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 .
Status of Claims
In the reply filed on 02 March 2026, applicant has made the following changes: amendment to claim 10.
Claims 1-20 are currently pending and have been examined.
Appropriate correction is required.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/166,427 and Application No. 63/507,047 fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. For claims 1, 12, and 16 the prior-filed application does not provide support for “detecting, by the computer system, a user input within the user interface that is associated with a first interactive element in the interactive elements” and “displaying, within the user interface, in response to detecting the user input, information associated with the first interactive element based on the genomic data structure or the clinical event data structures.” Examiner cannot find disclosure of a computer system detecting a user input within a user interface that is associated with a first interactive element in the interactive elements and displaying within the user interface information associated with the first interactive element based on the genomic data structure or the clinical event data structures in response to detecting the user input respectively in the prior filed application. For claims 2, 13, and 17 the prior-filed application does not provide support for “retrieving the description of the clinical event associated with the first interactive element from a corresponding clinical event data structure” and “displaying a tooltip containing the retrieved description.” Examiner cannot find disclosure of retrieving the description and displaying a tooltip in the prior filed application. For claims 3 and 18 the prior-filed application does not provide support for “retrieving the value of the measurement associated with the first interactive element from a corresponding clinical event data structure” and “displaying a tooltip containing the retrieved value.” Examiner cannot find disclosure of retrieving the measurement and displaying a tooltip in the prior filed application. For claims 5, 14, and 19 the prior-filed application does not provide support for “wherein the genomic data structure includes prevalence of each of a plurality of variants within the cancer cell genome data measured at each of the plurality of times” and “displaying, on the fishplot, a value of the prevalence of a first variant corresponding to the first interactive element.” Examiner cannot find disclosure of the genomic data structure including prevalence of each of a plurality of variants within the cancer cell genome data measured at each of the plurality of times and prevalence being displayed on a fishplot in the prior filed application. For claim 10 the prior-filed application does not provide support for “ingesting electronic medical records associated with the patient” and “processing the ingested electronic medical records using a large language model (LLM) to de-duplicate redundant entries and to deidentify the patient.” Examiner cannot find disclosure of electronic medical records being ingested for processing by an LLM for de-duplication of redundant entries as well as to deidentify the patient. For claims 11 and 15 the prior-filed application does not provide support for “training a model based on the plurality of genetic datasets and the plurality of clinical datasets” and “applying the trained model to the genetic dataset of the first patient and the clinical data associated with the first patient to generate a recommended treatment for the first patient.” Examiner cannot find disclosure of any disclosure of model training and applying the trained model to generate a recommended treatment. Accordingly, claims 1-20 are not entitled to the benefit of the prior application.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process (claims 1-11), an article of manufacture (claims 12-14), and system (claims 16-20).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recite steps of
generating, by a computer system, a visualization that represents progression of a cancer genome, wherein generating the visualization comprises:
obtaining a genetic dataset derived from cancer cell samples of a patient, the genetic dataset including cancer cell genome data measured at each of a plurality of times;
generating a genomic data structure that represents the genetic dataset;
obtaining clinical data associated with the patient, wherein the clinical data includes a plurality of clinical events;
generating clinical event data structures to represent each of the plurality of clinical events based on the clinical data; and
causing a user interface to display the visualization with interactive elements that are populated based on the genomic data structure and the clinical event data structures;
detecting, by the computer system, a user input within the user interface that is associated with a first interactive element in the interactive elements; and
displaying, within the user interface, in response to detecting the user input, information associated with the first interactive element based on the genomic data structure or the clinical event data structures.
Claims 11 and 16 recite similar limitations as claim 1 but for the recitation of generic computer components such as one or more processors and one or more non-transitory computer readable storage media.
These steps for generating a visualization that represents progression of a cancer genome, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity but for recitation of generic computer components but for recitation of generic computer components. That is, nothing in the claim element precludes the italicized portions from managing personal behavior or relationships or interactions between people through organizing the activity around modeling cancer evolution in a manner that helps doctors facilitate treatment decisions for a patient. This could be analogized to considering historical usage information while inputting data. If a claim limitation, under its broadest reasonable interpretation, covers performance as organizing human activity but for the recitation of generic computer components, then it falls within the “Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements, non-italicized portions identified above for claims 1, 11, and 16, do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of by a computer system; causing a user interface to display the visualization with interactive elements that are populated based on the genomic data structure and the clinical event data structures; detecting, by the computer system, a user input within the user interface that is associated with a first interactive element in the interactive elements; and displaying, within the user interface, in response to detecting the user input, information associated with the first interactive element based on the genomic data structure or the clinical event data structures; one or more processors; and one or more non-transitory computer readable storage media storing executable computer program instructions, the computer program instructions when executed by the one or more processors amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as by a computer system; causing a user interface to display the visualization with interactive elements that are populated based on the genomic data structure and the clinical event data structures; detecting, by the computer system, a user input within the user interface that is associated with a first interactive element in the interactive elements; and displaying, within the user interface, in response to detecting the user input, information associated with the first interactive element based on the genomic data structure or the clinical event data structures; one or more processors; and one or more non-transitory computer readable storage media storing executable computer program instructions, the computer program instructions when executed by the one or more processors, e.g., commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-11, 13-15, and 17-20 reciting particular aspects of generating a visualization that represents progression of a cancer genome including
[Claims 2, 13, & 17] wherein the clinical event data structures include, for each of the clinical events, a description of the corresponding clinical event;
wherein the visualization comprises a swimmer plot that represents one or more of the clinical events as respective interactive elements; and
wherein displaying information in response to the user interaction with the first interactive element comprises:
retrieving the description of the clinical event associated with the first interactive element from a corresponding clinical event data structure; and
displaying a tooltip containing the retrieved description;
[Claims 3 & 18] wherein the clinical event data structures include, for each of a plurality of laboratory measurement events, a value of a measurement taken at each of the laboratory measurement events;
wherein the visualization comprises a line graph that represents one or more laboratory measurement events as respective interactive elements; and
wherein displaying information in response to the user interaction with the first interactive element comprises:
retrieving the value of the measurement associated with the first interactive element from a corresponding clinical event data structure; and
displaying a tooltip containing the retrieved value;
[Claim 4] wherein the clinical event data structures include:
for each of the clinical events, a description of the corresponding clinical event; and for each of a plurality of laboratory measurement events, a value of a measurement taken at each of the laboratory measurement events;
wherein the visualization comprises a swimmer plot that represents one or more of the clinical events within a specified period of time as respective interactive elements; and wherein the visualization comprises a line graph that represents one or more laboratory measurement events in the specified period of time as respective interactive elements;
[Claims 5, 14, & 19] wherein the genomic data structure includes prevalence of each of a plurality of variants within the cancer cell genome data measured at each of the plurality of times;
wherein the visualization comprises a fishplot that represents prevalence of each of the plurality of variants across a specified time period; and
wherein displaying information in response to the user interaction with the first interactive element comprises:
displaying, on the fishplot, a value of the prevalence of a first variant corresponding to the first interactive element;
[Claim 6] wherein the visualization further comprises a phylogenetic tree that represents an evolutionary relationship between each of the plurality of variants and in which the plurality of variants are represented as respective interactive elements; and
wherein the user interaction with the first interactive element comprises a selection of the first variant from the phylogenetic tree;
[Claim 7] wherein the visualization further comprises a table that lists gene variants detected in the genetic dataset;
wherein the user interaction comprises a selection of a first gene variant from the table; and
wherein displaying information in response to the user interaction with the first interactive element comprises:
displaying, on the fishplot, variant nomenclature of the first gene variant and allele frequency of the first gene variant;
[Claim 8] wherein generating the visualization comprises:
generating two or more visualizations from:
a swimmer plot that represents one or more of the clinical events as respective interactive elements across a first specified time period;
a line graph that represents one or more laboratory measurement events as respective interactive elements across the first specified time period; or
a fishplot that represents prevalence of each of a plurality of variants within the cancer cell genome data across the first specified time period;
receiving a user input to modify the first specified time period to a second specified time period; and
modifying the two or more visualizations to represent corresponding clinical events, laboratory measurement events, or prevalence of each of the plurality of variants during the second specified time period;
[Claims 9 & 20] wherein obtaining the genetic dataset comprises:
ingesting a plurality of genome files in non-standard formats; and
filtering the plurality of ingested genome files to generate a filtered genome file that includes the cancer cell genome data measured at each of the plurality of times;
[Claim 10] wherein obtaining the clinical data associated with the patient comprises:
ingesting electronic medical records associated with the patient; and
processing the ingested electronic medical records using a large language model (LLM) to de-duplicate redundant entries and to de-identify the patient;
[Claims 11 & 15] wherein the patient is a first patient, and wherein the method further comprises:
obtaining a plurality of genetic datasets derived from cancer cell samples of a plurality of other patients;
obtaining a plurality of clinical datasets associated with the plurality of other patients;
training a model based on the plurality of genetic datasets and the plurality of clinical datasets; and
applying the trained model to the genetic dataset of the first patient and the clinical data associated with the first patient to generate a recommended treatment for the first patient;
these italicized portions are methods of organizing human activity but for the recitation of generic computer components since they merely describe types of data and determinations that can be performed by humans. The italicized portion containing the recitation of the training the model has been treated as part of the abstract idea, specifically as mathematical calculations (e.g., provide probabilities for new data items [0082]) which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance).
Step 2A Prong 2
Dependent claims 2-3, 5, 7-11, 13-15, 17, and 19-20 recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claims 2, 13, and 17 (wherein displaying information in response to the user interaction with the first interactive element comprises:
retrieving the description of the clinical event associated with the first interactive element from a corresponding clinical event data structure; and displaying a tooltip containing the retrieved description); claims 3 & 18 (wherein displaying information in response to the user interaction with the first interactive element comprises: retrieving the value of the measurement associated with the first interactive element from a corresponding clinical event data structure; and displaying a tooltip containing the retrieved value); claims 5, 14, and 19 (wherein displaying information in response to the user interaction with the first interactive element comprises: displaying, […], a value of the prevalence of a first variant corresponding to the first interactive element); claim 7 (wherein displaying information in response to the user interaction with the first interactive element comprises: displaying, […], variant nomenclature of the first gene variant and allele frequency of the first gene variant); claim 8 (receiving a user input to modify the first specified time period to a second specified time period); claims 9 & 20 (wherein obtaining the genetic dataset comprises: ingesting a plurality of genome files in non-standard formats; and filtering the plurality of ingested genome files to generate a filtered genome file that includes the cancer cell genome data measured at each of the plurality of times); claim 10 (wherein obtaining the clinical data associated with the patient comprises: ingesting electronic medical records associated with the patient; and
processing the ingested electronic medical records using a large language model (LLM) to de-duplicate redundant entries and to de-identify the patient); and, claim 11 (applying the trained model) amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
Dependent claims 2-3, 5, 7-11, 13-15, 17, and 19-20 recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, e.g., commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f). Also, see [0049] which provides examples of off-the-shelf computer devices, off-the-shelf processors, and examples of types of memory. There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Therefore, in consideration of all the facts, the present invention is not patent-eligible invention under USC 101.
Claim Rejections - 35 USC § 102
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 9-12, 15-16, 18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Colley et al. (US20230223121A1).
Regarding claim 1, Colley discloses generating, by a computer system, a visualization that represents progression of a cancer genome ([0248] “oncological and research computer workstations typically include conventional interface devices like one or more large flat panel display screens for presenting data representations and a keyboard, mouse, or other mechanical input device for entering information, manipulating interface tools and presenting many different data representations” [1263] “As one example, a GUI can include intuitive menu options, selectable features, color and/or highlighting to indicate relative importance of data, and sliding-scale timelines for the viewing of disorder progression”)
wherein generating the visualization comprises:
obtaining a genetic dataset derived from cancer cell samples of a patient, the genetic dataset including cancer cell genome data measured at each of a plurality of times ([0149] “Transcriptome profiling of tumor samples by standard RNA (ribonucleic acid) sequencing methods measures the average gene expression of the cell types present in the sample at the time of sampling” [2170] “samples collected from a certain patient (e.g., from different tissue types, body sites, samples collected at different times, etc.)” [0375] “In exemplary embodiments, the method comprises (a) receiving a subject's sample […] (c) preparing a CDSI report for the subject based on the PD-L1 expression status identified in step (b), wherein the CDSI report comprises […] data from images of the subject's tumor or cancer, image features, clinical data of the subject, epigenetic data of the subject”)
generating a genomic data structure that represents the genetic dataset ([1257] “A lab specialist is trained to acquire and process patient and/or tissue samples to generate genomic data”)
obtaining clinical data associated with the patient, wherein the clinical data includes a plurality of clinical events ([0174] “Typical partners include treating physicians and oncology laboratories, one or each of which may provide data to the provider in order for the provider to perform analysis and provide treatment planning services. For example, a partner physician may provide clinical data such about a particular patient such as, without limitation, the patient's cancer state, while a laboratory may provide accompanying information about the patient and/or may provide tissue samples (i.e., tumor biopsies) of the patient's cancerous cells.”)
generating clinical event data structures to represent each of the plurality of clinical events based on the clinical data ([1316] “FIG. 129 is a graphical user interface (GUI) 3750 that can be implemented in system 3200 to manage a number of operational tasks, and specifically, to enable clinical data structuring and abstraction work on a large scale.” [1343] ““Structured” clinical data refers to clinical data that has been ingested into a structured format governed by a data schema. As one simple example, structured clinical data may be patient name, diagnosis date, and a list of medications, arranged in a JSON format.”)
and causing a user interface to display the visualization with interactive elements that are populated based on the genomic data structure and the clinical event data structures ([2981] “The present system and user interface provide an intuitive, efficient method for patient selection and cohort definition given specific inclusion and/or exclusion criteria.” [2982] “The modeling and visualization framework set forth herein may enable users to interactively explore auto-detected patterns in the clinical and genomic data of their filtered patient cohort, and to analyze the relationship of those patterns to therapeutic response and/or survival likelihood”)
detecting, by the computer system, a user input within the user interface that is associated with a first interactive element in the interactive elements ([1043] “A physician may click each structured contour to obtain an additional level of detail of information.”)
and displaying, within the user interface, in response to detecting the user input, information associated with the first interactive element based on the genomic data structure or the clinical event data structures ([1043] “Clicking the structured contour may isolate it visually for the physician. In the case of a tumor contour, the additional level of detail may include supporting information such as tumor volume, longest 3D diameter, or other features.”)”)
Regarding claim 3, Colley discloses wherein the clinical event data structures include, for each of a plurality of laboratory measurement events, a value of a measurement taken at each of the laboratory measurement events ([1123] “The distribution of TMB varied by cancer type. […] We found that there is a population of hypermutated tumors with significantly higher TMB than the overall distribution of TMB for solid tumors. […] These hypermutated tumors are referred to as TMB-high, which are defined as tumors with a TMB greater than 9 mutations/Mb. This threshold was established by testing for the enrichment of tumors with orthogonally defined hypermutation (MSI-H) in a larger clinical database using the hypergeometric test. In this group, all MSI-H samples are in the TMB-high population (FIGS. 37 and 38).”)
wherein the visualization comprises a line graph that represents one or more laboratory measurement events as respective interactive elements ([0466] “FIG. 38 includes additional data related to TMB generated using techniques that are consistent with at least some aspects of the present disclosure related to the exemplary xT panel” Also, see Figure 38.)
and wherein displaying information in response to the user interaction with the first interactive element comprises:
retrieving the value of the measurement associated with the first interactive element from a corresponding clinical event data structure ([Figure 38] For a retrieved TMB value of 20, a MSI status specifying MSS/MSE has a log2(CYT+1) value of 9.)
and displaying a tooltip containing the retrieved value
([1043] “Clicking the structured contour may isolate it visually for the physician. In the case of a tumor contour, the additional level of detail may include supporting information such as tumor volume, longest 3D diameter, or other features.”)
Regarding claim 9, Colley discloses wherein obtaining the genetic dataset comprises: ingesting a plurality of genome files in non-standard formats ([1245] “The results of sequencing (herein, the “raw sequencing data”) may be passed through a bioinformatics pipeline […] This message may contain sample identifiers, as well as the location of BAM files. A BAM file (.bam) is the binary version of a SAM file. A SAM file (.sam) is a tab-delimited text file that contains sequence alignment data (such as the raw sequencing data).”)
and filtering the plurality of ingested genome files to generate a filtered genome file that includes the cancer cell genome data measured at each of the plurality of times ([1246] “The bioinformatics pipeline may receive the raw sequencing results and process them to identify genetic variants that are expressed in the patient's DNA or RNA. [0149] “Transcriptome profiling of tumor samples by standard RNA (ribonucleic acid) sequencing methods measures the average gene expression of the cell types present in the sample at the time of sampling”)
Regarding claim 10, Colley discloses wherein obtaining the clinical data associated with the patient comprises:
ingesting electronic medical records associated with the patient ([0988] “Referring now to FIG. 10 , an exemplary multi-micro-service process 1000 for ingesting a clinical medical record”)
and processing the ingested electronic medical records using a large language model (LLM) to de-duplicate redundant entries ([3232] “A patient's medication record consists of both EHR and Curated MR that contain a combination of redundant and sometimes contradictory information. ‘Harmonization’ refers to the set of heuristics learned from machine learning algorithms, internal, and external publications and medical experts, to remove these redundancies”)
and to de-identify the patient ([0951] “a “data vault to clinical mart” orchestration may take stable points in time of the data published to data vault by other orchestrations; transform the data into a mart model, and transform the mart data through a de-identification pipeline” [0957] “a de-identification module which accesses system data, scrubs that data to remove any specific patient identification information and then serves up the de-identified data to the application platform”)
Regarding claim 11, Colley discloses wherein the patient is a first patient
([0089] “optimize ailment treatments based on specific patient disease state”)
and wherein the method further comprises:
obtaining a plurality of genetic datasets derived from cancer cell samples of a plurality of other patients ([1080] “Gene expression data generated (as previously described) was combined with publicly available gene expression data for cancer samples and normal tissue samples to create a Reference Database. For this analysis, we specifically include data from The Cancer Genome Atlas (TCGA) Project and Genotype-Tissue Expression (GTEx) project”)
obtaining a plurality of clinical datasets associated with the plurality of other patients ([1330] “As one non-limiting example, if a user were to select the “Cancer Center Lung” project, 89 new cases would then become available immediately for abstraction to clinical data structurers, and abstractors on their interface.”)
training a model based on the plurality of genetic datasets and the plurality of clinical datasets ([1261] “A machine learning algorithm (MLA) or a neural network (NN) may be trained from a training data set. For a disease state, an exemplary training data set may include the clinical and molecular details of a patient such as those curated from the Electronic Health Record or genetic sequencing reports.”)
and applying the trained model to the genetic dataset of the first patient and the clinical data associated with the first patient to generate a recommended treatment for the first patient ([1454] “An exemplary device may be any device capable of receiving user input and capturing data a physician may desire to compare against an exemplary cohort to generate treatment recommendations. An exemplary cohort may be a patient cohort, such as a group of patients with similarities; those similarities may include diagnoses, responses to treatment regimens, genetic profiles, and/or other medical, geographic, demographic, clinical, molecular, or genetic features.” [2255] “In some embodiments, a model in accordance with the present disclosure may be applied to a new tumor to compare to another type of tumor and to find similarities between other tumor types, identify a match to the other tumor type, and/or recommend a treatment that is effective against the other tumor type to treat the new tumor.”)
Regarding claim 12, Colley discloses a non-transitory computer readable storage medium storing executable computer program instructions, the computer program instructions when executed by one or more processors of a system causing the system to: ([Claim 32] “A non-transitory computer-readable storage medium having stored thereon program code instructions that, when executed by a processor, cause the processor to”)
generate a visualization that represents progression of a cancer genome,
([0248] “oncological and research computer workstations typically include conventional interface devices like one or more large flat panel display screens for presenting data representations and a keyboard, mouse, or other mechanical input device for entering information, manipulating interface tools and presenting many different data representations” [1263] “As one example, a GUI can include intuitive menu options, selectable features, color and/or highlighting to indicate relative importance of data, and sliding-scale timelines for the viewing of disorder progression”)
wherein generating the visualization comprises:
obtaining a genetic dataset derived from cancer cell samples of a patient, the genetic dataset including cancer cell genome data measured at each of a plurality of times ([0149] “Transcriptome profiling of tumor samples by standard RNA (ribonucleic acid) sequencing methods measures the average gene expression of the cell types present in the sample at the time of sampling” [0375] “In exemplary embodiments, the method comprises (a) receiving a subject's sample […] (c) preparing a CDSI report for the subject based on the PD-L1 expression status identified in step (b), wherein the CDSI report comprises […] data from images of the subject's tumor or cancer, image features, clinical data of the subject, epigenetic data of the subject”)
generating a genomic data structure that represents the genetic dataset;
([1257] “A lab specialist is trained to acquire and process patient and/or tissue samples to generate genomic data”)
obtaining clinical data associated with the patient, wherein the clinical data includes a plurality of clinical events ([0174] “Typical partners include treating physicians and oncology laboratories, one or each of which may provide data to the provider in order for the provider to perform analysis and provide treatment planning services. For example, a partner physician may provide clinical data such about a particular patient such as, without limitation, the patient's cancer state, while a laboratory may provide accompanying information about the patient and/or may provide tissue samples (i.e., tumor biopsies) of the patient's cancerous cells.”)
generating clinical event data structures to represent each of the plurality of clinical events based on the clinical data ([1316] “FIG. 129 is a graphical user interface (GUI) 3750 that can be implemented in system 3200 to manage a number of operational tasks, and specifically, to enable clinical data structuring and abstraction work on a large scale.” [1343] ““Structured” clinical data refers to clinical data that has been ingested into a structured format governed by a data schema. As one simple example, structured clinical data may be patient name, diagnosis date, and a list of medications, arranged in a JSON format.”)
and causing a user interface to display the visualization with interactive elements that are populated based on the genomic data structure and the clinical event data structures ([2981] “The present system and user interface provide an intuitive, efficient method for patient selection and cohort definition given specific inclusion and/or exclusion criteria.” [2982] “The modeling and visualization framework set forth herein may enable users to interactively explore auto-detected patterns in the clinical and genomic data of their filtered patient cohort, and to analyze the relationship of those patterns to therapeutic response and/or survival likelihood”)
detect a user input within the user interface that is associated with a first interactive element in the interactive elements;
([1043] “A physician may click each structured contour to obtain an additional level of detail of information.”)
and display, within the user interface, in response to detecting the user input, information associated with the first interactive element based on the genomic data structure or the clinical event data structures.
([1043] “Clicking the structured contour may isolate it visually for the physician. In the case of a tumor contour, the additional level of detail may include supporting information such as tumor volume, longest 3D diameter, or other features.”)
Regarding claim 15, the limitations are rejected for the same reasons as claim 11.
Regarding claim 16, Colley discloses one or more processors ([1150] “a process running on a processor”)
and one or more non-transitory computer readable storage media storing executable computer program instructions, the computer program instructions when executed by the one or more processors causing the system to: ([Claim 32] “A non-transitory computer-readable storage medium having stored thereon program code instructions that, when executed by a processor, cause the processor to”)
generate a visualization that represents progression of a cancer genome, ([0248] “oncological and research computer workstations typically include conventional interface devices like one or more large flat panel display screens for presenting data representations and a keyboard, mouse, or other mechanical input device for entering information, manipulating interface tools and presenting many different data representations” [1263] “As one example, a GUI can include intuitive menu options, selectable features, color and/or highlighting to indicate relative importance of data, and sliding-scale timelines for the viewing of disorder progression”)
wherein generating the visualization comprises:
obtaining a genetic dataset derived from cancer cell samples of a patient, the genetic dataset including cancer cell genome data measured at each of a plurality of times ([0149] “Transcriptome profiling of tumor samples by standard RNA (ribonucleic acid) sequencing methods measures the average gene expression of the cell types present in the sample at the time of sampling” [0375] “In exemplary embodiments, the method comprises (a) receiving a subject's sample […] (c) preparing a CDSI report for the subject based on the PD-L1 expression status identified in step (b), wherein the CDSI report comprises […] data from images of the subject's tumor or cancer, image features, clinical data of the subject, epigenetic data of the subject”)
generating a genomic data structure that represents the genetic dataset; ([1257] “A lab specialist is trained to acquire and process patient and/or tissue samples to generate genomic data”)
obtaining clinical data associated with the patient, wherein the clinical data includes a plurality of clinical events ([0174] “Typical partners include treating physicians and oncology laboratories, one or each of which may provide data to the provider in order for the provider to perform analysis and provide treatment planning services. For example, a partner physician may provide clinical data such about a particular patient such as, without limitation, the patient's cancer state, while a laboratory may provide accompanying information about the patient and/or may provide tissue samples (i.e., tumor biopsies) of the patient's cancerous cells.”)
generating clinical event data structures to represent each of the plurality of clinical events based on the clinical data ([1316] “FIG. 129 is a graphical user interface (GUI) 3750 that can be implemented in system 3200 to manage a number of operational tasks, and specifically, to enable clinical data structuring and abstraction work on a large scale.” [1343] ““Structured” clinical data refers to clinical data that has been ingested into a structured format governed by a data schema. As one simple example, structured clinical data may be patient name, diagnosis date, and a list of medications, arranged in a JSON format.”)
and causing a user interface to display the visualization with interactive elements that are populated based on the genomic data structure and the clinical event data structures ([1316] “FIG. 129 is a graphical user interface (GUI) 3750 that can be implemented in system 3200 to manage a number of operational tasks, and specifically, to enable clinical data structuring and abstraction work on a large scale.” [1343] ““Structured” clinical data refers to clinical data that has been ingested into a structured format governed by a data schema. As one simple example, structured clinical data may be patient name, diagnosis date, and a list of medications, arranged in a JSON format.”)
detect a user input within the user interface that is associated with a first interactive element in the interactive elements ([1043] “A physician may click each structured contour to obtain an additional level of detail of information.”)
and display, within the user interface, in response to detecting the user input, information associated with the first interactive element based on the genomic data structure or the clinical event data structures ([1043] “Clicking the structured contour may isolate it visually for the physician. In the case of a tumor contour, the additional level of detail may include supporting information such as tumor volume, longest 3D diameter, or other features.”).
Regarding claim 18, the limitations are rejected for the same reasons as claim 3.
Regarding claim 20, the limitations are rejected for the same reasons as claim 9.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35
U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied 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 2, 13, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Colley et al. (US20230223121A1) in view of Chia et al. (Current and Evolving Methods to Visualize Biological Data in Cancer Research).
Regarding claim 2, Colley discloses wherein the clinical event data structures include, for each of the clinical events, a description of the corresponding clinical event ([1039] “Timeline 1509 includes a set of patient care cells 1570, 1580, etc., each of which corresponds to a meaningful care related event associated with treatment of the patient's cancer state.”)
and wherein displaying information in response to the user interaction with the first interactive element comprises:
retrieving the description of the clinical event associated with the first interactive element from a corresponding clinical event data structure
([1673] “The display of one or more of FIGS. 174-176 may include an information icon 4188 next to one or more of the treatment regimens, the selection of which may launch a web browser on the mobile device 4012 or another screen in the application 4010 to provide more information about the selected regimen.”)
and displaying a tooltip containing the retrieved description
([1673] “Alternatively, the text of each regimen or the field or row in which the regimen is shown may include an embedded hyperlink, accessible, such as by pressing and holding on the text of the regimen, hovering over the regimen using the cursor discussed above, etc.”)
Colley does not explicitly disclose however Chia teaches wherein the visualization comprises a swimmer plot that represents one or more of the clinical events as respective interactive elements ([pg. 5] “A swimmer (or swim-lane) plot aims to show multiple pieces of information about a given dataset in one plot (36). […] each patient is represented as a single bar, in this case horizontal. But rather than using tumor response as the other axis, swimmer plots use time and overlay the patient’s bar with multiple other pieces of information that enrich and qualify basic time-to-event metrics (36). By setting a clear symbol legend to depict patients’ response to therapy, one is able to review the timeframes of treatment, the point at which a response to treatment occurred, and determine which patients achieved a longer duration on treatment […] . The swimmer’s plot allows for a clear graphical display of the duration of response and reveals which patients continue to benefit from the treatment.” Also, see Figure 3.)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Colley a swimmer plot that represents one or more of the clinical events as respective interactive elements as taught by Chia since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 13, the limitations are rejected for the same reasons as claim 2.
Regarding claim 17, the limitations are rejected for the same reasons as claim 2.
Claims 4 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Colley et al. (US20230223121A1) in view of Chia et al. (Current and Evolving Methods to Visualize Biological Data in Cancer Research) and further in view of Anderson et al. (Assessing lead time of selected ovarian cancer biomarkers: a nested case–control study).
Regarding claim 4, Colley discloses wherein the clinical event data structures include:
for each of the clinical events, a description of the corresponding clinical event ([1039] “Timeline 1509 includes a set of patient care cells 1570, 1580, etc., each of which corresponds to a meaningful care related event associated with treatment of the patient's cancer state.”)
and for each of a plurality of laboratory measurement events, a value of a measurement taken at each of the laboratory measurement events ([1123] “The distribution of TMB varied by cancer type. […] We found that there is a population of hypermutated tumors with significantly higher TMB than the overall distribution of TMB for solid tumors. […] These hypermutated tumors are referred to as TMB-high, which are defined as tumors with a TMB greater than 9 mutations/Mb. This threshold was established by testing for the enrichment of tumors with orthogonally defined hypermutation (MSI-H) in a larger clinical database using the hypergeometric test. In this group, all MSI-H samples are in the TMB-high population (FIGS. 37 and 38 ).”)
Colley does not explicitly disclose however Chia teaches wherein the visualization comprises a swimmer plot that represents one or more of the clinical events within a specified period of time as respective interactive elements ([pg. 5] “A swimmer (or swim-lane) plot aims to show multiple pieces of information about a given dataset in one plot (36). […] each patient is represented as a single bar, in this case horizontal. But rather than using tumor response as the other axis, swimmer plots use time and overlay the patient’s bar with multiple other pieces of information that enrich and qualify basic time-to-event metrics (36). By setting a clear symbol legend to depict patients’ response to therapy, one is able to review the timeframes of treatment, the point at which a response to treatment occurred, and determine which patients achieved a longer duration on treatment […] . The swimmer’s plot allows for a clear graphical display of the duration of response and reveals which patients continue to benefit from the treatment.” Also, see Figure 3.)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Colley a swimmer plot that represents one or more of the clinical events within a specified period of time as respective interactive elements as taught by Chia since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Colley in view of Chia does not explicitly disclose however Anderson teaches and wherein the visualization comprises a line graph that represents one or more laboratory measurement events in the specified period of time as respective interactive elements ([pg. 30] “Similar CA125 protein levels were observed between cancer patients and control subjects until approximately 3 years before diagnosis of ovarian cancer, at which point (by visual inspection), the mean marker level among cancer patients began to rise (Figure 2, A). A similar pattern, though less pronounced, was observed for HE4 protein levels and to a lesser extent, for mesothelin protein levels (Figure 2, B and C). Levels of B7-H4 and DcR3 in cancer patients and control subjects were indistinguishable throughout the study (Figure 2, D and E). Spondin-2 levels showed a slight increase over time among cancer patients resulting in a small separation during the final year before diagnosis (Figure 2, F).” Also, see Figure 2.)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Colley and Chia a line graph that represents one or more laboratory measurement events in the specified period of time as respective interactive elements as taught by Anderson since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 8, Colley discloses receiving a user input to modify the first specified time period to a second specified time period ([2977] “the user to zoom in to a shorter time period, for example, going from about a 5-year window to about a 1-year window.”)
and modifying the two or more visualizations to represent corresponding clinical events, laboratory measurement events, or prevalence of each of the plurality of variants during the second specified time period ([2977] “the user to zoom in to a shorter time period, for example, going from about a 5-year window to about a 1-year window.” [2978] “Turning now to FIGS. 397-400 , the user interface also may be configured to modify its display and present survival information of smaller groups within the subset by receiving user inputs corresponding to additional grouping or sorting criteria. Those criteria may be clinical or molecular factors, such as any of the beginning or ending events, as well as gender, gene, histology, regimens, smoking status, stage, surgical procedures, etc.” [2979] “updating the user interface to include separate survival plots for each regimen”)
Colley does not explicitly disclose however Chia teaches wherein generating the visualization comprises:
generating two or more visualizations from:
a swimmer plot that represents one or more of the clinical events as respective interactive elements across a first specified time period ([pg. 5] “A swimmer (or swim-lane) plot aims to show multiple pieces of information about a given dataset in one plot (36). […] each patient is represented as a single bar, in this case horizontal. But rather than using tumor response as the other axis, swimmer plots use time and overlay the patient’s bar with multiple other pieces of information that enrich and qualify basic time-to-event metrics (36). By setting a clear symbol legend to depict patients’ response to therapy, one is able to review the timeframes of treatment, the point at which a response to treatment occurred, and determine which patients achieved a longer duration on treatment […] . The swimmer’s plot allows for a clear graphical display of the duration of response and reveals which patients continue to benefit from the treatment.” Also, see Figure 3.)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Colley a swimmer plot that represents one or more of the clinical events as respective interactive elements across a first specified time period as taught by Chia since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Colley in view of Chia does not explicitly disclose however Anderson teaches a line graph that represents one or more laboratory measurement events as respective interactive elements across the first specified time period; or
a fishplot that represents prevalence of each of a plurality of variants within the cancer cell genome data across the first specified time period ([pg. 30] “Similar CA125 protein levels were observed between cancer patients and control subjects until approximately 3 years before diagnosis of ovarian cancer, at which point (by visual inspection), the mean marker level among cancer patients began to rise (Figure 2, A). A similar pattern, though less pronounced, was observed for HE4 protein levels and to a lesser extent, for mesothelin protein levels (Figure 2, B and C). Levels of B7-H4 and DcR3 in cancer patients and control subjects were indistinguishable throughout the study (Figure 2, D and E). Spondin-2 levels showed a slight increase over time among cancer patients resulting in a small separation during the final year before diagnosis (Figure 2, F).” Also, see Figure 2.)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Colley and Chia a line graph that represents one or more laboratory measurement events as respective interactive elements across the first specified time period; or a fishplot that represents prevalence of each of a plurality of variants within the cancer cell genome data across the first specified time period as taught by Anderson since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 5, 7, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Colley et al. (US20230223121A1) in view of Miller et al. (Visualizing tumor evolution with the fishplot package for R).
Regarding claim 5, Colley does not explicitly disclose however Miller teaches wherein the genomic data structure includes prevalence of each of a plurality of variants within the cancer cell genome data measured at each of the plurality of times; wherein the visualization comprises a fishplot that represents prevalence of each of the plurality of variants across a specified time period ([pg.1] “To enable the creation of these plots in a robust and automatable fashion, we have developed an R package (“fishplot”) that takes estimates of subclonal prevalence at different timepoints” [pg. 2] “We have applied fishplot to a number of different cancer genomics studies, and three representative results are displayed in Fig. 1)
and wherein displaying information in response to the user interaction with the first interactive element comprises:
displaying, on the fishplot, a value of the prevalence of a first variant corresponding to the first interactive element (([pg.1] “To enable the creation of these plots in a robust and automatable fashion, we have developed an R package (“fishplot”) that takes estimates of subclonal prevalence at different timepoints” [pg. 2] “Lastly, we created a model of AML31, a patient that was sampled with ultra-deep sequencing at many timepoints, allowing even very rare (<1 % Variant Allele Frequency) subclones to be detected [9] (Fig. 1c).”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Colley wherein the genomic data structure includes prevalence of each of a plurality of variants within the cancer cell genome data measured at each of the plurality of times; wherein the visualization comprises a fishplot that represents prevalence of each of the plurality of variants across a specified time period; and, displaying, on the fishplot, a value of the prevalence of a first variant corresponding to the first interactive element as taught by Miller since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 7, Colley discloses wherein the visualization further comprises a table that lists gene variants detected in the genetic dataset;
([1068] “The systems and methods described above may be used with a variety of sequencing panels. One exemplary panel, the 595 gene xT panel referred to above (See again the FIG. 27 series of figures), is focused on actionable mutations. An exemplary 650 gene solid tumor XT panel is shown at Appendix E.” Also, see Figure(s) 27 which show the visualization in table format.)
wherein the user interaction comprises a selection of a first gene variant from the table ([1072] “Subsequent to selection, patients were binned by pre-specified cancer type and filtered for only those variants being classified as therapeutically relevant. The gene set was then filtered for only those genes having greater than 5 variants across the entire group so as to select for recurrently mutated genes.”)
Colley does not explicitly disclose however Miller teaches and
wherein displaying information in response to the user interaction with the first interactive element comprises:
displaying, on the fishplot, variant nomenclature of the first gene variant and allele frequency of the first gene variant ([pg. 2] “Lastly, we created a model of AML31, a patient that was sampled with ultra-deep sequencing at many timepoints, allowing even very rare (<1 % Variant Allele Frequency) subclones to be detected [9] (Fig. 1c).”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Colley displaying, on the fishplot, variant nomenclature of the first gene variant and allele frequency of the first gene variant as taught by Miller since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 14, the limitations are rejected for the same reasons as claim 5.
Regarding claim 19, the limitations are rejected for the same reasons as claim 5.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Colley et al. (US20230223121A1) in view of Miller et al. (Visualizing tumor evolution with the fishplot package for R) and further in view of Smith et al. (E-scape: interactive visualization of single-cell phylogenetics and cancer evolution).
Regarding claim 6, Colley in view of Miller does not explicitly disclose however Smith teaches wherein the visualization further comprises a phylogenetic tree that represents an evolutionary relationship between each of the plurality of variants and in which the plurality of variants are represented as respective interactive elements ([pg. 549] “We have developed E-scape (evolutionary landscapes) as an open-source, browser-based visualization suite (Supplementary Software) to render complex relationships between cancer evolution data in an intuitive, interactive framework for biomedical investigators (Fig. 1). Also, see Figure 1 “The tree illustrates the evolutionary relationships between cells.”)
and wherein the user interaction with the first interactive element comprises a selection of the first variant from the phylogenetic tree ([pg. 549] “The CellScape toolbar is equipped with several features that enable toggling between tree layouts, incorporating optional edge distances, and selecting genomic profiles of interest to highlight corresponding cells in the phylogeny.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Colley and Miller a phylogenetic tree that represents an evolutionary relationship between each of the plurality of variants and in which the plurality of variants are represented as respective interactive elements; and, the user interaction with the first interactive element comprises a selection of the first variant from the phylogenetic tree as taught by Smith since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Response to Arguments
The arguments filed on 02 March 2026 have been considered, but are not fully persuasive.
Regarding the claim objection, applicant has amended claim 10 to fix “deidentiy” that was spelled incorrectly. Therefore, the claim objection has withdrawn.
Regarding the priority, applicant has does not concede and states they reserve the right to address the issue should it become relevant to any cited art. Therefore, applicant is not entitled to priority of App# 63/507, 047.
Regarding the drawings, applicant has submitted new drawings that correct the issue raised in the objection. Therefore, the drawing objection has been withdrawn.
Regarding the USC 101 rejection, applicant argues on pages 13 to 16 that the claims do not recite an abstract idea and is not methods of organizing human activity. Applicant states that the claims recite specific technical data-processing and visualization steps for processing genomic and clinical data and generating interactive visualizations which do not fall within the enumerated sub-groupings and the characterization made by Office Action is broad it does not identify the subgrouping. Applicant argues that the present claims, even considered abstract, are directly analogous to USPTO Example 37. Applicant also indicates similarity to Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc., 880 F.3d 1356, 1362-63, 125 USPQ2d 1436, 1440-41 (Fed. Cir. 2018). Applicant asserts that the claims recite a specific manner of displaying cancer genomic and clinical data through interactive visualizations that respond to user input and solves the problem ([0014] of specification) of where conventional fail to offer visualizations without incorporation of cancer genomics. The limitations of claims 9 & 20 are pointed to by the applicant as reciting features not performable by humans as further described in [0047] & [0050] with respect to a filtering module, a clonal population structure generator, and a Bayesian hierarchical model. Regarding Step 2B, the applicant argues that the combination of elements represents a non-conventional and non-generic arrangement that addresses the specific technical problem identified in the specification and thus provides significantly more. Applicant requests withdrawal of the USC 101 rejection.
Examiner disagrees with the applicant’s arguments. Examiner asserts that the present claims are far from being non-abstract. A proper analysis was conducted with even the subgrouping identified contrary to the applicant’s assertion. Claiming of manipulating data by generating data structures to visualize clinical and genetic data does not automatically overcome the abstract idea; this is clear in MPEP 2106. The applicant attempts to stretch the claim limitations to argue that the claims are not abstract. Examiner points to the USPTO October 2019 Guidance (also incorporated in MPEP 2106) which states that claims can recite an abstract idea even if they are claimed as being performed on a computer. The USPTO October 2019 Guidance is clear in that the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite an abstract idea even though the limitations may not be entirely performed by humans. Examiner points out that the computers in the claims are not used in a specific, inventive way. The claims are very outcome- focused and do not detail how each of the outcomes are reached. For instance, the applicant’s generic claim doesn’t delineate the steps of how the argued generating steps are specifically done; there is no clarity on the actual computer processing or how the computer is programmed to achieve the results in a non-abstract way different from how humans handle data. One of ordinary skill in the art would understand that applicant’s invention is clearly directed to judicial exception. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to ‘implement[ing] the abstract idea of intermediated settlement on a generic computer’, it cannot save OIP's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). Even if the claims nominally recites computer components that are rooted in technology, there is no recitation of how the computer components are specifically programmed to distinguish from generic computer processes. Thus, the present claim(s) are still not eligible under Step 2A Prong 1.
With respect to Step 2A Prong 2, examiner asserts the present specification provides a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art. The MPEP provides that improvements to the functioning of a computer or to any other technology or technical field can signal eligibility, see MPEP 2106.05(a), and provides examples of improvements to computer functionality, MPEP 2106.05(a)(I), and improvements to any other technology of technical field, MPEP 2106.05(a)(I). “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool”. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification' s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general-purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691. Unlike Enfish, the instant claimed invention appears to improve upon a judicial exception rather than a problem in the software arts or computer technology. Rather than improving a computer's algorithm (i.e., solving a technically based problem), the claimed invention purports to solve the non-technological problem of the lack of existing technologies that incorporate cancer genomics to model cancer evolution in a manner that facilitates treatment decisions ([0014] of the specification) through data-processing and visualization (see pg. 14 of applicant’s arguments). Examiner points out that the problem outlined by the specification does not point to any issue with the computer functionality of computer-based technologies, rather an abstract problem with respect to cancer data. In other words, one of the main/glaring issues with the present invention is that the problem solved by the applicant is not a technological problem; claim clearly does not solve a computing problem. The present claims are not analogous to USPTO Example 37 as there is no improvement to the operation of the interface technology. The present claims are also certainly not analogous to the claims in the Core Wireless Licensing S.A.R.L. case that were directed to an improvement in the functioning of computers, particularly those with small screens. In this present application, applicant is clearly improving upon the abstract idea; applicant’s specification does not describe a problem with existing user interfaces. All the applicant’s asserted innovation seems to be is in the type of data being shown and its utility to the user (see [0014] of the specification & pg. 14 of applicant’s arguments); this is NOT a new technical way for a computer to function. MPEP 2106 is clear that patents are not granted to inventions that use a computer to collect, manipulate, and display data. Next, examiner points out that claims 9 & 20 were further evaluated under Step 2A Prong 2 and were determined not to be eligible under USC 101. Applicant’s citation of [0047] & [0050] of the specification further supports the examiner’s arguments as those cited portions simply assert “do it on a computer” without delineating the steps on the computer functionality of the filtering module and the clonal population structure generator. Applicant’s current claims aren’t meaningful and do not help integrate the judicial exception into a practical application. Again, the applicant’s own specification does not support the assertion that the improvement is of a technological nature.
The examiner asserts the following facts that the applicant would not be able to dispute:
1) the invention does NOT involve a novel algorithm or data structure that significantly improves the computer's functionality,
2) the invention does NOT involve a new hardware component or configuration that works with the computer to achieve a specific technical benefit, and
3) the computer is NOT used in a completely new way demonstrating a significant technical advancement.
Improvement to the abstract idea is not an improvement to computer technology.
Thus, examiner does not see how the present claims improve the functioning of a computer or provide improvements to any other technology or technical field.
The claimed invention very clearly appears to be similar to the example of improvements that are insufficient to show an improvement in computer-functionality such as arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). See MPEP 2106.05(a)(I)(viii). Examiner points out that the claimed limitations have no indication in the specification that the operations recited invoke any inventive programming, require any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is implemented using other than generic computer components to perform generic computer functions. See DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (fed Cir. 2014) (“[A]fter Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.”). Most importantly, in DDR Holdings & unlike the present claims, the claims at issue specified how interactions with the Internet were manipulated to yield a desired result—a result that overrode the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink. 773 F.3d at 1258; 113 USPQ2d at 1106. The examiner also points out that there is no indication in the specification that the claimed invention affects a transformation or reduction of a particular article to a different state or thing. To show an involvement of a computer assists in improving technology, the claims must recite details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (MPEP 2106.05(a)(II)). In Finjan, Inc. v. Blue Coat Systems the courts found that the claims were “directed to a non-abstract improvement in computer functionality…” (MPEP 2106.04(d)). The present invention does not meet the condition set forth by the courts and thus does not integrate the judicial exception into a practical application.
With respect to Step 2B, in comparison to Bascom, examiner points out that Bascom is not similar to the present application because Bascom claimed a technical improvement in the art i.e., a technology-based solution to filter content on the internet while the present application is not presenting an improvement to computer technology (as indicated above). Additionally, the use of a computer or other machinery in its ordinary capacity for economic or other tasks or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See 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). Applicant’s claims do not recite unconventional steps that improve a conventional system. Thus, "significantly more" standard has not been satisfied. The applicant has not demonstrated that their invention is inventive and thus the present invention is still not patent-eligible under USC 101. Therefore, the USC 101 rejection is strongly maintained.
Regarding the USC 102 rejection, applicant argues that Colley does not teach "cancer cell genome data measured at each of a plurality of times." Applicant states that the claims require obtaining genetic sequencing data from the same patient measured at multiple discrete time points where the claimed limitation is necessary to track cancer genome progression over time through the interactive visualizations. Applicant also argues that Colley does not teach "displaying ... information associated with the first interactive element based on the genomic data structure." Applicant points out [1043] of Colley describes imaging data while the claims specify displaying information that is based on the genomic data structure generated from genetic sequencing data. Applicant requests withdrawal of the USC 102 rejection.
Examiner disagrees with the applicant’s arguments. Examiner asserts that Colley teaches “cancer cell genome data measured at each of a plurality of times" through the sampling and measuring performed on the patient. Colley has robust disclosure in [0149] to [0375], and even further explained/clarified in [2170] as well as Figure 241. With respect to Colley not disclosing the "displaying ... information associated with the first interactive element based on the genomic data structure", examiner points out that applicant uses conjunctive language hence the claim construction indicates that displaying does not have to be based on the genomic data structure. Therefore, the USC 102 rejection is maintained.
Regarding the USC 103 rejection, applicant argues that none of the additional cited references cure the deficiency of Colley and requests withdrawal of the USC 103 rejection.
Examiner disagrees and asserts that Colley has no deficiency. Thus, the remaining references still apply under USC 103. Therefore, the USC 103 rejection has been maintained.
Prior Art Cited but Not Relied Upon
Harbig, T. A., Nusrat, S., Mazor, T., Wang, Q., Thomson, A., Bitter, H., ... & Gehlenborg, N. (2021). Oncothreads: visualization of large-scale longitudinal cancer molecular data. Bioinformatics, 37(Supplement_1), i59-i66.
This reference is relevant since it discloses a similar design choice as the present invention to visualize cancer data.
US20210125731A1
This reference is relevant since it discloses visualizing patient progression and survival.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/WINSTON R FURTADO/Examiner, Art Unit 3687