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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/20/2026 has been entered.
Status of the Application
Claims 1-27 are currently pending in this case and have been examined and addressed below.
Claim 1 is currently amended.
Claim 27 is added.
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.
Independent claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Claim 1 is drawn to a process. As such, independent claim 1 is drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Independent Claim 1: A method for automatically providing guidance to a patient in real-time, comprising:
receiving multiple input data streams at a server processing system, wherein the multiple input data streams include a stream of current medical data for a patient, a stream of personal preference data for the patient, a stream of current situational data for the patient, and one or more streams of current environmental characterization data relevant to the patient, wherein the stream of current medical data conveys a current health condition of the patient;
executing an artificial intelligence model at the server processing system to automatically generate a recommendation for the patient in real-time based on the multiple input data streams, wherein the recommendation for the patient is based on interaction outcomes of multi-variate patient treatment functions that require execution of the artificial intelligence module to identify and characterize in real-time, wherein the recommendation for the patient facilitates mitigation of one or more of an adverse condition and an adverse situation associated with the patient;
automatically determining whether or not the recommendation for the patient as generated by the artificial intelligence model is compliant with the stream of personal preference data for the patient;
upon automatically determining that the recommendation for the patient is compliant with the stream of personal preference data for the patient, automatically conveying the recommendation from the server processing system to a client computing system of the patient to provide for implementation of the recommendation before occurrence of the adverse condition or the adverse situation associated with the patient;
and upon determining that the recommendation for the patient is not compliant with the stream of personal preference data for the patient, quarantining the recommendation for the patient for a manual review.
(Examiner notes: The above claim terms underlined are additional elements that fall under Step 2A - Prong Two analysis section detailed below)
These steps amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or behaviors; and managing human mental activity (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people). Therefore, the concepts of receiving multiple input data streams, generating a recommendation, determining whether or not the recommendation is compliant with input data streams, conveying the recommendation when the recommendation is deemed compliant, and quarantining the recommendation for manual review when the recommendation is deemed as non-compliant are directed to managing personal interactions or personal behavior.
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
Claim 1 recites the use of a server processing system, in this case to receive multiple input data streams. The claim also recites a client computing system, in this case to receive the recommendation. The server processing system and the client computing system are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer MPEP § 2106.05(f)(2). The claim further recites the use of executing an artificial intelligence model, in this case to automatically generate a recommendation only recites the artificial intelligence model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) amounting to instruction to implement the abstract idea using a general purpose computer.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception.
As discussed above in “Step 2A – Prong 2”, the identified additional elements, such as server processing system, executing an artificial intelligence model, and client computing system in independent claim 1 are equivalent to adding the words “apply it” on a generic computer. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the computer and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”). Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements are directed to generic computer component and functions being used to perform the abstract idea.
Applicant’s own disclosure in paragraph [0036] “the PGS 100 interfaces with one or more other data processing/computing systems that have information relative to the patient 101. For example, in some embodiments, the PGS 100 interfaces with one or more of a home security system, a remote monitoring camera system, a home automation system, an automobile, a remote patient monitoring device, a medical device, an in-home air monitoring device, a wearable air monitoring device, an in-home appliance, an environment control system (e.g., thermostat, humidifier, de-humidifier, air filter, etc.), among essentially any other device/system that is associated with the patient 101 and that is capable of data communication with the data acquisition system 103 of the PGS 100”. Additionally, paragraph [0086] acknowledges that “the present invention may be practiced with various computer system configurations including servers, cloud systems, hand-held devices, microprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers and the like. The invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a wire-based or wireless network”. Paragraph [0055] discloses the “various types of machine learning algorithms that can be utilized to form and improve the personalized patient AI model 107-1. In some embodiments, the deep learning engine 105 utilizes methods associated with supervised learning, unsupervised learning, and/or reinforced learning, as known in the art of machine learning (artificial intelligence)”. Furthermore, paragraph [0039] acknowledges PGS 100 is a machine learning system that implements AI to consume the multiple input data streams 151-1 to 151-N and creatively and automatically generate output in real-time that is beneficial to the health and well-being of the patient 101, where the output takes the form of recommendations, coaching, and/or information”. Paragraphs [0025] and [0076] discloses “the natural language processor 109 itself is implemented by one or more AI models…and… the natural language processor 109 is implemented by an artificial intelligence model”. Also, paragraph [0078] discloses “a graphical user interface, e.g., the user interface 300, configured for display on a computing system of the patient”.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, independent claim 1 is not eligible subject matter under 35 USC 101.
Similarly to the independent claim 1, its dependent claims 2-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: As for the dependent claims 2-27, the claims are drawn to a process, as their independent claim. Therefore, similarly to the independent claims, the dependent claims are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
The dependent claim 2 is directed to the current health condition.
The dependent claim 3 is directed to the current medical data which includes current body temp, current heart rate, current respiration rate, current blood pressure, fetal heart rate, blood oxygen saturation level, or an electrocardiogram.
The dependent claim 4 is directed to current medical data which includes a current bodyweight and current body measurements.
The dependent claim 5 is directed to current medical data which includes a current medical diagnosis.
The dependent claim 6 is directed to current medical data which includes a current image of body parts.
The dependent claim 7 is directed to the stream of current situational data which includes a current location.
The dependent claim 8 is directed to the stream of current situational data which includes a current listing of calendared events.
The dependent claim 9 is directed to the stream of current situational data which includes a current daily schedule.
The dependent claim 10 is directed to the stream of current situational data which includes an activity being performed.
The dependent claim 11 is directed to the stream of current environmental characterization data which includes an outdoor temperature value, a humidity value, a barometric pressure value, an air quality index value, a value for particulate matter sized at less than or equal to about 2.5 micrometers, a heat index value, a wind speed value, a wind direction, a visibility distance value, or an insect/animal vector distribution.
The dependent claim 12 is directed to the stream of current environmental characterization data which includes air quality measurements within a current vicinity.
The dependent claim 13 is directed to the stream of current environmental characterization data which includes air quality measurements along an anticipated travel route.
The dependent claim 14 is directed to case data being used for training.
The dependent claim 15 is directed to support bi-directional communication.
The dependent claim 16 is directed to articulating the recommendation.
The dependent claim 18 is directed to moderate the automatic generation of the recommendation, receive a current profile that specifies preferences, and ensure the recommendation is conveyed.
The dependent claim 19 is directed to provide feedback.
The dependent claim 20 is directed to the preferences which includes budget sensitivity, time restrictions, sleep patterns, dietary preferences, meal times, exercise preferences, entertainment preferences, working hours, work location, travel preferences, travel times, communication preferences, restaurant preferences, grocer preferences, or wellness provider preferences.
The dependent claim 21 is directed to connecting data from the multiple data streams.
The dependent claim 22 is directed to display the recommendation.
The dependent claim 23 is directed to provide bi-directional communication.
The dependent claim 24 is directed to automatically identify a condition or situation that will have an adverse impact when left unmitigated and suggest a recommendation that will mitigate the condition or situation.
The dependent claim 25 is directed to automatically identify an action that will have a beneficial impact when performed and generate a recommendation encourage the action.
The dependent claim 26 is directed to automatically identify information for conveyance and generate a recommendation to convey the information.
The dependent claim 27 is directed to automatically determining whether or not the recommendation for the patient is complaint with the stream of personal preference data for the patient includes performing a probabilistic confidence assessment to determine a confidence level that the recommendation is appropriate for conveyance to the patient, and requiring the confidence level to meet or exceed a specified confidence level threshold value to allow conveyance of the recommendation to the patient.
Each of these steps of the preceding dependent claims 2-27 only serve to further limit or specify the features of independent claim 1 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
As such, the Examiner concludes that the preceding claims recite an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two:
Claims 14, 17-19, and 26-27 recite the use of an artificial intelligence model, in this case to automatically generate a recommendation, trained with case data, only recites the artificial intelligence model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) amounting to instruction to implement the abstract idea using a general purpose computer.
Claims 15 and 23 recite the use of a patient guidance system, only as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) amounting to instruction to implement the abstract idea using a general purpose computer.
Claims 15-17 recite the use of a natural language processor, in this case to conduct bi-directional communication and articulate the recommendation, only recites the natural language processor as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 21 recites the use of a one or more applications executing on a computing device, only as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2).
Claim 22 recites the use of a graphical user interface on a computing system, in this case to display the recommendation, only recites the graphical user interface on a computing system as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B:
As discussed above in “Step 2A – Prong 2”, the identified additional elements, such as the artificial intelligence model, patient guidance system, natural language processor, one or more applications executing on a computing device, and graphical user interface on a computing system dependent claims 2-27 are equivalent to adding the words “apply it” on a generic computer. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the computer and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”). Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements are directed to generic computer component and functions being used to perform the abstract idea.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, dependent claims 1-27 are not eligible subject matter 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 1-3, 5-14, and 21-26 are rejected under 35 U.S.C. 103 as being unpatentable over Bitran (US-20170039344-A1)[hereinafter Bitran], in view of Patton (US-20200163727-A1)[hereinafter Patton].
As per Claim 1, Bitran discloses a method for automatically providing guidance to a patient in real-time in paragraphs [0003] and [0015] and [0047] (a method for a health recommender (Examiner notes that the health recommender provides guidance to the patient in regards to treating a health condition)), comprising: receiving multiple input data streams at a server processing system, wherein the multiple input data streams include a stream of current medical data for a patient, a stream of personal preference data for the patient, a stream of current situational data for the patient, and one or more streams of current environmental characterization data relevant to the patient, wherein the stream of current medical data conveys a current health condition of the patient in paragraphs [0015] and [0017] and [0019-0020] and [0022-0023] (receives user synonymous to patient data at a computing system (synonymous to a server processing system), wherein the medical data includes the user's electronic medical record which consists of the preexisting medical conditions of the user, inferred data (referring to the personal preference data of the patient), non-medical data (referring to the current environmental characterization data relevant to the patient), and geolocation data (referring to the current situational data of the patient)); executing an artificial intelligence model at the server processing system to automatically generate a recommendation for the patient in real-time based on the multiple input data streams in paragraphs [0035] and [0037] (a Bayesian machine-learning algorithm (synonymous to the artificial intelligence model) at the computing system to determine a health recommendation based on user's electronic medical record, identified health condition, time and location-based data, and health insurance information, wherein these all come from the user data), wherein the recommendation for the patient facilitates mitigation of one or more of an adverse condition and an adverse situation associated with the patient in paragraph [0038] (the recommendation for the user includes staying indoors using supplemental oxygen, using indoor air filter, and increasing nebulizer use to treat the worsening asthma (synonymous to an adverse situation) of the user (Examiner notes that the recommendations listed facilitates mitigation in the worsening asthma, wherein the worsening asthma is considered to be an adverse situation associated with the patient. Also, examiner notes that the adverse situation meets the one or more limitations)); automatically determining whether or not the recommendation for the patient as generated by the artificial intelligence model is compliant with the stream of personal preference data for the patient in paragraphs [0035] and [0039] (determining whether the recommendation generated by Bayesian machine-learning algorithm is compliant with the individual preferences (Examiner notes that the health recommender automatically determines if the recommendation of medical services are in network based on the individual preferences (synonymous to the recommendation being compliant with the personal preference data)); upon automatically determining that the recommendation for the patient is compliant with the stream of personal preference data for the patient, automatically conveying the recommendation from the server processing system to a client computing system of the patient to provide for implementation of the recommendation before occurrence of the adverse condition or the adverse situation associated with the patient in paragraphs [0035] and [0039] and [0056] (upon determining that the recommendation of medical services is within network based on individual preferences, presenting the recommendation from the from the computing system to a computing device of the user to recommend the user to the appropriate healthcare professionals before an adverse situation occurs); and upon determining that the recommendation for the patient is not compliant with the stream of personal preference data for the patient, quarantining the recommendation for the patient for a manual review in paragraph [0039] (upon determining that the recommendation of medical services is out of network based on individual preferences, the excluded results of the recommended medical services can be viewed once selected (Examiner notes that excluding the recommendations of medical services out of network indicates that the recommendations were quarantined for manual review)).
Bitran discloses generating a recommendation but does not disclose the recommendation being based on interaction outcomes of multivariate patient treatment functions in order to be identified and characterized in real-time. However, Patton discloses wherein the recommendation for the patient is based on interaction outcomes of multi-variate patient treatment functions that require execution of the artificial intelligence module to identify and characterize in real-time in paragraphs [0015] and [0042] (a treatment recommendation for the patient is based the probable results (synonymous to the interaction outcome) of different treatment options (synonymous to multi-variate patient treatment functions) executed by an algorithm (synonymous to an artificial intelligence module) to identify and characterize in real time).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for automatically providing guidance to a patient in real-time, as disclosed by Bitran, to be combined with the recommendation being based on interaction outcomes of multi-variate patient treatment function, as disclosed by Patton, for the purpose of generating optimal treatment outcomes [0004-0005].
As per Claim 2, Bitran and Patton disclose the method as recited in claim 1, Bitran also discloses wherein the current health condition of the patient is one or more of a woman trying to conceive, a woman that is currently pregnant, and a woman that is within two years postpartum in paragraphs [0035-0036] (the health condition of the user, wherein the health condition determines the type of recommendation, may include women that are pregnant (Examiner notes that women who are pregnant meets the one or more limitations of the current health condition)).
As per Claim 3, Bitran and Patton disclose the method as recited in claim 2, Bitran also discloses wherein the current medical data for the patient includes one or more of a current body temperature, a current heart rate, a current respiration rate, a current blood pressure, a fetal heart rate, a blood oxygen saturation level, and an electrocardiogram in paragraph [0019] (the medical data may comprise of user's electronic medical record, biometric data, wherein biometric data includes heart rate, blood pressure, and body temperature, and medical device data (Examiner notes that the heart rate, blood pressure, and body temperature meets the one or more limitations of medical data)).
As per Claim 5, Bitran and Patton disclose the method as recited in claim 3, Bitran also discloses wherein the current medical data for the patient includes a current medical diagnosis in paragraphs [0023] and [0027] and [0034-0037] (the user's electronic medical record includes current medications, allergies, preexisting medical conditions, wherein the medical conditions can describe diseases and syndromes and their associated symptoms and signs, past medical screenings and procedures, past hospitalizations and visits (Examiner notes that the patient's medical history, symptoms, and signs are factors of a current medical diagnosis)).
As per Claim 6, Bitran and Patton disclose the method as recited in claim 3, Bitran also discloses wherein the current medical data for the patient includes a current image of one or more body parts in paragraph [0031] (medical data may also include a picture of a skin lesion to be identified at a later time (Examiner notes that the picture skin lesion on the user's body is an example of an image of a body part)).
As per Claim 7, Bitran and Patton disclose the method as recited in claim 2, Bitran also discloses wherein the stream of current situational data for the patient includes a current location of the patient in paragraph [0017] (geolocation data includes GPS coordinate data, wherein the coordinate data includes time stamp, latitude, longitude, and altitude, that is obtained by a GPS receiver on a computing device).
As per Claim 8, Bitran and Patton disclose the method as recited in claim 2, Bitran also discloses wherein the stream of current situational data for the patient includes a current listing of calendared events for the patient in paragraphs [0059] and [0062] (the user's geolocation data is used to provide a recommended health service based on the user's predicted location during an available timeslot in the future according to the user's calendar (Examiner notes that the predicted location during an available time slot shows the scheduled events including time and location, wherein time and location are included in geolocation data, that are in the patient's calendar)).
As per Claim 9, Bitran and Patton disclose the method as recited in claim 2, Bitran also discloses wherein the stream of current situational data for the patient includes a current daily schedule for the patient in paragraphs [0046] and [0059] and [0062] (based on the user's geolocation data, the health recommender notes that the user has a busy schedule which is contributing to a lack of sleep and then recommends a schedule change (Examiner notes that a busy schedule shows that the user had many activities or events planned, wherein the planned events/activities include time stamps and specific locations which are included in geolocation data, throughout the day, week, or for an accumulated amount of time)).
As per Claim 10, Bitran and Patton disclose the method as recited in claim 2, Bitran also discloses wherein the stream of current situational data for the patient includes an activity currently being performed by the patient in paragraphs [0015-0016] and [0053] (geolocation data received from the computing device, wherein the computing device may be a smart phone, tablet computing device, a wearable computing device, a personal computer or a computerized medical device, includes the geographic location and the velocity of the user (Examiner notes that the velocity of the user describes if the user is actively moving or not and the user's speed and direction)).
As per Claim 11, Bitran and Patton disclose the method as recited in claim 2, Bitran also discloses wherein the one or more streams of current environmental characterization data includes one or more of an outdoor temperature value, a humidity value, a barometric pressure value, an air quality index value, a value for particulate matter sized at less than or equal to about 2.5 micrometers, a heat index value, a wind speed value, a wind direction, a visibility distance value, and an insect/animal vector distribution in paragraph [0018] (the non-medical data may comprise of weather data (Examiner notes that National Oceanic and Atmospheric Administration, NOAA, considers weather data to include temperature, humidity, wind speed and direction, and atmospheric pressure. Also, the weather data meets the one or more limitation of current environmental characterization data)).
As per Claim 12, Bitran and Patton disclose the method as recited in claim 2, Bitran also discloses wherein the one or more streams of current environmental characterization data includes one or more air quality measurements within a current vicinity of the patient in paragraphs [0018] and [0038] (the non-medical data may comprise of air quality measurements, wherein air quality includes air pollen and pollutant concentrations, in the vicinity of the user).
As per Claim 13, Bitran and Patton disclose the method as recited in claim 12, Bitran also discloses wherein the one or more streams of current environmental characterization data includes one or more air quality measurements along an anticipated travel route of the patient in paragraphs [0018] and [0038] (the non-medical data may comprise of air quality measurements, wherein air quality includes air pollen and pollutant concentrations which may be displayed on an interactive map showing the temporal and geographic distribution (Examiner notes that temporal and geographic distribution shows how the air pollen and pollutant concentration change over time in a geographical area, wherein the geographical area includes travel routes)).
As per Claim 14, Bitran and Patton disclose the method as recited in claim 1, Bitran also discloses further comprising: using case data for a population of patients to train the artificial intelligence model in paragraphs [0021] and [0025-0026] and [0035-0037] (the machine learning algorithm is informed and modified over time based on context information (referring to the case data), wherein the information is the combined time and location-based data, wherein the combined data is a global aggregated time and location-based history that includes the time and location-based history correlated in the first and second correlator, wherein the correlators correlate a plurality of medical and non-medical data from an user population), wherein the case data for a given patient within the population of patients includes actions taken and corresponding outcomes as a function of time in paragraphs [0033-0034] (the combined time and location-based data for a user includes past medical history, medications, past hospitalizations, family history, social history, occupational history, and environmental history (Examiner notes that the user's personal and medical history corresponds to the actions taken and corresponding outcomes as a function of time. For example, the reference discloses that if a patient reports shortness of breath, the patient's combined time and location-based data may be evaluated to correlate the shortness of breath with the patient's asthma, which was affected due to the recent environmental history. In the evaluation, the shortness of breath can also be associated with the time point when the patient started a medication (referring to the action taken in response to having asthma) and an inference of an adverse effect (referring to the corresponding outcome as a function of time due to the patient having a shortness of breath after taking the medication for a period of time))), the case data for the given patient also including one or more of the multiple input data streams for the given patient as a function of time during periods of time relevant to the actions taken and corresponding outcomes present in the case data for the given patient in paragraphs [0021] and [0025-0026] and [0033-0037] (the combined time and location-based data for a user includes a plurality of medical and non-medical data, wherein the data is associated with time-stamped geolocation data, that is relevant to the user's past medical history, medications, past hospitalizations, family history, social history, occupational history, and environmental history).
As per Claim 21, Bitran and Patton disclose the method as recited in claim 1, Bitran also discloses wherein one or more of the multiple input data streams are received from one or more applications executing on a computing device of the patient in paragraphs [0018] and [0020] (the personal assistant interpretation engine receives user data from a search application, and an electronic personal assistant application program executed on the user computing device, wherein the applications provide user data including medical, non-medical, and geolocation data to the personal assistant interpretation engine).
As per Claim 22, Bitran and Patton disclose the method as recited in claim 1, further comprising: Bitran also discloses directing display of a graphical user interface on a computing system of the patient, the graphical user interface including a region for displaying the recommendation for the patient in real-time in paragraphs [0046] and [0055] and [0074] (a graphical user interface displays on the computing device of the user, an area for making health recommendations).
As per Claim 23, Bitran and Patton disclose the method as recited in claim 22, Bitran also discloses wherein the region provides for bi-directional communication between a patient guidance system and the patient in paragraphs [0015-0016] and [0030] (user feedback is solicited in regards to the effectiveness of the recommendation, which the feedback is then transmitted to the electronic personal assistant application server, wherein the server is a part of the computer system's server system, which decides the type of recommendation that is sent to the computing device of the user (Examiner notes that the computing system's program soliciting user feedback to the recommendation is an example of bi-directional communication)).
As per Claim 24, Bitran and Patton disclose the method as recited in claim 1, further comprising: Bitran also discloses executing the artificial intelligence model to automatically identify a condition or a situation that will adversely impact the patient when left unmitigated, wherein the recommendation for the patient is generated to suggest an action by the patient that will mitigate the condition or the situation in paragraphs [0035] and [0037-0038] (the machine learning algorithm located in the health recommender is configured to identify the worsening asthma (referring to a condition that will adversely impact the patient when left unmitigated) of the user and output a recommendation to stay indoors, use supplemental oxygen, use indoor air filter, and increase nebulizer use (Examiner notes that the recommended treatments will mitigate the worsening asthma)).
As per Claim 25, Bitran and Patton disclose the method as recited in claim 1, further comprising: Bitran also discloses executing the artificial intelligence model to automatically identify an action that will beneficially impact the patient when performed, wherein the recommendation for the patient is generated to encourage performance of the action by the patient in paragraphs [0037] and [0039] (the machine learning algorithm located in the health recommender may advise a patient with a mild, self-limiting headache to try an NSAID medication at home (Examiner notes that based on the symptoms and signs or user data received, wherein the symptom and sign was the mild, self-limiting headache, the algorithm identified an action that would beneficially impact the patient which would treat the headache)).
As per Claim 26, Bitran and Patton disclose the method as recited in claim 1, further comprising: Bitran also discloses executing the artificial intelligence model to automatically identify information for conveyance to the patient, wherein the recommendation for the patient is generated to convey the identified information in paragraphs [0036-0037] (the machine learning algorithm located in the health recommender is configured to identify a health condition, differential diagnoses, individuals with symptoms related to the flu epidemic and will output this information alongside the recommendation to the user).
Claims 4 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bitran (US-20170039344-A1)[hereinafter Bitran], in view of Patton (US-20200163727-A1)[hereinafter Patton], in view of Aranke (US-11830623-B1)[hereinafter Aranke].
As per Claim 4, Bitran and Patton disclose the method as recited in claim 3.
Bitran and Patton do not disclose the following limitations. However, Aranke discloses wherein the current medical data for the patient includes a current body weight and one or more current body measurements in column 15 lines 12-42 (clinical data (referring to the current medical data) includes patient-physician encounter data, wherein the encounter data includes the patient's weight and height).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for automatically providing guidance to a patient in real-time, as disclosed by Bitran and Patton, to be combined with including body weight and measurements in medical data, as disclosed by Aranke, for the purpose of providing a way to improve managing health conditions [column 1 lines 18-52].
As per Claim 18, Bitran and Patton disclose the method as recited in claim 1, further comprising: Bitran also discloses receiving a current profile for the patient that specifies personal preferences of the patient in paragraphs [0020] and [0022] (the personal assistant interpretation engine receives user data for the user profile which includes inferred data (referring to the personal preferences of the patient)).
Bitran and Patton do not disclose the following limitations. However, Aranke discloses and moderating the artificial intelligence model with regard to automatic generation of the recommendation for the patient to ensure that the recommendation for the patient is compatible with the current profile for the patient in column 14 lines 12-40 and column 16 lines 8 - 59 and column 19 line 23-column 20 line 29 (the machine learning engine provides curated data inputs, performs training on the machine learning models using the curated data inputs, and approves and publishes the machine learning models, wherein the curated data inputs come from the user profile (referring to the current profile for the patient) and the machine learning models are used to determine and generate real-time recommendations of actionable interventions by the total health index engine and the condition management engine which are both communicatively coupled to the processor that displays the recommendation to the user (Examiner notes that the machine learning engine, wherein the engine trains the machine learning models on the data used in the user profile, going through the process of approving and publishing the machine learning models is an method of the engine ensuring the recommendation is compatible with the current profile for the patient, wherein the machine learning models determine and generate the recommendations)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for automatically providing guidance to a patient in real-time, as disclosed by Bitran and Patton, to be combined with moderating the artificial intelligence model for automatic generation of the recommendation, as disclosed by Aranke, for the purpose of providing a way to improve managing health conditions [column 1 lines 18-52].
As per Claim 19, Bitran, Patton, and Aranke disclose the method as recited in claim 18.
Bitran and Patton do not disclose the following limitations. However, Aranke discloses further comprising: providing feedback into the artificial intelligence model, the feedback based on the moderating in column 16 line 8 - column 17 line 47 (the machine learning engine may provide label identification hints and patterns, perform training, wherein the training may include supervised learning, approval and publish model versions, perform scoring model parameter tuning, or create scoring accuracy thresholds for the machine learning model (Examiner notes that feedback is provided into the artificial intelligence model through supervised learning)).
As per Claim 20, Bitran, Patton, and Aranke disclose the method as recited in claim 18, Bitran also discloses wherein the personal preferences of the patient include one or more of budget sensitivity, time restrictions, sleep patterns, dietary preferences, meal times, exercise preferences, entertainment preferences, working hours, work location, travel preferences, travel times, communication preferences, restaurant preferences, grocer preferences, and wellness provider preferences in paragraph [0022] (the inferred data includes social history, wherein social history includes occupation and living conditions, and health maintenance information, wherein the health maintenance information includes exercise habits, diet information, sleep data, therapy and counseling history, and health provider preferences (Examiner notes that the inferred data meets the one or more limitations of the preferences)).
Claims 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Bitran (US-20170039344-A1)[hereinafter Bitran], in view of Patton (US-20200163727-A1)[hereinafter Patton], in view of Leonard (US-20160321415-A1)[hereinafter Leonard].
As per Claim 15, Bitran and Patton disclose the method as recited in claim 1.
Bitran and Patton do not disclose the following limitations. However, Leonard discloses further comprising: conducting bi-directional communication between a patient guidance system and the patient without human intervention through operation of a natural language processor in paragraphs [0060] and [0062-0063] and [0070] (the system begins to automatically listen when it is detected the patient is in a clinical conversation based on sensing, subsequently the system interprets the conversation using natural language processing techniques and generates the summary, wherein the summary includes follow up actions (Examiner notes that the interpretation of the conversation which occurs due to the natural language processing techniques and the summary generated is an example of a natural language processor supporting bi-directional communication between the system and patient without human intervention)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant's invention of a method for automatically providing guidance to a patient in real-time, as disclosed by Bitran and Patton, to be combined with conducting bi-directional communication through a natural language processor, as disclosed by Leonard, for the purpose of automatically generating a summary of the interaction and follow up actions in order to decrease the odds of misunderstanding the discussed medical information and improve the impact on healthcare outcomes and costs [0001] and [0007].
As per Claim 16, Bitran, Patton, and Leonard disclose the method as recited in claim 15.
Bitran and Patton do not disclose the following limitations. However, Leonard discloses wherein the recommendation for the patient is articulated by the natural language processor in paragraphs [0060] and [0062-0063] and [0070] (the follow up actions are generated once the natural language processing techniques interpret the conversation).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant's invention of a method for automatically providing guidance to a patient in real-time, as disclosed by Bitran and Patton, to be combined with the recommendation being articulated by the natural language processor, as disclosed by Leonard, for the purpose of automatically generating a summary of the interaction and follow up actions in order to decrease the odds of misunderstanding the discussed medical information and improve the impact on healthcare outcomes and costs [0001] and [0007].
As per Claim 17, Bitran, Patton, and Leonard disclose the method as recited in claim 15.
Bitran and Patton do not disclose the following limitations. However, Leonard discloses wherein the natural language processor is implemented by the artificial intelligence model in paragraph [0070] (the natural language processing techniques are a part of the artificial intelligence module (referring to the artificial intelligence model)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant's invention of a method for automatically providing guidance to a patient in real-time, as disclosed by Bitran and Patton, to be combined with the natural language processor being implemented by the artificial intelligence model, as disclosed by Leonard, for the purpose of automatically generating a summary of the interaction and follow up actions in order to decrease the odds of misunderstanding the discussed medical information and improve the impact on healthcare outcomes and costs [0001] and [0007].
Claim 27 are rejected under 35 U.S.C. 103 as being unpatentable over Bitran (US-20170039344-A1)[hereinafter Bitran], in view of Patton (US-20200163727-A1)[hereinafter Patton], in view of Allen (US-20180082030-A1)[hereinafter Allen].
As per Claim 27, Bitran and Patton disclose the method as recited in claim 1, wherein automatically determining whether or not the recommendation for the patient as generated by the artificial intelligence model is compliant with the stream of personal preference data for the patient.
Bitran and Patton do not disclose the following limitations. However, Allen discloses performing a probabilistic confidence assessment to determine a confidence level that the recommendation as generated by the artificial intelligence model is appropriate for conveyance to the patient, and requiring the confidence level to meet or exceed a specified confidence level threshold value to allow conveyance of the recommendation as generated by the artificial intelligence model to the patient in paragraphs [0118] and [0133] and Figure 5 (determining a confidence score that the treatment recommendation is valid for the patient, and requiring the confidence score to meet the threshold in order for the treatment recommendation to be presented to the patient).
It would have been obvious to one of ordinary still in the art to include in the method for automatically providing guidance to a patient in real-time of Bitran and Patton with determining a confidence level that the recommendation is appropriate for conveyance to the patient and requiring the confidence level to meet or exceed a specified confidence level threshold value to allow the recommendation to be conveyed to the patient as taught by Allen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably a method for automatically providing guidance to a patient in real-time that determines a confidence level that the recommendation is appropriate for conveyance to the patient and requiring the confidence level to meet or exceed a specified confidence level threshold value to allow the recommendation to be conveyed to the patient.
Response to Arguments
Applicant’s arguments, see Page 8, “Rejections under 35 U.S.C. 112”, filed 01/20/2026, with respect to claim 1 have been fully considered and are persuasive. The claim rejection of claim 1 under 35 U.S.C. 112 has been withdrawn.
Applicant's arguments, see Pages 9-15, “Rejections under 35 U.S.C. 101”, filed 01/20/2026 with respect to claims 1-26 have been fully considered but they are not persuasive.
Applicant argues that the amended claim 1 does not recite an abstract idea. Examiner respectfully disagrees. The amended claim limitations are directed to receiving and analyzing data. The limitations merely recite receiving multiple input data streams, generating a recommendation, determining whether or not the recommendation is compliant with input data streams, conveying the recommendation when the recommendation is deemed compliant, and quarantining the recommendation for manual review when the recommendation is deemed as non-compliant, which are activities performed by medical staff, which falls into the abstract grouping of certain methods of organizing human activity because it is the business relations of medical staff and patients. Additionally, the claim limitations involve managing personal behaviors or interactions between people.
Applicant argues that a sufficiently clear and specific explanation has not been provided as to why claim is rejected under 35 U.S.C. 101. Examiner respectfully disagrees. In addition to the amended claim limitations reciting an abstract idea, the claim does not provide an improvement to technology. The claims merely recite receiving multiple input data streams, generating a recommendation, determining whether or not the recommendation is compliant with input data streams, conveying the recommendation when the recommendation is deemed compliant, and quarantining the recommendation for manual review when the recommendation is deemed as non-compliant, which are a part of the abstract idea. An improvement to the abstract ideas of receiving multiple input data streams, generating a recommendation, determining whether or not the recommendation is compliant with input data streams, conveying the recommendation when the recommendation is deemed compliant, and quarantining the recommendation for manual review when the recommendation is deemed as non-compliant does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(II) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology."). The courts indicated in TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48, that gathering and analyzing information using conventional techniques and providing the output is not sufficient to show an improvement to technology. The claim language and instant application fails to provide details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Here, the improvement is to receiving multiple input data streams, generating a recommendation, determining whether or not the recommendation is compliant with input data streams, conveying the recommendation when the recommendation is deemed compliant, and quarantining the recommendation for manual review when the recommendation is deemed as non-compliant. There is no indication in the disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Merely adding generic computer components to perform the method is not sufficient. Furthermore, the claims are not directed to something significantly more than the idea itself. The use of the server processing system, artificial intelligence model, client computing system, patient guidance system, natural language processor, one or more applications executing on a computing device, and graphical user interface on a computing system to carry out the steps of the abstract idea is merely applying the abstract idea to general purpose computer components which amounts to mere instructions to apply the exceptions, see MPEP 2106.05(f)(2). The courts indicated in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984, that “a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer” is not enough to qualify as significantly more.
Applicant's arguments, see Pages 14-15, “Double Patenting”, filed 01/20/2026 with respect to claims 1-26 have been fully considered.
Applicant argues that the claims of the subject application and the claims of co-pending U.S. Patent Application No. 18/307, 752 are subject to change as each of the patent applications is prosecuted to issuance. Therefore, the double patenting rejection has been withdrawn due to the amended claim limitations of the subject application.
Applicant’s arguments, see Pages 16-19, “Rejections under 35 U.S.C. 103” filed 01/20/2026 with respect to claims 1-26 have been fully considered.
With regards to claims 1-3, 5-14, and 21-26, Applicant argues that Bitran and Patton do not teach or suggest all of the features of amended claim 1 to render the claim prima facie obvious under 35 U.S.C. 103. Examiner respectfully disagrees and points Applicant to the updated rejection and citations in the 103 rejections above. In response to the argument that the references do not teach an artificial intelligence model to provide for real-time identification and characterization of interaction outcomes of multi-variate patient treatment functions, Examiner respectfully disagrees. Patton discloses in [0042] an optimal treatment recommendation generated by machine learning algorithm that is based on the probable results of the one or more suggested treatment plans with a high probability of success, wherein the machine learning algorithm identifies and characterizes the treatment plans in real-time in order to determine the optimal treatment plan out of the multi-variate suggested treatment plans. As per the rejections of claims 4, 15-17, and 18-20, Applicant argues that the dependent claims are patentable for the same reason as its independent claim. Examiner respectfully disagrees and points Applicant to the updated rejection and citations in the 103 rejections above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fraenkel L. “Incorporating patients' preferences into medical decision making” (2013) teaches on the importance on including patients’ preferences in the decision making process in regards to determining patient care.
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/K.N.W./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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