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 11/17/2025, the following occurred: Claims 1, 9, 11 and 19 have been amended.
Claims 1-20 are pending and have been examined.
Priority
This application claims priority to U.S. Provisional Patent Application No. 63/484,376 dated 2/10/2023.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: “cluster analysis platform configured to display” (claims 1 and 11), a functionality that is coextensive with the recited apparatus processor implementing the instructions.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f), it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected for lacking written description.
Claims 1 and 11 are rejected under 35 U.S.C. §112(a) for lacking written description for the recitation of “convert the . . . data into machine-readable medium” (claim 1 being representative). This is a new matter rejection. New matter added to the claims shall be rejected under 112a. MPEP § 2163.06(I).
The Applicant’s disclosure describes “a machine-readable storage medium” in [0146] similarly to how most specifications describe “machine-readable medium”; however, the Specification does not appear to use “convert” along with “machine-readable medium” as used in these limitations (see [0031], “In some cases, a binarization process may include converting an image from color or greyscale to black-and-white (i.e., a binary image)”). As such, the Applicant’s disclosure fails to provide support for this/these limitation(s). There is no disclosure describing that conversion of data into a machine-readable medium.
The rejection of independent claims 1 and 11 also applies to dependent claims 2-10 and 12-20.
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.
Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Appropriate correction is required.
Claims 1 and 11 recite “convert the . . . data into machine-readable medium” (claim 1 being representative). A “machine-readable medium” usually refers to memory, a signal, etc., which is described in [0146] similarly to how most specifications describe “machine-readable medium.” It is unclear whether the data is stored in a medium or the data is converted into machine readable data or some other meaning. The specification does not appear to use “convert” along with “machine-readable medium” as used in these limitations (see [0031], “In some cases, a binarization process may include converting an image from color or greyscale to black-and-white (i.e., a binary image)”), which adds to the issue.
The rejection of independent claims 1 and 11 also applies to dependent claims 2-10 and 12-20.
Dependent claims 2 and 12 recite “generating the one or more augmented data sets comprises identifying textual data from the at least one data file using optical character recognition” (emphasis added) (claim 2 being representative). However, claims 1 and 11 recite “converting… the heterogeneous electronic health record data into machine-readable medium by preprocessing the heterogeneous electronic health record data using a feature extraction process” followed by “generating… one or more augmented data sets as a function of… and the one or more vitality data sets comprising the converted machine-readable medium” (claim 1 being representative). As explained in a Subject Matter Free of Prior Art section at the end of this Office action, the BRI of the converting step reads on performing optical character recognition on unstructured data of an electronic health record. The dependent claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by the “comprising” language replaces the features recited in the parent claim.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 11 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Eligibility Analysis Step 1 (YES): Claims 1 and 11 fall into at least one of the statutory categories (i.e., machine or process).
Eligibility Analysis Step 2A1 (YES): The claims recite an abstract idea. The identified abstract idea is for identifying clusters based on augmented data sets.
The limitations of receive one or more vitality data sets comprising heterogeneous electronic health record data; convert the heterogeneous electronic health record data into machine-readable medium by preprocessing the heterogeneous electronic health record data using a feature extraction process; retrieve auxiliary information for the one or more vitality data sets comprising converted machine-readable medium using data manipulations; generate one or more augmented data sets as a function of the auxiliary information retrieved using data manipulations and the one or more vitality data sets comprising the converted machine-readable medium; generate at least one cluster as a function of the one or more augmented data sets using data manipulations; generate a similarity datum as a function of the at least one cluster generated using data manipulations and the one or more augmented data sets; generate a user interface data structure comprising one or more of the similarity datum, the at least one cluster, the auxiliary information, and the one or more vitality data sets; and generate a cluster analysis platform using the user interface data structure, wherein the cluster analysis platform is configured to display the similarity datum in a graphical format (claim 1 being representative) is a process that under the broadest reasonable interpretation (BRI) covers a method of organizing human activity but for the recitation of generic computer component language (discussed below in 2A2). That is, other than reciting the generic computer component language, the claimed invention amounts to a human following a series of rules or steps to identify cluster(s) based on augmented data set(s), which is a method of managing personal behavior or relationships or interactions between people. For example, but for the generic computer component language, the claims encompass a person generating augmented data set(s) as a function of the auxiliary information generated using data manipulations and the vitality data set(s). Likewise, but for the generic computer component language, the claims encompass a person generating a similarity datum as a function of cluster(s) generated using data manipulations and the augmented data set(s). Likewise, but for the processor and memory, the claims encompass a person generating a user interface data structure; generating a cluster analysis platform using the generated user interface data structure; and displaying the similarity datum in a graphical format. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (MPEP § 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people but for the recitation of generic computer component language, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. See additionally MPEP 2106. Accordingly, the claims recite an abstract idea.
The claims further recite using a retrained classifier, the classifier is trained by: receiving classifier training data comprising the generated one or more augmented data sets using the trained auxiliary machine learning model as input correlated to a plurality of clusters as output; iteratively training the classifier using the classifier training data; retraining the classifier as a function of user feedback related to the accuracy of the output (Claim 1 being representative). When given its broadest reasonable interpretation (BRI) in light of the disclosure, the training of a classifier model in the manner described in the identified abstract idea, supra, represents the creation of mathematical interrelationships between data. See Applicant’s disclosure, e.g., at para. 45: (“may generate a classifier using a classification algorithm, defined as a process whereby a computing device 104 derives a classifier from training data. Classification may be performed using… logistic regression and/or… decision trees”). As such, the training of the classifier represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Eligibility Analysis Step 2A2 (NO):
The judicial exception, the above-identified abstract idea, is not integrated into a practical application. In particular, the claims recite the additional elements of a processor and a memory communicatively connected to the processor (claims 1 and 11) and a user interface (claims 1 and 11) that implement the identified abstract idea (represented by claim 1). The additional elements aforementioned are not described by the applicant and are recited at a high-level of generality (i.e., a generic computer or computer component performing a generic computer or computer component function that facilitates the identified abstract idea) such that these amount no more than mere instructions to apply the exception using a generic computer component (see Specification e.g., at para. 72: “Devices 201-209 generally include computer devices or systems, such as personal computers, mobile devices, servers, or the like”; and para. 150: “any suitable processor, such as without limitation…”) See MPEP § 2106.04(d)(I). Accordingly, even in combination, 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. The claims are directed to an abstract idea.
The claims further recite the additional elements of using a trained auxiliary machine learning model (claims 1 and 11) for the function of retrieving data (claim 1) or generating data (claim 11). The additional element is not described by the Applicant, is recited at a high-level of generality and is merely invoked as a tool to perform an existing process (MPEP § 2106.05(f)(2), see case involving a commonplace business method or mathematical algorithm being applied on a general-purpose computer within the “Other examples”), such that this amounts no more than mere instructions to apply the abstract idea using a general-purpose computer (see Specification at para. 49: “processor 108 may use a machine learning model such as any machine learning model as described in this disclosure in order to retrieve and/or generate auxiliary information 128 and/or quantitative data points 132”). See MPEP § 2106.04(d)(I); and Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014). Accordingly, even in combination, 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. The claims are directed to an abstract idea.
Eligibility Analysis Step 2B (NO):
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 processor and a memory communicatively connected to the processor (claims 1 and 11) and a user interface (claims 1 and 11) to perform the method (represented by claim 1) amount no more than mere instructions to apply the exception using a generic computer or generic computer component. Mere instructions to apply an exception using generic computer(s) and/or generic computer component(s) cannot provide an inventive concept (“significantly more”). See MPEP § 2106.05(f).
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 element of using a trained auxiliary machine learning model (claims 1 and 11) to perform the method amounts no more than mere instructions to “apply it” with the exception by invoking an algorithm merely as a tool to perform an existing process (i.e., only recites the algorithm as a tool to retrieve or generate data), in this case to retrieve auxiliary information for the one or more vitality data sets (claim 1) or to generate one or more augmented data sets (claim 11). The use of a trained machine learning algorithm in its ordinary capacity to perform task(s) in the identified abstract idea does not provide an inventive concept (“significantly more”). See MPEP § 2106.05(f). Accordingly, alone or in combination, the additional element does not provide significantly more. Thus, the claims are not patent eligible.
Dependent claims 2-10 and 12-20, when analyzed as a whole, are similarly rejected under 35 U.S.C. §101 because the additional limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. The claims, when considered alone or as an ordered combination, either (1) merely further define the abstract idea, (2) do not further limit the claim to a practical application, or (3) do not provide an inventive concept such that the claims are subject matter eligible.
Claim(s) 2, 4-6, 12, 14-16 merely further describe(s) the abstract idea (e.g., the auxiliary information, retrieving the auxiliary information, identifying treatment event(s), retrieving quantitative data associated with each of the vitality data sets for each treatment event, using optical character recognition). See analysis, supra. See also MPEP 2106.05(d)(II) citing Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (“optical character recognition”).
Claim(s) 3 & 13 merely further describe(s) the additional element of a graphical user interface, which is a generic computer component performing generic computer implementation. See analysis, supra.
Claim(s) 7 & 17 merely further describe(s) the additional element(s) of the processor (e.g., receiving the one or more vitality data sets, determining at least one missing element within the one or more vitality data sets, retrieving the auxiliary information as a function of the missing element). See analysis, supra.
Claim(s) 8 & 18 merely further describe(s) (the processor having the auxiliary module) retrieving the auxiliary information… as a function of a web crawler, which is considered generally linking the use of the abstract idea to a particular technological environment or technical field. See analysis, supra. Further, and for completeness, the prior art of record indicates that using a web crawler is a well-understood, routine, and conventional element (see US 2022/0188659 A1 to Krasnoslobodtsev and US 2021/0090694 A1 to Colley at para. 2563). Note: The function of web crawling is analogous to the well-understood, routine, conventional use of optical character recognition for collecting data.
Claims 9 & 19 merely further describe the abstract idea of using a classifier machine learning model for the function of classifying the one or more augmented data sets to the at least one cluster. The Applicant has described the classifier machine learning to encompass simplistic mathematical models such as logistic regression and/or decision trees (see Spec. at para. 45). The classifier machine learning model itself is considered to be part of the identified abstract idea because it falls under data manipulations that humans perform and thus is part of the rules or instructions. Alternately, when given its BRI in light of the Specification, the use of the classifier machine learning model to classify data in the manner described in the identified abstract idea represents the creation of mathematical interrelationships between data. Thus, the classifier machine learning model is interpreted to be part of the identified abstract idea, supra.
Alternately, Claims 9 & 19 merely further describe(s) using a (trained) classifier machine learning model for the function of classifying the one or more augmented data sets to the at least one cluster. The additional element is not described by the Applicant, is recited at a high-level of generality and is merely invoked as a tool to perform an existing process (MPEP § 2106.05(f)(2), see case involving a commonplace business method or mathematical algorithm being applied on a general-purpose computer within the “Other examples”), such that this amounts no more than mere instructions to apply the abstract idea using a general-purpose computer (see Specification at para. 45: “may generate a classifier using a classification algorithm, defined as a process whereby a computing device 104 derives a classifier from training data. Classification may be performed using… logistic regression and/or… decision trees”). See MPEP § 2106.04(d)(I); and Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014). Accordingly, even in combination, 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. The claims are directed to an abstract idea.
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 element of using a classifier machine learning model to perform the method amounts no more than mere instructions to “apply it” with the exception by invoking an algorithm merely as a tool to perform an existing process (i.e., only recites the algorithm as a tool to classify data to a cluster of data), in this case to receive augmented data and output cluster data. The use of a trained machine learning algorithm in its ordinary capacity to perform task(s) in the identified abstract idea does not provide an inventive concept (“significantly more”). See MPEP § 2106.05(f). Accordingly, alone or in combination, the additional element does not provide significantly more. Thus, the claims are not patent eligible.
Claims 10 & 20 merely further describe the abstract idea of “generating prediction training data comprising a plurality of augmented data sets correlated to a plurality of predictive outputs; iteratively train a prediction machine learning model as a function of the prediction training data and the similarity datum to generate a trained prediction machine learning model; and determine the predictive outputs using the trained prediction machine learning model”. The Specification at para. 67 describes the training as being performed by a regression model. Each of the training of the prediction machine learning model and the prediction machine learning model itself are considered to be part of the identified abstract idea because they fall under data manipulations that humans perform and thus are part of the rules or instructions. Alternately, when given its BRI in light of the Specification, the training of the prediction machine learning model and the prediction machine learning model itself described in the identified abstract idea represent the creation of mathematical interrelationships between data. As such, the training of the prediction machine learning model and the prediction machine learning model represent a mathematical concept. Either way, the limitation is interpreted to be part of the identified abstract idea, supra.
Alternately, Claims 10 & 20 further recite(s) the additional element of iteratively training a prediction machine learning model as a function of the prediction training data and the similarity datum that implements the identified abstract idea. The additional element is not described by the Applicant, is recited at a high-level of generality and is merely invoked as a tool to perform an existing process (MPEP § 2106.05(f)(2), see case involving a commonplace business method or mathematical algorithm being applied on a general-purpose computer within the “Other examples”), such that this amounts no more than mere instructions to apply the abstract idea using a general-purpose computer (see Specification, e.g., at para. 19, 69 and 105). See MPEP § 2106.04(d)(I); and Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014). Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Claims 10 & 20 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 element of iteratively training a prediction machine learning model as a function of the prediction training data and the similarity datum to perform the method amounts no more than mere instructions to “apply it” with the exception by invoking an algorithm merely as a tool to perform an existing process (i.e., only recites the algorithm as a tool to apply data to an algorithm and report the results), in this case to receive input data and output output data. The use of a trained machine learning algorithm in its ordinary capacity to perform tasks in the identified abstract idea does not provide an inventive concept (“significantly more”). See MPEP § 2106.05(f). Accordingly, alone or in combination, the additional element does not provide significantly more. Thus, the claims are not patent eligible.
Response to Arguments
Claim Objection
Regarding the objection to claims 1, 9, 11 and 19, the amendments presented alleviate the minor informalities. The claim objections are withdrawn.
Rejections under 35 U.S.C. § 112(b)
Regarding the rejection of claims 1-20, the amendments presented by the Applicant alleviate the past issues. The past rejections are withdrawn. Applicant’s amendments introduce new issues with respect to 112(a) and (b).
Rejections under 35 U.S.C. § 101
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments but does not find them persuasive for at least the following reasons. Applicant argues:
For Step 2A, Prong one
A1. “The claim recites, among other elements, a processor and memory configured to "receive one or more vitality data sets comprising heterogeneous electronic health record data" and "convert the heterogeneous electronic health record data into machine-readable medium by preprocessing the heterogeneous electronic health record data using a feature extraction process." This is a technical data-processing operation, not a human or economic activity” (Remarks, pg. 12).
Re. argument A1: The Examiner respectfully submits the basis of rejection as afforded by RCE. Given the BRI and in light of the Specification, the claims as drafted are directed to certain methods of organizing human activity along with a mathematical concept. The exemplary claim portions amount to a human following a series of rules to receive data comprising heterogeneous (i.e., various, diverse, different types of) electronic health record data (e.g., including unstructured and structured data) and converting these data by preprocessing these data using a feature extraction process, which is a method of managing personal behavior (CMOHA) that a person can most certainly do with or without generic computer implementation. Other claim portions can be characterized as either creation of mathematical relationships between data (Mathematical Concept) (or as part of the rules because they fall under data manipulations that humans perform) (alternately CMOHA). The Examiner respectfully asserts that the characterization of these identified abstract ideas as a single identified abstract idea, including the iterative training and retraining of the machine learning models, is fair.
A2. “Heterogeneous electronic health records are inherently digital, machine-stored data originating from multiple medical data sources, such as imaging systems, text notes, and structured diagnostic codes. These records are not manually handled by individuals in the claimed process; rather, they are processed automatically by a computing device that performs specialized preprocessing steps…” (Remarks, pg. 12).
Re. argument A2: The Examiner respectfully submits that the steps, as recited, are mere generic computer implementation of the identified abstract idea (“apply it” on a computer). For example, the abstract idea includes generic pre-processing by extracting features of the received data. Whether a human can perform the abstract idea is not part of the Methods of Organizing Human Activity inquiry. CMOHA includes a human following data processing steps executed by computational devices in a generic manner / at a high level of generality; thus, the rejection allows for the preprocessing to be performed on a generic computer.
Further, there are feature extraction preprocessing techniques that humans can most certainly do. Humans in the past have performed extraction of features in heterogeneous sets of patient health data. For example, humans can redact data prior to scanning documents into a digital image document (i.e., unstructured data that may be structured, e.g., with conventional optical character recognition). Automating a broadly claimed feature extraction preprocessing step, e.g., to perform redaction after scanning documents into electronic patient health records, means that the generic preprocessing function can very well be performed manually or on the computer; and is therefore a manual process executed by computational devices.
The Examiner notes that the argued feature of “converts mixed-format, semi-structured data into standardized, machine-readable form” is not claimed (and there are also related issues of indefiniteness). The argued feature of “OCR process” is not introduced until dependent claims 2 and 12. Regardless, the use of OCR is described in a conventional manner / high level optical character recognition without detailing how optical character recognition on a computer might differ from optical character recognition by a human. Past exemplary decisions have been made where, e.g., pixel analysis was recited in claim sets in a detailed manner to differentiate the way humans would analyze pixels and the way a computer implements the analysis.
The Examiner notes that the claims do not recite any “glyph” features or any of the exemplary non-limiting features.
A3. “feature extraction may reduce dimensionality of representation and may make the recognition process computationally more efficient” (Remarks, pg. 12).
Re. argument A3: The Examiner respectfully submits that the Applicant has not claimed the specifics of such a feature extraction process. Also, respectfully, this evidence requires further deliberation once the claims are amended to recite details specifying the particular feature extraction process.
As another example, the argued feature of a feature vector is not claimed. As these features are not claimed, applicant’s arguments that they “constitute computational transformations that improve how computer systems handle, interpret, and normalize multi-format data” are rendered moot.
A4. “This type of operation directly addresses a technical problem in computer technology, specifically the inability of conventional systems to efficiently process unstructured or heterogeneous medical data from multiple incompatible sources” (Remarks, pg. 13).
Re. argument A4: see response to argument A3. The Examiner notes that the Applicant has not claimed converting “unstructured” data. Heterogeneous medical data is not synonymous with unstructured data as it may include one or more of unstructured, semi-structured, and already structured data.
A5. “the claim's recitation of generating a "user interface data structure" and a "cluster analysis platform configured to display the similarity datum in a graphical format" further reinforces that the invention is directed to technological improvements in computer operation” (Remarks, pg. 13).
Re. argument A5: The Examiner respectfully asserts that the claimed steps are high level generic computer implementation (e.g., generating data and displaying data in a non-specific graphical format). The Specification at [0063] states: “As used in this disclosure, “user interface data structure” is a data structure representing a specialized formatting of data on a computer configured such that the information can be effectively presented for a user interface. User interface data structure may include clusters 160, vitality data sets 120, auxiliary information 128, similarity datum 180, and the like.” The Specification here specifies the displayable data rather than describing this “specialized” formatting. The Specification at [0063] also describes a graphical format “for the purposes of this disclosure is a visual representation of textual information”. The use of the processor to implement a display module to visually display various types of data including textual information is generic computer implementation. After careful consideration, these steps amount no more than mere instructions to apply the abstract idea on a generic computer.
A6. “The generation of such a data structure is a computer operation that transforms raw analytical results into a dynamic, visual representation accessible through a graphical interface.” (Remarks, pg. 13).
Re. argument A6: The Examiner respectfully asserts that the claims are directed to the identified abstract idea. Certain methods of organizing human activity includes interaction of a person with a computer. MPEP 2106.04(a)(2)(II). These computer interactions involve high level / generic computer functions (e.g., generic output of data, generic generation and display of display data). The recited computer generates the user interface data structure (i.e., generates one or more of the recited data) in a high-level manner lacking the required level of detail to remove the claims from recitation of an abstract idea (or to have the computer integrate the abstract idea into a practical application). See response to argument A5.
Regarding the generation of output data for display, in the context of software patents (which includes machine learning patents), the Alice/Mayo Test step-one inquiry determines “whether the claims focus on ‘the specific asserted improvement in computer capabilities… or, instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool.’” Recentive Analytics, Inc. v. Fox Corp., pg. 10 (quoting Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303 (Fed. Cir. 2018)). The Examiner respectfully submits that the Applicant has not claimed machine learning itself but, as argued previously, “the use of machine learning algorithms to automate and optimize clustering tasks” (see Office action mailed 05/16/2025 at pg. 39). The claim limitations rely on the use of generic machine learning models to carry out the claimed methods for generating data, and the machine learning technology described in the patents is conventional. See, e.g., Specification at para. 19 (requiring “machine-learning process may utilize supervised, unsupervised, lazy-learning processes and/or neural networks”). See also, e.g., Specification at para. 45 (requiring “[c]lassification may be performed using… logistic regression and/or… decision trees”). For comparison, see Recentive Analytics, Inc. v. Fox Corp., pg. 11.
A7. “The cluster analysis platform is not an abstract concept of presenting data to users, but a specific, technical implementation that enables efficient rendering of model-derived similarity data in a graphical interface” (Remarks, pg. 13-14).
Re. argument A7: The Examiner respectfully submits that the processor is recited at a high level of generality and is a generic computer component performing generic computer functions. This is not a practical application by any measure provided for in the 2019 subject matter eligibility guidance (incorporated into the most recent version of the MPEP). Also, the use of the processor and the cluster analysis platform (i.e., module) for presenting data, as argued, does not provide an improvement within the meaning of that word; the processor is not made to physically run faster, utilize fewer resources, or run more efficiently (e.g., when rendering display data). Notably, Applicant does not argue that the processor experiences increased efficiency in rendering data using the module, merely that this improvement may be enabled by the processing module in some way.
A8. “Moreover, there is no indication anywhere in the claim that the steps of converting, generating, or displaying are performed by or on behalf of a human for purposes of managing commercial or social relationships” (Remarks, pg. 14).
Re. argument A8: The Examiner asserts that following rules or instructions is an example of managing personal behavior, which is a certain method of organizing human activity. The recited steps of converting, generating, or displaying data are a series of rules or instructions that a person would follow to perform health data processing and analytics using basic tools of scientific work (i.e., a computer). The Examiner notes that multiple CAFC court decisions that were found to recite a method of organizing human activity did not actively recite a person or persons performing the steps of the claims (see, e.g., EPG, TLI communications, Ultramercial).
A9. “The Office further asserts…” (Remarks, pg. 14).
Re. argument A9: The Examiner respectfully submits the basis of rejection, which characterizes certain portions as a mathematical concept. These are not the recited portions characterized as mathematical concept.
A10. “the limitations of claim 1 do not recite a judicial exception merely because the claimed operations may involve or rely upon mathematical concepts. The claim does not "set forth or describe any mathematical relationships, calculations, formulas, or
equations using words or mathematical symbols." … the claim as a whole does not require or recite any particular mathematical algorithm, computation, or formula…” (Remarks, pg. 15-16).
Re. argument A10: The Examiner respectfully submits that the claims are directed to CMOHA along with a Mathematical Concept. See analysis, supra. The claims could even be directed to CMOHA alone. Regardless, certain identified limitations recite “using a retrained classifier, the classifier is trained by: receiving classifier training data comprising the generated one or more augmented data sets using the trained auxiliary machine learning model as input correlated to a plurality of clusters as output; iteratively training the classifier using the classifier training data; retraining the classifier as a function of user feedback related to the accuracy of the output”. This training algorithm represents the creation of mathematical interrelationships between data. As such, the Examiner asserts that the training of the classifier model and the use of a classifier model itself represent a mathematical concept that is interpreted to be part of the identified abstract idea.
Note: Unlike training of this machine learning model, the training (i.e., algorithm) of the trained auxiliary machine learning model used to train the auxiliary machine learning model is not specified, and use of the pre-trained machine learning model is considered “apply it”. See Recentive Analytics, Inc. v. Fox, Corp., pg. 10-11. Note: MPEP § 2106.04(a)(2)(B) states that “there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.”
For Step 2A, Prong two
A11. “Claim 1 integrates any alleged mathematical operations or abstract ideas into a technological process that improves how computing systems process, normalize, and visualize heterogeneous medical data. The recited steps of receiving heterogeneous electronic health record data; converting that data into a machine-readable medium using a feature extraction process; and generating augmented data sets and clusters through iterative machine-learning training collectively represent a specialized computer-implemented pipeline that goes far beyond performing mathematical calculations in the abstract. These steps operate on inherently digital data, transform it into a standardized machine-encoded format, and produce relevant similarity outputs that can be rendered for user interpretation in real time” (Remarks, pg. 16-17).
Re. argument A11: The Examiner respectfully disagrees. The abstract idea, including the iterative training process, cannot solve a technological problem (see Office action mailed 05/16/2025 at, e.g., pg. 40, last para.; see also Recentive Analytics v. Fox. Corp., pg. 12). Although the identified abstract idea may be improved, an improved abstract idea is still an abstract idea. Only additional elements may provide a practical application.
Further, the additional elements are not improved in the claims as drafted.
The processor and memory are each recited at a high level of generality and are generic computer components. The use of the processor and memory for receiving, converting, retrieving, generating (includes training), and displaying data, as drafted, does not provide an improvement within the meaning of that word; the processor and memory are not made to physically run faster, utilize fewer resources, or run more efficiently. There is no described improvement to the processor or memory, and the trained auxiliary machine learning model is also not improved. The Specification does not set forth an improvement within the meaning of the word; the excerpt provided describes how data visualization may be improved for the user experience (if an X-Y chart and/or color-coded similarity matrix were recited) rather than how the graphical format (i.e., output data) could improve computer display functionality (e.g., the aforementioned speed, utilization, efficiency of the processor and/or memory). Therefore, the Examiner should not determine the claim improves technology. MPEP 2106.04(d)(1). As for performing operations in “real time”, utilizing a computer or tool to perform an abstract idea in a faster or more accurate manner is utilizing a computer as designed and is insufficient to provide a practical application or significantly more. See Alice Corp. Also, this is not a practical application by any measure provided for in the 2019 Patent Eligibility Guidance or the most recent version of the MPEP.
The Examiner notes that the claims do not recite “a standardized machine-encoded format”, “an X-Y chart”, “a similarity matrix”, or “a color-coded matrix”. Nonetheless, humans were certainly capable of creating and presenting these types of data visualization displays from numerical datasets prior to the advent of computer systems.
A12. “This improvement to graphical rendering and user interfacing, such as automatically structuring the output for visualization of correlated patient clusters, is a tangible enhancement to the computer's ability to convey complex information efficiently and accurately” (Remarks, pg. 17-18).
Re. argument A12: The Examiner respectfully disagrees. The claims recite generic computer functionality, which computers were certainly able to do prior. Even without computers, people have rendered numerical datasets as visually appealing presentations for patients, students, colleagues and stakeholders. Again, utilizing a computer or tool to perform an abstract idea in a faster or more accurate manner is utilizing a computer as designed and is insufficient to provide a practical application or significantly more.
A13. “The claim's iterative training and retraining steps also represent a practical application of machine learning. The classifier is continuously updated as a function of user feedback related to output accuracy, allowing the system to dynamically improve its clustering performance without manual data re-labeling or code modification” (Remarks, pg. 18).
Re. argument A13: The Examiner respectfully submits that the classifier model, unlike the trained auxiliary machine learning model, is not recited as machine learning technology. Assuming arguendo that the claimed classifier was machine learning technology, the iterative training and retraining steps in the claims do not represent a technological improvement or practical application of machine learning technology. “Recentive’s own representations about the nature of machine learning vitiate this argument: Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. See, e.g., Opposition Br. 9 (“[U]sing a machine learning technique[]… necessarily includes [an] iterative[] training step . . . .” (internal quotation marks and citation omitted)); Transcript at 26:21–24 (“[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input”)”. Recentive Analytics v. Fox, Corp., pg. 12.
For Step 2B:
A14. “This specific, non-conventional combination of limitations is directed to a technical solution to problems arising from the need to combine, normalize, and visually present disparate electronic health record formats” (Remarks, pg. 19).
Re. argument A14: Certain argued features are part of the identified abstract idea. While the abstract idea may be improved (i.e., recited in a non-generic manner), an improved abstract idea is still an abstract idea. Only additional elements may provide a practical application or significantly more. As previously stated, the Applicant’s claims do not claim machine learning itself, and the claims rely on the use of the generic pre-trained auxiliary machine learning model to implement the abstract idea on a generic computer. The disclosed auxiliary machine learning model described in the specification is conventional. The asserted improvements are instead a process / arrangement of steps that qualifies as an abstract idea for which computers are invoked merely as a tool. Nothing in the claims, even in combination, would transform the claimed invention into something “significantly more” than the abstract idea of identifying/generating cluster data through application of machine learning.
A15. “Conventional systems do not automatically convert mixed-format clinical records into unified machine-readable vectors, combine the converted data using an auxiliary machine-learning model, or iteratively retrain a classifier based on feedback to improve clustering accuracy” (Remarks, pg. 19).
Re. argument A15: The Examiner respectfully submits that Applicant has not claimed the argued feature of “unified machine-readable vectors”. The Examiner also respectfully submits that the iterative training process is conventional. See response to argument A13, citing Recentive Analytics v. Fox, Corp., pg. 12.
A16. “These ordered operations improve computer functionality by enabling automated ingestion and standardization of heterogeneous medical data and by producing a dynamically generated graphical interface that visualizes similarity relationships in real time” (Remarks, pg. 19).
Re. argument A16: The additional elements do not provide an inventive concept. The additional elements of the generic computer processor and memory are merely invoked as tool(s) to apply the abstract idea (e.g., generating and displaying at a high level of generality) and cannot provide an inventive concept (“significantly more”). See MPEP § 2106.05(f). See also Recentive Analytics, Inc. v. Fox Corp., pg. 10 (quoting Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303 (Fed. Cir. 2018). The additional element of the trained machine learning model is also used in its ordinary capacity to perform task(s) in the identified abstract idea, which does not provide an inventive concept (“significantly more”). See MPEP § 2106.05(f). Unlike Berkheimer, the combination of additional elements is no more than well-understood, routine, conventional activities previously known to the industry, which are recited at the high level of generality. As further explained by Recentive Analytics, Inc., the requirement that the machine learning model be applied to a new environment to supply data or to be iteratively trained in the claims does not represent a technological improvement. (Id. at pg. 12).
Regarding the rejection of Claims 2-20, the Applicant has not offered any arguments with respect to these claims other than to reiterate the argument(s) present for the analogous claim or the claim from which they depend. As such, the rejection of these claims is also maintained.
Subject Matter Free of Prior Art
The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within the independent claims 1 and 11 as follows (claim 1 being representative):
receiving one or more vitality data sets comprising heterogeneous electronic health record data; converting the heterogeneous electronic health record data into machine-readable medium by preprocessing the heterogenous electronic health record data using a feature extraction process; retrieve auxiliary information for the one or more vitality data sets comprising converted machine-readable medium using a trained auxiliary machine learning model; generate one or more augmented data sets as a function of the auxiliary information retrieved using the trained auxiliary machine learning model and the one or more vitality data sets comprising the converted machine-readable medium; generate at least one cluster as a function of the one or more augmented data sets using a retrained classifier, the classifier is trained by: receiving classifier training data comprising the generated one or more augmented data sets… iteratively training the classifier… retraining the classifier as a function of user feedback related to an accuracy of the output; generate a similarity datum as a function of the at least one cluster generated using the retrained classifier and the one or more augmented data sets.
The Examiner notes that previously, there was no converting step. Boussios in view of Barve can render obvious the converting step with proper motivation. However, the highlighted generating step mapped to transforming Boussios’s unstructured data as a function of Boussios’s subtype definitions generated in view of Nference’s one or more trained neural network models (see Office action mailed 05/16/2025 at pg. 22-23). Mapping to this step can no longer rely upon Boussios’s unstructured data transformation as earlier converting step is now mapped to transformation of Boussios’s unstructured data.
Training of the retrained classifier also relies upon “the one or more augmented data sets”, which no longer maps to Boussios’s transformed unstructured data.
Generating at least one cluster also relies upon “the one or more augmented data sets”, which no longer maps to Boussios’s transformed unstructured data.
Generating a similarity datum also relies upon “the one or more augmented data sets”, which no longer maps to Boussios’s transformed unstructured data.
The determination of subject matter eligibility for independent claims 1 and 11 also applies to their dependents (claims 2-10 and 12-20) for the specified reason(s).
The most remarkable prior art of record is as follows:
Boussios et al. (US 11,862,346 B1; “Boussios” herein) for teaching identification of patient sub-cohorts and corresponding quantitative definitions of subtypes as a classification system for medical conditions… the computer system processes the patient data for the cohort to group patients into sub-cohorts of similar patients, i.e., each sub-cohort includes patients who have similar medical fact patterns in their patient data. See Abstract.
Nference, Inc. (WO 2021/178,689 A1; “Nference” herein) for teaching systems and methods for computing with private healthcare data including de-identification method, entity tagging models, obfuscation. See Abstract.
Staples, II et al. (US 2021/0202093 A1; “Staples” herein) for teaching systems, methods, and devices to identify members of a cohort. See Abstract.
Barve et al. (US 2022/0115100 A1; “Barve” herein) for teaching system and methods for retrieving clinical information based on clinical patient data. See Abstract and para. 0002-0005, 0058.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Foschini et al. (US 2023/0245777 A1) for teaching systems and methods for self-supervised learning based on naturally-occurring patterns of missing data. See Abstract.
Wagner et al. (US 2022/0189636 A1) for teaching patient vitality data (Fig. 9), machine learning (see Fig. 3), training data set (Fig. 9), auxiliary information (age, gender) (Fig. 4), patient cohort analysis (Fig. 10A), model descriptions (Fig. 11I-11L).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jessica M Webb whose telephone number is (469)295-9173. The examiner can normally be reached Mon-Fri 9:00am-3:00pm CST.
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/J.M.W./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683