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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2020-094723, filed on 05/29/2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/17/2022 was considered by the examiner.
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.
Claim 1 recites elements including “a signal acquisition unit that acquires a signal”, “ a processing unit that calculates immunostaining conditions”, and “an output unit that outputs the immunostaining conditions”. The recited “units” are generic placeholders for “means”, each unit is modified by functional language (e.g. a signal acquisition unit that acquires a signal), and each lacks sufficient structural language modifying the “means” for performing the function. The specification recites an information processing device (paragraph [0008] and Fig. 2) including a signal acquisition unit (paragraphs [0035]-[0037]), a processing unit (paragraph [0038]), and an output unit (paragraphs [0008] and [0023]). Said elements are therefore interpreted to include means of signal acquisition, processing, and outputting information, respectfully, including a generic computing device.
Claim 16, dependent on claim 1, further recites “a presentation unit that presents support information”, which is taught in the specification ([0023] and Fig. 2). Similar interpretation is used as claim 1, such that the presentation unit is interpreted to include means of presenting information, including generic computing device comprising user interface.
Claim 17 recites similar elements, wherein the generic placeholder for “means” is “step”, and similar interpretation applies. The elements are modified by functional language (e.g. a signal acquisition step in which a signal derived from a sample including a biological sample is acquired) and no sufficient structural language modifies the “step”. Similar interpretation applies to “function” elements of claim 18 and the “unit” elements of claim 19. Claim 19 further recites “a detection unit that detects a signal derived from the strained sample”, which is detailed in the specification ([0035] “For example, the signal acquisition unit 11 acquires signals detected by a flow cytometer, a microscope, various photodetectors and the like”), which is interpreted to include means of detection (“a flow cytometer, a microscope, various photodetectors and the like”) for acquiring signals (such as images) and includes tools, devices, and optical/visual means of detection (“photodetectors and the like”). Claim 20 further recites “a staining unit that stains the sample using a reagent”, which is recited in the specification (paragraph [0011] and Fig. 5, “staining unit 32”), and is interpreted as means of staining a sample utilizing some reagent, including means of introducing a chemical or substance, for the purposes of staining the sample, such as by dye, fluorescent, or other substance that produces a detectable difference. Claim 21 further recites “an analysis unit that analyzes the sample”, which similarly recites a generic placeholder for “means”, functional language, and lacks structural language modifying said “means”, which is recited in the specification (Fig. 5 “analysis unit”), and is interpreted to include means of analyzing including a generic computer or a human user ([0006] “In addition, in order to detect and/or analyze target molecules, setting of staining conditions using a reagent containing binding molecules is directly performed by a user who performs detection and/or analysis, but even for the experienced user, it is a time-consuming task.”).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, Claim 1 recites:
“An information processing device, comprising:
a signal acquisition unit that acquires a signal derived from a sample including a biological sample;
a processing unit that calculates immunostaining conditions of a reagent for the sample based on the signal; and
an output unit that outputs the immunostaining conditions, wherein the signal includes a signal derived from the reagent containing target molecules in the biological sample and binding molecules that are able to bind to the target molecules and/or a signal derived from non-target molecules and the reagent.”
Claim 17 recites:
“An information processing method, comprising:
a signal acquisition step in which a signal derived from a sample including a biological sample is acquired;
a processing step in which immunostaining conditions of a reagent for the sample are calculated based on the signal; and
an output step in which the immunostaining conditions are output, wherein the signal includes a signal derived from a reagent containing target molecules in the biological sample and binding molecules that are able to bind to the target molecules and/or a signal derived from non-target molecules and the reagent.”
Claim 18 recites:
“A computer program causing a computer to implement:
a signal acquisition function of acquiring a signal derived from a sample including a biological sample;
a processing function of calculating immunostaining conditions of a reagent for the sample based on the signal; and
an output function of outputting the immunostaining conditions, wherein the signal includes a signal derived from a reagent containing target molecules in the biological sample and binding molecules that are able to bind to the target molecules and/or a signal derived from non-target molecules and the reagent.”
Claim 19 recites:
“A target molecule detection system, comprising:
a signal acquisition unit that acquires a signal derived from a sample including a biological sample;
a processing unit that calculates immunostaining conditions of a reagent for the sample based on the signal;
an output unit that outputs the immunostaining conditions; and
a detection unit that detects a signal derived from the stained sample based on the output immunostaining conditions, wherein the signal includes a signal derived from a reagent containing target molecules in the biological sample and binding molecules that are able to bind to the target molecules and/or a signal derived from non-target molecules and the reagent.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above; the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 1 recites a device (i.e. machine), Claim 17 recites a process, and Claim 19 recites a system (i.e. machine), and therefore each falls within a statutory category.
Independent claim 18 is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 18 recites “a computer program” in the preamble, and therefore amounts to “software per se”, thus is not directed to a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portion of representative claim 17 comprises process steps that fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category.
Individually and collectively, the steps of claim 17:
“a signal acquisition step in which a signal derived from a sample including a biological sample is acquired”;
“a processing step in which immunostaining conditions of a reagent for the sample are calculated based on the signal”; and
“an output step in which the immunostaining conditions are output”
may be performed as mental processes. A signal acquisition step in which a signal is acquired is at step of collecting information, which may be performed as mental processes. A processing step in which conditions of a reagent are calculated is an analytical step, said analysis may be performed as mental processes. An output step in which conditions are output is the display of certain results of the analysis, which may be performed as mental processes. The type of high-level information collecting and analyzing data recited in these elements has been found by the Federal Circuit to constitute patent ineligible matter (see Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind).
Similar limitations comprise the mental processes type abstract idea recited by independent claims 1, and 18-19. Claim 1 recites a signal acquisition unit that acquires a signal (collecting information), a processing unit that calculates immunostaining conditions (analysis), and an output unit that outputs the immunostaining conditions (display of certain results of the analysis), each of which may be performed as mental processes. Claim 18 recites a signal acquisition function of acquiring a signal (collecting information), a processing function of calculating immunostaining conditions (analysis), and an output function of outputting the immunostaining conditions (display of certain results of the analysis), each of which may be performed as mental processes. Claim 19 recites a signal acquisition unit that acquires a signal (collecting information), a processing unit that calculates immunostaining conditions (analysis), an output unit that outputs the immunostaining conditions (display of certain results of the analysis), and a detection unit that detects a signal derived from the stained sample (collecting information) each of which may be performed as mental processes.
Step 2A, Prong Two of the analysis entails determining whether a claim includes additional elements that integrate the recited judicial exception (e.g., abstract idea) into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 17 does not include additional elements that integrate the recited abstract idea into a practical application. Based on the individual and collective limitations of claim 17, applying a broadest reasonable interpretation, the most significant of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” in any combination appear to integrate the abstract idea to technologically improve any aspect of a system that may be used to implement the highlighted steps such a generic computer. The claim as a whole entails acquiring information, analyzing said information, and outputting results of the analysis. Therefore, any alleged improvement must be an improvement in the abstract idea process steps, which is not an improvement in technology (MPEP 2106.05(a).II “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”).
Regarding application of the judicial exception with, or by use of, a particular machine, none of the “additional elements” in any combination recite a particular machine, and no particularized manner of implementing the abstract idea process steps is recited.
Regarding effectuation of a transformation or reduction of a particular article to a different state or thing, the claim includes no such transformation or reduction. Instead, the claim as a whole entails gathering or otherwise obtaining (e.g. acquiring) information, analyzing (e.g. processing) said information, and displaying (e.g. outputting) the results of said analysis. The output of information does not amount to a transformation of a particular article to a different state.
Similar analysis applies to independent claims 1, 18, and 19. Claim 1 recites additional elements including “an information processing device”, “ a signal acquisition unit”, “a processing unit”, and “an output unit”, which are recited generically (and amount to a generic computer), do not utilize the abstract idea processing steps in a particularized manner, and do not effect a transformation of a particular article. Claim 19 recites similar additional elements as independent claim 1, and “a target molecule detection system” and “a detection unit”, which is recited generically and does not integrate the abstract idea into a particular application. Claim 18 recites additional elements including “a computer program”, “a computer”, “a signal acquisition function”, “a processing function”, and “an output function”, which amounts to mere instruction to implement the abstract idea in a computer environment (MPEP 2106.05(f)).
The above additional elements, considered individually and in combination with the claim elements reciting an abstract idea do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under Step 2B.
Regarding Step 2B, independent claims 1 and 17-19, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in the relevant art as evidenced by the prior art of record as indicated in the rejections under 35 U.S.C. §103.
Independent claims 1 and 17-19 are therefore not patent eligible.
Dependent claims 2-16 and 20-21 provide additional features/steps which are part of an expanded algorithm that includes the abstract idea of the independent claims (Step 2A, Prong One). Claims 2 and 14 further detail the signal acquired and processed. Claims 3-13 further detail the analysis step, which may be performed as mental processes and/or mathematical operations. Claim 15 further details the output conditions of the output unit. Claim 16 recites “a presentation unit that presents support information”, which amounts to displaying certain results of the analysis, which may be performed as mental processes. Claim 20 recites a “staining unit that stains the sample using the reagent”. This limitation amounts to extra-solution activity to the target molecule detection system, and therefore does not integrate the judicial exception into a practical application. Claim 21 recites “an analysis unit that analyzes the sample” which is an analytical processes, which may be performed by mental processes. None of dependent claims 2-18 and 20 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and all fail the “significantly more” test under the step 2B for the same reasons as discussed with regards to the independent claims.
The dependent claims 2-16 and 20-21 therefore are also ineligible subject matter.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-13, and 15-21 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Day et al. (US 20190293637A1).
Regarding claim 1, Day teaches An information processing device (Abstract; [0023] lines 1-8, “The present invention also features an automated staining machine comprising a system adapted to perform a method of the present invention. The automated staining machine may comprise a memory coupled to a processor, wherein the memory stores computer-readable instructions that, when executed by the processor, cause the automated staining machine to perform operations for a method of the present invention.”.”), comprising:
a signal acquisition unit that acquires a signal derived from a sample ([0010] “The present invention provides methods for amplifying a signal for a target molecule in a sample (e.g., methods for amplifying a signal for a target molecule in a formalin-fixed paraffin-embedded (FFPE) tissue sample). The method may comprise treating the tissue sample with a deparaffinization reagent and treating the tissue sample with an antigen retrieval reagent. The method may further comprise treating the sample with a protease, e.g., before applying the biomarker-specific agent specific for the target protein biomarker.”; [0058]- [0059] “Detectable label or Detectable moiety: A molecule or material that can produce a detectable signal (such as a visual, electrical, or other signal) that indicates the presence and/or concentration of the detectable moiety or label deposited on the sample (or the presence and/or amount of a target (such as a protein or nucleic acid) in a sample). Detectable labels are well known to one of ordinary skill in the art. A detectable signal can be generated by any known or yet to be discovered mechanism including absorption, emission and/or scattering of a photon (including radio frequency, microwave frequency, infrared frequency, visible frequency and ultra-violet frequency photons).” ) including a biological sample ([0052] “Biological sample: As used herein, the term “biological sample” shall refer to any material obtained from a subject capable of being tested for the presence or absence of a biomarker or target molecule.”);
a processing unit that calculates immunostaining conditions of a reagent for the sample based on the signal ([0067] lines 1-5, “Histochemistry (e.g., see Immunohistochemistry (IHC) also): A method of determining the presence or distribution of a target molecule in a sample by detecting interaction of the target molecule with a specific binding agent, such as an antibody, that can be detected.”; IHC of [0068] and quantitative IHC (qIHC) of [0087]-[0088]); and
an output unit that outputs the immunostaining conditions ([0140] lines 1-7, “Image analysis systems may feature one or more computing devices such as desktop computers, laptop computers, tablets, smartphones, servers, application-specific computing devices, or any other type(s) of electronic device(s) capable of performing the techniques and operations described herein. In some embodiments, the image analysis system may be implemented as a single device.”; [0144] lines 1-6, “After acquiring an image, the image analysis system may pass the image to an object identifier, which functions to identify and mark relevant objects and other features within the image that will later be used for scoring. In the present invention, biomarkers appear as punctate dots that can be quantified.”), wherein the signal includes a signal derived from the reagent containing target molecules in the biological sample and binding molecules that are able to bind to the target molecules ([0054] lines 1-6, “Biomarker-specific agent: Any compound or composition that binds to a biomarker or a specific structure within that biomarker in a manner that permits a specific detection of the biomarker in a sample. Examples include: antibodies and antigen binding fragments thereof; and engineered specific binding structures”, lines 21-34 “(Descriptions of such engineered specific binding structures are reviewed by Wurch et al., Development of Novel Protein Scaffolds as Alternatives to Whole Antibodies for Imaging and Therapy: Status on Discovery Research and Clinical Validation, Current Pharmaceutical Biotechnology, Vol. 9, pp. 502-509 (2008), the content of which is incorporated by reference); and fusion proteins including at least a first domain capable of specifically binding to the biomarker (e.g. an antigen binding fragment of an antibody or a target-binding portion of a protein that binds to the biomarker) and a second portion that is adapted to facilitate binding of detection reagents to the fusion protein (e.g., a biotin label, an epitope tag, an Ig fragment, etc.).”; [0111] lines1-4, “a biomarker-specific agent is applied to the sample, wherein the biomarker-specific agent is specific for and binds to the target in the sample.”) and/or a signal derived from non-target molecules and the reagent.
Regarding claim 2, teaches The information processing device according to claim 1, wherein the signal includes at least one of a signal (punctate dot signals), a specific signal/background ([0107] Without wishing to limit the present invention to any theory or mechanism, it was thought that polyclonal antibodies would generate off-target binding and result in background signal (because of their inherent increased ability to bind things other than the intended antigen). For example, the RNA methods require the use of a monoclonal antibody for detecting the labeled probe. It was surprisingly found that this was not observed with a polyclonal antibody used in the methods herein (the polyclonal antibody did not generate detectable background signal). Thus, in certain embodiments, a polyclonal antibody may be used for the qIHC methods herein (for detecting the target-specific binding agent, e.g., the primary antibody). In certain embodiments, a monoclonal antibody is used for the qIHC methods herein (for detecting the target-specific binding agent, e.g., the primary antibody).), and a specific signal/non-specific signal ([0073] “Non-Biotin detection techniques have gained popularity because they are devoid of such limitations of Avidin-Biotin detection as nonspecific background staining due to the endogenous biotin that is abundant in different types of animal tissues, including kidney, brain, and placenta.”).
Regarding claim 3, teaches The information processing device according to claim 1, wherein the processing unit calculates immunostaining conditions of the reagent for the sample including the biological sample based on signals derived from the sample stained with a plurality of reagent concentrations ([0151] “Repeated Experiment 1 using optimal reagent concentrations and five tonsil and five Her2 3in1 xenograft slides. Results demonstrated increased sensitivity of the high-sensitivity detection system (method of the present invention) compared to the RTD OptiView amp DAB detection system.”) and a threshold value ([0087] lines 4-8, “the methods herein produce visible punctate dots (that in certain embodiments allow for the counting of individual molecules (e.g., target molecules)), e.g., by reducing the threshold number of molecules needed to generate a visible signal.”)
Regarding claim 4, teaches The information processing device according to claim 3, wherein the threshold value is the maximum signal among the signals derived from the sample stained with a plurality of reagent concentrations ([0138] “In certain embodiments, the stained tissue sections may comprise a concentration-dependent stain (e.g., a stain that has different chromatic properties at different concentrations). Thus, the methods of unmixing the digital image of the tissue section may account for the effects of light scattering and how, at varying stain concentrations, light scattering may change the proportions of RGB channel signals in detected light. This may feature selecting an optimal color reference vector for the concentration-dependent stain selected from a set of color reference vectors for the concentration-dependent stain (wherein each color reference vector in the set describes or characterizes the concentration-dependent stain at a different concentration level), and unmixing the image using the selected optimal color reference vector.”). The concentration-dependent stain, which produces a color dependent on the concentration level, indicates a corresponding threshold value (i.e. a maximum) for the reagent at a concertation.
Regarding claim 5, teaches The information processing device according to claim 1, wherein the processing unit calculates immunostaining conditions of the reagent for the sample including the biological sample based on the signal derived from the sample stained with at least one reagent concentration and reagent information referred to in a database ([0137] lines 1-5, “The unmixing process extracts stain-specific channels to determine local concentrations of individual stains using color reference vectors, or reference spectra, that are well-known for standard types of tissue and stain combinations.”; [0133] lines 1-3, “Images generated by the scanning platform may be transferred to an image analysis system or to a server or database accessible by an image analysis system”).
Regarding claim 6, teaches The information processing device according to claim 1, wherein the processing unit calculates immunostaining conditions of the reagent for the sample including the biological sample based on the signals derived from the sample stained with a plurality of reagent concentrations ([0151] “Repeated Experiment 1 using optimal reagent concentrations and five tonsil and five Her2 3in1 xenograft slides. Results demonstrated increased sensitivity of the high-sensitivity detection system (method of the present invention) compared to the RTD OptiView amp DAB detection system.”) and a threshold value ([0087] lines 4-8, “the methods herein produce visible punctate dots (that in certain embodiments allow for the counting of individual molecules (e.g., target molecules)), e.g., by reducing the threshold number of molecules needed to generate a visible signal.”) extracted from region information (region of interest (ROI); [0144] “After acquiring an image, the image analysis system may pass the image to an object identifier, which functions to identify and mark relevant objects and other features within the image that will later be used for scoring. In the present invention, biomarkers appear as punctate dots that can be quantified. Thus, the objects identified in the image analysis are the punctate dots within the ROI (e.g., whole tumor, invasive margin, tumor core, peri-tumoral region, etc.).”).
Regarding claim 7, teaches The information processing device according to claim 6, wherein the processing unit determines a region (region of interest (ROI); [0144] “After acquiring an image, the image analysis system may pass the image to an object identifier, which functions to identify and mark relevant objects and other features within the image that will later be used for scoring. In the present invention, biomarkers appear as punctate dots that can be quantified. Thus, the objects identified in the image analysis are the punctate dots within the ROI (e.g., whole tumor, invasive margin, tumor core, peri-tumoral region, etc.).”) based on the signal and/or bright field image ([0011] lines 21-23, “The detectable moiety (e.g., chromogen) may be visible as a punctate dot using microscopy, e.g., brightfield microscopy”; [0131] lines 1-2, “In some embodiments, the imaging apparatus is a brightfield imager slide scanner.”). The region
Regarding claim 8, teaches The information processing device according to claim 7, wherein the region includes morphology information of the biological sample ([0110] lines 1-5, “Biomarker-stained sections may optionally be additionally stained with a contrast agent (such as a hematoxylin stain) to visualize macromolecular structures, identify morphological features or morphologically relevant areas, either manually or automatically.”).
Regarding claim 9, teaches The information processing device according to claim 8, wherein the morphology information includes a cell membrane and a nuclear distribution ([0069] lines 1-5, “Counterstaining is the staining of tissue sections with dyes that allow one to see the entire “landscape” of the tissue section and serve as a reference for the main color used for the detection of tissue targets. Such dyes can stain cell nuclei, the cell membrane, or the entire cell.”).
Regarding claim 10, teaches The information processing device according to claim 8, wherein the morphology information includes a cell morphology obtained by segmentation ([0139] lines 21-27, “the computer system may automatically suggest an ROI without any direct input from the user (termed an “automated ROI annotation”). For example, a previously trained tissue segmentation function or other pattern recognition function may be applied to an unannotated image to identify the desired morphological region to use as an ROI.”).
Regarding claim 11, teaches The information processing device according to claim 7, wherein the processing unit compares the plurality of determined regions ([0139] lines 7-15, “For example, the user may delineate one or more regions on the digital image, which the system then automatically transforms into a complete ROI. For example, if the desired ROI is an invasive margin (IM) region, a user can delineate (e.g., by outlining, tracing) an IM region or a whole tumor (WT) region, and the system applies a pattern recognition function that uses computer vision and machine learning to identify regions having similar morphological characteristics to an IM region.”). The pattern recognition function that identifies regions having similar morphological characteristics is comparing a plurality of determined regions.
Regarding claim 12, teaches The information processing device according to claim 11, wherein the processing unit compares the plurality of regions including at least one of a cell membrane, a cell nucleus ([0069] lines 1-5, “Counterstaining is the staining of tissue sections with dyes that allow one to see the entire “landscape” of the tissue section and serve as a reference for the main color used for the detection of tissue targets. Such dyes can stain cell nuclei, the cell membrane, or the entire cell.”), a specific binding region ([0087] lines 20-25, “FIG. 4B shows a native or modified (e.g., haptenated, tagged) antibody (e.g., monoclonal antibody) binding to a target biomarker. Either a monoclonal or polyclonal secondary antibody conjugate (e.g., HRP) is used to bind to the target-specific binding agent (e.g., the primary antibody).”) and a non- specific binding region and analyzes localization of the regions ([0143] lines 1-3, “The image analysis system may also include an object identifier, a region of interest (ROI) generator, a user-interface module, and a scoring engine.”).
Regarding claim 13, teaches The information processing device according to claim 11, wherein the plurality of determined regions are single cells, and wherein the processing unit excludes signals derived from single cells that overlap and/or are adjacent to each other within a predetermined distance ([0139] lines 16-20, “In cases in which ROI generation is semi-automated, the user may be given an option to modify the ROI annotated by the computer system, such as by expanding the ROI, annotating regions of the ROI or objects within the ROI to be excluded from analysis, etc.”; [0145] “The object identifier and the annotation of the ROI (ROI generator) may be implemented in any order. For example, the object identifier may be applied to the entire image first. The positions and features of the identified objects may then be stored and recalled when the ROI generator is implemented. A score can be generated by a scoring engine upon generation of the ROI. Alternatively, the ROI generator can be implemented first. In this case, the object identifier may be implemented only on the ROI, or it may still be implemented on the whole image. It may also be possible to implement the object identifier and the ROI generator simultaneously.”). The information of positions of identified objects includes overlapping cells, and the exclusion from analysis based on the regions of interest includes excluding signals of such overlapping cells.
Regarding claim 15, teaches The information processing device according to claim 1, wherein the output unit outputs ([0058] “Detectable label or Detectable moiety: A molecule or material that can produce a detectable signal (such as a visual, electrical, or other signal) that indicates the presence and/or concentration of the detectable moiety or label deposited on the sample (or the presence and/or amount of a target (such as a protein or nucleic acid) in a sample). Detectable labels are well known to one of ordinary skill in the art.”), as the immunostaining conditions, at least one of an antibody clone, an antibody concentration ([0060] lines 1-7, “When conjugated to a specific binding molecule (for example, an antibody or nucleic acid probe), the detectable label can be used to locate and/or quantify the target to which the specific binding molecule is directed. A detectable label can be detected directly or indirectly, and several different detectable labels can be used in combination to detect one or more targets.”), an antigen-antibody reaction time, a reaction temperature, antigen activation conditions, a composition of a reaction solution, and stirring conditions.
Regarding claim 16, teaches The information processing device according to claim 1, further comprising a presentation unit that presents support information for staining conditions to a user based on the output immunostaining conditions ([0143] lines 1-3, “The image analysis system may also include an object identifier, a region of interest (ROI) generator, a user-interface module, and a scoring engine.”; [0147] lines 7-12, “The methods herein provide scoring of a tissue section using the punctate dot signals for one or more biomarkers labeled in the tissue section. For example, image analysis software may input the quantitated biomarkers (object metric) into a pre-determined scoring function to obtain a score for the tissue section.”). The score is the support information.
Regarding claim 17, teaches An information processing method (Abstract; [0023] lines 1-8, “The present invention also features an automated staining machine comprising a system adapted to perform a method of the present invention. The automated staining machine may comprise a memory coupled to a processor, wherein the memory stores computer-readable instructions that, when executed by the processor, cause the automated staining machine to perform operations for a method of the present invention.”; [0141] “The image analysis system may include a memory, a processor, and a display. The memory may include any combination of any type of volatile or non-volatile memories, such as random-access memories (RAMs), read-only memories such as an Electrically-Erasable Programmable Read-Only Memory (EEPROM), flash memories, hard drives, solid state drives, optical discs, and the like. The processor may include one or more processors of any type, such as central processing units (CPUs), graphics processing units (GPUs), special-purpose signal or image processors, field-programmable gate arrays (FPGAs), tensor processing units (TPUs), and so forth.”), comprising:
a signal acquisition step in which a signal derived from a sample ([0010] “The present invention provides methods for amplifying a signal for a target molecule in a sample (e.g., methods for amplifying a signal for a target molecule in a formalin-fixed paraffin-embedded (FFPE) tissue sample). The method may comprise treating the tissue sample with a deparaffinization reagent and treating the tissue sample with an antigen retrieval reagent. The method may further comprise treating the sample with a protease, e.g., before applying the biomarker-specific agent specific for the target protein biomarker.”; [0058]- [0059] “Detectable label or Detectable moiety: A molecule or material that can produce a detectable signal (such as a visual, electrical, or other signal) that indicates the presence and/or concentration of the detectable moiety or label deposited on the sample (or the presence and/or amount of a target (such as a protein or nucleic acid) in a sample). Detectable labels are well known to one of ordinary skill in the art. A detectable signal can be generated by any known or yet to be discovered mechanism including absorption, emission and/or scattering of a photon (including radio frequency, microwave frequency, infrared frequency, visible frequency and ultra-violet frequency photons).” ) including a biological sample ([0052] “Biological sample: As used herein, the term “biological sample” shall refer to any material obtained from a subject capable of being tested for the presence or absence of a biomarker or target molecule.”) is acquired;
a processing step in which immunostaining conditions of a reagent for the sample are calculated based on the signal ([0067] lines 1-5, “Histochemistry (e.g., see Immunohistochemistry (IHC) also): A method of determining the presence or distribution of a target molecule in a sample by detecting interaction of the target molecule with a specific binding agent, such as an antibody, that can be detected.”; IHC of [0068] and quantitative IHC (qIHC) of [0087]-[0088]); and
an output step in which the immunostaining conditions are output ([0140] lines 1-7, “Image analysis systems may feature one or more computing devices such as desktop computers, laptop computers, tablets, smartphones, servers, application-specific computing devices, or any other type(s) of electronic device(s) capable of performing the techniques and operations described herein. In some embodiments, the image analysis system may be implemented as a single device.”; [0144] lines 1-6, “After acquiring an image, the image analysis system may pass the image to an object identifier, which functions to identify and mark relevant objects and other features within the image that will later be used for scoring. In the present invention, biomarkers appear as punctate dots that can be quantified.”), wherein the signal includes a signal derived from a reagent containing target molecules in the biological sample and binding molecules that are able to bind to the target molecules ([0054] lines 1-6, “Biomarker-specific agent: Any compound or composition that binds to a biomarker or a specific structure within that biomarker in a manner that permits a specific detection of the biomarker in a sample. Examples include: antibodies and antigen binding fragments thereof; and engineered specific binding structures”, lines 21-34 “(Descriptions of such engineered specific binding structures are reviewed by Wurch et al., Development of Novel Protein Scaffolds as Alternatives to Whole Antibodies for Imaging and Therapy: Status on Discovery Research and Clinical Validation, Current Pharmaceutical Biotechnology, Vol. 9, pp. 502-509 (2008), the content of which is incorporated by reference); and fusion proteins including at least a first domain capable of specifically binding to the biomarker (e.g. an antigen binding fragment of an antibody or a target-binding portion of a protein that binds to the biomarker) and a second portion that is adapted to facilitate binding of detection reagents to the fusion protein (e.g., a biotin label, an epitope tag, an Ig fragment, etc.).”; [0111] lines1-4, “a biomarker-specific agent is applied to the sample, wherein the biomarker-specific agent is specific for and binds to the target in the sample.”) and/or a signal derived from non-target molecules and the reagent.
Regarding claim 18, teaches A computer program (Abstract; [0148] “The present invention also provides computing systems and computer algorithms for use with automated systems described herein.”) causing a computer ([0023] lines 1-8, “The present invention also features an automated staining machine comprising a system adapted to perform a method of the present invention. The automated staining machine may comprise a memory coupled to a processor, wherein the memory stores computer-readable instructions that, when executed by the processor, cause the automated staining machine to perform operations for a method of the present invention.”; [0141] “The image analysis system may include a memory, a processor, and a display. The memory may include any combination of any type of volatile or non-volatile memories, such as random-access memories (RAMs), read-only memories such as an Electrically-Erasable Programmable Read-Only Memory (EEPROM), flash memories, hard drives, solid state drives, optical discs, and the like. The processor may include one or more processors of any type, such as central processing units (CPUs), graphics processing units (GPUs), special-purpose signal or image processors, field-programmable gate arrays (FPGAs), tensor processing units (TPUs), and so forth.”) to implement:
a signal acquisition function of acquiring a signal derived from a sample ([0010] “The present invention provides methods for amplifying a signal for a target molecule in a sample (e.g., methods for amplifying a signal for a target molecule in a formalin-fixed paraffin-embedded (FFPE) tissue sample). The method may comprise treating the tissue sample with a deparaffinization reagent and treating the tissue sample with an antigen retrieval reagent. The method may further comprise treating the sample with a protease, e.g., before applying the biomarker-specific agent specific for the target protein biomarker.”; [0058]- [0059] “Detectable label or Detectable moiety: A molecule or material that can produce a detectable signal (such as a visual, electrical, or other signal) that indicates the presence and/or concentration of the detectable moiety or label deposited on the sample (or the presence and/or amount of a target (such as a protein or nucleic acid) in a sample). Detectable labels are well known to one of ordinary skill in the art. A detectable signal can be generated by any known or yet to be discovered mechanism including absorption, emission and/or scattering of a photon (including radio frequency, microwave frequency, infrared frequency, visible frequency and ultra-violet frequency photons).” ) including a biological sample ([0052] “Biological sample: As used herein, the term “biological sample” shall refer to any material obtained from a subject capable of being tested for the presence or absence of a biomarker or target molecule.”);
a processing function of calculating immunostaining conditions of a reagent for the sample based on the signal ([0067] lines 1-5, “Histochemistry (e.g., see Immunohistochemistry (IHC) also): A method of determining the presence or distribution of a target molecule in a sample by detecting interaction of the target molecule with a specific binding agent, such as an antibody, that can be detected.”; IHC of [0068] and quantitative IHC (qIHC) of [0087]-[0088]); and
an output function of outputting the immunostaining conditions ([0140] lines 1-7, “Image analysis systems may feature one or more computing devices such as desktop computers, laptop computers, tablets, smartphones, servers, application-specific computing devices, or any other type(s) of electronic device(s) capable of performing the techniques and operations described herein. In some embodiments, the image analysis system may be implemented as a single device.”; [0144] lines 1-6, “After acquiring an image, the image analysis system may pass the image to an object identifier, which functions to identify and mark relevant objects and other features within the image that will later be used for scoring. In the present invention, biomarkers appear as punctate dots that can be quantified.”), wherein the signal includes a signal derived from a reagent containing target molecules in the biological sample and binding molecules that are able to bind to the target molecules ([0054] lines 1-6, “Biomarker-specific agent: Any compound or composition that binds to a biomarker or a specific structure within that biomarker in a manner that permits a specific detection of the biomarker in a sample. Examples include: antibodies and antigen binding fragments thereof; and engineered specific binding structures”, lines 21-34 “(Descriptions of such engineered specific binding structures are reviewed by Wurch et al., Development of Novel Protein Scaffolds as Alternatives to Whole Antibodies for Imaging and Therapy: Status on Discovery Research and Clinical Validation, Current Pharmaceutical Biotechnology, Vol. 9, pp. 502-509 (2008), the content of which is incorporated by reference); and fusion proteins including at least a first domain capable of specifically binding to the biomarker (e.g. an antigen binding fragment of an antibody or a