collection tube configured to collect the biological sample having passed through the flow path of claim 32; wherein the cell type to be determined includes neutrophil, lymphocyte, monocyte, eosinophil, or basophil of claim 34; wherein the cell type to be determined includes immature granulocyte, tumor cell, lymphoblast, plasma cell, atypical lymphocyte, reactive lymphocyte, nucleated erythrocyte, or megakaryocyte of claim 35; wherein the cell type to be determined includes nucleated erythrocyte of claim 36; wherein the cell type to be determined includes abnormal cell, and the processing part is configured to: output information indicating that the abnormal cell is contained in the biological sample if a predetermined number of the analysis target cells have been determined to be the abnormal cell on the basis of the results outputted from the deep learning algorithm of claim 37; wherein the processing part comprises a CPU and an accelerator configured to assist arithmetic processing performed by the CPU of claim 42 and wherein the accelerator comprises a GPU of claim 43. However, these limitations are taught by Suzuki.
Suzuki teaches that the light source is a semiconductor laser light source. Suzuki teaches The beam spot forming system 220 is configured such that light emitted from the light source unit 221 passes a collimator lens 222 and a condenser lens 223 to be emitted to each of the first and second measurement specimens which is flowing in the flow cell 211. The light source unit 221 is a semiconductor laser light source. Light emitted from the light source unit 221 is laser light in the range of blue light wavelengths. The wavelength of light emitted from the light source unit 221 is set to be not less than 400 nm and not greater than 435 nm. In Embodiment 1, the wavelength of light emitted from the light source unit 221 is about 405 nm. (Para. [0028]).
Regarding claim 30, Suzuki teaches the first waveform data includes third waveform data including a third type of the signal values relating to forward scattered light from each of the analysis target cells and fourth waveform data including a fourth type of the signal values relating to side scattered light from each of the analysis target cells. Suzuki teaches "The light source unit 221 emits light to the first measurement specimen flowing in the flow cell 211 during the first measurement, and emits light to the second measurement specimen flowing in the flow cell 211 during the second measurement. When the first measurement specimen is irradiated with the light from the light source unit 221, first scattered light, second scattered light, and first fluorescence occur from each blood cell in the first measurement specimen. When the second measurement specimen is irradiated with light from the light source unit 221, third scattered light, fourth scattered light, and second fluorescence occur from each blood cell in the second measurement specimen. In the first measurement, the light receivers 231, 243, 252 receive first scattered light, second scattered light, and first fluorescence, respectively. In the second measurement, the light receivers 231, 243, 252 receive third scattered light, fourth scattered light, and second fluorescence, respectively. Each of the light receivers 231, 243, 252 outputs a signal based on the received light, to the signal processing circuit 16. Detailed configuration of the optical detection unit 14 will be described later with reference to FIG. 2." (para. [0019]) and "On the basis of the signal outputted from each of the light receivers 231, 243, 252, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell, and calculates the peak value, the width, the area, and the like of the waveform. The signal processing circuit 16 outputs to the measurement controller 11 the peak values of the waveforms obtained from the signals based on the first scattered light, the second scattered light, the third scattered light, the fourth scattered light, the first fluorescence, and the second fluorescence, as first scattered light information, second scattered light information, third scattered light information, fourth scattered light information, first fluorescence information, and second fluorescence information, respectively. On the basis of the signal outputted from the electric-resistance-type detection unit 15, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell and outputs the peak value of the waveform as blood cell information to the measurement controller 11." (Para. [0021]). Suzuki also teaches "As shown in FIG. 2, the optical detection unit 14 includes a sheath flow system 210, a beam spot forming system 220, a forward scattered light receiving system 230, a side scattered light receiving system 240, and a fluorescence receiving system 250. The configuration of the optical system of the optical detection unit 14 may be changed as appropriate other than the configuration shown in FIG 2." (Para. [0026]) and "The forward scattered light receiving system 230 is configured such that the first and third scattered light is received by the light receiver 231. The light receiver 231 is a photodiode. The light receiver 231 outputs an electric signal that corresponds to the intensity of each of the received first and third scattered light. The side scattered light receiving system 240 is configured such that second and fourth scattered light is collected by a side condenser lens 241, and is reflected by a dichroic mirror 242, to be received by the light receiver 243. The light receiver 243 is a photodiode. The light receiver 243 outputs an electric signal that corresponds to the intensity of each of the received second and fourth scattered light." (Para. [0030]).
Regarding claim 32, Suzuki teaches an electric resistance detector including an aperture portion having the flow path inside thereof, a sample nozzle configured to supply the biological sample to the flow path, and a collection tube configured to collect the biological sample having passed through the flow path. Suzuki teaches "Containers respectively containing a diluent 111, a hemolyzing agent 112, a fluorescence-labeled antibody reagent 113, a hemolyzing agent 114, and a staining solution 115 are connected to the specimen preparation unit 13. The diluent 111 is also used as a sheath liquid for causing a measurement specimen to flow in a flow cell 211 of the optical detection unit 14 and in a flow cell of the electric-resistance-type detection unit 15." (para. [0011]). The sheath liquid of Suzuki corresponds to the recited sample nozzle and the flow cell of Suzuki corresponds to the recited collection tube.
Regarding claim 34, Suzuki teaches wherein the cell type to be determined includes neutrophil, lymphocyte, monocyte, eosinophil, or basophil. Suzuki teaches "The processing unit 21 uses the first scattered light information and the second scattered light information, to classify blood cells contained in the first measurement specimen into at least lymphocyte, monocyte, and granulocyte, and count them. The processing unit 21 uses the first fluorescence information and the second scattered light information, to identify and count CD4-positive T-cells in the first measurement specimen. The processing unit 21 uses the first fluorescence information and the second scattered light information, to identify and count eosinophils in the first measurement specimen. Granulocytes include neutrophils and eosinophils, and thus, on the basis of the granulocyte identification and the eosinophil identification, the processing unit 21 identifies and counts neutrophils in the first measurement specimen. The processing unit 21 uses the third scattered light information and the second fluorescence information, to count malaria-infected red blood cells in the second measurement specimen. The processing unit 21 uses the blood cell information to count red blood cells and platelets in the third measurement specimen. The details of the process to be performed by the processing unit 21 will be described later with reference to FIG 3." (Para. [0024]).
Regarding claim 35, Suzuki teaches wherein the cell type to be determined includes immature granulocyte, tumor cell, lymphoblast, plasma cell, atypical lymphocyte, reactive lymphocyte, nucleated erythrocyte, or megakaryocyte. Suzuki teaches "Analyzing the state of cell surface antigens included in a blood sample is effective in diagnosis of diseases. For example, in a subject infected with HIV (Human Immunodeficiency Virus), the number of CD4-positive T-cells in blood decreases as the disease condition progresses. On the basis of the number of CD4-positive T-cells in the blood sample, infection with HIV and progress of disease condition thereof can be diagnosed. Japanese Laid-Open Patent Publication No. 2001-91513 describes a method in which: a first fluorescence-labeled antibody for recognizing white blood cells, a second fluorescence-labeled antibody for recognizing an antigen that changes its expression in accordance with the maturity stage of neutrophilic cells, and a third fluorescence-labeled antibody for recognizing an antigen that changes its expression in accordance with the maturity stage of immature granulocytic cells are used, to classify and count immature granulocytes having different degrees of maturity on the basis of scattered light intensity and three types of fluorescence." (Para. [0002]).
Regarding claim 36, Suzuki teaches wherein the cell type to be determined includes nucleated erythrocyte. Suzuki teaches "Said another measurement may be measurement for identifying and counting nucleated red blood cells and white blood cells, for example. In this case, the specimen preparation unit 13 mixes a blood sample 101, another hemolyzing agent, and another staining solution together, to prepare another measurement specimen. In preparation of said another measurement specimen, red blood cells are hemolyzed, and nucleic acid and cell organelles of nucleated red blood cells and white blood cells are stained. Said another measurement specimen is caused to flow in the flow cell 211, similarly to the first and second measurement specimens. On the basis of forward scattered light and fluorescence occurring from blood cells in said another measurement specimen irradiated with light, a scattergram is created. Regions are set on the created scattergram, whereby identification and counting of nucleated red blood cells and white blood cells are performed." (Para. [0054]).
Regarding claim 37, Suzuki teaches wherein the cell type to be determined includes abnormal cell, and the processing part is configured to: output information indicating that the abnormal cell is contained in the biological sample if a predetermined number of the analysis target cells have been determined to be the abnormal cell on the basis of the results outputted from the deep learning algorithm. Suzuki teaches " In step S206, the processing unit 21 identifies and counts malaria-infected red blood cells and white blood cells." (para. [0050]). Malaria-infected red blood cells of Suzuki corresponds to the recited abnormal cell.
Regarding claim 42, Suzuki teaches wherein the processing part comprises a CPU and an accelerator configured to assist arithmetic processing performed by the CPU. Suzuki teaches "The processing unit 21 is a CPU, for example. The processing unit 21 receives signals outputted by components of the information processing unit 20, and controls the components of the information processing unit 20. The processing unit 21 performs communication with the measurement unit 10. The storage unit 22 is a ROM, a RAM, a hard disk, and the like. The processing unit 21 executes processes on the basis of programs stored in the storage unit 22." (Para. [0023]) and "On the basis of the signal outputted from each of the light receivers 231, 243, 252, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell, and calculates the peak value, the width, the area, and the like of the waveform. The signal processing circuit 16 outputs to the measurement controller 11 the peak values of the waveforms obtained from the signals based on the first scattered light, the second scattered light, the third scattered light, the fourth scattered light, the first fluorescence, and the second fluorescence, as first scattered light information, second scattered light information, third scattered light information, fourth scattered light information, first fluorescence information, and second fluorescence information, respectively. On the basis of the signal outputted from the electric-resistance-type detection unit 15, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell and outputs the peak value of the waveform as blood cell information to the measurement controller 11." (Para. [0021]). The signal processing circuit of Suzuki corresponds to the recited accelerator.
Regarding claim 43, Suzuki teaches wherein the accelerator comprises a GPU. Suzuki teaches "The processing unit 21 is a CPU, for example. The processing unit 21 receives signals outputted by components of the information processing unit 20, and controls the components of the information processing unit 20. The processing unit 21 performs communication with the measurement unit 10. The storage unit 22 is a ROM, a RAM, a hard disk, and the like. The processing unit 21 executes processes on the basis of programs stored in the storage unit 22." (Para. [0023])
It would have been prima facia obvious to combine the teachings of WO 2018/203568 A1 with Suzuki. A person of ordinary skill in the art would have been motivated to modify the method of WO 2018/203568 A1 to include the waveform data that is related to the forward scattered light and the side scattered light of the cells as taught by Suzuki to capture the different aspects of the particle because the forward scattered light reflects information regarding the size of the particle, side scattered light reflects internal information of the particle, and fluorescence reflects the degree of staining of the particle (Para. [0029]). A person of ordinary skill in the art would have also been motivated to modify the method of WO 2018/203568 A1 to include an electric resistance detector as taught by Suzuki to apply voltage to the specimen to catch changes in voltage caused by passage of each blood cell, thereby detecting the blood cell (para. [0020]). It would have also been prima facia to modify WO 2018/203568 A1 to include a sample nozzle in the form of a sheath and a collection tube in the form of a flow cell as taught by Suzuki to direct the flow of the sample and to store the sample. It would have also been prima facia to modify WO 2018/203568 A1 to include determining the presence of the following cell types neutrophil, lymphocyte, monocyte, eosinophil, basophil, immature granulocyte, tumor cell, lymphoblast, plasma cell, atypical lymphocyte, reactive lymphocyte, nucleated erythrocyte, megakaryocyt or nucleated erythrocyte in a sample and to identify abnormal cells as taught by Suzuki to determine disease condition. It would have also been prima facia to modify WO 2018/203568 A1 to include processing part and an accelerator comprising of a CPU to assist in with arithmetic processing as taught by Suzuki in order to process the signals. There would have been a reasonable expectation of success, since both WO 2018/203568 A1 with Suzuki teach methods that pertain to the use of flow cytometry to analyze samples.
Claim 38 is rejected under 35 U.S.C. 103 as being unpatentable over WO 2018/203568 A1 (WO 2018/203568 A1, published 11/08/2018; as cited on the 09/21/2021 IDS Document) as applied to claim 27 above, in view of Ozasa (US 9,664,669 B2, Date of patent: May 30, 2017; as cited on the attached "Notice of References Cited" form 892).
Regarding independent claim 27, WO 2018/203568 A1 teaches A cell analyzer configured to determine a cell type of each of analysis target cells contained in a biological sample. WO 2018/203568 A1 teaches "The cell evaluation system to which the present invention is applied is a cell evaluation system that physically measures one or more cells from a cell group and evaluates the cells." (Page 4 of 13 of PDF, Para. 1).
WO 2018/203568 A1 teaches by using a deep learning algorithm having a neural network structure, the cell analyzer comprising a processing part, wherein the processing part is configured to: obtain, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through a predetermined area in a flow path; input, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm, determine the cell type of each of the analysis target cells. WO 2018/203568 A1 teaches "The evaluation unit 6 searches for cell specific information in the cells flowing through the imaging flow cytometer analysis unit 3' based on the time-series waveform obtained in this way. The cell specifying information here is a concept including any information for specifying a cell flowing through the imaging flow cytometer analysis unit 3'. For example, cell phenotype, cell characteristics, cell type, intracellular molecular localization, Information for defining intracellular structures, cell morphology, cell maturity, important cell characterization, and the like. In the following example, a case where the cell type of the cell flowing through the imaging flow cytometer analysis unit 3 ' is searched as an example of the cell specifying information will be described." (page 9 of 13 of PDF, para. 3) and "As shown in FIG. 9, the time-series waveform is arranged on the left side through this association degree, and the cell identification information (cell type) is arranged on the right side through this association degree. The degree of association is the same as in the example of FIG. 4 described above, but in this example, the time-series waveform indicates which of the cell identification information is more relevant." (page 9 of 13 of PDF, para. 5). WO 2018/203568 A1 also teaches "Note that the association degree may be constituted by a model that can be updated through so-called machine learning, or may be constituted by a neural network. In addition, this association degree may be configured by a network on the assumption that deep learning is performed." (page 9 of 13 of PDF, para. 7). The evaluating unit of WO 2018/203568 A1 corresponds to the recited processing unit.
WO 2018/203568 A1 does not teach wherein the biological sample is a urine sample of claim 38. However, this limitation is taught by Ozasa.
Regarding claim 38, Ozasa teaches wherein the biological sample is a urine sample. Ozasa teaches "Disclosed is a urine specimen analyzing method which improves the detection of casts in a urine specimen by flowing a measurement sample containing a urine specimen through a flow cell, irradiating light on the measurement sample flowing through the flow cell, generating a signal waveform indicating a temporal change of intensity of light given off by the measurement sample, and detecting casts distinguishably from mucus threads contained in the urine specimen, based on information related to respective slope at both end sides of the signal waveform corresponding to each formed element contained in the urine specimen." (Abstract)
It would have been prima facia obvious to combine the teachings of WO 2018/203568 A1 with Ozasa. A person of ordinary skill in the art would have been motivated to modify the method of WO 2018/203568 A1 to include a step of analyzing urine samples as taught by Ozasa to detect and count elements contained in urine. There would have been a reasonable expectation of success, since both WO 2018/203568 A1 with Ozasa teach methods that pertain to the use of flow cytometry to analyze samples.
Claims 39-41 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2018/203568 A1 (WO 2018/203568 A1, published 11/08/2018; as cited on the 09/21/2021 IDS Document) as applied to claim 27 above, in view of Fukama (US 8,252,593 B2, published Aug. 8, 2012; as cited on the attached "Notice of References Cited" form 892).
Regarding independent claim 27, WO 2018/203568 A1 teaches A cell analyzer configured to determine a cell type of each of analysis target cells contained in a biological sample. WO 2018/203568 A1 teaches "The cell evaluation system to which the present invention is applied is a cell evaluation system that physically measures one or more cells from a cell group and evaluates the cells." (Page 4 of 13 of PDF, Para. 1).
WO 2018/203568 A1 teaches by using a deep learning algorithm having a neural network structure, the cell analyzer comprising a processing part, wherein the processing part is configured to: obtain, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through a predetermined area in a flow path; input, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm, determine the cell type of each of the analysis target cells. WO 2018/203568 A1 teaches "The evaluation unit 6 searches for cell specific information in the cells flowing through the imaging flow cytometer analysis unit 3' based on the time-series waveform obtained in this way. The cell specifying information here is a concept including any information for specifying a cell flowing through the imaging flow cytometer analysis unit 3'. For example, cell phenotype, cell characteristics, cell type, intracellular molecular localization, Information for defining intracellular structures, cell morphology, cell maturity, important cell characterization, and the like. In the following example, a case where the cell type of the cell flowing through the imaging flow cytometer analysis unit 3 ' is searched as an example of the cell specifying information will be described." (page 9 of 13 of PDF, para. 3) and "As shown in FIG. 9, the time-series waveform is arranged on the left side through this association degree, and the cell identification information (cell type) is arranged on the right side through this association degree. The degree of association is the same as in the example of FIG. 4 described above, but in this example, the time-series waveform indicates which of the cell identification information is more relevant." (page 9 of 13 of PDF, para. 5). WO 2018/203568 A1 also teaches "Note that the association degree may be constituted by a model that can be updated through so-called machine learning, or may be constituted by a neural network. In addition, this association degree may be configured by a network on the assumption that deep learning is performed." (page 9 of 13 of PDF, para. 7). The evaluating unit of WO 2018/203568 A1 corresponds to the recited processing unit.
WO 2018/203568 A1 does not teach wherein the signal values in the waveform data include a first signal value at first having reached a predetermined threshold and signal values obtained during a predetermined time after the first signal values of claim 39; wherein the processing part is configured to: obtain the analysis data by applying a predetermined process to the waveform data of claim 40; and wherein the predetermined process includes noise removal, baseline correction, or normalization of claim 41. However, these limitations are taught by Fukama.
Regarding claim 39, Fukama teaches wherein the signal values in the waveform data include a first signal value at first having reached a predetermined threshold and signal values obtained during a predetermined time after the first signal values. Fukama teaches "If i is greater than or equal to five in step S55 (YES in step S55), the CPU 51 a reads out the analysis result of the sample for reproducibility check obtained through five measurements from the hard disc 51 d, and determines whether or not the variation in five analysis results is within a predetermined range, that is, the difference of the minimum value and the maximum value of the five analysis results is within a predetermined range (step S57). If the variation in five analysis results exceeds the predetermined range (NO in step S57), the first measurement unit 2 is assumed to be abnormal, and thus the CPU 51 a displays an abnormal warning screen on the image display unit 52 (step S58), and terminates the process." (col. 13, para. 5).
Regarding claim 40, Fukama teaches wherein the processing part is configured to: obtain the analysis data by applying a predetermined process to the waveform data. Fukuma teaches "FIG. 10 is a view describing an outline of a procedure of the automatic calibration operation of the sample analyzer 1. As shown in the figure, in the automatic calibration operation of the sample analyzer 1 according to the present embodiment, the same sample A is first measured continuously five times by the first measurement unit 2, and whether the variation in the analysis result by the five measurements is within a predetermined range is checked (reproducibility check). Thereafter, the reference concentration of the calibrator is input to the sample analyzer 1, the calibrator is measured five times by the first measurement unit 2, and the correction value of the first measurement unit 2 is calculated by the analysis result of the calibrator and the reference concentration. Thereafter, the sample A is again measured once by the first measurement unit 2, and the analysis result by a new correction value is calculated. Subsequently, the sample A is measured five times by the second measurement unit 3, and whether the variation in the analysis result by five measurements is within a predetermined range is checked. The reproducibility check of the analysis result of the second measurement unit 3 is carried out in such manner. The analysis result obtained by the measurement of the sample A of the first measurement unit 2 after the calculation of the correction value is then set as a target value (reference concentration) of the calibration of the second measurement unit 3, the analysis result by five measurements of the sample A of the second measurement unit 3 is read out from the hard disc 51 d, and the correction value of the second measurement unit 3 is calculated by the analysis result and the target value." (Col. 11, para. 3)
Regarding claim 41, Fukama teaches wherein the predetermined process includes noise removal, baseline correction, or normalization. Fukama teaches "First, the automatic calibration operation in which the sample analyzer 1 according to the present embodiment automatically performs the calibration of the first measurement unit 2 and the second measurement unit 3 will be described. The automatic calibration operation includes automatically performing a series of operations of automatically conveying the sample rack L inserted with the sample container T of the calibrator and the sample container T of the sample for reproducibility check or the normal sample at a predetermined position (holding position 1 and holding position 2 in the present embodiment), checking the reproducibility of the analysis result of the first measurement unit 2 before the calibration by the sample for reproducibility check, performing calibration of the first measurement unit 2 by the calibrator, checking the reproducibility of the analysis result of the second measurement unit 2 before the calibration by the sample for reproducibility check, and performing the calibration of the second measurement unit 3. The calibrator is a sample which concentration of the component is known, wherein the calibration of the measurement unit is performed by defining the correction value (correction data) of the analysis result so that the numerical value of the analysis result of the calibrator matches the concentration (hereinafter referred to as 'reference concentration'). The normal sample is usually used for the sample for reproducibility check." (Col. 11. Para. 2).
It would have been prima facia obvious to combine the teachings of WO 2018/203568 A1 with Fukama. A person of ordinary skill in the art would have been motivated to modify the method of WO 2018/203568 A1 to include a step of setting the threshold values for signal values as taught by Fukama to determine abnormal values. A person of ordinary skill in the art would have also been motivated to modify the method of WO 2018/203568 A1 to include a step of normalizing or calibrating the values as taught by Fukama to remove unwanted background signals. There would have been a reasonable expectation of success, since both WO 2018/203568 A1 with Fukama teach methods that pertain to the use of flow cytometry to analyze samples.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 27-28, 31, 34-35 and 44 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-5, 7, 9, 11 and 20 of copending Application No. 17691761 (reference application, 03/10/2022 claims). Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims are involved with analyzing biological samples to determine cell type using artificial intelligence. Both group of claims are also involved with obtaining waveform data of the cells by applying light to the sample. Overall, the difference is that the claims of the instant application are broader in scope than the claims of the reference application and thus the instant claims are anticipated by the reference application (see MPEP 804.II.B.2). See table below for a mapping of the claims of the reference application that anticipate the claims of the instant application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not been patented.
Instant app. #17480683, 06/21/2024
Reference app. #17691739, 03/10/2022
27. (New) A cell analyzer configured to determine a cell type of each of analysis target cells contained in a biological sample, by using a deep learning algorithm having a neural network structure, the cell analyzer comprising a processing part, wherein the processing part is configured to: obtain, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through a predetermined area in a flow path; input, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm, determine the cell type of each of the analysis target cells.
28. (New) The cell analyzer of claim 27, wherein the waveform data includes a plurality of types of waveform data.
44. (New) A cell analysis method for determining a cell type of each of analysis target cells contained in a biological sample, by using a deep learning algorithm having a neural network structure, the cell analysis method comprising: causing each of the analysis target cells to pass through a predetermined area in a flow path; obtaining, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through the predetermined area, inputting, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm, determining the cell type of each of the analysis target cells.
45. (New) A computer-readable storage medium having stored therein a computer program for determining a cell type of each of analysis target cells contained in a biological sample, by using a deep learning algorithm having a neural network structure, the computer program being configured to cause a processing part to execute a process comprising: obtaining, for each of the analysis target cells, waveform data including signal values derived from each of the analysis target cells passing through a predetermined area in a flow path, inputting, for each of the analysis target cells, analysis data corresponding to the obtained waveform data to the deep learning algorithm; and on the basis of results outputted from the deep learning algorithm, determining the cell type of each of the analysis target cells.
1. An analysis method for analyzing a specimen containing cells, the analysis method comprising: applying light to a measurement sample prepared from the specimen and detecting light generated from cells; obtaining, with respect to each of a plurality of cells contained in the specimen, feature data of the cell on the basis of the detected light; analyzing the feature data with use of an artificial intelligence algorithm, thereby classifying each of the cells into a plurality of cell types; and displaying information based on a result of the classifying.
7. The analysis method of claim 1, wherein the analyzing of the data includes inputting the feature data of one cell into a deep learning algorithm, and obtaining, as an output from the deep learning algorithm, a result of classifying the cell into a plurality of cell types.
9. The analysis method of claim 1, wherein the detecting includes detecting light generated as a result of a cell passing through a flow cell to which light is applied, and the obtaining of the feature data includes obtaining a waveform signal that changes over time in accordance with the detected light.
11. The analysis method of claim 1, wherein the analyzing of the data is performed by using a processor and a parallel-processing processor that operates under an order of the processor.
20. An analyzer configured to analyze a specimen containing cells, the analyzer comprising: a sample preparation part configured to prepare a measurement sample from the specimen; a detection part configured to apply light to the measurement sample prepared by the sample preparation part, and to detect light generated from cells; a signal processing part configured to obtain, with respect to each of a plurality of cells contained in the specimen, feature data of the cell on the basis of the detected light; a display part; and at least one processor configured to analyze the feature data with use of an artificial intelligence algorithm, thereby classifying each of the cells into a plurality of cell types, the at least one processor being configured to cause the display part to display information based on a result of the classifying.
34. (New) The cell analyzer of claim 33, wherein the cell type to be determined includes neutrophil, lymphocyte, monocyte, eosinophil, or basophil.
35. (New) The cell analyzer of claim 33, wherein the cell type to be determined includes immature granulocyte, tumor cell, lymphoblast, plasma cell, atypical lymphocyte, reactive lymphocyte, nucleated erythrocyte, or megakaryocyte.
4. The analysis method of claim 1, wherein the plurality of cell types include lymphocyte, monocyte, eosinophil, neutrophil, basophil, and abnormal blood cell.
5. The analysis method of claim 3, wherein the abnormal blood cell includes at least one of immature granulocyte, blast, and abnormal lymphocyte.
Copending application fails to teach the limitations of claims 29-30, 32-33 and 36-43. Copending application fails to teach A computer-readable storage medium having stored therein a computer program for determining a cell type of each of analysis target cells contained in a biological sample of claim 45. However, these limitations are taught by WO 2018/203568 A1, Suzuki, Ozasa and Fukama as discussed below.
Claims 29, 33 and 45 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-5, 7, 9, 11 and 20 of copending Application No. 17691739 (reference application, 03/10/2022 claims) in view of WO 2018/203568 A1 (WO 2018/203568 A1, published 11/08/2018; as cited on the 09/21/2021 IDS Document).
Regarding claim 29, WO 2018/203568 A1 teaches a light source configured to apply a light to each of the analysis target cells passing through the predetermined area in the flow path, wherein the plurality of types of waveform data includes first waveform data including a first type of the signal values relating to scattered light from each of the analysis target cells and second waveform data including a second type of the signal values relating to fluorescence light from each of the analysis target cells. WO 2018/203568 A1 teaches "The light source 2 emits light necessary for analysis in the imaging flow cytometer analysis unit 3. Based on the imaging flow cytometry method, the imaging flow cytometer analysis unit 3 disperses individual cells in a fluid, finely flows down the fluid, and optically analyzes the cells to obtain cell shape information. Details of the imaging flow cytometer analysis unit 3 will be described later." (page 4 of 13 of PDF, para. 10) and The first objective lens 32 focuses the light supplied from the light source 2 into the flow path 31, collects the return light reflected from the flow path 31, and sends this to the first beam splitter 33 . The first beam splitter 33 plays a role of allowing the light emitted from the light source 2 to pass through as it is and reflecting the return light from the first objective lens 32. (page 5 of 13 of PDF, para. 1). WO 2018/203568 A1 teaches "A flow cytometry method has been proposed as a cell measurement technique in the case of performing analysis using such a single cell (single cell). This flow cytometry method is a technique in which individual cells are dispersed in a fluid, and the fluid is finely flowed down and optically analyzed. An apparatus using this technique is called a flow cytometer. In this flow cytometry method, by irradiating excitation light while flowing fine particles such as cells to be observed in the flow path at high speed, and obtaining the total amount of fluorescence brightness and scattered light emitted from individual cells, The observation object can be evaluated." (page 3 of 13 of PDF, para. 5)
Regarding claim 33, WO 2018/203568 A1 teaches wherein the biological sample is a blood sample. WO 2018/203568 A1 teaches "In the present invention, in addition to supervised learning data based on a learned model created as described above, a semi-teacher based on unlabeled data that is not associated with a positive time-series waveform and a negative time-series waveform Of course, the cell specifying information may be specified based on the learning. For example, it is useful when most of the labeling is known, such as cancer cells in blood, when most do not know how to label." (page 10 of 13 of PDF, para. 5).
Regarding independent claim 45, WO 2018/203568 A1 teaches A computer-readable storage medium having stored therein a computer program for determining a cell type of each of analysis target cells contained in a biological sample. WO 2018/203568 A1 teaches "This control part 5 is comprised with a personal computer (PC), a portable terminal, a smart phone, a wearable terminal, a tablet type terminal etc., for example." (page 4 of 13 of PDF, para. 11) and "The computer is caused to evaluate the cell based on the biological measurement information linked to the searched measurement information for reference." (page 10 of 13 of PDF, para. 5).
It would have been prima facia obvious to combine the teachings of copending application with WO 2018/203568 A1. A person of ordinary skill in the art would have been motivated to modify the method of copending application to include the waveform data that is related to scattered light of the cells as taught by WO 2018/203568 A1 to capture the aspects of the cells. A person of ordinary skill in the art would have also been motivated to modify the method of copending application to include a step of analyzing blood samples as taught by WO 2018/203568 A1 to analyze the cell types in the sample. A person of ordinary skill in the art would have also been motivated to modify the method of copending application to include a computer-readable storage medium having stored therein a computer program as taught by WO 2018/203568 A1 to facilitate the storage and processing of data. There would have been a reasonable expectation of success, since both copending application with WO 2018/203568 A1 teach methods that pertain to the use of flow cytometry to analyze samples.
Claims 30, 32, 34-37 and 42-43 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-5, 7, 9, 11 and 20 of copending Application No. 17691739 (reference application, 03/10/2022 claims) in view of Suzuki (EP 3 073 265 A1, published 28.09.2016; as cited on the 01/30/2023 IDS Document).
Regarding claim 30, Suzuki teaches the first waveform data includes third waveform data including a third type of the signal values relating to forward scattered light from each of the analysis target cells and fourth waveform data including a fourth type of the signal values relating to side scattered light from each of the analysis target cells. Suzuki teaches "The light source unit 221 emits light to the first measurement specimen flowing in the flow cell 211 during the first measurement, and emits light to the second measurement specimen flowing in the flow cell 211 during the second measurement. When the first measurement specimen is irradiated with the light from the light source unit 221, first scattered light, second scattered light, and first fluorescence occur from each blood cell in the first measurement specimen. When the second measurement specimen is irradiated with light from the light source unit 221, third scattered light, fourth scattered light, and second fluorescence occur from each blood cell in the second measurement specimen. In the first measurement, the light receivers 231, 243, 252 receive first scattered light, second scattered light, and first fluorescence, respectively. In the second measurement, the light receivers 231, 243, 252 receive third scattered light, fourth scattered light, and second fluorescence, respectively. Each of the light receivers 231, 243, 252 outputs a signal based on the received light, to the signal processing circuit 16. Detailed configuration of the optical detection unit 14 will be described later with reference to FIG. 2." (para. [0019]) and "On the basis of the signal outputted from each of the light receivers 231, 243, 252, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell, and calculates the peak value, the width, the area, and the like of the waveform. The signal processing circuit 16 outputs to the measurement controller 11 the peak values of the waveforms obtained from the signals based on the first scattered light, the second scattered light, the third scattered light, the fourth scattered light, the first fluorescence, and the second fluorescence, as first scattered light information, second scattered light information, third scattered light information, fourth scattered light information, first fluorescence information, and second fluorescence information, respectively. On the basis of the signal outputted from the electric-resistance-type detection unit 15, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell and outputs the peak value of the waveform as blood cell information to the measurement controller 11." (Para. [0021]). Suzuki also teaches "As shown in FIG. 2, the optical detection unit 14 includes a sheath flow system 210, a beam spot forming system 220, a forward scattered light receiving system 230, a side scattered light receiving system 240, and a fluorescence receiving system 250. The configuration of the optical system of the optical detection unit 14 may be changed as appropriate other than the configuration shown in FIG 2." (Para. [0026]) and "The forward scattered light receiving system 230 is configured such that the first and third scattered light is received by the light receiver 231. The light receiver 231 is a photodiode. The light receiver 231 outputs an electric signal that corresponds to the intensity of each of the received first and third scattered light. The side scattered light receiving system 240 is configured such that second and fourth scattered light is collected by a side condenser lens 241, and is reflected by a dichroic mirror 242, to be received by the light receiver 243. The light receiver 243 is a photodiode. The light receiver 243 outputs an electric signal that corresponds to the intensity of each of the received second and fourth scattered light." (Para. [0030]).
Regarding claim 32, Suzuki teaches an electric resistance detector including an aperture portion having the flow path inside thereof, a sample nozzle configured to supply the biological sample to the flow path, and a collection tube configured to collect the biological sample having passed through the flow path. Suzuki teaches "Containers respectively containing a diluent 111, a hemolyzing agent 112, a fluorescence-labeled antibody reagent 113, a hemolyzing agent 114, and a staining solution 115 are connected to the specimen preparation unit 13. The diluent 111 is also used as a sheath liquid for causing a measurement specimen to flow in a flow cell 211 of the optical detection unit 14 and in a flow cell of the electric-resistance-type detection unit 15." (para. [0011]). The sheath liquid of Suzuki corresponds to the recited sample nozzle and the flow cell of Suzuki corresponds to the recited collection tube.
Regarding claim 36, Suzuki teaches wherein the cell type to be determined includes nucleated erythrocyte. Suzuki teaches "Said another measurement may be measurement for identifying and counting nucleated red blood cells and white blood cells, for example. In this case, the specimen preparation unit 13 mixes a blood sample 101, another hemolyzing agent, and another staining solution together, to prepare another measurement specimen. In preparation of said another measurement specimen, red blood cells are hemolyzed, and nucleic acid and cell organelles of nucleated red blood cells and white blood cells are stained. Said another measurement specimen is caused to flow in the flow cell 211, similarly to the first and second measurement specimens. On the basis of forward scattered light and fluorescence occurring from blood cells in said another measurement specimen irradiated with light, a scattergram is created. Regions are set on the created scattergram, whereby identification and counting of nucleated red blood cells and white blood cells are performed." (Para. [0054]).
Regarding claim 37, Suzuki teaches wherein the cell type to be determined includes abnormal cell, and the processing part is configured to: output information indicating that the abnormal cell is contained in the biological sample if a predetermined number of the analysis target cells have been determined to be the abnormal cell on the basis of the results outputted from the deep learning algorithm. Suzuki teaches " In step S206, the processing unit 21 identifies and counts malaria-infected red blood cells and white blood cells." (para. [0050]). Malaria-infected red blood cells of Suzuki corresponds to the recited abnormal cell.
Regarding claim 42, Suzuki teaches wherein the processing part comprises a CPU and an accelerator configured to assist arithmetic processing performed by the CPU. Suzuki teaches "The processing unit 21 is a CPU, for example. The processing unit 21 receives signals outputted by components of the information processing unit 20, and controls the components of the information processing unit 20. The processing unit 21 performs communication with the measurement unit 10. The storage unit 22 is a ROM, a RAM, a hard disk, and the like. The processing unit 21 executes processes on the basis of programs stored in the storage unit 22." (Para. [0023]) and "On the basis of the signal outputted from each of the light receivers 231, 243, 252, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell, and calculates the peak value, the width, the area, and the like of the waveform. The signal processing circuit 16 outputs to the measurement controller 11 the peak values of the waveforms obtained from the signals based on the first scattered light, the second scattered light, the third scattered light, the fourth scattered light, the first fluorescence, and the second fluorescence, as first scattered light information, second scattered light information, third scattered light information, fourth scattered light information, first fluorescence information, and second fluorescence information, respectively. On the basis of the signal outputted from the electric-resistance-type detection unit 15, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell and outputs the peak value of the waveform as blood cell information to the measurement controller 11." (Para. [0021]). The signal processing circuit of Suzuki corresponds to the recited accelerator.
Regarding claim 43, Suzuki teaches wherein the accelerator comprises a GPU. Suzuki teaches "The processing unit 21 is a CPU, for example. The processing unit 21 receives signals outputted by components of the information processing unit 20, and controls the components of the information processing unit 20. The processing unit 21 performs communication with the measurement unit 10. The storage unit 22 is a ROM, a RAM, a hard disk, and the like. The processing unit 21 executes processes on the basis of programs stored in the storage unit 22." (Para. [0023])
It would have been prima facia obvious to combine the teachings of copending application with Suzuki. A person of ordinary skill in the art would have been motivated to modify the method of copending application to include the waveform data that is related to the forward scattered light and the side scattered light of the cells as taught by Suzuki to capture the different aspects of the particle because the forward scattered light reflects information regarding the size of the particle, side scattered light reflects internal information of the particle, and fluorescence reflects the degree of staining of the particle (Para. [0029]). A person of ordinary skill in the art would have also been motivated to modify the method of copending application to include an electric resistance detector as taught by Suzuki to apply voltage to the specimen to catch changes in voltage caused by passage of each blood cell, thereby detecting the blood cell (para. [0020]). It would have also been prima facia to modify copending application to include a sample nozzle in the form of a sheath and a collection tube in the form of a flow cell as taught by Suzuki to direct the flow of the sample and to store the sample. It would have also been prima facia to modify copending application to include determining the presence of nucleated erythrocyte in a sample and to identify abnormal cells as taught by Suzuki to determine disease condition. It would have also been prima facia to modify copending application to include processing part and an accelerator comprising of a CPU to assist in with arithmetic processing as taught by Suzuki in order to process the signals. There would have been a reasonable expectation of success, since both copending application with Suzuki teach methods that pertain to the use of flow cytometry to analyze samples.
Claim 38 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-5, 7, 9, 11 and 20 of copending Application No. 17691739 (reference application, 03/10/2022 claims), in view of Ozasa (US 9,664,669 B2, Date of patent: May 30, 2017; as cited on the attached "Notice of References Cited" form 892).
Regarding claim 38, Ozasa teaches wherein the biological sample is a urine sample. Ozasa teaches "Disclosed is a urine specimen analyzing method which improves the detection of casts in a urine specimen by flowing a measurement sample containing a urine specimen through a flow cell, irradiating light on the measurement sample flowing through the flow cell, generating a signal waveform indicating a temporal change of intensity of light given off by the measurement sample, and detecting casts distinguishably from mucus threads contained in the urine specimen, based on information related to respective slope at both end sides of the signal waveform corresponding to each formed element contained in the urine specimen." (Abstract)
It would have been prima facia obvious to combine the teachings of copending application with Ozasa. A person of ordinary skill in the art would have been motivated to modify the method of copending application to include a step of analyzing urine samples as taught by Ozasa to detect and count elements contained in urine. There would have been a reasonable expectation of success, since both copending application with Ozasa teach methods that pertain to the use of flow cytometry to analyze samples.
Claims 39-41 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-5, 7, 9, 11 and 20 of copending Application No. 17691739 (reference application, 03/10/2022 claims) in view of Fukama (US 8,252,593 B2, published Aug. 8, 2012; as cited on the attached "Notice of References Cited" form 892).
Regarding claim 39, Fukama teaches wherein the signal values in the waveform data include a first signal value at first having reached a predetermined threshold and signal values obtained during a predetermined time after the first signal values. Fukama teaches "If i is greater than or equal to five in step S55 (YES in step S55), the CPU 51 a reads out the analysis result of the sample for reproducibility check obtained through five measurements from the hard disc 51 d, and determines whether or not the variation in five analysis results is within a predetermined range, that is, the difference of the minimum value and the maximum value of the five analysis results is within a predetermined range (step S57). If the variation in five analysis results exceeds the predetermined range (NO in step S57), the first measurement unit 2 is assumed to be abnormal, and thus the CPU 51 a displays an abnormal warning screen on the image display unit 52 (step S58), and terminates the process." (col. 13, para. 5).
Regarding claim 40, Fukama teaches wherein the processing part is configured to: obtain the analysis data by applying a predetermined process to the waveform data. Fukuma teaches "FIG. 10 is a view describing an outline of a procedure of the automatic calibration operation of the sample analyzer 1. As shown in the figure, in the automatic calibration operation of the sample analyzer 1 according to the present embodiment, the same sample A is first measured continuously five times by the first measurement unit 2, and whether the variation in the analysis result by the five measurements is within a predetermined range is checked (reproducibility check). Thereafter, the reference concentration of the calibrator is input to the sample analyzer 1, the calibrator is measured five times by the first measurement unit 2, and the correction value of the first measurement unit 2 is calculated by the analysis result of the calibrator and the reference concentration. Thereafter, the sample A is again measured once by the first measurement unit 2, and the analysis result by a new correction value is calculated. Subsequently, the sample A is measured five times by the second measurement unit 3, and whether the variation in the analysis result by five measurements is within a predetermined range is checked. The reproducibility check of the analysis result of the second measurement unit 3 is carried out in such manner. The analysis result obtained by the measurement of the sample A of the first measurement unit 2 after the calculation of the correction value is then set as a target value (reference concentration) of the calibration of the second measurement unit 3, the analysis result by five measurements of the sample A of the second measurement unit 3 is read out from the hard disc 51 d, and the correction value of the second measurement unit 3 is calculated by the analysis result and the target value." (Col. 11, para. 3)
Regarding claim 41, Fukama teaches wherein the predetermined process includes noise removal, baseline correction, or normalization. Fukama teaches "First, the automatic calibration operation in which the sample analyzer 1 according to the present embodiment automatically performs the calibration of the first measurement unit 2 and the second measurement unit 3 will be described. The automatic calibration operation includes automatically performing a series of operations of automatically conveying the sample rack L inserted with the sample container T of the calibrator and the sample container T of the sample for reproducibility check or the normal sample at a predetermined position (holding position 1 and holding position 2 in the present embodiment), checking the reproducibility of the analysis result of the first measurement unit 2 before the calibration by the sample for reproducibility check, performing calibration of the first measurement unit 2 by the calibrator, checking the reproducibility of the analysis result of the second measurement unit 2 before the calibration by the sample for reproducibility check, and performing the calibration of the second measurement unit 3. The calibrator is a sample which concentration of the component is known, wherein the calibration of the measurement unit is performed by defining the correction value (correction data) of the analysis result so that the numerical value of the analysis result of the calibrator matches the concentration (hereinafter referred to as 'reference concentration'). The normal sample is usually used for the sample for reproducibility check." (Col. 11. Para. 2).
It would have been prima facia obvious to combine the teachings of copending application with Fukama. A person of ordinary skill in the art would have been motivated to modify the method of copending application to include a step of setting the threshold values for signal values as taught by Fukama to determine abnormal values. A person of ordinary skill in the art would have also been motivated to modify the method of copending application to include a step of normalizing or calibrating the values as taught by Fukama to remove unwanted background signals. There would have been a reasonable expectation of success, since both copending application with Fukama teach methods that pertain to the use of flow cytometry to analyze samples.
Claims 27-28, 31, 34-35, 37 and 44 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-4, 8-9, 13-15, 17 and 19-20 of copending Application No. 17691761 (reference application, 03/10/2022 claims). Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims are involved with analyzing biological samples to determine cell type using artificial intelligence. Both group of claims are also involved with obtaining waveform data of the cells by applying light to the sample. Overall, the difference is that the claims of the instant application are broader in scope than the claims of the reference application and thus the instant claims are anticipated by the reference application (see MPEP 804.II.B.2). See table below for a mapping of the claims of the reference application that anticipate the claims of the instant application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not been patented.
Copending application fails to teach the limitations of claims 29-30, 32-33, 36 and 43. Copending application fails to teach A computer-readable storage medium having stored therein a computer program for determining a cell type of each of analysis target cells contained in a biological sample of claim 45. However, these limitations are taught by WO 2018/203568 A1, Suzuki, Ozasa and Fukama as discussed below.
Claims 29 and 45 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-4, 8-9, 13-15, 17 and 19-20 of copending Application No. 17691761 (reference application, 03/10/2022 claims) in view of WO 2018/203568 A1 (WO 2018/203568 A1, published 11/08/2018; as cited on the 09/21/2021 IDS Document).
Regarding claim 29, WO 2018/203568 A1 teaches a light source configured to apply a light to each of the analysis target cells passing through the predetermined area in the flow path, wherein the plurality of types of waveform data includes first waveform data including a first type of the signal values relating to scattered light from each of the analysis target cells and second waveform data including a second type of the signal values relating to fluorescence light from each of the analysis target cells. WO 2018/203568 A1 teaches "The light source 2 emits light necessary for analysis in the imaging flow cytometer analysis unit 3. Based on the imaging flow cytometry method, the imaging flow cytometer analysis unit 3 disperses individual cells in a fluid, finely flows down the fluid, and optically analyzes the cells to obtain cell shape information. Details of the imaging flow cytometer analysis unit 3 will be described later." (page 4 of 13 of PDF, para. 10) and The first objective lens 32 focuses the light supplied from the light source 2 into the flow path 31, collects the return light reflected from the flow path 31, and sends this to the first beam splitter 33 . The first beam splitter 33 plays a role of allowing the light emitted from the light source 2 to pass through as it is and reflecting the return light from the first objective lens 32. (page 5 of 13 of PDF, para. 1). WO 2018/203568 A1 teaches "A flow cytometry method has been proposed as a cell measurement technique in the case of performing analysis using such a single cell (single cell). This flow cytometry method is a technique in which individual cells are dispersed in a fluid, and the fluid is finely flowed down and optically analyzed. An apparatus using this technique is called a flow cytometer. In this flow cytometry method, by irradiating excitation light while flowing fine particles such as cells to be observed in the flow path at high speed, and obtaining the total amount of fluorescence brightness and scattered light emitted from individual cells, The observation object can be evaluated." (page 3 of 13 of PDF, para. 5)
Regarding independent claim 45, WO 2018/203568 A1 teaches A computer-readable storage medium having stored therein a computer program for determining a cell type of each of analysis target cells contained in a biological sample. WO 2018/203568 A1 teaches "This control part 5 is comprised with a personal computer (PC), a portable terminal, a smart phone, a wearable terminal, a tablet type terminal etc., for example." (page 4 of 13 of PDF, para. 11) and "The computer is caused to evaluate the cell based on the biological measurement information linked to the searched measurement information for reference." (page 10 of 13 of PDF, para. 5).
It would have been prima facia obvious to combine the teachings of copending application with WO 2018/203568 A1. A person of ordinary skill in the art would have been motivated to modify the method of copending application to include the waveform data that is related to scattered light of the cells as taught by WO 2018/203568 A1 to capture the aspects of the cells. A person of ordinary skill in the art would have also been motivated to modify the method of copending application to include a step of analyzing blood samples as taught by WO 2018/203568 A1 to analyze the cell types in the sample. A person of ordinary skill in the art would have also been motivated to modify the method of copending application to include a computer-readable storage medium having stored therein a computer program as taught by WO 2018/203568 A1 to facilitate the storage and processing of data. There would have been a reasonable expectation of success, since both copending application with WO 2018/203568 A1 teach methods that pertain to the use of flow cytometry to analyze samples.
Claims 30, 32, 34-37 and 42-43 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-5, 7, 9, 11 and 20 of copending Application No. 17691739 (reference application, 03/10/2022 claims) in view of Suzuki (EP 3 073 265 A1, published 28.09.2016; as cited on the 01/30/2023 IDS Document).
Regarding claim 30, Suzuki teaches the first waveform data includes third waveform data including a third type of the signal values relating to forward scattered light from each of the analysis target cells and fourth waveform data including a fourth type of the signal values relating to side scattered light from each of the analysis target cells. Suzuki teaches "The light source unit 221 emits light to the first measurement specimen flowing in the flow cell 211 during the first measurement, and emits light to the second measurement specimen flowing in the flow cell 211 during the second measurement. When the first measurement specimen is irradiated with the light from the light source unit 221, first scattered light, second scattered light, and first fluorescence occur from each blood cell in the first measurement specimen. When the second measurement specimen is irradiated with light from the light source unit 221, third scattered light, fourth scattered light, and second fluorescence occur from each blood cell in the second measurement specimen. In the first measurement, the light receivers 231, 243, 252 receive first scattered light, second scattered light, and first fluorescence, respectively. In the second measurement, the light receivers 231, 243, 252 receive third scattered light, fourth scattered light, and second fluorescence, respectively. Each of the light receivers 231, 243, 252 outputs a signal based on the received light, to the signal processing circuit 16. Detailed configuration of the optical detection unit 14 will be described later with reference to FIG. 2." (para. [0019]) and "On the basis of the signal outputted from each of the light receivers 231, 243, 252, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell, and calculates the peak value, the width, the area, and the like of the waveform. The signal processing circuit 16 outputs to the measurement controller 11 the peak values of the waveforms obtained from the signals based on the first scattered light, the second scattered light, the third scattered light, the fourth scattered light, the first fluorescence, and the second fluorescence, as first scattered light information, second scattered light information, third scattered light information, fourth scattered light information, first fluorescence information, and second fluorescence information, respectively. On the basis of the signal outputted from the electric-resistance-type detection unit 15, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell and outputs the peak value of the waveform as blood cell information to the measurement controller 11." (Para. [0021]). Suzuki also teaches "As shown in FIG. 2, the optical detection unit 14 includes a sheath flow system 210, a beam spot forming system 220, a forward scattered light receiving system 230, a side scattered light receiving system 240, and a fluorescence receiving system 250. The configuration of the optical system of the optical detection unit 14 may be changed as appropriate other than the configuration shown in FIG 2." (Para. [0026]) and "The forward scattered light receiving system 230 is configured such that the first and third scattered light is received by the light receiver 231. The light receiver 231 is a photodiode. The light receiver 231 outputs an electric signal that corresponds to the intensity of each of the received first and third scattered light. The side scattered light receiving system 240 is configured such that second and fourth scattered light is collected by a side condenser lens 241, and is reflected by a dichroic mirror 242, to be received by the light receiver 243. The light receiver 243 is a photodiode. The light receiver 243 outputs an electric signal that corresponds to the intensity of each of the received second and fourth scattered light." (Para. [0030]).
Regarding claim 32, Suzuki teaches an electric resistance detector including an aperture portion having the flow path inside thereof, a sample nozzle configured to supply the biological sample to the flow path, and a collection tube configured to collect the biological sample having passed through the flow path. Suzuki teaches "Containers respectively containing a diluent 111, a hemolyzing agent 112, a fluorescence-labeled antibody reagent 113, a hemolyzing agent 114, and a staining solution 115 are connected to the specimen preparation unit 13. The diluent 111 is also used as a sheath liquid for causing a measurement specimen to flow in a flow cell 211 of the optical detection unit 14 and in a flow cell of the electric-resistance-type detection unit 15." (para. [0011]). The sheath liquid of Suzuki corresponds to the recited sample nozzle and the flow cell of Suzuki corresponds to the recited collection tube.
Regarding claim 36, Suzuki teaches wherein the cell type to be determined includes nucleated erythrocyte. Suzuki teaches "Said another measurement may be measurement for identifying and counting nucleated red blood cells and white blood cells, for example. In this case, the specimen preparation unit 13 mixes a blood sample 101, another hemolyzing agent, and another staining solution together, to prepare another measurement specimen. In preparation of said another measurement specimen, red blood cells are hemolyzed, and nucleic acid and cell organelles of nucleated red blood cells and white blood cells are stained. Said another measurement specimen is caused to flow in the flow cell 211, similarly to the first and second measurement specimens. On the basis of forward scattered light and fluorescence occurring from blood cells in said another measurement specimen irradiated with light, a scattergram is created. Regions are set on the created scattergram, whereby identification and counting of nucleated red blood cells and white blood cells are performed." (Para. [0054]).
Regarding claim 37, Suzuki teaches wherein the cell type to be determined includes abnormal cell, and the processing part is configured to: output information indicating that the abnormal cell is contained in the biological sample if a predetermined number of the analysis target cells have been determined to be the abnormal cell on the basis of the results outputted from the deep learning algorithm. Suzuki teaches " In step S206, the processing unit 21 identifies and counts malaria-infected red blood cells and white blood cells." (para. [0050]). Malaria-infected red blood cells of Suzuki corresponds to the recited abnormal cell.
Regarding claim 42, Suzuki teaches wherein the processing part comprises a CPU and an accelerator configured to assist arithmetic processing performed by the CPU. Suzuki teaches "The processing unit 21 is a CPU, for example. The processing unit 21 receives signals outputted by components of the information processing unit 20, and controls the components of the information processing unit 20. The processing unit 21 performs communication with the measurement unit 10. The storage unit 22 is a ROM, a RAM, a hard disk, and the like. The processing unit 21 executes processes on the basis of programs stored in the storage unit 22." (Para. [0023]) and "On the basis of the signal outputted from each of the light receivers 231, 243, 252, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell, and calculates the peak value, the width, the area, and the like of the waveform. The signal processing circuit 16 outputs to the measurement controller 11 the peak values of the waveforms obtained from the signals based on the first scattered light, the second scattered light, the third scattered light, the fourth scattered light, the first fluorescence, and the second fluorescence, as first scattered light information, second scattered light information, third scattered light information, fourth scattered light information, first fluorescence information, and second fluorescence information, respectively. On the basis of the signal outputted from the electric-resistance-type detection unit 15, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell and outputs the peak value of the waveform as blood cell information to the measurement controller 11." (Para. [0021]). The signal processing circuit of Suzuki corresponds to the recited accelerator.
Regarding claim 43, Suzuki teaches wherein the accelerator comprises a GPU. Suzuki teaches "The processing unit 21 is a CPU, for example. The processing unit 21 receives signals outputted by components of the information processing unit 20, and controls the components of the information processing unit 20. The processing unit 21 performs communication with the measurement unit 10. The storage unit 22 is a ROM, a RAM, a hard disk, and the like. The processing unit 21 executes processes on the basis of programs stored in the storage unit 22." (Para. [0023])
It would have been prima facia obvious to combine the teachings of copending application with Suzuki. A person of ordinary skill in the art would have been motivated to modify the method of copending application to include the waveform data that is related to the forward scattered light and the side scattered light of the cells as taught by Suzuki to capture the different aspects of the particle because the forward scattered light reflects information regarding the size of the particle, side scattered light reflects internal information of the particle, and fluorescence reflects the degree of staining of the particle (Para. [0029]). A person of ordinary skill in the art would have also been motivated to modify the method of copending application to include an electric resistance detector as taught by Suzuki to apply voltage to the specimen to catch changes in voltage caused by passage of each blood cell, thereby detecting the blood cell (para. [0020]). It would have also been prima facia to modify copending application to include a sample nozzle in the form of a sheath and a collection tube in the form of a flow cell as taught by Suzuki to direct the flow of the sample and to store the sample. It would have also been prima facia to modify copending application to include determining the presence of nucleated erythrocyte in a sample and to identify abnormal cells as taught by Suzuki to determine disease condition. It would have also been prima facia to modify copending application to include processing part and an accelerator comprising of a CPU to assist in with arithmetic processing as taught by Suzuki in order to process the signals. There would have been a reasonable expectation of success, since both copending application with Suzuki teach methods that pertain to the use of flow cytometry to analyze samples.
Claim 38 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-4, 8-9, 13-15, 17 and 19-20 of copending Application No. 17691761 (reference application, 03/10/2022 claims), in view of Ozasa (US 9,664,669 B2, Date of patent: May 30, 2017; as cited on the attached "Notice of References Cited" form 892).
Regarding claim 38, Ozasa teaches wherein the biological sample is a urine sample. Ozasa teaches "Disclosed is a urine specimen analyzing method which improves the detection of casts in a urine specimen by flowing a measurement sample containing a urine specimen through a flow cell, irradiating light on the measurement sample flowing through the flow cell, generating a signal waveform indicating a temporal change of intensity of light given off by the measurement sample, and detecting casts distinguishably from mucus threads contained in the urine specimen, based on information related to respective slope at both end sides of the signal waveform corresponding to each formed element contained in the urine specimen." (Abstract)
It would have been prima facia obvious to combine the teachings of copending application with Ozasa. A person of ordinary skill in the art would have been motivated to modify the method of copending application to include a step of analyzing urine samples as taught by Ozasa to detect and count elements contained in urine. There would have been a reasonable expectation of success, since both copending application with Ozasa teach methods that pertain to the use of flow cytometry to analyze samples.
Claims 39-41 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-4, 8-9, 13-15, 17 and 19-20 of copending Application No. 17691761 (reference application, 03/10/2022 claims) in view of Fukama (US 8,252,593 B2, published Aug. 8, 2012; as cited on the attached "Notice of References Cited" form 892).
Regarding claim 39, Fukama teaches wherein the signal values in the waveform data include a first signal value at first having reached a predetermined threshold and signal values obtained during a predetermined time after the first signal values. Fukama teaches "If i is greater than or equal to five in step S55 (YES in step S55), the CPU 51 a reads out the analysis result of the sample for reproducibility check obtained through five measurements from the hard disc 51 d, and determines whether or not the variation in five analysis results is within a predetermined range, that is, the difference of the minimum value and the maximum value of the five analysis results is within a predetermined range (step S57). If the variation in five analysis results exceeds the predetermined range (NO in step S57), the first measurement unit 2 is assumed to be abnormal, and thus the CPU 51 a displays an abnormal warning screen on the image display unit 52 (step S58), and terminates the process." (col. 13, para. 5).
Regarding claim 40, Fukama teaches wherein the processing part is configured to: obtain the analysis data by applying a predetermined process to the waveform data. Fukuma teaches "FIG. 10 is a view describing an outline of a procedure of the automatic calibration operation of the sample analyzer 1. As shown in the figure, in the automatic calibration operation of the sample analyzer 1 according to the present embodiment, the same sample A is first measured continuously five times by the first measurement unit 2, and whether the variation in the analysis result by the five measurements is within a predetermined range is checked (reproducibility check). Thereafter, the reference concentration of the calibrator is input to the sample analyzer 1, the calibrator is measured five times by the first measurement unit 2, and the correction value of the first measurement unit 2 is calculated by the analysis result of the calibrator and the reference concentration. Thereafter, the sample A is again measured once by the first measurement unit 2, and the analysis result by a new correction value is calculated. Subsequently, the sample A is measured five times by the second measurement unit 3, and whether the variation in the analysis result by five measurements is within a predetermined range is checked. The reproducibility check of the analysis result of the second measurement unit 3 is carried out in such manner. The analysis result obtained by the measurement of the sample A of the first measurement unit 2 after the calculation of the correction value is then set as a target value (reference concentration) of the calibration of the second measurement unit 3, the analysis result by five measurements of the sample A of the second measurement unit 3 is read out from the hard disc 51 d, and the correction value of the second measurement unit 3 is calculated by the analysis result and the target value." (Col. 11, para. 3)
Regarding claim 41, Fukama teaches wherein the predetermined process includes noise removal, baseline correction, or normalization. Fukama teaches "First, the automatic calibration operation in which the sample analyzer 1 according to the present embodiment automatically performs the calibration of the first measurement unit 2 and the second measurement unit 3 will be described. The automatic calibration operation includes automatically performing a series of operations of automatically conveying the sample rack L inserted with the sample container T of the calibrator and the sample container T of the sample for reproducibility check or the normal sample at a predetermined position (holding position 1 and holding position 2 in the present embodiment), checking the reproducibility of the analysis result of the first measurement unit 2 before the calibration by the sample for reproducibility check, performing calibration of the first measurement unit 2 by the calibrator, checking the reproducibility of the analysis result of the second measurement unit 2 before the calibration by the sample for reproducibility check, and performing the calibration of the second measurement unit 3. The calibrator is a sample which concentration of the component is known, wherein the calibration of the measurement unit is performed by defining the correction value (correction data) of the analysis result so that the numerical value of the analysis result of the calibrator matches the concentration (hereinafter referred to as 'reference concentration'). The normal sample is usually used for the sample for reproducibility check." (Col. 11. Para. 2).
It would have been prima facia obvious to combine the teachings of copending application with Fukama. A person of ordinary skill in the art would have been motivated to modify the method of copending application to include a step of setting the threshold values for signal values as taught by Fukama to determine abnormal values. A person of ordinary skill in the art would have also been motivated to modify the method of copending application to include a step of normalizing or calibrating the values as taught by Fukama to remove unwanted background signals. There would have been a reasonable expectation of success, since both copending application with Fukama teach methods that pertain to the use of flow cytometry to analyze samples.
Claims 27, 31, 40, 42 and 44 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 12-14 and 18 of copending Application No. 18185814 (reference application, 03/17/2023 claims). Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims are involved with analyzing biological samples to determine cell type using artificial intelligence. Both group of claims are also involved with obtaining waveform data of the cells by applying light to the sample. Overall, the difference is that the claims of the instant application are broader in scope than the claims of the reference application and thus the instant claims are anticipated by the reference application (see MPEP 804.II.B.2). See table below for a mapping of the claims of the reference application that anticipate the claims of the instant application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not been patented.
Copending application fails to teach the limitations of claims 28-30, 32-39, 41 and 43. Copending application fails to teach A computer-readable storage medium having stored therein a computer program for determining a cell type of each of analysis target cells contained in a biological sample of claim 45. However, these limitations are taught by WO 2018/203568 A1, Suzuki, Ozasa and Fukama as discussed below.
Claims 28-29, 33 and 45 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-4, 8-9, 13-15, 17 and 19-20 of copending Application No. 17691761 (reference application, 03/10/2022 claims) in view of WO 2018/203568 A1 (WO 2018/203568 A1, published 11/08/2018; as cited on the 09/21/2021 IDS Document).
Regarding claim 28, WO 2018/203568 A1 teaches the waveform data includes a plurality of types of waveform data. WO 2018/203568 A1 teaches "Incidentally, this degree of association may be replaced with a neuron of a neural network. In such a case, a learned model is constructed in which one or more cell identification information for a combination of a plurality of time-series waveforms is associated through association. In actual discrimination, one or more pieces of cell specifying information may be selected based on the method described above." (page 9 of 13 of PDF, para. 9).
Regarding claim 29, WO 2018/203568 A1 teaches a light source configured to apply a light to each of the analysis target cells passing through the predetermined area in the flow path, wherein the plurality of types of waveform data includes first waveform data including a first type of the signal values relating to scattered light from each of the analysis target cells and second waveform data including a second type of the signal values relating to fluorescence light from each of the analysis target cells. WO 2018/203568 A1 teaches "The light source 2 emits light necessary for analysis in the imaging flow cytometer analysis unit 3. Based on the imaging flow cytometry method, the imaging flow cytometer analysis unit 3 disperses individual cells in a fluid, finely flows down the fluid, and optically analyzes the cells to obtain cell shape information. Details of the imaging flow cytometer analysis unit 3 will be described later." (page 4 of 13 of PDF, para. 10) and The first objective lens 32 focuses the light supplied from the light source 2 into the flow path 31, collects the return light reflected from the flow path 31, and sends this to the first beam splitter 33 . The first beam splitter 33 plays a role of allowing the light emitted from the light source 2 to pass through as it is and reflecting the return light from the first objective lens 32. (page 5 of 13 of PDF, para. 1). WO 2018/203568 A1 teaches "A flow cytometry method has been proposed as a cell measurement technique in the case of performing analysis using such a single cell (single cell). This flow cytometry method is a technique in which individual cells are dispersed in a fluid, and the fluid is finely flowed down and optically analyzed. An apparatus using this technique is called a flow cytometer. In this flow cytometry method, by irradiating excitation light while flowing fine particles such as cells to be observed in the flow path at high speed, and obtaining the total amount of fluorescence brightness and scattered light emitted from individual cells, The observation object can be evaluated." (page 3 of 13 of PDF, para. 5)
Regarding claim 33, WO 2018/203568 A1 teaches wherein the biological sample is a blood sample. WO 2018/203568 A1 teaches "In the present invention, in addition to supervised learning data based on a learned model created as described above, a semi-teacher based on unlabeled data that is not associated with a positive time-series waveform and a negative time-series waveform Of course, the cell specifying information may be specified based on the learning. For example, it is useful when most of the labeling is known, such as cancer cells in blood, when most do not know how to label." (page 10 of 13 of PDF, para. 5).
Regarding independent claim 45, WO 2018/203568 A1 teaches A computer-readable storage medium having stored therein a computer program for determining a cell type of each of analysis target cells contained in a biological sample. WO 2018/203568 A1 teaches "This control part 5 is comprised with a personal computer (PC), a portable terminal, a smart phone, a wearable terminal, a tablet type terminal etc., for example." (page 4 of 13 of PDF, para. 11) and "The computer is caused to evaluate the cell based on the biological measurement information linked to the searched measurement information for reference." (page 10 of 13 of PDF, para. 5).
It would have been prima facia obvious to combine the teachings of copending application with WO 2018/203568 A1. A person of ordinary skill in the art would have been motivated to modify the method of copending application to include the plurality of waveform data that is related to scattered light of the cells as taught by WO 2018/203568 A1 to capture the aspects of the cells. A person of ordinary skill in the art would have also been motivated to modify the method of copending application to include a step of analyzing blood samples as taught by WO 2018/203568 A1 to analyze the cell types in the sample. A person of ordinary skill in the art would have also been motivated to modify the method of copending application to include a computer-readable storage medium having stored therein a computer program as taught by WO 2018/203568 A1 to facilitate the storage and processing of data. There would have been a reasonable expectation of success, since both copending application with WO 2018/203568 A1 teach methods that pertain to the use of flow cytometry to analyze samples.
Claims 30, 32, 34-37 and 42-43 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-5, 7, 9, 11 and 20 of copending Application No. 17691739 (reference application, 03/10/2022 claims) in view of Suzuki (EP 3 073 265 A1, published 28.09.2016; as cited on the 01/30/2023 IDS Document).
Regarding claim 30, Suzuki teaches the first waveform data includes third waveform data including a third type of the signal values relating to forward scattered light from each of the analysis target cells and fourth waveform data including a fourth type of the signal values relating to side scattered light from each of the analysis target cells. Suzuki teaches "The light source unit 221 emits light to the first measurement specimen flowing in the flow cell 211 during the first measurement, and emits light to the second measurement specimen flowing in the flow cell 211 during the second measurement. When the first measurement specimen is irradiated with the light from the light source unit 221, first scattered light, second scattered light, and first fluorescence occur from each blood cell in the first measurement specimen. When the second measurement specimen is irradiated with light from the light source unit 221, third scattered light, fourth scattered light, and second fluorescence occur from each blood cell in the second measurement specimen. In the first measurement, the light receivers 231, 243, 252 receive first scattered light, second scattered light, and first fluorescence, respectively. In the second measurement, the light receivers 231, 243, 252 receive third scattered light, fourth scattered light, and second fluorescence, respectively. Each of the light receivers 231, 243, 252 outputs a signal based on the received light, to the signal processing circuit 16. Detailed configuration of the optical detection unit 14 will be described later with reference to FIG. 2." (para. [0019]) and "On the basis of the signal outputted from each of the light receivers 231, 243, 252, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell, and calculates the peak value, the width, the area, and the like of the waveform. The signal processing circuit 16 outputs to the measurement controller 11 the peak values of the waveforms obtained from the signals based on the first scattered light, the second scattered light, the third scattered light, the fourth scattered light, the first fluorescence, and the second fluorescence, as first scattered light information, second scattered light information, third scattered light information, fourth scattered light information, first fluorescence information, and second fluorescence information, respectively. On the basis of the signal outputted from the electric-resistance-type detection unit 15, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell and outputs the peak value of the waveform as blood cell information to the measurement controller 11." (Para. [0021]). Suzuki also teaches "As shown in FIG. 2, the optical detection unit 14 includes a sheath flow system 210, a beam spot forming system 220, a forward scattered light receiving system 230, a side scattered light receiving system 240, and a fluorescence receiving system 250. The configuration of the optical system of the optical detection unit 14 may be changed as appropriate other than the configuration shown in FIG 2." (Para. [0026]) and "The forward scattered light receiving system 230 is configured such that the first and third scattered light is received by the light receiver 231. The light receiver 231 is a photodiode. The light receiver 231 outputs an electric signal that corresponds to the intensity of each of the received first and third scattered light. The side scattered light receiving system 240 is configured such that second and fourth scattered light is collected by a side condenser lens 241, and is reflected by a dichroic mirror 242, to be received by the light receiver 243. The light receiver 243 is a photodiode. The light receiver 243 outputs an electric signal that corresponds to the intensity of each of the received second and fourth scattered light." (Para. [0030]).
Regarding claim 32, Suzuki teaches an electric resistance detector including an aperture portion having the flow path inside thereof, a sample nozzle configured to supply the biological sample to the flow path, and a collection tube configured to collect the biological sample having passed through the flow path. Suzuki teaches "Containers respectively containing a diluent 111, a hemolyzing agent 112, a fluorescence-labeled antibody reagent 113, a hemolyzing agent 114, and a staining solution 115 are connected to the specimen preparation unit 13. The diluent 111 is also used as a sheath liquid for causing a measurement specimen to flow in a flow cell 211 of the optical detection unit 14 and in a flow cell of the electric-resistance-type detection unit 15." (para. [0011]). The sheath liquid of Suzuki corresponds to the recited sample nozzle and the flow cell of Suzuki corresponds to the recited collection tube.
Regarding claim 36, Suzuki teaches wherein the cell type to be determined includes nucleated erythrocyte. Suzuki teaches "Said another measurement may be measurement for identifying and counting nucleated red blood cells and white blood cells, for example. In this case, the specimen preparation unit 13 mixes a blood sample 101, another hemolyzing agent, and another staining solution together, to prepare another measurement specimen. In preparation of said another measurement specimen, red blood cells are hemolyzed, and nucleic acid and cell organelles of nucleated red blood cells and white blood cells are stained. Said another measurement specimen is caused to flow in the flow cell 211, similarly to the first and second measurement specimens. On the basis of forward scattered light and fluorescence occurring from blood cells in said another measurement specimen irradiated with light, a scattergram is created. Regions are set on the created scattergram, whereby identification and counting of nucleated red blood cells and white blood cells are performed." (Para. [0054]).
Regarding claim 37, Suzuki teaches wherein the cell type to be determined includes abnormal cell, and the processing part is configured to: output information indicating that the abnormal cell is contained in the biological sample if a predetermined number of the analysis target cells have been determined to be the abnormal cell on the basis of the results outputted from the deep learning algorithm. Suzuki teaches " In step S206, the processing unit 21 identifies and counts malaria-infected red blood cells and white blood cells." (para. [0050]). Malaria-infected red blood cells of Suzuki corresponds to the recited abnormal cell.
Regarding claim 42, Suzuki teaches wherein the processing part comprises a CPU and an accelerator configured to assist arithmetic processing performed by the CPU. Suzuki teaches "The processing unit 21 is a CPU, for example. The processing unit 21 receives signals outputted by components of the information processing unit 20, and controls the components of the information processing unit 20. The processing unit 21 performs communication with the measurement unit 10. The storage unit 22 is a ROM, a RAM, a hard disk, and the like. The processing unit 21 executes processes on the basis of programs stored in the storage unit 22." (Para. [0023]) and "On the basis of the signal outputted from each of the light receivers 231, 243, 252, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell, and calculates the peak value, the width, the area, and the like of the waveform. The signal processing circuit 16 outputs to the measurement controller 11 the peak values of the waveforms obtained from the signals based on the first scattered light, the second scattered light, the third scattered light, the fourth scattered light, the first fluorescence, and the second fluorescence, as first scattered light information, second scattered light information, third scattered light information, fourth scattered light information, first fluorescence information, and second fluorescence information, respectively. On the basis of the signal outputted from the electric-resistance-type detection unit 15, the signal processing circuit 16 extracts a waveform that corresponds to the blood cell and outputs the peak value of the waveform as blood cell information to the measurement controller 11." (Para. [0021]). The signal processing circuit of Suzuki corresponds to the recited accelerator.
Regarding claim 43, Suzuki teaches wherein the accelerator comprises a GPU. Suzuki teaches "The processing unit 21 is a CPU, for example. The processing unit 21 receives signals outputted by components of the information processing unit 20, and controls the components of the information processing unit 20. The processing unit 21 performs communication with the measurement unit 10. The storage unit 22 is a ROM, a RAM, a hard disk, and the like. The processing unit 21 executes processes on the basis of programs stored in the storage unit 22." (Para. [0023])
It would have been prima facia obvious to combine the teachings of copending application with Suzuki. A person of ordinary skill in the art would have been motivated to modify the method of copending application to include the waveform data that is related to the forward scattered light and the side scattered light of the cells as taught by Suzuki to capture the different aspects of the particle because the forward scattered light reflects information regarding the size of the particle, side scattered light reflects internal information of the particle, and fluorescence reflects the degree of staining of the particle (Para. [0029]). A person of ordinary skill in the art would have also been motivated to modify the method of copending application to include an electric resistance detector as taught by Suzuki to apply voltage to the specimen to catch changes in voltage caused by passage of each blood cell, thereby detecting the blood cell (para. [0020]). It would have also been prima facia to modify copending application to include a sample nozzle in the form of a sheath and a collection tube in the form of a flow cell as taught by Suzuki to direct the flow of the sample and to store the sample. It would have also been prima facia to modify copending application to include determining the presence of nucleated erythrocyte in a sample and to identify abnormal cells as taught by Suzuki to determine disease condition. It would have also been prima facia to modify copending application to include processing part and an accelerator comprising of a CPU to assist in with arithmetic processing as taught by Suzuki in order to process the signals. There would have been a reasonable expectation of success, since both copending application with Suzuki teach methods that pertain to the use of flow cytometry to analyze samples.
Claim 38 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-4, 8-9, 13-15, 17 and 19-20 of copending Application No. 17691761 (reference application, 03/10/2022 claims), in view of Ozasa (US 9,664,669 B2, Date of patent: May 30, 2017; as cited on the attached "Notice of References Cited" form 892).
Regarding claim 38, Ozasa teaches wherein the biological sample is a urine sample. Ozasa teaches "Disclosed is a urine specimen analyzing method which improves the detection of casts in a urine specimen by flowing a measurement sample containing a urine specimen through a flow cell, irradiating light on the measurement sample flowing through the flow cell, generating a signal waveform indicating a temporal change of intensity of light given off by the measurement sample, and detecting casts distinguishably from mucus threads contained in the urine specimen, based on information related to respective slope at both end sides of the signal waveform corresponding to each formed element contained in the urine specimen." (Abstract)
It would have been prima facia obvious to combine the teachings of copending application with Ozasa. A person of ordinary skill in the art would have been motivated to modify the method of copending application to include a step of analyzing urine samples as taught by Ozasa to detect and count elements contained in urine. There would have been a reasonable expectation of success, since both copending application with Ozasa teach methods that pertain to the use of flow cytometry to analyze samples.
Claims 39 and 41 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-4, 8-9, 13-15, 17 and 19-20 of copending Application No. 17691761 (reference application, 03/10/2022 claims) in view of Fukama (US 8,252,593 B2, published Aug. 8, 2012; as cited on the attached "Notice of References Cited" form 892).
Regarding claim 39, Fukama teaches wherein the signal values in the waveform data include a first signal value at first having reached a predetermined threshold and signal values obtained during a predetermined time after the first signal values. Fukama teaches "If i is greater than or equal to five in step S55 (YES in step S55), the CPU 51 a reads out the analysis result of the sample for reproducibility check obtained through five measurements from the hard disc 51 d, and determines whether or not the variation in five analysis results is within a predetermined range, that is, the difference of the minimum value and the maximum value of the five analysis results is within a predetermined range (step S57). If the variation in five analysis results exceeds the predetermined range (NO in step S57), the first measurement unit 2 is assumed to be abnormal, and thus the CPU 51 a displays an abnormal warning screen on the image display unit 52 (step S58), and terminates the process." (col. 13, para. 5).
Regarding claim 41, Fukama teaches wherein the predetermined process includes noise removal, baseline correction, or normalization. Fukama teaches "First, the automatic calibration operation in which the sample analyzer 1 according to the present embodiment automatically performs the calibration of the first measurement unit 2 and the second measurement unit 3 will be described. The automatic calibration operation includes automatically performing a series of operations of automatically conveying the sample rack L inserted with the sample container T of the calibrator and the sample container T of the sample for reproducibility check or the normal sample at a predetermined position (holding position 1 and holding position 2 in the present embodiment), checking the reproducibility of the analysis result of the first measurement unit 2 before the calibration by the sample for reproducibility check, performing calibration of the first measurement unit 2 by the calibrator, checking the reproducibility of the analysis result of the second measurement unit 2 before the calibration by the sample for reproducibility check, and performing the calibration of the second measurement unit 3. The calibrator is a sample which concentration of the component is known, wherein the calibration of the measurement unit is performed by defining the correction value (correction data) of the analysis result so that the numerical value of the analysis result of the calibrator matches the concentration (hereinafter referred to as 'reference concentration'). The normal sample is usually used for the sample for reproducibility check." (Col. 11. Para. 2).
It would have been prima facia obvious to combine the teachings of copending application with Fukama. A person of ordinary skill in the art would have been motivated to modify the method of copending application to include a step of setting the threshold values for signal values as taught by Fukama to determine abnormal values. A person of ordinary skill in the art would have also been motivated to modify the method of copending application to include a step of normalizing or calibrating the values as taught by Fukama to remove unwanted background signals. There would have been a reasonable expectation of success, since both copending application with Fukama teach methods that pertain to the use of flow cytometry to analyze samples.
Conclusion
No claims are allowed.
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/K.K./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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