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 .
Response to Amendment
The following Office action in response to communications received June 3, 2026. Claims 1-24 have been canceled. Claims 25-34 have been added. Therefore, claims 25-34 are pending and addressed below.
Applicant’s amendments to the claims are sufficient to overcome the Claim Objections rejections set forth in the previous office action dated February 4, 2026.
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 25-34 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Independent claim 25 is directed to an abstract idea consisting of collecting information, analyzing the information, creating a digital representation, generating predictive and simulated outcomes, identifying and presenting interventions, tracking changes and effectiveness, collecting additional information, and generating alternative interventions.
Independent claim 25 recites, in part:
“collecting, via data modalities, multimodal data comprising exposome data and biological data”; “analyzing via multimodal artificial intelligence”; “creating . . . a digital twin”; “generating . . . personalized predictive outcomes”; “applying the personalized interventions to the digital twin to generate simulated outcomes”; “identifying . . . personalized interventions that resulted in a positive predictive outcome”; “presenting the personalized interventions”; “tracking . . . behavioral changes . . . and effectiveness”; “continuously collecting . . . additional multimodal data”; and “generating . . . alternative personalized interventions.”
The limitations of claims 25–34, as drafted, under their broadest reasonable interpretation, cover the performance of:
Mental processes, including observation, evaluation, judgment, comparison, and recommendation. The claim recites collecting “multimodal data,” analyzing that data, creating a “digital twin,” generating “personalized predictive outcomes,” applying “personalized interventions” to generate “simulated outcomes,” identifying interventions associated with “a positive predictive outcome,” tracking “behavioral changes” and “effectiveness,” and generating “alternative personalized interventions.” These recitations encompass evaluating information to select and recommend an intervention. The claim does not recite administering any intervention, altering a physical state, controlling a device, or performing a claimed physical treatment.
Certain methods of organizing human activity, including managing personal behavior and healthcare-related interactions. The claim recites “presenting the personalized interventions to the subject couple,” “tracking . . . behavioral changes,” tracking “effectiveness,” and generating “alternative personalized interventions.” These limitations manage health-related recommendations and follow-up interactions involving the subject couple.
But for the recitation of generic implementation elements, claim 25 recites collecting information concerning a subject couple, evaluating the information, predicting and simulating possible outcomes, selecting and presenting interventions, tracking behavioral changes and effectiveness, and revising an intervention based on later information.
Claims 26–34 recite the same abstract idea with additional information-source, information-content, subject-category, or record-organization limitations. Claim 26 recites “one or more sensing devices.” Claim 27 recites “bidirectional data exchange.” Claim 28 recites “not adhering,” “lifestyle,” and “environmental factors.” Claim 29 recites “exposome data and biological data.” Claim 30 recites “an evidence database.” Claim 31 recites a man, woman, sperm donor, or egg donor. Claim 32 recites “a reproductive family health archive.” Claim 33 recites “data collected from one or more offspring.” Claim 34 recites “wearable sensors and at-home monitors.” These limitations do not change the character of the claim from the identified abstract information-collection, analysis, prediction, simulation, and recommendation activity.
The claims recite additional elements such as: “data modalities”; “one or more sensing devices”; “multimodal artificial intelligence”; “a server accessible via a network”; “a cyber-physical system”; “a digital twin” and “a digital representation”; “an evidence database”; “a reproductive family health archive”; “wearable sensors and at-home monitors”; and presenting information to a “subject couple.”
These elements are recited at a high level of generality. The claims use the recited elements to collect information, analyze information, store information, update information, generate outcomes, generate recommendations, and present recommendations. The claims do not recite a specific improvement to sensing, computer operation, network operation, server operation, database operation, model architecture, or data acquisition.
The recited “server accessible via a network” merely identifies an environment for performing the claimed information collection, analysis, storage, and presentation. The recited “cyber-physical system” and “digital twin” do not integrate the judicial exception into a practical application. The recited “one or more sensing devices,” “wearable sensors,” and “at-home monitors” merely identify sources of input information. The recited “reproductive family health archive” and “data collected from one or more offspring” merely identify the content and organization of stored information. The claims do not recite an improvement to record storage, retrieval, indexing, database operation, or data security.
The additional elements do not integrate the abstract idea into a practical application because they do not improve the functioning of a computer, network, sensor, model architecture, database, or any other technology. They merely apply the abstract information-collection, analysis, prediction, simulation, and recommendation activity in a generic computing environment and limit the activity to reproductive-health information, a subject couple, donor-related information, family information, or offspring information.
The claims do not recite administering treatment, performing an assisted-reproduction procedure, controlling medical equipment, modifying a physiological parameter, modifying a biological sample, or causing another physical transformation. “Presenting the personalized interventions” is the communication of information resulting from the claimed analysis, it is not a claimed treatment action.
The claims do not recite a particular improvement in “multimodal artificial intelligence.” The claims invoke “multimodal artificial intelligence” only to analyze data, create a digital twin, generate predictions, generate simulated outcomes, identify interventions, track information, and generate alternative interventions. The claims do not recite a specific artificial-intelligence architecture, training technique, technical constraint, processing improvement, or resource-management improvement.
The ordered combination of claim elements adds nothing significantly more than the abstract idea itself. The recited “data modalities,” “one or more sensing devices,” “multimodal artificial intelligence,” “server accessible via a network,” “cyber-physical system,” “digital twin,” “evidence database,” “reproductive family health archive,” “wearable sensors,” and “at-home monitors” perform generic functions of collecting, receiving, transmitting, storing, accessing, analyzing, simulating, generating, updating, and presenting information.
The claims do not recite a non-conventional arrangement of the listed elements or a particular technological improvement produced by their arrangement.
The following claim limitations are insignificant extra-solution activity:
“collecting, via data modalities, multimodal data.” This limitation merely collects input information used by the abstract analysis.
“executing on a server accessible via a network.” This limitation merely identifies a generic computing and communication environment for processing information.
“presenting the personalized interventions to the subject couple.” This limitation merely outputs information produced by the abstract analysis.
“tracking behavioral changes” and “continuously collecting . . . additional multimodal data.” These limitations merely obtain additional information for repeated performance of the abstract analysis.
“an evidence database” and “a reproductive family health archive.” These limitations merely store and organize information used in the abstract analysis.
“data collected from one or more offspring.” This limitation merely specifies additional informational content.
“wearable sensors and at-home monitors.” These limitations merely identify generic devices that collect input information; the claims do not recite an improvement to the operation of those devices.
Accordingly, claims 25–34 are directed to an abstract idea without significantly more and therefore are not patent eligible under 35 USC 101.
Claim Rejections - 35 USC § 103
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 25-34 are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0005200 A1 to Zimmerman et al., in view of US 2019/0108912 A1 to Spurlock et al., and further in view of US 2019/0209022 A1 to Sobol et al.
Claim 25:
“A method of providing personalized reproductive health care to a subject couple using a couple-based digital reproductive health platform, the method comprising: a. collecting, via data modalities, multimodal data comprising exposome data and biological data related to a subject couple, wherein the biological data comprises omics data; b. analyzing via multimodal artificial intelligence executing on a server accessible via a network, the collected multimodal data related to the subject couple; c. creating, via multimodal artificial intelligence, a digital twin of the subject couple by integrating data from the data modalities in real time within a cyber-physical system, the digital twin comprising a digital representation of each individual of the subject couple; d. generating, using multimodal artificial intelligence, personalized predictive outcomes to inform personalized interventions based on the analyzed collected multimodal data related to the subject couple; e. applying the personalized interventions to the digital twin to generate simulated outcomes; f. identifying, using multimodal artificial intelligence, personalized interventions that resulted in a positive predictive outcome when implemented in the digital twin of the subject couple, and presenting the personalized interventions to the subject couple; g. tracking, using multimodal artificial intelligence, behavioral changes of the subject couple and effectiveness of the presented personalized interventions over a predetermined follow-up period; h. continuously collecting, using multimodal artificial intelligence, additional multimodal data related to the subject couple over the specified follow-up period; and i. generating, by the multimodal artificial intelligence, alternative personalized interventions for the subject couple based on the tracked behavioral changes and effectiveness data.”
Zimmerman et al. teaches:
“collecting, via data modalities, multimodal data.” Zimmerman et al. teaches “a combination of patient medical record data, image data, genetic information, and historical information” and teaches that the combination is “extracted from one or more information systems.” See Zimmerman et al., paragraph 0004. Zimmerman et al. further teaches “EMR 210 information, images 220, genetic data 230, laboratory results 240, demographic information 250, social history 260,” “sensor data,” and “location data.” See Zimmerman et al., paragraph 0044.
“biological data.” Zimmerman et al. teaches “genetic information,” “genetic data,” “laboratory results,” “genomics,” and “clinical notes.” See Zimmerman et al., paragraphs 0004 and 0044. The quoted “genetic information,” “genetic data,” “laboratory results,” and “genomics” are interpreted as the claimed “biological data.”
“analyzing via multimodal artificial intelligence.” Zimmerman et al. teaches that “the patient digital twin 130 can be used to apply patient-related heterogenous data with artificial intelligence e.g., machine learning, deep learning, etc. and digitized medical knowledge to enable health outcomes.” See Zimmerman et al., paragraph 0046.
“executing on a server accessible via a network.” Zimmerman et al. teaches “a plurality of health-focused systems 1710-1712” “communicating via a cloud 1720 with a server 1730 and associated data store 1740.” See Zimmerman et al., paragraph 0135.
“creating . . . a digital twin.” Zimmerman et al. teaches “a patient digital twin of a first patient” and teaches that the patient digital twin includes “a data structure created from a combination of patient medical record data, image data, genetic information, and historical information.” See Zimmerman et al., paragraph 0004.
“a digital representation.” Zimmerman et al. teaches that the data are “arranged in the data structure to form a digital representation of the first patient.” See Zimmerman et al., paragraph 0004.
“integrating data from the data modalities in real time within a cyber-physical system.” Zimmerman et al. teaches that the digital twin includes “a physical object in real space,” “a digital twin of that physical object that exists in a virtual space,” “a link for data flow from real space to virtual space,” and “a link for information flow from virtual space to real space and virtual sub-spaces.” See Zimmerman et al., paragraph 0031. Zimmerman et al. teaches that “sensors connected to the physical object e.g., the patient 110 can collect data and relay the collected data 120 to the digital twin 130” and that “a real-time or substantially real-time” digital description allows the system to predict a condition. See Zimmerman et al., paragraph 0033. The quoted physical-object, digital-twin, and two-way data-flow disclosures are interpreted as the claimed “cyber-physical system.”
“generating . . . personalized predictive outcomes.” Zimmerman et al. teaches that a patient digital twin is “combinable with one or more rules to generate, using the processor, a recommendation for a patient health outcome based on modeling the patient digital twin as instructed by the one or more rules.” See Zimmerman et al., paragraph 0004. Zimmerman et al. further teaches that the patient digital twin “can be analyzed in the digital twin environment 135 to predict future behavior, condition, progression, etc., of the patient 110.” See Zimmerman et al., paragraph 0042.
“personalized interventions.” Zimmerman et al. teaches “specific recommended actions to be taken e.g., by the patient 110 and/or healthcare practitioner.” See Zimmerman et al., paragraph 0079. The quoted “specific recommended actions” are interpreted as the claimed “personalized interventions” because Zimmerman et al. states that the recommended course of action “can be customized for that particular patient 110.” See Zimmerman et al., paragraph 0079.
“applying the personalized interventions to the digital twin to generate simulated outcomes.” Zimmerman et al. teaches that the “patient digital twin is to be arranged for query and simulation via the processor.” See Zimmerman et al., paragraph 0004. Zimmerman et al. further teaches that “the patient digital twin 130 can be used to simulate, predict” patient risk and that a healthcare provider can understand “how the patient 110 may react to a variety of treatments in a variety of scenarios.” See Zimmerman et al., paragraphs 0043 and 0078.
“identifying . . . personalized interventions that resulted in a positive predictive outcome.” Zimmerman et al. teaches that “an action plan e.g., a patient care plan, etc. can be created from the synthesized output” and that “the action plan can be incorporated into the patient digital twin 130, for example, to model the patients 110 response to the action plan.” See Zimmerman et al., paragraph 0058. The quoted creation of an action plan and modeling of a patient response are interpreted as identifying an intervention associated with an outcome from the digital twin.
“presenting the personalized interventions to the subject couple.” Zimmerman et al. teaches that “a coordinated care action plan for the patient 110 can be communicated to authorized stakeholders.” See Zimmerman et al., paragraph 0058. The quoted “communicated to authorized stakeholders” is interpreted as the claimed presenting of interventions.
“tracking . . . behavioral changes.” Zimmerman et al. teaches “behavioral choices 340” including “diet 610, exercise 620, alcohol 630, tobacco 640, drugs 650, sexual behavior 660, extreme sports 670, hygiene 680.” See Zimmerman et al., paragraph 0051. Zimmerman et al. teaches that “behavioral choices 340 observed in and/or documented with respect to the patient 110 can be reflected in the patients digital twin 130.” See Zimmerman et al., paragraph 0051.
“over a predetermined follow-up period.” Zimmerman et al. teaches “at block 910, a change or scheduled follow-up is initiated” and teaches that “the process 900 can then loop upon the next change to allow the patient digital twin 130 to be updated.” See Zimmerman et al., paragraphs 0055 and 0060. The quoted “scheduled follow-up” and looping process are interpreted as a follow-up period.
“continuously collecting . . . additional multimodal data.” Zimmerman et al. teaches “data 120 dynamically provided from a source e.g., from the patient 110, practitioner, health information system, sensor, etc.” See Zimmerman et al., paragraph 0035. Zimmerman et al. further teaches “post-event feedback” generated from “image analysis, sensor data evaluation, test results, human feedback, etc.” and teaches that the feedback is “provided to the digital twin 130 for improved modeling, parameter modification, etc.” See Zimmerman et al., paragraph 0065.
“alternative personalized interventions.” Zimmerman et al. teaches that “feedback from user experience can be used to generate tips/suggestions, instructions, etc., that can be incorporated in the digital twin 130, provided to a user, etc.” See Zimmerman et al., paragraph 0063. Zimmerman et al. also teaches that when new data arrive, “the information is automatically analyzed and used to update the digital twin 130 and provide one or more recommendations and/or further actions based on the twin 130 modeled interactions.” See Zimmerman et al., paragraph 0066. The quoted “tips/suggestions, instructions,” “recommendations,” and “further actions” are interpreted as the claimed “alternative personalized interventions.”
Zimmerman et al. fails to explicitly teach:
“providing personalized reproductive health care to a subject couple using a couple-based digital reproductive health platform.”
“exposome data.”
“omics data.”
“a digital twin of the subject couple” comprising “a digital representation of each individual of the subject couple.”
“effectiveness of the presented personalized interventions.”
“generating . . . alternative personalized interventions for the subject couple based on the tracked behavioral changes and effectiveness data.”
Spurlock et al. teaches:
“omics data.” Spurlock et al. teaches that the system can use “laboratory assay results” and that “laboratory tests, such as sequencing, expression profiling, blood tests, or the like can be provided to the machine learning system.” See Spurlock et al., paragraph 0011. Spurlock et al. also teaches “obtaining a sample from the individual,” “performing an assay on the sample,” and “sequencing the nucleic acid, such that the clinical results include sequences or expression level.” See Spurlock et al., paragraphs 0014 and 0049. The quoted “sequencing,” “expression profiling,” “sequences,” and “expression level” are interpreted as the claimed “omics data.”
“exposome data.” Spurlock et al. teaches that data sources may include “geographic data” and that data sets may include “environmental data” and “geographic data.” See Spurlock et al., paragraphs 0015, 0018, and 0052. The quoted “environmental data” and “geographic data” are interpreted as the claimed “exposome data.”
“biological data.” Spurlock et al. teaches “genetic data,” “laboratory test results,” “phenotypic data,” “clinical data,” and “laboratory assay results.” See Spurlock et al., paragraphs 0011, 0015, 0018, and 0052. The quoted disclosures are interpreted as the claimed “biological data.”
“analyzing via multimodal artificial intelligence.” Spurlock et al. teaches “an autonomous machine learning system” that “discovers associations in data from a plurality of data sources obtained from a population and correlates the associations to health status of patients in the population.” See Spurlock et al., paragraph 0013.
“personalized predictive outcomes.” Spurlock et al. teaches “providing patient data from an individual and predicting, by the machine learning system, a health state for the individual when the patient data presents one or more of the discovered associations.” See Spurlock et al., paragraph 0013.
“effectiveness of the presented personalized interventions.” Spurlock et al. teaches that outcomes provided to a machine-learning algorithm may include “treatment selection, mortality, comorbidity, disease severity, treatment compliance, known response to a treatment i.e., effectiveness of treatment, and quality of life.” See Spurlock et al., paragraph 0043. The quoted “known response to a treatment i.e., effectiveness of treatment” is interpreted as the claimed “effectiveness of the presented personalized interventions.”
“alternative personalized interventions.” Spurlock et al. teaches that where an algorithm is trained on treatment outcomes, “it can then be used to predict a patients responsiveness to various disease specific therapies” and that the method may include “recommending a treatment based in part on the prediction where a certain treatment will only be recommended for patients likely to respond thereto.” See Spurlock et al., paragraph 0044. The quoted “recommending a treatment” based on predicted responsiveness is interpreted as generating an “alternative personalized intervention.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include methods as taught by Spurlock et al. within the patient digital twin taught by Zimmerman et al. with the motivation of Spurlock et al. teaching that the machine-learning system discovers associations in data from a plurality of sources, thereby correlating the associations to health status, predicts a health state for an individual, and recommends a treatment based on predicted responsiveness to disease-specific therapies. See Spurlock et al., paragraphs 0011, 0043–0044.
Zimmerman et al. and Spurlock et al. fail to explicitly teach:
“collecting, via data modalities, multimodal data comprising exposome data and biological data” via a wearable sensing device.
“continuously collecting . . . additional multimodal data” by a wearable sensing device.
Sobol et al. teaches:
“collecting, via data modalities, multimodal data.” Sobol et al. teaches “acquiring, with numerous sensors that are formed as part of the wearable electronic device, at least one of environmental data, activity data and physiological data.” See Sobol et al., paragraph 0042.
“exposome data.” Sobol et al. teaches “environmental data.” See Sobol et al., paragraph 0042. The quoted “environmental data” is interpreted as the claimed “exposome data.”
“biological data.” Sobol et al. teaches “physiological data.” See Sobol et al., paragraph 0042. The quoted “physiological data” is interpreted as the claimed “biological data.”
“continuously collecting . . . additional multimodal data.” Sobol et al. teaches that the wearable electronic device permits “continuous monitoring” and that monitoring and analysis can take place “in actual user environments, such as a home, assisted living community or the like.” See Sobol et al., paragraph 0117.
“analyzing via multimodal artificial intelligence executing on a server accessible via a network.” Sobol et al. teaches “determining, using a machine learning model, a health condition of the individual” based on data acquired by the wearable electronic device. See Sobol et al., paragraph 0044. Sobol et al. teaches that the machine-learning model “is executed on at least one of the wearable electronic device, a backhaul server and cloud.” See Sobol et al., paragraph 0047.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the methods taught by Sobol et al. with the patient digital twin taught by Zimmerman et al. and Spurlock et al. with the motivation of using the acquired information taught by Sobol et al. to determine a health condition using a machine-learning model, thereby allowing the model to execute on a wearable device, a backhaul server, or cloud. See Sobol et al., paragraphs 0042-0047.
As per Claim 26, Zimmerman et al., Spurlock et al. and Sobol et al. teach:
“The method of claim 25, wherein the exposome data is collected via one or more sensing devices.”
Zimmerman et al. teaches:
“collected via one or more sensing devices.” Zimmerman et al. teaches that “sensors connected to the physical object e.g., the patient 110 can collect data and relay the collected data 120 to the digital twin 130.” See Zimmerman et al., paragraph 0033. Zimmerman et al. further teaches that “medical devices, monitoring devices, biometric sensors, locational sensors, communication systems, collaboration systems, etc., can be used to measure and/or otherwise capture social/environmental information 330.” See Zimmerman et al., paragraph 0052.
Zimmerman et al. fails to explicitly teach:
“the exposome data.”
Sobol et al. teaches:
“the exposome data is collected via one or more sensing devices.” Sobol et al. teaches “acquiring, with numerous sensors that are formed as part of the wearable electronic device, at least one of environmental data, activity data and physiological data.” See Sobol et al., paragraph 0042. The quoted “environmental data” acquired using “numerous sensors” is interpreted as “exposome data” collected via sensing devices.
The obviousness of combining the teachings of Zimmerman et al., Spurlock et al. and Sobol et al. are discussed in the rejection of claim 25 and incorporated herein.
As per Claim 27, Zimmerman et al., Spurlock et al. and Sobol et al. teach:
“The method of claim 25, wherein the cyber-physical system establishes bidirectional data exchange between the digital twin and the subject couple on a continuous or periodic basis.”
Zimmerman et al. teaches:
“the cyber-physical system establishes bidirectional data exchange between the digital twin and the subject couple.” Zimmerman et al. teaches “a physical object in real space,” “a digital twin of that physical object that exists in a virtual space,” “a link for data flow from real space to virtual space,” and “a link for information flow from virtual space to real space and virtual sub-spaces.” See Zimmerman et al., paragraph 0031. Zimmerman et al. further teaches “a patient 110 in a real space 115 providing data 120 to a digital twin 130 in a virtual space 135” and “the digital twin 130 and/or its virtual space 135 provide information 140 back to the real space 115.” See Zimmerman et al., paragraph 0032.
“on a continuous or periodic basis.” Zimmerman et al. teaches “a real-time or substantially real-time” digital description. See Zimmerman et al., paragraph 0033. Zimmerman et al. further teaches “at block 910, a change or scheduled follow-up is initiated” and that “the process 900 can then loop upon the next change to allow the patient digital twin 130 to be updated.” See Zimmerman et al., paragraphs 0055 and 0060. The quoted “real-time or substantially real-time,” “scheduled follow-up,” and looping update disclosures are interpreted as a continuous or periodic basis.
The obviousness of combining the teachings of Zimmerman et al., Spurlock et al. and Sobol et al. are discussed in the rejection of claim 25 and incorporated herein.
As per Claim 28, Zimmerman et al., Spurlock et al. and Sobol et al. teach:
“The method of claim 25, wherein the alternative personalized interventions are generated in response to the subject couple not adhering to one or more of the presented personalized interventions during the specified follow-up period, and wherein the generated alternative personalized interventions target either or both of lifestyle and environmental factors of the subject couple.”
Zimmerman et al. teaches:
“lifestyle . . . factors.” Zimmerman et al. teaches “behavioral choices 340” including “diet 610, exercise 620, alcohol 630, tobacco 640, drugs 650, sexual behavior 660, extreme sports 670, hygiene 680.” See Zimmerman et al., paragraph 0051. The quoted “behavioral choices” are interpreted as the claimed “lifestyle” factors.
“environmental factors.” Zimmerman et al. teaches “environmental factors 330” including “home 710, air 720, water 730, pets 740, chemicals 750, family 760.” See Zimmerman et al., paragraph 0052.
“not adhering.” Zimmerman et al. teaches that the disclosed “social/environmental factors 710-760 can influence patient 110 behavior, health, recovery, adherence to protocol, etc.” See Zimmerman et al., paragraph 0052. The quoted “adherence to protocol” is interpreted as the claim’s “adhering to one or more of the presented personalized interventions.”
“alternative personalized interventions.” Zimmerman et al. teaches “feedback from user experience can be used to generate tips/suggestions, instructions, etc.” and that new data can be used “to update the digital twin 130 and provide one or more recommendations and/or further actions.” See Zimmerman et al., paragraphs 0063 and 0066. The quoted “tips/suggestions, instructions,” “recommendations,” and “further actions” are interpreted as “alternative personalized interventions.”
Zimmerman et al. fails to explicitly teach:
“alternative personalized interventions are generated in response to the subject couple not adhering to one or more of the presented personalized interventions during the specified follow-up period.”
“effectiveness” used to generate the “alternative personalized interventions.”
Spurlock et al. teaches:
“not adhering.” Spurlock et al. teaches that an outcome identified by a machine-learning algorithm may include “treatment compliance.” See Spurlock et al., paragraph 0018. Spurlock et al. further teaches that known patient outcomes may include “treatment compliance” and “known response to a treatment i.e., effectiveness of treatment.” See Spurlock et al., paragraph 0043. The quoted “treatment compliance” is interpreted as the claim’s “adhering to one or more of the presented personalized interventions.”
“alternative personalized interventions.” Spurlock et al. teaches that the method may include “recommending a treatment based in part on the prediction where a certain treatment will only be recommended for patients likely to respond thereto.” See Spurlock et al., paragraph 0044. The quoted “recommending a treatment” based on predicted response is interpreted as generating an alternative personalized intervention.
The obviousness of combining the teachings of Zimmerman et al., Spurlock et al. and Sobol et al. are discussed in the rejection of claim 25 and incorporated herein.
As per Claim 29, Zimmerman et al., Spurlock et al. and Sobol et al. teach:
“The method of claim 25, wherein continuously collecting additional multimodal data related to the subject couple comprises collecting both exposome data and biological data.”
Zimmerman et al. teaches:
“continuously collecting additional multimodal data.” Zimmerman et al. teaches that “data 120” are “dynamically provided from a source e.g., from the patient 110, practitioner, health information system, sensor, etc.” See Zimmerman et al., paragraph 0035. Zimmerman et al. teaches that the digital twin can evolve based on “available health data, machine-learning, human feedback, medical event processing, new or updated digital medical knowledge, and post-event feedback.” See Zimmerman et al., paragraph 0066.
“biological data.” Zimmerman et al. teaches “patient medical record data, image data, genetic information, and historical information,” as well as “genetic data,” “laboratory results,” “genomics,” and “clinical notes.” See Zimmerman et al., paragraphs 0004 and 0044. The quoted disclosures are interpreted as “biological data.”
Zimmerman et al. fails to explicitly teach:
“collecting both exposome data and biological data.”
Spurlock et al. teaches:
“exposome data.” Spurlock et al. teaches “environmental data” and “geographic data.” See Spurlock et al., paragraphs 0015, 0018, and 0052. The quoted “environmental data” and “geographic data” are interpreted as “exposome data.”
“biological data.” Spurlock et al. teaches “genetic data,” “laboratory test results,” “phenotypic data,” “clinical data,” and “laboratory assay results.” See Spurlock et al., paragraphs 0011, 0015, 0018, and 0052. The quoted disclosures are interpreted as “biological data.”
The obviousness of combining the teachings of Zimmerman et al., Spurlock et al. and Sobol et al. are discussed in the rejection of claim 25 and incorporated herein.
As per Claim 30, Zimmerman et al., Spurlock et al. and Sobol et al. teach:
“The method of claim 25, wherein the collected multimodal data and the continuously collected additional multimodal data provide an evidence database from which the multimodal artificial intelligence learns and generates future personalized interventions for the subject couple.”
Zimmerman et al. teaches:
“the collected multimodal data and the continuously collected additional multimodal data.” Zimmerman et al. teaches data including “EMR 210 information, images 220, genetic data 230, laboratory results 240, demographic information 250, social history 260,” “sensor data,” and “location data.” See Zimmerman et al., paragraph 0044. Zimmerman et al. teaches “post-event feedback” from “image analysis, sensor data evaluation, test results, human feedback, etc.” used to update the digital twin. See Zimmerman et al., paragraph 0065.
“an evidence database.” Zimmerman et al. teaches that “some or all information can also be aggregated for population-based health analytics, management, etc.” See Zimmerman et al., paragraph 0045. Zimmerman et al. further teaches “a plurality of health-focused systems 1710-1712” communicating “via a cloud 1720 with a server 1730 and associated data store 1740.” See Zimmerman et al., paragraph 0135. The quoted aggregation and associated data store are interpreted as an “evidence database.”
“multimodal artificial intelligence learns.” Zimmerman et al. teaches that the patient digital twin can use “artificial intelligence e.g., machine learning, deep learning, etc.” and that the twin “can evolve over time based on available health data, machine-learning, human feedback, medical event processing, new or updated digital medical knowledge, and post-event feedback.” See Zimmerman et al., paragraphs 0046 and 0066.
“generates future personalized interventions.” Zimmerman et al. teaches that new information can be “automatically analyzed and used to update the digital twin 130 and provide one or more recommendations and/or further actions based on the twin 130 modeled interactions.” See Zimmerman et al., paragraph 0066. The quoted “recommendations and/or further actions” are interpreted as “future personalized interventions.”
Zimmerman et al. fails to explicitly teach:
That the data are used so that learned associations are added back into the data and the system continues to learn from data including the learned associations.
Spurlock et al. teaches:
“an evidence database from which the multimodal artificial intelligence learns.” Spurlock et al. teaches “discovering via an autonomous machine learning system associations in data from a plurality of data sources obtained from a population and correlating the associations to health status of patients in the population.” See Spurlock et al., paragraph 0013. Spurlock et al. further teaches “adding the discovered associations into the data as events and continuing to discover associations in the data that includes the initially-discovered associations.” See Spurlock et al., paragraph 0013. Spurlock et al. teaches that “the association itself can be added back into the data sources 207 as an entry 203 itself” and the system may “continue to discover 115 other associations in the data 207 that includes the initially-discovered association 311.” See Spurlock et al., paragraph 0039.
“generates future personalized interventions.” Spurlock et al. teaches “recommending a treatment based in part on the prediction where a certain treatment will only be recommended for patients likely to respond thereto.” See Spurlock et al., paragraph 0044. The quoted recommendation based on prediction is interpreted as generating a “future personalized intervention.”
The obviousness of combining the teachings of Zimmerman et al., Spurlock et al. and Sobol et al. are discussed in the rejection of claim 25 and incorporated herein.
As per Claim 31, Zimmerman et al., Spurlock et al. and Sobol et al. teach:
“The method of claim 25, wherein the subject couple comprises one of a man and a woman, a woman and a sperm donor, or a man and an egg donor.”
Zimmerman et al. teaches:
“subject couple.” Zimmerman et al. teaches “patient and family history,” “family and/or friend demographics,” and “family and/or friend input.” See Zimmerman et al., paragraphs 0044–0045 and 0071. Zimmerman et al. also teaches that “the patient digital twin 130 can facilitate collaboration among friends, family, care providers, etc., for the patient 110” and that multiple people can “view, interact with, and draw conclusions from the same digital twin 130.” See Zimmerman et al., paragraph 0040. The quoted family and multiple-people disclosures are interpreted as involving associated persons; Zimmerman et al. does not use the exact claim term “subject couple.”
Zimmerman et al. fails to explicitly teach:
“the subject couple comprises one of a man and a woman, a woman and a sperm donor, or a man and an egg donor.”
Spurlock et al. teaches:
“a man and a woman,” as to biological and genetic-information sources. Spurlock et al. teaches that “genetic data can be obtained, for example, by conducting an assay on a sample from a male or female that identifies variants present within DNA.” See Spurlock et al., paragraph 0051.
“a sperm donor,” as to a sperm biological-information source. Spurlock et al. teaches that body fluids may include “semen.” See Spurlock et al., paragraph 0051. The quoted “semen” disclosure is interpreted as a biological sample that can be obtained from a sperm source.
“an egg donor,” as to female reproductive biological-information sources. Spurlock et al. teaches that body fluids may include “maternal blood,” “amniotic fluid,” “menstrual fluid,” and “follicular fluid of the ovary.” See Spurlock et al., paragraph 0051. The quoted female reproductive biological-sample disclosures are interpreted as information from a female reproductive source.
The obviousness of combining the teachings of Zimmerman et al., Spurlock et al. and Sobol et al. are discussed in the rejection of claim 25 and incorporated herein.
As per Claim 32, Zimmerman et al., Spurlock et al. and Sobol et al. teach:
“The method of claim 25, further comprising a reproductive family health archive, wherein the reproductive family health archive comprises an archive of data collected from each subject couple using the couple-based digital health platform.”
Zimmerman et al. teaches:
“an archive of data.” Zimmerman et al. teaches that data used to create a patient digital twin include “patient medical record data, image data, genetic information, and historical information.” See Zimmerman et al., paragraph 0004. Zimmerman et al. teaches that the patient digital twin includes “patient and family history,” “lab test results,” “prescription information,” “friend and social network information,” “image data,” “genomics,” “clinical notes,” “sensor data,” and “location data.” See Zimmerman et al., paragraph 0044.
“an archive of data collected.” Zimmerman et al. teaches that “data input to the digital twin 130 is processed by an ingestion engine” and that “some or all information can also be aggregated for population-based health analytics, management, etc.” See Zimmerman et al., paragraph 0045. Zimmerman et al. further teaches “a server 1730 and associated data store 1740.” See Zimmerman et al., paragraph 0135. The quoted “aggregated” information and “associated data store” are interpreted as an archive of data.
Zimmerman et al. fails to explicitly teach:
“a reproductive family health archive.”
“an archive of data collected from each subject couple using the couple-based digital health platform.”
Spurlock et al. teaches:
“an archive of data.” Spurlock et al. teaches that types of clinical data include “health records medical records, administrative data, claims data, patient or disease registries, health surveys, clinical trial data, and test results such as clinical laboratory assay results.” See Spurlock et al., paragraph 0025.
“data collected from each subject couple.” Spurlock et al. teaches that “each entry 203 in the data is specific to one patient from the population, and assigned to a pre-defined category.” See Spurlock et al., paragraph 0034. The quoted patient-specific entries are interpreted as records associated with respective persons; Spurlock et al. does not expressly disclose entries collected from “each subject couple.”
The obviousness of combining the teachings of Zimmerman et al., Spurlock et al. and Sobol et al. are discussed in the rejection of claim 25 and incorporated herein.
As per Claim 33, Zimmerman et al., Spurlock et al. and Sobol et al. teach:
“The method of claim 32, wherein the reproductive family health archive further comprises data collected from one or more offspring of the subject couple.”
Zimmerman et al. teaches:
“data collected from one or more offspring.” Zimmerman et al. teaches that “the digital twin 130 of a child patient 110 may be implemented as a child reference digital twin organized according to certain standard or typical child characteristics, with a particular digital twin instance representing the particular child patient 110.” See Zimmerman et al., paragraph 0041. Zimmerman et al. further teaches that “multiple child patients” can be represented by child-digital-twin instances and that a “digital twin aggregate can be used to identify differences, similarities, trends, etc., between children.” See Zimmerman et al., paragraph 0041. The quoted child-patient disclosures are interpreted as data from offspring.
“an archive.” Zimmerman et al. teaches that information may be “aggregated for population-based health analytics, management, etc.” and teaches “a server 1730 and associated data store 1740.” See Zimmerman et al., paragraphs 0045 and 0135.
Zimmerman et al. fails to explicitly teach:
“the reproductive family health archive further comprises data collected from one or more offspring of the subject couple.”
Spurlock et al. teaches:
“data collected from one or more offspring.” Spurlock et al. teaches that a machine-learning system may differentiate between “early-onset forms of a disease e.g., juvenile forms” and “late-onset forms of a disease e.g., adult forms.” See Spurlock et al., paragraph 0018. Spurlock et al. teaches patient-specific data that may include “genetic data,” “laboratory test results,” “phenotypic data,” “environmental data,” “clinical data,” and “treatment data.” See Spurlock et al., paragraphs 0015, 0018, and 0052. The quoted “juvenile forms” and patient-specific data are interpreted as health data for child patients.”
The obviousness of combining the teachings of Zimmerman et al., Spurlock et al. and Sobol et al. are discussed in the rejection of claim 25 and incorporated herein.
As per Claim 34, Zimmerman et al., Spurlock et al. and Sobol et al. teach:
“The method of claim 26, wherein the sensing devices comprise either or both of wearable sensors and at-home monitors.”
Zimmerman et al. teaches:
“at-home monitors.” Zimmerman et al. teaches that “one or more technology sensors can be used to gather patient-related information.” See Zimmerman et al., paragraph 0069. Zimmerman et al. teaches “digital meters, chair sensors, fitness trackers, exercise machines, smart scales, diabetes blood sugar test, and/or other health tracker.” See Zimmerman et al., paragraph 0069. Zimmerman et al. also teaches “home access 530” and “telemedicine access 540.” See Zimmerman et al., paragraph 0050. The quoted technology sensors and home-access disclosures are interpreted as “at-home monitors.”
Zimmerman et al. fails to explicitly teach:
“wearable sensors.”
Sobol et al. teaches:
“wearable sensors.” Sobol et al. teaches “a wearable electronic device” and teaches “acquiring, with numerous sensors that are formed as part of the wearable electronic device, at least one of environmental data, activity data and physiological data.” See Sobol et al., paragraphs 0041–0042. The quoted numerous sensors formed as part of the wearable electronic device are interpreted as “wearable sensors.”
“at-home monitors.” Sobol et al. teaches that the wearable electronic device permits “continuous monitoring” and that the monitoring can take place in “actual user environments, such as a home, assisted living community or the like.” See Sobol et al., paragraph 0117. The quoted home monitoring disclosure is interpreted as “at-home monitors.”
The obviousness of combining the teachings of Zimmerman et al., Spurlock et al. and Sobol et al. are discussed in the rejection of claim 25 and incorporated herein.
Response to Arguments
Applicant's arguments, filed on June 3, 2026 with respect to argument in the remarks, have been considered but are moot in view of the new ground(s) of rejection necessitated by the new claims added to Claims 25-34 21.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pub. No.: US 20250061995 A1: The couple-based digital reproductive health platform system has a server including a reproductive health application, machine learning algorithms, multimodal artificial intelligence (120), reproductive health graphical user interface, operating memory, and a communications interface, where the server is accessible through a network. A data store is provided in communication with the server. A controller is provided to execute stored program instructions for collecting data related to a subject couple (180), analyzing the collected data related to the subject couple, creating a digital copy of the subject couple based on the analyzed collected data, generating personalized interventions (124) or predictive outcomes based on the analyzed collected data related to the subject couple, and tracking results data of the presented personalized interventions or predictive outcomes implemented by the subject couple.
WO 2020247498 A1: The computer-based method involves collecting patient and caregiver dyadic data by multiple computer devices associated with a health system (130), such that the patient and caregiver dyadic data is received from a patient and a caregiver user computing device (110, 120). The cancer or non-cancer pain events data or cancer-related or other disease-related symptom events data of the patient (101) is received. The patient and caregiver dyadic data is related to the cancer or non-cancer pain events data or cancer-related or other disease-related symptom events data of patient. The real- time personalized intervention information is generated for the patient and caregiver (103) based on the relation, and the real-time personalized intervention information is communicated to the patient user computing device and caregiver user computing device for appropriate action to be undertaken, to anyone or more of the following: the caregiver, the patient, or both the caregiver and patient.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD B WINSTON III whose telephone number is (571)270-7780. The examiner can normally be reached M-F 1030 to 1830.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.B.W/ Examiner, Art Unit 3683
/ROBERT W MORGAN/ Supervisory Patent Examiner, Art Unit 3683
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