DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to Applicant’s communication filed on May 5, 2025.
Claims 1, 2, 4, 10-12 and 15 have been amended and are hereby entered.
Claims 1-20 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 5, 2025 has been entered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a plurality of neural network modules configured to output a plurality modality-specific embeddings, wherein each neural network module of the plurality of neural network modules is configured to input data in modality of the plurality of modalities and output a modality-specific embedding” and “a contrastive loss module configured to input the plurality of modality-specific embeddings and output a vector embedding” in claims 1 and 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Applicant specification recites “Aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software module.” in paragraph 143. Based on Applicant’s description, the Examiner interprets the neural network modules and the contrastive loss module to be purely software being executed by a computer/hardware.
If applicant does not intend 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 avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 an abstract idea without significantly more.
Step 1 analysis:
Claims 1 and 11 are directed to an apparatus and a method respectively and therefore all fall into one of the four statutory categories. (Step 1: Yes, the claims fall into one of the four statutory categories).
Step 2A analysis - Prong one:
The substantially similar independent apparatus and method claims, taking claim 1 as exemplary, recite the following limitations: receive a query identifying a plurality of modalities of data, wherein each modality of the plurality of modalities of data represents data captured using a distinct diagnostic process; retrieve user data of a user, wherein the user data comprises medical data corresponding to each modality of the plurality of modalities of data; instantiate a …[algorithm]…, wherein instantiating the …[algorithm]…further comprises: instantiating, based on the query, a plurality of …[algorithms]…configured to output a plurality modality-specific embeddings, wherein each …[algorithm]…of the plurality of …[algorithms]… is configured to input data in modality of the plurality of modalities and output a modality-specific embedding; and connecting the output of each of the plurality of …[algorithms]…to at least a …[algorithm]…configured to input the plurality of modality-specific embeddings and output a vector embedding; generate a vector embedding of the user data using the …[algorithms]…the user data, wherein generating the vector embedding comprises: inputting at least a portion of the user data…; and generating the vector embedding using the at least one …[algorithm]…to reduce dimensionality of the inputted at least a portion of the user data to create the vector embedding having a compact vector representation; generate a plurality of cohorts of retrospective users using cohort data extracted…based on a query input, wherein generating the plurality of cohorts comprises generating a set of vector embeddings of the cohort data using the …[algorithm]… and the cohort data; classify, based on the vector embedding, having reduced dimensionality and compact vector representation, and the set of vector embeddings, the user data to at least a cohort of the plurality of cohorts of the retrospective users; and output the at least a cohort...
The series of steps as recited above describe managing personal behavior or relationships or interactions between people including following rules or instructions and are thus are grouped as certain methods of organizing human activity which are abstract ideas.
Further, the limitations of “instantiate a neural network architecture, wherein instantiating the neural network architecture further comprises: instantiating, based on the query, a plurality of neural network modules configured to output a plurality modality-specific embeddings, wherein each neural network module of the plurality of neural network modules is configured to input data in modality of the plurality of modalities and output a modality-specific embedding; and connecting the output of each of the plurality of neural network modules to at least a contrastive loss module configured to input the plurality of modality-specific embeddings and output a vector embedding; generate a vector embedding of the user data using the neural network architecture and the user data, wherein generating the vector embedding comprises: inputting at least a portion of the user data into at least one neural network module of the plurality of neural network modules; and generating the vector embedding using the at least one neural network module to reduce dimensionality of the inputted at least a portion of the user data to create the vector embedding having a compact vector representation;” is a process that under broadest reasonable interpretation covers a mathematical concept that includes mathematical relationships, mathematical formulas or equations, and mathematical calculations but for the recitation of generic computer component language (discussed below at Step 2A2). That is, other than reciting the generic computer component language, the claim recites a procedure for preprocessing data and generating vector embeddings which encompasses a mathematical concept. Applicant’s specification describes the steps regarding generating of the vector embeddings to be math (see e.g., paras 38, 46-47, 64 and 86-89). The Examiner notes that the mathematical concept need not be expressed in mathematical symbols. MPEP § 2106.04(a)(2)(I). Accordingly, the claim recites an abstract idea.
The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes. The combination analysis of all the abstract ideas still leads to the determination that the limitations as a whole are grouped as certain methods of organizing human activity. (Step 2A – Prong 1: Yes, the claims are abstract).
Step 2A analysis - Prong two:
Claims 1 and 11 recite additional elements beyond the abstract idea. Claims 1 and 11 recite a cohort database, a neural network architecture, a plurality of neural network modules, a contrastive loss module, and a user interface. Claim 1 further recites a processor, computer-readable storage medium and instructions. Claim 11 further recites a computing device. The claims are applying generic computer components to the recited abstract limitations. The recited instructions, neural network architecture, neural network modules and contrastive loss module appear to be software and are interpreted as such. See 112f interpretation above for the interpretations of the neural network modules and the contrastive loss module.
This judicial exception is not integrated into a practical application. In particular, the claims recite a cohort database, a neural network architecture, a plurality of neural network modules, a contrastive loss module, a user interface, a processor, computer-readable storage medium, instructions and a computing device which are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exceptions using a generic computer component. For example, Applicant’s specification explains that the processor may read instructions, perform computing tasks, receive data inputs, generate outputs, etc. (see Applicant’s specification paras 3, 12, 146-153). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Therefore, Claims 1 and 11 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional claimed elements are not integrated into a practical application).
Step 2B analysis:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); iv) storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. See MPEP §2106.05(d)(II).
This listing is not meant to imply that all computer functions are well‐understood, routine, conventional activities, or that a claim reciting a generic computer component performing a generic computer function is necessarily ineligible. Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking). On the other hand, courts have held computer-implemented processes to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic. See MPEP §2106.05(d)(II) – emphasis added.
Here, the steps are receiving or transmitting data over a network; storing and retrieving information in memory – all of which have been recognized by the courts as well-understood, routine and conventional functions. See MPEP 2106.05(d)(II).
The claims are directed to an abstract idea with additional generic computer elements that do not add meaningful limitations to the abstract idea because they require no more than a generic computer to perform generic computer functions that are well-understood, routine, and conventional activities previously known in the industry.
For the next step of the analysis, it must be determined whether the limitations present in the claims represent a patent-eligible application of the abstract idea. A claim directed to a judicial exception must be analyzed to determine whether the elements of the claim, considered both individually and as an ordered combination are sufficient to ensure that the claim as a whole amounts to significantly more than the exception itself.
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of well-understood, routine, and conventional activities previously known to the industry. Further, the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention. See MPEP 2106.05(d).
Applicant’s specification discloses the following:
Applicant describes embodiments of the disclosure at a very high level to include the use of a wide variety of processors, large language models, memories, machine learning models, neural networks, input/output systems, interfaces, displays, storage devices, etc. (see Applicant’s Spec. paras 12, 43, 56, 130, 147-153).
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The collective functions appear to be implemented using conventional computer systemization.
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 cohort database, a neural network architecture, a plurality of neural network modules, a contrastive loss module, a user interface, a processor, computer-readable storage medium, instructions and a computing device to perform all of the steps discussed above amount to no more than mere instructions to apply the exceptions using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims do not provide an inventive concept significantly more than the abstract idea. Accordingly, these additional elements, when considered separately and as an ordered combination, 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: No, the claims do not provide significantly more).
Dependent Claims 2-10 and 12-20 further define the abstract idea that is presented in independent Claims 1 and 11 respectively, and are further grouped as certain methods of organizing human activity and are abstract for the same reasons and basis as presented above. Further, Claims 2-3, 6, 12-13 and 16-17 recite additional elements beyond the abstract idea. Claims 2-3 and 12-13 recite a preliminary classifier. Claims 6-7 and 16-17 recite a machine-learning model. These additional elements are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. For example, as noted above, the Applicant’s specification indicates the use of known machine-learning models (see Applicant’s specification paras 109, 135).
Accordingly, these additional elements, when considered separately and as an ordered combination, 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 do not recite additional elements that integrate the judicial exception into a practical application when considered both individually and as an ordered combination. Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to abstract ideas without significantly more.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Brisimi et al. (Brisimi, T. S., Xu, T., Wang, T., Dai, W., Adams, W. G., & Paschalidis, I. Ch. (2018). Predicting chronic disease hospitalizations from electronic health records: An interpretable classification approach. Proceedings of the IEEE, 106(4), 690–707. https://doi.org/10.1109/jproc.2017.2789319) in view of Cui et al. (Cui, S., Wang, J., Zhong, Y., Liu, H., Wang, T., & Ma, F. (2024). Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions. Proceedings of the 2024 SIAM International Conference on Data Mining (SDM), 361–369. https://doi.org/10.1137/1.9781611978032.41), further in view of Banerjee et al. (US 20230107415).
Regarding Claim 1, Brisimi discloses the following limitations:
An apparatus for classifying a user to a cohort of retrospective users, wherein the apparatus comprises: at least a processor; a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to: (Brisimi discloses using a variety of machine learning methods indicating the use of a processor, computer-readable medium and instructions configuring the processor to perform the desired functions. – see Abstract, page 690)
receive a query identifying a plurality of modalities of data, wherein each modality of the plurality of modalities of data represents data captured using a distinct diagnostic process; (Brisimi discloses that the used data (receive a query identifying a plurality of modalities of data) focuses on patients with at least one heart-related diagnosis or procedure record such as diagnosis, lab tests, etc. (data captured using a distinct diagnostic process). – Section V pages 697-698; Tables 1-2)
retrieve user data of a user, wherein the user data comprises medical data corresponding to each modality of the plurality of modalities of data; (Brisimi discloses that they base their predictions on the patients’ medical history data (user data of a user), recent and more distant, as described in their electronic health records (EHRs) (comprises medical data corresponding to each modality of the plurality of modalities of data). – abstract; Section V pages 697-698; Tables 1-2)
generate a vector embedding of the user data using…the user data; (Brisimi discloses that the data sets collected for the study include a patients’ EHRs (using the user data) and are preprocessed. Preprocessing includes summarizing the medical factors in the history of a patient which results in a vector of 212 features for each patient (generate a vector embedding using the user data). – See Section V page 697; Section V-A, page 698)
generate a plurality of cohorts of retrospective users using cohort data extracted from a cohort database based on a query input, wherein generating the plurality of cohorts comprises generating a set of vector embeddings of the cohort data using…the cohort data; (Brisimi discloses that the data sets collected for the study include patients’ EHRs (cohort data extracted from a cohort database) obtained from the Boston Medical Center and their affiliated Community Health Centers (from a cohort database). The data sets and study were focused on patients with at least one heart-related diagnosis or procedure record (based on a query input - Section V, page 697). Preprocessing the data includes summarizing the medical factors in the history of a patient which results in a vector of 212 features for each patient (generating a set of vector embeddings of the cohort data using the cohort data - Section V-A, page 698). – Section V, pages 697-698; Section V-A, page 698)
classify, based on the vector embedding,…, and the set of vector embeddings, the user data to at least a cohort of the plurality of cohorts of the retrospective users; (Brisimi discloses clustering and classifying (classify… to at least a cohort of the plurality of cohorts) each patient based on their medical history from electronic health records (EHRs) (the user data) by using feature vectors (based on the vector embedding and the set of vector embeddings) to make a prediction. Further, identifying clusters of patients who share the same set of discriminative features and, at the same time, develop per-cluster sparse classifiers using these features – abstract; Section I-A, page 692; Section III, page 692)
and output the at least a cohort through a user interface. (Brisimi discloses that the machine learning models result (output) in patient clusters based on their medical history. – abstract; Section VII, page 702, col 2-page 703, col 1)
Brisimi does not disclose the following limitations met by Cui:
instantiate a neural network architecture, wherein instantiating the neural network architecture further comprises: instantiating, based on the query, a plurality of neural network modules configured to output a plurality modality-specific embeddings, (Cui teaches AutoFM (instantiate a neural network architecture), a novel Neural Architecture Search (NAS) framework (a plurality of neural network modules) designed for automatically fusing multi-modal EHR data. An overview of the multimodal EHR data embeddings is in Figure 1. Cui teaches inputting four modalities (e.g., continuous events, discrete events, patient demographics and clinical notes) to generate modality-specific embeddings (output a plurality modality-specific embeddings). – abstract, page 361; Figure 1, page 363; Section 3.1, page 363; Section 4.1, page 366)
wherein each neural network module of the plurality of neural network modules is configured to input data in modality of the plurality of modalities and output a modality-specific embedding; (Cui teaches inputting four modalities (e.g., continuous events, discrete events, patient demographics and clinical notes) (configured to input data in modality of the plurality of modalities) to generate modality-specific embeddings (output a modality-specific embedding). -– Figure 1, page 363; Section 3.1, page 363; Section 4.1, page 366)
and connecting the output of each of the plurality of neural network modules to at least a contrastive loss module configured to input the plurality of modality-specific embeddings and output a vector embedding; (Cui teaches Multi-modal Fusion Search which obtains the output features for all modalities (configured to input the plurality of modality-specific embeddings) from the first stage, we apply a fixed max pooling operation to the encodings of sequence features over the sequence length dimension. The loss function used is cross entropy (connecting to at least a contrastive loss module). To obtain a comprehensive representation of the entire EHR data, we linearly combine all the node features from the multi-modal fusion module [g1, · · · , gC] (see Figure 1) (output a vector embedding). – Figure 1, page 363; Section 3.2.2, page 364; Section 3.3, page 365; Section 4.1, page 366; Section 4.6, page 368)
generate a vector embedding of the user data using the neural network architecture…; generating a set of vector embeddings of the cohort data using the neural network architecture… (Cui teaches using a neural network (using the neural network architecture) to generate modality-specific vectors, denoted as [z1, z2, z3, z4] ∈ Rde, from four modality inputs (generate a vector embedding of the user data). They extract data from the MIMICIII dataset and specifically focus on the 17,710 patients (23,620 ICU visits) recorded from 2008 to 2012 (generating a set of vector embeddings of the cohort data). – Figure 1, page 363; Section 4.1, page 366)
wherein generating the vector embedding comprises: inputting at least a portion of the user data into at least one neural network module of the plurality of neural network modules; (Cui teaches that, as depicted in Figure 1, our proposed AutoFM framework (t least one neural network module of the plurality of neural network modules) takes multiple heterogeneous EHR data as input (inputting at least a portion of the user data into at least one neural network module). – Figure 1, page 362; Section 3 page 362; Section 3.1 page 363)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of data preprocessing as disclosed by Brisimi (see Section V-A page 698 and Section V-C page 699) to incorporate the use of neural networks for preprocessing data as taught by Cui in order to eliminate the need for human intervention (see Cui Section 1, page 361).
Brisimi and Cui do not disclose the following limitations met by Banerjee:
and generating the vector embedding using the at least one neural network module to reduce dimensionality of the inputted at least a portion of the user data to create the vector embedding having a compact vector representation; (Banerjee teaches that a user can evaluate the multi-modal data archetypes to determine that one or more dimensions of the latent space represent multi-modal data that is substantially irrelevant, e.g., to a medical condition of interest. In response, the machine learning system can remove the specified dimensions of the latent space (reduce dimensionality of the inputted at least a portion of the user data), thus reducing the dimensionality of the latent space, and as a result, reducing consumption of computational resources (e.g., memory and computing power) during clustering of the multi-modal data embeddings in the latent space (create the vector embedding having a compact vector representation). – paras 178-179, 189)
classify, based on the vector embedding, having reduced dimensionality and compact vector representation… (Banerjee teaches determining a respective classification score for each patient category in a set of patient categories based on the embedding of the multi-modal data (based on the vector embedding, having reduced dimensionality and compact vector representation) characterizing the patient and classifying the patient (classify) as being included in a corresponding patient category from the set of patient categories based on the classification scores. The latent space (i.e., in which the machine learning system clusters multi-modal data embeddings to identify patient categories), is not directly interpretable. Lack of interpretability can limit the applicability of machine learning systems and their outputs – paras 8, 178-179)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the machine learning method for predicting disease as disclosed by Brisimi to incorporate machine learning systems to reduce the dimensionality of the latent space as taught by Banerjee in order to reduce the consumption of computational resources (see Banerjee para 179).
Regarding Claim 2, Brisimi, Cui and Banerjee disclose all the limitations above and further disclose the following limitations:
wherein generating the plurality of cohorts of retrospective users further comprises: generating a plurality of preliminary cohorts as a function of populating the cohort database; (Brisimi discloses starting with an initial (e.g., random or using some clustering algorithm) cluster assignment (generating a plurality of preliminary cohorts) of the positive samples and then alternating between the two modules. – Section IV-B, page 695-696)
and classifying the user to corresponding preliminary cohorts of the plurality of preliminary cohort using a preliminary classifier. (Brisimi discloses using machine learning models (using a preliminary classifier) for clustering and classifying each patient (classifying the user to corresponding preliminary cohorts) based on their medical history from electronic health records (EHRs) by using feature vectors to make a prediction. The process starts with an initial (e.g., random or using some clustering algorithm) cluster assignment (preliminary cohorts). – abstract; Section I-A, page 692; Section III, page 692; Section IV-B, page 695-696)
Regarding Claim 3, Brisimi, Cui and Banerjee disclose all the limitations above and further disclose the following limitations:
wherein using the preliminary classifier comprises: training the preliminary classifier with training data comprising a plurality of user data correlated to a plurality of preliminary cohorts; (Brisimi discloses training a classification model (training the preliminary classifier) with training data (training data) obtained from sampling a random subset of the original data (comprising a plurality of user data correlated to a plurality of preliminary cohorts). – Section III-B, page 693; Section IV-B, page 695; Section V-C, page 700)
and outputting the corresponding preliminary cohorts classified to the user data. (Brisimi discloses that the initial cluster (corresponding preliminary cohorts) assignments (classified to the user data) are output. - Section IV-B, page 695-696)
Regarding Claim 4, Brisimi, Cui and Banerjee disclose all the limitations above and further disclose the following limitations:
wherein generating the plurality of cohorts of retrospective users further comprises modifying the at least a cohort based on an intersection of the preliminary cohorts. (Brisimi discloses re-clustering (modifying) positive samples given all the estimated classifiers (intersection). The process starts with an initial (e.g., random or using some clustering algorithm) cluster assignment (the preliminary cohorts) of the positive samples and then alternates between the two modules. Algorithm 1 orchestrates the alternating optimization process; given samples’ assignment to clusters, it obtains the optimal per-cluster SLSVM classifiers and calls the reclustering procedure (modifying) described in Algorithm 2. Section IV-B, page 695-696; Algorithm 2, page 696)
Regarding Claim 5, Brisimi, Cui and Banerjee disclose all the limitations above and further disclose the following limitations:
wherein the at least a processor is further configured to extract biomarkers of user data to implement in a query criteria-based search of the cohort database. (Brisimi discloses that they use the following features (predictors): age, diabetes, smoking, treated and untreated systolic blood pressure, total cholesterol, high-density lipo-protein (HDL), and body mass index (BMI) (biomarkers) - which are all extracted features from the patients EHRs (user data) and the data sets from the EHRs are focused on patients with at least one heart-related diagnosis or procedure record (a query criteria-based search). – see Section V, pages 697-698, Tables 1-2; Section VI, page 700)
Regarding Claim 6, Brisimi, Cui and Banerjee disclose all the limitations above and further disclose the following limitations:
wherein extracting the biomarkers comprises implementing a machine-learning model to conduct a temporal analysis on time-series data of the user data. (Brisimi discloses preprocessing the data over a time interval (a temporal analysis on time-series data of the user data). – see Section V, page 698)
Regarding Claim 7, Brisimi, Cui and Banerjee disclose all the limitations above and further disclose the following limitations:
wherein extracting the biomarkers comprises implementing a machine-learning model to create measurements of biomarkers related to a plurality of biological structures of the user. (Brisimi discloses that the features selected are: age, race, gender, average over the entire patient history of the hemoglobin A1c, or HbA1c for short (which measures average blood sugar concentrations for the preceding two to three months) (measurements of biomarkers), and the number of emergency room visits over the entire patient history. – see section VI, page 700, col 2)
Regarding Claim 8, Brisimi, Cui and Banerjee disclose all the limitations above and further disclose the following limitations:
wherein the query input comprises a criterium comprising a modality. (Brisimi discloses that the data sets obtained are focused on patients with at least one heart-related diagnosis or procedure record (a criterium comprising a modality). – see Section V, page 697 and Table 1; Section V, page 698 and Table 2)
Regarding Claim 9, Brisimi, Cui and Banerjee disclose all the limitations above and further disclose the following limitations:
wherein the at least a cohort comprises a plurality of comorbidities. (Brisimi discloses that cluster 1 contains diabetes patients with chronic cerebrovascular disease, skin ulcers, hypertension, an abnormal glucose tolerance test, and other complications as a result of diabetes. Cluster 2 contains patients with diabetes complicating pregnancy. Cluster 3 contains patients with less acute disease, combining diabetes with hypertension. – see Section VII, page 702, col 2-page 703, col 1)
Regarding Claim 10, Brisimi, Cui and Banerjee disclose all the limitations above and further disclose the following limitations:
wherein the computer-readable storage medium contains instructions further configuring the at least a processor to calculate a performance, comprising an AUC value, of a classification model on each of the plurality of comorbidities. (Brisimi discloses evaluating performance (calculate a performance) of the various algorithms (of a classification model) by using the area under the ROC curve (comprising an AUC value). – see Section VI, page 700, col 1-2)
Regarding Claim 11, this claim recites substantially similar limitations to those recited in claim 1 above; thus, the same rejection applies.
Regarding Claim 12, this claim recites substantially similar limitations to those recited in claim 2 above; thus, the same rejection applies.
Regarding Claim 13, this claim recites substantially similar limitations to those recited in claim 3 above; thus, the same rejection applies.
Regarding Claim 14, this claim recites substantially similar limitations to those recited in claim 4 above; thus, the same rejection applies.
Regarding Claim 15, this claim recites substantially similar limitations to those recited in claim 5 above; thus, the same rejection applies.
Regarding Claim 16, this claim recites substantially similar limitations to those recited in claim 6 above; thus, the same rejection applies.
Regarding Claim 17, this claim recites substantially similar limitations to those recited in claim 7 above; thus, the same rejection applies.
Regarding Claim 18, this claim recites substantially similar limitations to those recited in claim 8 above; thus, the same rejection applies.
Regarding Claim 19, this claim recites substantially similar limitations to those recited in claim 9 above; thus, the same rejection applies.
Regarding Claim 20, this claim recites substantially similar limitations to those recited in claim 10 above; thus, the same rejection applies.
Response to Arguments
Regarding rejections under 35 USC § 112(b) to Claims 1-20, Applicant’s arguments have been fully considered and are persuasive. Examiner has withdrawn the rejection.
Regarding rejections under 35 USC § 101 to Claims 1-20, Applicant’s arguments have been fully considered, and are not persuasive. The rejection has been updated in light of latest amendments. Applicant argues:
(a) Applicant submits that representative claim 1, at least as amended, recites additional elements that integrate any alleged judicial exception into a practical application by providing an improvement in technology. The improvement in technology includes, inter alia, a multi-stage provision of generating a vector embedding of user data using a neural network module to reduce dimensionality of the raw user data to create the vector embedding having a compact vector representation, thereby, and advantageously, improving downstream computational efficiency in performing complex machine-learning operations. This, advantageously, can provide a computationally robust approach to enhancing alignment between a specific user's medical condition and its classification, and as such improves problems related to the issue of accuracy of predictive analytics, and leads to accurately tailored treatments and subsequent enhancements in effectiveness, and ultimately elevates the standard of patient care. (p. 4-5).
Regarding (a), 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. Further, Examiner notes that the stated problems of accuracy in predictive analysis and tailored treatments are interpreted as not being rooted in technology. The problems are not caused by nor related to computer technology and the claims do not provide any limitations that may be interpreted as technical improvements to computer technology. The claimed invention is using a computer as a tool and any improvement present is an improvement to the abstract idea of, to paraphrase, classifying patient data.
(b) Additionally, Applicant submits that the limitations as recited in claim 1, at least as amended, do not recite any abstract idea, such as certain methods of organizing human activity and/or mathematical concepts, much like Example 47, claim 3, listed in the July 2024 Subject Matter Eligibility Examples (see also, 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence). Applicant submits that Applicant's representative claim 1 and the allowable claims presented in Example 47 are analogous, at least, because both provide a technological solution to a problem by automatedly processing and transforming information for downstream analysis and implementation by utilizing a specific multi-stage scheme for neural network and/or machine- learning activities. (p. 5).
Regarding (b), Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicate 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 technological 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).
Claim 3 in Example 47 in the July 2024 Subject Matter Eligibility Examples was found eligible under Step A2 Prong 2 because the additional elements recited in the claim integrated the abstract idea into a practical application because the claim improved the technical field of network intrusion detection.
Here, the stated problems are not caused by nor related to computer technology and the claims do not provide any limitations that may be interpreted as technical improvements to computer technology. As noted in response to argument (a) above, the stated problems related to the issue of accuracy of predictive analytics are interpreted as not being rooted in technology. Because no technological problem is present, the claims do not provide a practical application.
(c) Applicant further respectfully asserts that representative claim 1 contains limitations amounting to an inventive concept. Claim 1 as amended contains multiple additional elements that do not recite the allegedly abstract idea. These include, without limitation, the generation of a vector embedding of user data using a neural network module to reduce dimensionality of the raw user data to create the vector embedding having a compact vector representation, thereby, and advantageously, improving downstream computational efficiency in performing complex machine-learning operations. (p. 6).
Regarding (c), 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.
Amended claim 1 recites “wherein generating the vector embedding comprises: inputting at least a portion of the user data into at least one neural network module of the plurality of neural network modules; and generating the vector embedding using the at least one neural network module to reduce dimensionality of the inputted at least a portion of the user data to create the vector embedding having a compact vector representation;”. These limitations are interpreted as part of the abstract idea and not as additional elements because they fall under the abstract idea grouping of mathematical concepts. That is, other than reciting the generic computer component language, the claim recites a procedure for preprocessing data and generating vector embeddings which encompasses a mathematical concept.
Further, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above in the updated rejection with respect to integration of the abstract idea into a practical application, the additional element identified amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
(d) Moreover, Applicant respectfully asserts that these limitations are not "well-understood, routine, [and] conventional activities." Id. Neither the instant disclosure nor the record of prosecution in this matter includes any admission that any limitation of claim 1 as amended is "well-understood, routine, [and] conventional." The references of record in this matter also do not contain any such characterization, and there is no court case or printed publication supporting the conclusion that the above-described limitations are "well-understood, routine, [and] conventional." Applicant respectfully submits therefore that the recitation of the above limitations, both individually and as an ordered combination with other claim elements, amounts to "significantly more" than any allegedly abstract idea for at least this reason. Additionally, as discussed below, Applicant's claims amount to an "inventive concept" because they are not taught by the relevant art. (p. 6-7).
Regarding (d), Examiner respectfully disagrees. MPEP 2106.05(d) states: “Another consideration when determining whether a claim recites significantly more than a judicial