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 .
DETAILED ACTION
Response to Amendment
In the Amendment dated 18 February 2026, the following occurred:
Claims 1, 2, 5, 9, 17, and 19 were amended.
Claims 4, 8, 10, 16, and 18 were canceled.
Claims 1-3, 5, 9, 11-15, 17, 19, and 20 are pending.
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted on 02 June 2026 is in compliance with the provisions of 37 CFR 1.97 and has been fully considered by the Examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 17, 19, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 17 recites “a diagnostic result comprising one of: the patient having no cancer... each of the one or more pages being associated with a particular primary tumor site.” The claim is indefinite because it is unclear how each of the pages can be associated with a particular primary tumor site if the patient has a diagnostic result of “no cancer.”
By virtue of their dependence from Claim 17, this basis of rejection also applies to dependent Claims 19 and 20.
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 1-3, 5, 9, 11-15, 17, 19, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 9, and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite method for managing medical data, and therefore meet step 1.
Step 2A1
The limitations of (Claim 1) creating a patient record for a patient…, the patient record comprising an identifier of the patient and a network of interconnected data objects related to medical data associated with the patient…; retrieving… a medical record for the patient, wherein the medical record comprises unstructured data; …identify[ing] text in the medical record; …correlat[ing] a portion of the identified text with a corresponding field [of displayed data], thereby identifying a set of data elements of the identified text correlated to corresponding data types of the corresponding fields; receiving identification of a primary cancer associated with the medical record via [the displayed data], wherein cancers are categorized as primary cancers or metastases of a primary cancer, and wherein receiving the identification of the primary cancer comprises: identifying the primary cancer by analyzing the set of data elements and the corresponding data types, displaying the [data] comprising a prompt for a user to confirm the primary cancer identification, and receiving user confirmation of the primary cancer identification…; in response to receiving the identification of the primary cancer, creating a primary cancer object in the patient record, the primary cancer object having a field including the primary cancer; storing the medical record linked to the primary cancer object in the patient record…; receiving, via user input…, medical data for the patient; determining that the medical data for the patient is associated with the primary cancer; storing the medical data for the patient linked to the primary cancer object in the patient record…, wherein storing the medical data includes storing the identified text… in association with the field; retrieving… at least a subset of the medical data for the patient, including medical data…; and causing display… of the at least the subset of the medical data for the patient for performing clinical decision making, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
The limitations of (Claim 9) storing… a patient record for a patient…; storing, to the patient record…, a first data object corresponding to a data element for a tumor mass of a primary cancer, the first data object including an attribute specifying a site of the tumor mass, wherein cancers are categorized as primary cancers or metastases of a primary cancer; receiving… diagnosis information corresponding to the primary cancer; analyzing the diagnosis information to identify a correlation between the diagnosis information and to the tumor mass; based on identifying the correlation between the diagnosis information and the tumor mass, storing… a second data object corresponding to the diagnosis information, the second data object connected to the first data object…; receiving… treatment information corresponding to the primary cancer; analyzing the treatment information to identify a correlation between the treatment information and to the tumor mass; based on identifying the correlation between the treatment information and the tumor mass, storing… a third data object corresponding to the treatment information, the third data object connected to the first data object…; and updating… by: importing medical data…; …identify[ing] text in the medical data; …correlat[ing] a portion of the identified text with a corresponding field of [displayed data], thereby identifying a set of data elements of the identified text correlated to corresponding data types of the corresponding fields; parsing the correlated imported medical data to identify a particular data element associated with the patient and the primary cancer; storing the particular data element to a sixth data object in association with the first data object; retrieving… one or more of the attributes specifying the site of the tumor mass, the diagnosis information, and/or the treatment information for clinical decision making, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
The limitations of (Claim 17) receiving… input medical data of a patient associated with a plurality of data categories, the plurality of data categories being associated with an oncology workflow operation; generating structured medical data of the patient based on the input medical data, the structured medical data being generated to support the oncology workflow operation to generate a diagnostic result comprising one of: the patient having no cancer, the patient having a primary cancer, the patient having multiple primary cancers, or the patient having a carcinoma of unknown primary sites, wherein cancers are categorized as primary cancers or metastases of a primary cancer; and displaying… the structured medical data and a history of the diagnostic results of the patient with respect to a time in a timeline…, to enable a clinical decision to be made based on the history of the diagnosis results, wherein… the input medical data [is received] for mapping the input medical data into fields to generate the structured medical data; and wherein… the structured medical data [is organized] into one or more pages, each of the one or more pages being associated with a particular primary tumor site, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
That is, other than reciting methods implemented by a computer, the claimed invention amounts to managing personal behavior or interaction between people. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a graphical user interface (claim 1), databases (claims 1 and 9), a diagnostic computer and network (claim 9), and a portal comprising a data entry interface (claim 17) that implement the identified abstract idea. The computing elements are not exclusively described by the applicant and are recited at a high-level of generality (i.e., generic computer components, see, e.g., Para. 0275) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. 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. Further, receiving data is considered insignificant extra solution activity such as pre-solution activity e.g., data gathering (performed by receiving/transmitting/etc.) See MPEP 2106.05(g).
The claim further recites the additional elements of applying an Optical Character Recognition (OCR) machine learning model to identify text, and applying a Natural Language Processing (NLP) machine learning model to correlate a portion of the identified text with a corresponding field. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the machine learning models to the data merely confines the use of the abstract idea (i.e., the trained models) to a particular technological environment or field of use (the noted types of ML) and thus fails to add an inventive concept to the claims. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a graphical user interface, a network, a database, and a portal were considered to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. The prior art of record indicates that displaying data on a graphical user interface is well-understood, routine, conventional activity in the field (see US 20200387810 to Hodgson; Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)." Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a first machine learning model, a second machine learning model, and a language learning model were determined to represent “apply it” on a generic computer. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(I)(A) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such, these claims are not patent eligible.
Claims 2, 3, 5, 11-15, 19, and 20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim 2 merely describes the medical record, which further defines the abstract idea.
Claim 3 merely describes a first medical record and a second medical record, receiving, identifying, analyzing, and storing, which further defines the abstract idea.
Claim 5 merely describes storing and parsing, which further defines the abstract idea.
Claims 11 and 12 merely describe receiving, analyzing, and storing, which further defines the abstract idea.
Claim 13 merely describes the second data object, which further defines the abstract idea.
Claim 14 merely describes identifying and transmitting, which further defines the abstract idea.
Claim 15 merely describes generating timestamps, which further defines the abstract idea.
Claim 19 merely describes receiving, creating, and populating, which further defines the abstract idea.
Claim 20 merely describes receiving and populating, which further defines the abstract idea.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 2 are rejected under 35 U.S.C. 103 as being unpatentable over Francois (U.S. 2016/0110523) in view of Colley et al. (U.S. 2021/0090694), referred to hereinafter as Colley, Reiner (U.S. 2010/0235330), and Michuda et al. (U.S. 2021/0142904), referred to hereinafter as Michuda.
REGARDING CLAIM 1
Francois teaches the claimed method for managing medical data comprising performing by a server computer:
creating a patient record for a patient in a unified patient database, the patient record comprising an identifier of the patient and a network of interconnected data objects related to medical data associated with the patient, the unified patient database including data from a plurality of sources; [Para. 0100 teaches creating a patient record in an EMR database. Para. 0125 teaches the patient record comprises an identifier of the patient (Patient ID) and information found in a medical record (Patient Data). Para. 0135 teaches an active diagnosis module (ADM) is linked to multiple EMRs of a patient. Para. 0384 teaches the database includes information from a variety of sources.]
receiving identification of a primary cancer associated with the medical record via the Graphical User Interface (GUI), wherein cancers are categorized as primary cancers or metastases of a primary cancer, and wherein receiving the identification of the primary cancer comprises: [Para. 0187 teaches presenting diagnoses in a hierarchical manner via a GUI. An example diagnosis is the high level diagnosis of “lung cancer,” where "high level" is interpreted as a primary category of cancer (along with the other options) and thus the presented cancers are in the primary cancer category. The Examiner notes that both categorizations are not required to be present ("categorized as primary...or metastases...").]
[…]
displaying the GUI comprising a prompt for a user to confirm the primary cancer identification, and [Para. 0100 teaches using a GUI to present input fields for a user to complete. Para. 0042 teaches instructing a user to fill in all information needed to confirm a diagnosis.]
receiving user confirmation of the primary cancer identification via the GUI; [Para. 0043 teaches when all required information has been properly filled out, the screen indicates that the diagnosis has been confirmed.]
retrieving, from the unified patient database, at least a subset of the medical data for the patient; and [Para. 0077 teaches at least some health information in the database is available for retrieval.]
causing display, via a user interface, of the at least the subset of the medical data for the patient for performing clinical decision making. [Para. 0126 teaches displaying patient data to the user. Para. 0152 teaches a user interface which enables users to retrieve data. Para. 0082 teaches a clinical decision support system component or an interface thereto.]
Francois may not explicitly teach
retrieving, from an external database, a medical record for the patient, wherein the medical record comprises unstructured data;
applying an Optical Character Recognition (OCR) machine learning model to identify text in the medical record;
applying a Natural Language Processing (NLP) machine learning model to correlate a portion of the identified text with a corresponding field of a graphical user interface (GUI), thereby identifying a set of data elements of the identified text correlated to corresponding data types of the corresponding fields;
…wherein storing the medical data includes storing the identified text to the unified patient database in association with the field;
retrieving, from the unified patient database, at least a subset of the medical data for the patient, including medical data originating from one or remote databases; and
causing display, via a user interface, of the at least the subset of the medical data for the patient for performing clinical decision making.
However, Colley teaches the following:
retrieving, from an external database, a medical record for the patient, wherein the medical record comprises unstructured data; [Para. 0462 teaches retrieving data from a database. Para. 0951 teaches extracting unstructured patient data. The patient data is stored in electronic medical records.]
applying an Optical Character Recognition (OCR) machine learning model to identify text in the medical record; [Para. 0383 teaches using an optical character recognition (OCR) method to determine data elements within health information from an electronic medical record.]
applying a Natural Language Processing (NLP) machine learning model to correlate a portion of the identified text with a corresponding field of a graphical user interface (GUI), thereby identifying a set of data elements of the identified text correlated to corresponding data types of the corresponding fields; [Para. 1533 teaches using a natural language processing (NLP) algorithm to identify if the text corresponds to a respective field in an application interface. Extracted patient information is populated into the mobile application for review by the user. Para. 02552 teaches a graphical user interface (GUI) that includes records, and is configured for user inputs.]
…wherein storing the medical data includes storing the identified text to the unified patient database in association with the field; [Para. 0991 teaches storing the identified text to the database.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the method of Francois to retrieve a medical record, apply an OCR model, and apply an NLP model as taught by Colley, with the motivation of improving treatment outcomes for certain diseases and conditions (see Colley at Para. 2444).
Francois in view of Colley may not explicitly teach
in response to receiving the identification of the primary cancer, creating a primary cancer object in the patient record, the primary cancer object having a field including the primary cancer;
storing the medical record linked to the primary cancer object in the patient record in the unified patient database;
receiving, via user input to the GUI, medical data for the patient;
determining that the medical data for the patient is associated with the primary cancer;
and storing the medical data for the patient linked to the primary cancer object in the patient record in the unified patient database…
However, Reiner teaches the following:
in response to receiving the identification of the primary cancer, creating a primary cancer object in the patient record, the primary cancer object having a field including the primary cancer; [Para. 0052 teaches recording association relationships by electronically linking two data elements.]
storing the medical record linked to the primary cancer object in the patient record in the unified patient database; [Para. 0071 teaches storing a medical record linked to association data (diagnoses).]
receiving, via user input to the GUI, medical data for the patient; [Para. 0092 teaches receiving data via user input. Para. 0020 teaches an input device with a user interface.]
determining that the medical data for the patient is associated with the primary cancer; [Para. 0119 teaches correlating generic association data (diagnoses) with personalized data derived from a patient’s data.]
and storing the medical data for the patient linked to the primary cancer object in the patient record in the unified patient database… [Para. 0094 teaches storing linked data elements and their relationship to a specific underlying pathology in an association database.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the method of Francois in view of Colley to link data elements, store a linked medical record, receive medical data, correlate patient data with association data, and store linked data elements as taught by Reiner, with the motivation of improving clinical outcomes (see Reiner at Para. 0190).
Francois in view of Colley and Reiner may not explicitly teach
identifying the primary cancer by analyzing the set of data elements and the corresponding data types, …
However, Michuda teaches the following:
identifying the primary cancer by analyzing the set of data elements and the corresponding data types, … [Para. 0240 teaches identifying the diagnosis of the cancer condition by differentiating the cancer condition between a new tumor and a recurrence of a previous tumor. Para. 0340 teaches identifying the type of cancer.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the method of Francois in view of Colley and Reiner to identify the primary cancer as taught by Michuda, with the motivation of improving access to personalized therapies (see Michuda at Para. 0010).
REGARDING CLAIM 2
Francois in view of Colley, Reiner, and Michuda teaches the method of claim 1.
Francois further teaches
the medical record for the patient is in a first format comprising a set of data elements correlated to corresponding data types. [Para. 0100 teaches a document having a preset input format (a template). The template contains appropriately labeled fields that request or permit entry of a specified type of data. The EMR system generates a medical record by assembling the information.]
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Francois in view of Colley, Reiner, Michuda, Barnes et al. (U.S. 2017/0076046), referred to hereinafter as Barnes, and Landes (U.S. 2021/0263971).
REGARDING CLAIM 3
Francois in view of Colley, Reiner, and Michuda teaches the method of claim 2.
Francois in view of Colley, Reiner, and Michuda may not explicitly teach
receiving a second medical record for the patient, wherein the second medical record is in a second format comprising unstructured data;
identifying, from the unstructured data, a data element associated with the primary cancer;
and based on the assigned data type and the identifying the data element is associated with the primary cancer, storing the data element linked to the primary cancer object in the patient record in the unified patient database.
However, Barnes teaches the following:
receiving a second medical record for the patient, wherein the second medical record is in a second format comprising unstructured data; [Claim 2 teaches receiving second clinical data for the patient. Para. 0250 teaches unstructured data.]
identifying, from the unstructured data, a data element associated with the primary cancer; [Para. 0194 teaches deriving, from reports, information such as cancer type.]
and based on the assigned data type and the identifying the data element is associated with the primary cancer, storing the data element linked to the primary cancer object in the patient record in the unified patient database. [Para. 0182 teaches aggregating clinical values for specific cancer types. Para. 0185 teaches storing relevant clinical information, by category. Para. 0222 teaches saving data to the system for patient records, or to the database.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the method of Francois in view of Colley, Reiner, and Michuda to receive second clinical data and identify data points associated with cancer as taught by Barnes, with the motivation of improving patient care (see Barnes at Para. 0083).
Francois in view of Colley, Reiner, Michuda, and Barnes may not explicitly teach
analyzing the unstructured data to assign the data element to a data type;
However, Landes teaches the following:
analyzing the unstructured data to assign the data element to a data type; [Para. 0021 teaches processing a database to assign data types. Para. 0039 teaches documents of the dataset are unstructured.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the method of Francois in view of Colley, Reiner, Michuda, and Barnes to analyze unstructured data to assign data types as taught by Landes, with the motivation of improving efficiency (see Landes at Para. 0026).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Francois in view of Colley, Reiner, Michuda, and Singh (U.S. 2019/0295726).
REGARDING CLAIM 5
Francois in view of Colley, Reiner, and Michuda teaches the method of claim 1.
Reiner further teaches
storing the medical record in the patient record; [Para. 0071 teaches storing a medical record linked to association data (diagnoses).]
Francois in view of Colley, Reiner, and Michuda may not explicitly teach
and parsing the medical record to determine that the patient record is not associated with a particular primary cancer, wherein displaying the medical record is responsive to determining that the patient record is not associated with a particular primary cancer.
However, Singh teaches the following:
and parsing the medical record to determine that the patient record is not associated with a particular primary cancer, wherein displaying the medical record is responsive to determining that the patient record is not associated with a particular primary cancer. [Para. 0056 teaches parsing medical records. Para. 0148 teaches determining the medical record is not associated with a hereditary cancer. Para. 0127 teaches displaying medical record information in the form of a menu.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the method of Francois in view of Colley, Reiner, and Michuda to parse medical records to determine that a record is not associated with a particular cancer as taught by Singh, with the motivation of transforming medical record data into actionable disease knowledge (see Singh at Para. 0006).
Claims 9 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Francois in view of Colley, Barnes, Reiner, and Singh.
REGARDING CLAIM 9
Francois teaches the claimed method for managing a unified patient database comprising performing by a server computer:
storing, to the unified patient database, a patient record for a patient, the patient record comprising a network of interconnected data objects, the unified patient database including data from a plurality of sources; [Para. 0100 teaches creating a patient record in an EMR database. Para. 0111 teaches a network of interconnected computer-readable media. Para. 0384 teaches the database includes information from a variety of sources. Data in a database is interconnected; the Applicant has not stated how the data is interconnected or even what interconnected entails.]
causing display, via a user interface, of one or more of the attribute specifying the site of the tumor mass, the diagnosis information, and/or the treatment information for clinical decision making. [Para. 0126 teaches displaying patient data (including a diagnosis and/or a treatment plan) to the user. Para. 0152 teaches a user interface which enables users to retrieve data. Para. 0082 teaches a clinical decision support system component or an interface thereto.]
Francois may not explicitly teach
applying an Optical Character Recognition (OCR) machine learning model to identify text in the medical data;
applying a Natural Language Processing (NLP) machine learning model to correlate a portion of the identified text with a corresponding field of a graphical user interface (GUI), thereby identifying a set of data elements of the identified text correlated to corresponding data types of the corresponding fields;
However, Colley teaches the following:
applying an Optical Character Recognition (OCR) machine learning model to identify text in the medical data; [Para. 0383 teaches using an optical character recognition (OCR) method to determine data elements within health information from an electronic medical record.]
applying a Natural Language Processing (NLP) machine learning model to correlate a portion of the identified text with a corresponding field of a graphical user interface (GUI), thereby identifying a set of data elements of the identified text correlated to corresponding data types of the corresponding fields; [Para. 1533 teaches using a natural language processing (NLP) algorithm to identify if the text corresponds to a respective field in an application interface. Extracted patient information is populated into the mobile application for review by the user. Para. 02552 teaches a graphical user interface (GUI) that includes records, and is configured for user inputs.]
Motivation to combine the teaching of Colley with the teaching of Francois is the same as that used with respect to claim 1 and is therefore reiterated here.
Francois in view of Colley may not explicitly teach
storing, to the patient record in the unified patient database, a first data object corresponding to a data element for a tumor mass of a primary cancer, the first data object including an attribute specifying a site of the tumor mass, wherein cancers are categorized as primary cancers or metastases of a primary cancer;
receiving, from a diagnostic computer, diagnosis information corresponding to the primary cancer;
analyzing the diagnosis information to identify a correlation between the diagnosis information and to the tumor mass;
based on identifying the correlation between the diagnosis information and the tumor mass, storing, to the unified patient database, a second data object corresponding to the diagnosis information, the second data object connected to the first data object via the network of interconnected data object;
receiving, from the diagnostic computer, treatment information corresponding to the primary cancer;
analyzing the treatment information to identify a correlation between the treatment information and to the tumor mass;
based on identifying the correlation between the treatment information and the tumor mass, storing, to the unified patient database, a third data object corresponding to the treatment information, the third data object connected to the first data object via the network of interconnected data objects; and
retrieving, from the unified patient database, one or more of the attributes specifying the site of the tumor mass, the diagnosis information, and/or the treatment information;
However, Barnes teaches the following:
storing, to the patient record in the unified patient database, a first data object corresponding to a data element for a tumor mass of a primary cancer, the first data object including an attribute specifying a site of the tumor mass, wherein cancers are categorized as primary cancers or metastases of a primary cancer; [Para. 0230 teaches saving information to a patient record. Para. 0106 teaches storing information in a database. Para. 0079 teaches tumor information (e.g., location, metastasis score), which is interpreted as “a first data object.”]
receiving, from a diagnostic computer, diagnosis information corresponding to the primary cancer; [Para. 0101 teaches accessing clinical data points such as diagnosis (e.g., type of cancer).]
analyzing the diagnosis information to identify a correlation between the diagnosis information and to the tumor mass; [Para. 0080 teaches charting diagnostic information to identify a correlation between diagnostic information and data points.]
based on identifying the correlation between the diagnosis information and the tumor mass, storing, to the unified patient database, a second data object corresponding to the diagnosis information, the second data object connected to the first data object via the network of interconnected data object; [Para. 0181 teaches adding correlations to the patient data. The location in the patient data where the data is stored (i.e., a file location) is interpreted as “a second data object.” The patient data tracker saves the generated charts in the database. Alternatively, the storage of the data in the patient data in the database is also a “network of the interconnected data objects,” the Examiner noting there is no description in the claims as to what this must or must not entail.]
receiving, from the diagnostic computer, treatment information corresponding to the primary cancer; [Para. 0195 teaches extracting treatment information, i.e., previous cancer related treatment information, from the EMR system.]
analyzing the treatment information to identify a correlation between the treatment information and to the tumor mass; [Para. 0080 teaches charting treatment information to identify a correlation between treatment information and data points.]
based on identifying the correlation between the treatment information and the tumor mass, storing, to the unified patient database, a third data object corresponding to the treatment information, the third data object connected to the first data object via the network of interconnected data objects. [Para. 0181 teaches adding correlations to the patient data. The location in the patient data where the data is stored (i.e., a file location) is interpreted as “a third data object.” The patient data tracker saves the generated charts in the database. Alternatively, the storage of the data in the patient data in the database is also a “network of the interconnected data objects,” the Examiner noting there is no description in the claims as to what this must or must not entail.]
retrieving, from the unified patient database, one or more of the attributes specifying the site of the tumor mass, the diagnosis information, and/or the treatment information; [Para. 0230 teaches saving to a patient record. Para. 0106 teaches storing information in a database. Para. 0077 teaches archiving information corresponding to cancer patient treatment plans. Para. 0079 teaches tumor information (e.g., location).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the method of Francois in view of Colley to receive second clinical data and identify data points associated with cancer as taught by Barnes, with the motivation of improving patient care (see Barnes at Para. 0083).
Francois in view of Colley and Barnes may not explicitly teach
updating the unified patient database by:
importing medical data from an external database;
parsing the imported medical data to identify a particular data element associated with the patient and the primary cancer; and
storing the particular data element to a sixth data object in association with the first data object.
However, Reiner teaches the following:
updating the unified patient database by:
importing medical data from an external database; [Para. 0118 teaches importing data from another institution’s database.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the method of Francois in view of Colley and Barnes to import medical data as taught by Reiner, with the motivation of improving clinical outcomes (see Reiner at Para. 0190).
Francois in view of Colley, Barnes, and Reiner may not explicitly teach
parsing the imported medical data to identify a particular data element associated with the patient and the primary cancer; and
storing the particular data element to a sixth data object in association with the first data object.
However, Singh teaches the following:
parsing the correlated imported medical data to identify a particular data element associated with the patient and the primary cancer; [Para. 0036 teaches extracting data items from the electronic medical record (imported medical data). The data items (data elements) include an indication of whether a patient’s family member has been afflicted with a cancer. Para. 0065 teaches that the parsing identifies cancer (interpreted as primary cancer) in the data.]
storing the particular data element to a sixth data object in association with the first data object. [Para. 0063 teaches that the result of the parsing and identification of cancer is stored in the EMR (the EMR of Francois). The location where the data is stored (i.e., a file location) is interpreted as “a sixth data object.” The data is stored in the EMR (interpreted to correspond to the EMR of Francois) and is thus stored “in association” with the data of Francois.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the method of Francois in view of Colley, Reiner, and Barnes to parse medical data and store data elements as taught by Singh, with the motivation of transforming medical record data into actionable disease knowledge (see Singh at Para. 0006).
REGARDING CLAIM 11
Francois in view of Colley, Barnes, Reiner, and Singh teaches the method of claim 9.
Francois further teaches
receiving, from the diagnostic computer, patient history data; [Para. 0126 teaches accessing a patient’s medical/surgical history.]
Barnes further teaches
analyzing the patient history data to identify a correlation between the patient history data and the tumor mass; [Para. 0080 teaches graphing values over time to identify a correlation between patient history data and data points.]
and based on identifying the correlation between the patient history data and the tumor mass, storing, to the unified patient database, a fourth data object corresponding to the patient history data, the fourth data object connected to the first data object via the network of interconnected data objects. [Para. 0181 teaches adding correlations to the patient data. The patient data tracker saves the generated charts in the database.]
REGARDING CLAIM 12
Francois in view of Colley, Barnes, Reiner, and Singh teaches the method of claim 9.
Francois further teaches
receiving, from the diagnostic computer, tumor mass information corresponding to a tumor mass at a metastasis site of the primary cancer; [Para. 0252 teaches gathering information corresponding to discovery of regional metastasis.]
Barnes further teaches
analyzing the tumor mass information to identify a correlation between the diagnosis information and the tumor mass; [Para. 0080 teaches charting diagnostic information to identify a correlation between diagnostic information and data points.]
and based on receiving the tumor mass information and identifying the first data object, storing, to the unified patient database, a fifth data object corresponding to the tumor mass information connected to the first data object via the network of interconnected data objects. [Para. 0181 teaches adding correlations to the patient data. The patient data tracker saves the generated charts in the database.]
REGARDING CLAIM 13
Francois in view of Colley, Barnes, Reiner, and Singh teaches the method of claim 9.
Francois further teaches
wherein the second data object includes one or more attributes selected from: a stage of the primary cancer, a biomarker, and a tumor size. [Para. 0437 teaches including a stage of the cancer and molecular markers.]
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Francois in view of Colley, Barnes, Reiner, Singh, and Landes.
REGARDING CLAIM 14
Francois in view of Colley, Barnes, Reiner, and Singh teaches the method of claim 9.
Francois in view of Colley, Barnes, Reiner, and Singh may not explicitly teach
identifying, from the unified patient database, a data element and a data type associated with the patient; and
transmitting, to an external system, the data element and the data type in structured form.
However, Landes teaches the following:
identifying, from the unified patient database, a data element and a data type associated with the patient; and [Para. 0021 teaches identifying, from the database, data types.]
transmitting, to an external system, the data element and the data type in structured form. [Para. 0042 teaches transmitting information to a computer system. Para. 0025 teaches a database that contains data in a structured form.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the method of Francois in view of Colley, Barnes, Reiner, and Singh to identify and transmit a data element/type as taught by Landes, with the motivation of improving efficiency (see Landes at Para. 0026).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Francois in view of Colley, Barnes, Reiner, Singh, and Badawi.
REGARDING CLAIM 15
Francois in view of Colley, Barnes, Reiner, and Singh teaches the method of claim 9.
Francois in view of Colley, Barnes, Reiner, and Singh may not explicitly teach
upon generating each of the first data object and the second data object, generating a first timestamp stored in association with the first data object indicating a time of creation of the first data object and a second timestamp stored in association with the second data object indicating the time of creation of the second data object.
However, Badawi teaches the following:
upon generating each of the first data object and the second data object, generating a first timestamp stored in association with the first data object indicating a time of creation of the first data object and a second timestamp stored in association with the second data object indicating the time of creation of the second data object. [Para. 0030 teaches generating a timestamp indicating a time of creation.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the method of Francois in view of Colley, Barnes, Reiner, and Singh to generate timestamps as taught by Badawi, with the motivation of improving efficiency (see Badawi at Para. 0003).
Claims 17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Francois in view of Colley, Reiner, Michuda, and Bjornerud et al. (WO 2010/115885), referred to hereinafter as Bjornerud.
REGARDING CLAIM 17
Francois teaches the claimed method of processing medical data to facilitate a clinical decision, comprising:
receiving, via a portal, input medical data of a patient associated with a plurality of data categories, the plurality of data categories being associated with an oncology workflow operation; [Para. 0422 teaches downloading, via a portal, medical data of a patient. A patient downloads a Dx app. Para. 0425 teaches a Dx app comprises or has associated with it one or more disease-specific apps. Disease-specific apps are available for oncology.]
wherein the portal comprises a data entry interface to receive the input medical data, and to map the input medical data into fields to generate the structured medical data; and [Para. 0100 teaches entry of health information by a contributor is facilitated by presenting a contributor with various input fields to complete. Para. 0422 teaches a portal. EMR data are used to populate fields to generate one or more active diagnosis modules (ADMs).]
wherein the data entry interface organizes the structured medical data into one or more pages… [Para. 0110 teaches organizing the EMR into one or more pages. The various elements of an EMR are stored in different fields of the database record.]
Francois may not explicitly teach
generating structured medical data of the patient based on the input medical data, the structured medical data being generated to support the oncology workflow operation to…
…a timeline in…
However, Colley teaches the following:
generating structured medical data of the patient based on the input medical data, the structured medical data being generated to support the oncology workflow operation to… [Para. 1304 teaches accepting input medical data. Para. 0968 teaches generating structured medical data of the patient.]
…a timeline in… [Para. 3081 teaches a patient timeline analysis module that permits a user to review the sequence of events in the clinical life of a patient.]
Motivation to combine the teaching of Colley with the teaching of Francois is the same as that used with respect to claim 1 and is therefore reiterated here.
Francois in view of Colley may not explicitly teach
and displaying, via the portal, the structured medical data and a history of the diagnostic results of the patient with respect to a time in […] the portal, to enable a clinical decision to be made based on the history of the diagnosis results…
However, Reiner teaches the following:
and displaying, via the portal, the structured medical data and a history of the diagnostic results of the patient with respect to a time in […] the portal, to enable a clinical decision to be made based on the history of the diagnosis results... [Para. 0117 teaches displaying a history of the diagnostic results of the patient, along with other data recorded at the time these tests were performed, as part of a decision support feature.]
Motivation to combine the teaching of Reiner with the teachings of Francois and Colley is the same as that used with respect to claim 1 and is therefore reiterated here.
Francois in view of Colley and Reiner may not explicitly teach
…generate a diagnostic result comprising one of: the patient having no cancer, the patient having a primary cancer, the patient having multiple primary cancers, or the patient having a carcinoma of unknown primary sites, wherein cancers are categorized as primary cancers or metastases of a primary cancer;
However, Michuda teaches the following:
…generate a diagnostic result comprising one of: the patient having no cancer, the patient having a primary cancer, the patient having multiple primary cancers, or the patient having a carcinoma of unknown primary sites, wherein cancers are categorized as primary cancers or metastases of a primary cancer; [Para. 0028 teaches including an indication as to whether the respective subject has a metastatic cancer or primary cancer.]
Motivation to combine the teaching of Michuda with the teachings of Francois, Colley, and Reiner is the same as that used with respect to claim 1 and is therefore reiterated here.
Francois in view of Colley, Reiner, and Michuda may not explicitly teach
each of the one or more pages being associated with a particular primary tumor site
However, Bjornerud teaches the following:
each of the one or more pages being associated with a particular primary tumor site [FIG. 13K, 13L teaches different pages being associated with different tumor locations.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the method of Francois in view of Colley, Reiner, and Michuda to associate each page with a particular tumor site as taught by Bjornerud, with the motivation of improving the accuracy, reproducibility, and objectiveness in the assessment of structural changes in a tumor (see Bjornerud at Pages 12-13).
REGARDING CLAIM 19
Francois in view of Colley, Reiner, Michuda, and Bjornerud teaches the claimed method of claim 17.
Colley further teaches
receiving, via the data entry interface, a first indication that a first subset of the medical data entered into a first page of the data entry interface associated with a first primary tumor site belongs to a second primary tumor site; [Para. 2351 teaches applying a model that identifies a plurality of cell-type profiles comprising a cell-type profile for a cell type of a second tumor type to data received from the patient having the first tumor type.]
and based on the first indication: creating a second page for the second primary tumor site; and populating the second page with the first subset of the medical data. [Para. 0307 teaches populating the plurality of data entry fields based on the original patient data. Para. 1518 teaches summaries of conclusions made from sequencing results span multiple pages.]
REGARDING CLAIM 20
Francois in view of Colley, Reiner, Michuda, and Bjornerud teaches the method of claim 19.
Colley further teaches
receiving, via the data entry interface, a second indication that a second subset of the medical data entered into the first page is related to a metastasis of the second primary tumor site; [Para. 1975 teaches requesting predictions of metastasis. A webform receives predictions and displays them through an interface containing one or more editable fields (data entry interface).]
and based on the second indication, populating the second page with the second subset of the medical data. [Para. 0307 teaches populating the plurality of data entry fields. Para. 1518 teaches summaries of conclusions made from sequencing results span multiple pages.]
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-3, 5, 9, 11-15, 17, 19, and 20, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Applicant argues:
…the claims are not directed to human behavior.
Regarding (a), the Examiner respectfully disagrees. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with the aid of a computer, would follow to process and store data. The Examiner notes that Applicant’s Background describes sourcing the data from multiple data sources, and then parsing through the data manually (see Spec. Para. 0002) as a clinician task. Furthermore, the Examiner submits that healthcare itself inherently represents the organization of human activity. Applicant has not pointed to anything in the claims that falls outside of this characterization. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to retrieve and organize medical data, the claimed invention is directed to an abstract idea.
…the claimed techniques solve a technical problem of how to automatically retrieve and organize medical data for better accessibility and usability.
Regarding (b), the Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem is not a technological problem caused by the technological environment to which the claims are confined. The problem of lack of accessibility and usability for the retrieval and organization of medical data was not a problem cause by the computer, it is a problem that existed and/or exists regardless of whether a computer is involved in the process. At best, Applicant’s identified problem is a medical data management problem. Because no technological problem is present, the claims do not provide a practical application.
… the example of an interaction for medical data analysis does not fall under any enumerated subgrouping. If the Examiner maintains the rejection, Applicant requests specific identification of the type of organizing human activity from the list in the MPEP, as is required by PTO guidance.
Regarding (c), the Examiner respectfully disagrees. As stated on Page 33 of the Office Action dated 22 October 2025, “the claim elements fall under a series of rules or instructions that a person or persons would follow to retrieve and organize medical data.” The type of organizing human activity from the list in the MPEP was identified as “managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).” It is unclear what additional information the Applicant believes is required.
The Examiner focuses on the hardware and software, ignoring the steps recited in the claims, which include a sequence of operations that provides improvements to the functioning of the computing system.
Regarding (d), the Examiner respectfully submits that the steps were not the focus of the Step 2A2 analysis of additional elements because they were determined to be part of the abstract idea in Step 2A1.
However, the Examiner respectfully disagrees that the sequence of operations provides improvements to the functioning of the computing system. MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves the functioning of a computer. See also MPEP 2106.05(a)(I). The technological environment of Applicant’s claim is a generally programmable processor (see Spec. Para. 0275). Applicant has not identified nor can the Examiner locate any physical improvement to the functioning of the computer that results from the implementation of Applicant’s claim. There is no indication that the computer is made to run faster, more efficiently, or utilize less power. In fact, the computer may be caused to operate slower and less efficiently through the implementation of Applicant’s claimed invention; we do not know. Because there is no improvement to the function of the computer, a practical application is not present.
…the claimed techniques provide the additional practical application of improving the efficiency and quality of care, improving the overall field of medical care.
Regarding (e), the Examiner respectfully disagrees. MPEP 2106.04(d)(1) states “the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” Here, there is no improvement to the computer nor is there an improvement to another technology. Because neither type of improvement is present in the claims, an improvement to technology is not present and there is no practical application.
Applicant’s argument that the field of medical care is a technology and the claimed invention improves this field is not reflected in the claimed invention. The claims are confined to a general-purpose computer and do not claim medical care. Moreover, the entire field of medical care is not reasonably understood to be a problem arising in technology, as it is instead a problem arising in healthcare. The claimed invention is using a computer as a tool and any improvement present is an improvement to the abstract idea of, to paraphrase, retrieving and organizing medical data. Finally, were Applicant’s line of reasoning correct, the invention in Alice Corp. would have been subject matter eligible because it was an improvement to the technology of settlement risk mitigation.
Similarly to the table at issue in Enfish, claims 1 and 9 recites specific features of the data structure that improve the accessibility and utility of the data therein, providing significantly more than any alleged abstract idea.
Regarding (f), the Examiner respectfully disagrees. The claimed features are merely invoking a computer as a tool to implement an abstract idea. Further, Applicant’s claimed invention is not even remotely like that in Enfish because Applicant’s claims are not directed to a new form of database structure. There is no improvement to how the computer physically operates in the claim.
(Core Wireless, 880 F.3d 1356, 1362 (2018)). Similarly here, claim 17 is directed to a particular manner of presenting information on a computing device in an improved fashion, providing significantly more than any alleged abstract idea.
Regarding (g), the Examiner respectfully disagrees that Applicant’s claimed invention is analogous to Core Wireless. In Core Wireless the claimed invention provided a technical solution to a technical problem. The disclosure of Core Wireless described a problem caused by the display device, namely that the screen of the display device was too small rendering it difficult to navigate menus. And, the claimed invention in Core Wireless solved this problem. No such problem is present in Applicant’s claimed invention. Applicant has not pointed to and the Examiner cannot find any technological problem caused by the computer (see response to argument (c), above).
The steps described above and delineated below constitute a specific, unconventional, and inventive sequence of operations… [T]he claimed operations use a combination of machine learning, multi-database integration, and specialized user interfaces to organize medical data based on primary cancer objects to facilitate streamlined diagnosis and treatment, which is unconventional. No evidence has been provided to the contrary.
Regarding (h), the Examiner respectfully disagrees. The “specific, unconventional, and inventive sequence of operations” referred to by the Applicant is the abstract idea. Only additional elements can provide an inventive concept under step 2B and there has been no showing by the Applicant that any of the additional elements provide such an inventive concept. Applicant is arguing that the abstract idea (the “sequence of operations”) is novel and thus eligible. To the contrary, MPEP 2106 clearly states that the novelty of an abstract idea is of no relevance to whether the claim is eligible or not. An improved abstract idea is still an abstract idea.
Rejections under 35 U.S.C. § 103
Regarding the rejection of Claims 1-3, 5, 9, 11-15, 17, 19, and 20, the Examiner has considered Applicant’s arguments; however, the arguments are not persuasive. Applicant argues:
…a network of interconnected data objects… The cited references do not describe any such interconnected structure of data objects.
Regarding (i), the Examiner respectfully disagrees. Francois at Para. 0135 teaches an active diagnosis module (ADM) is linked to multiple EMRs of a patient. This describes a structure of multiple data objects interconnected through the ADM/patient identifier. Notably, the claim does not state what these data object are.
Barnes is silent to a first data object corresponding to a data element for a tumor mass of a primary cancer…
Regarding (j), the Examiner respectfully disagrees. Barnes teaches storing tumor information in a patient record, including information specifying a location of the tumor and information concerning metastasis (Para. 0079, 0106, 0230). The tumor information is interpreted as the “first data object,” and the tumor location is interpreted as the attribute specifying a site of the tumor mass. The inclusion of a metastasis score distinguishes a primary tumor from a metastasis of a primary cancer.
…Reiner describes displaying things recorded at the same time, not with respect to different times in a timeline…
Regarding (k), the Examiner respectfully submits that Reiner was not relied upon to teach this feature, Colley was. Colley describes displaying things recorded at different times in a timeline, and therefore provides what Reiner lacks.
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Ginsburg (U.S. 2023/0238090) which discloses a system and method for aggregating and tracking medical delivery to a patient.
Abraham et al. (U.S. 2023/0113092) which discloses predicting a tumor primary lineage, cancer category or type, organ group, and/or histology.
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/CAMRYN B LEWIS/
Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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