Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Application
Claims 1-26 are currently pending in this case and have been examined and addressed below. This communication is a Final Rejection in response to the Amendments to the Claims and Remarks filed on 10/08/2025.
Claims 1-26 are currently amended.
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: Independent 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 operating a patient guidance system, comprising:
operating a data acquisition engine to receive multiple input data streams, wherein the multiple input data streams include a current profile for the patient that specifies personal preferences of the patient, a stream of current medical data for a 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 current profile for the patient includes a desired coaching intensity profile setting that specifies an intensity level at which the patient desires to receive coaching from the patient guidance system, wherein the stream of current medical data conveys a current health condition of the patient;
operating an artificial intelligence model to automatically generate a recommendation for the patient in real-time based on the multiple input data streams;
operating a moderator engine to apply a probabilistic confidence assessment to determine a confidence level that the recommendation for the patient as automatically generated by the artificial intelligence model is compatible with the current profile for the patient including the desired coaching intensity profile setting;
operating an output processor to convey the recommendation for the patient as automatically generated by the artificial intelligence model to the patient when the confidence level as determined by the moderator engine meets or exceeds a specified confidence level threshold value;
and operating the output processor to not convey the recommendation for the patient as automatically generated by the artificial intelligence model to the patient when the confidence level as determined by the moderator engine does not meet or exceed the specified confidence level threshold value so as to avoid unnecessary use of a data communication network over which the patient guidance system receives and transmits data.
(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, receiving multiple input data streams, generating a recommendation, applying a probabilistic confidence assessment to determine a confidence level that the recommendation is compatible with the patient profile, conveying the recommendation if the confidence level meets or exceeds a threshold, and not conveying the recommendation if the confidence level does not meet or exceed the threshold 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 patient guidance system. The claim also recites the use of a data acquisition engine, in this case to receive multiple input data streams. Additionally, claim 1 recites the use of a moderator engine, in this case to apply a probabilistic confidence assessment to determine a confidence level that the recommendation for the patient as automatically generated is compatible with the current profile for the patient including the desired coaching intensity profile setting. The claim further recites the use of an output processor, in this case to convey the recommendation for the patient as automatically generated to the patient when the confidence level as determined meets or exceeds a specified confidence level threshold value and to not convey the recommendation for the patient as automatically generated to the patient when the confidence level as determined does not meet or exceed the specified confidence level threshold value. The patient guidance system, data acquisition engine, moderator engine, and an output processor 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)).
Claim 1 recites the use of an artificial intelligence model, in this case to automatically generate a recommendation for the patient in real-time based on the multiple input data streams, 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.
Claim 1 recites the use of a data communication network over which the patient guidance system receives and transmits data, only as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
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 the patient guidance system, data acquisition engine, artificial intelligence model, moderator engine, and output processor 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.
Additionally, a data communication network over which the patient guidance system receives and transmits data in independent claim 1 is well-understood, routine, and conventional. The courts indicated in Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) that receiving or transmitting data over a network is well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP 2106.05(d)(II)).
Applicant’s own disclosure in paragraph [0053] acknowledges that the “data acquisition engine 103 implements a data filtering system that functions to filter data within the multiple input data streams 151-1 to 151-N to identify specific data relevant to the patient 101…the data acquisition engine 103 implements machine learning to analyze big data that is collected from a population of patients that may have characteristics similar to those of the patient 101”. Paragraph [0024] acknowledges that the “AI model(s) 107 to provide AI-based predictive analysis of cause-and-effect probabilistic correlations that are embedded (and often hidden) within the multiple input data streams 151-1 to 151-N… The AI model(s) 107 are trained by a cumulative pool of input data amassed over time from a large population of patients”. Paragraph [0025] and [0076] acknowledges that “the natural language processor 109 is implemented by an artificial intelligence model”. Furthermore, in paragraph [0052] the disclosure acknowledges that “the output processor 113 is defined to prepare and transmit the recommendations, coaching, and/or information for the patient 101, as generated by the PGS 100, within data packets over the cloud network 190 to the personal data communication device 102 of the patient 101. In these embodiments, the data packets are prepared by the output processor 113 in accordance with any known and available network communication protocol. In some embodiments, the output processor 113 includes a NIC to provide for packetization of outgoing data to be transmitted from the PGS 100. In some embodiments, the output processor is configured to communicate the output of the PGS 100 and the associated input data to a general data pool 116, as indicated by arrow 117. In some embodiments, the general data pool 116 is maintained within one or more computer readable media in a storage server system of the cloud network 190. However, in various embodiments, the general data pool 116 can be maintained within one or more computer readable media anywhere that is accessible by the PGS 100. Also, in some embodiments, the output processor 113 is configured to communicate information from the PGS 100 to any one or more of the data sources associated with the multiple input data streams 151-1 to 151-N, by way of the cloud network 190”. Paragraph [0040] acknowledges that the “moderator engine 111 is configured to operate in either an autonomous mode, a manual mode, or a hybrid mode… the moderator engine 111 provides the output generated by the deep learning engine 105 to a moderator portal (e.g., implemented as a graphical user interface) through which the output generated by the deep learning engine 105 can be reviewed and either approved or rejected by a human moderator before it is conveyed outside of the PGS 100 to the patient 101”. Additionally, paragraph [0036] discloses that the “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”.
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 2-26 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 2-26, the claims are drawn to a process, as their respective 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 of the patient.
The dependent claim 3 is directed to 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 convey the recommendation for the patient to a human moderator.
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 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.
Each of these steps of the preceding dependent claims 2-26 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:
Dependent claims 14, 18-19, and 24-26 recite the use of an artificial intelligence model is trained, in this case 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, 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, and automatically identify information for conveyance to the patient, wherein the recommendation for the patient is generated to convey the identified information, only recites the artificial intelligence model is trained 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.
Dependent claims 15-16 recite the use of a natural language processor, in this case to support bi-directional communication with the patient without human intervention 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.
Dependent claim 15 recites the use of a patient guidance system, 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)).
Dependent claim 17 recites the use of the natural language processor is implemented by the artificial intelligence model, 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.
Dependent claims 18-19 recite the use of a moderator engine, in this case to convey the recommendation for the patient to a human moderator and provide feedback, only recites the moderator engine 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)).
Dependent claims 21 recites the use of the data acquisition engine is configured for data connection with 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)).
Dependent claims 22-23 recite the use of a graphical user interface configured for display 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 this case to provide bi-directional communication, only recites the graphical user interface configured for display on a computing system of the patient, the graphical user interface including a region for displaying the recommendation for the patient in real-time 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)).
Dependent claims 24-26 recites the use of an artificial intelligence model, in this case to automatically identify a condition or situation that will have an adverse impact if left unmitigated, automatically identify an action that will have a beneficial impact, and automatically identify information for conveyance, 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:
As discussed above in “Step 2A – Prong 2”, the identified additional elements, such as the artificial intelligence model is trained, natural language processor, patient guidance system, the natural language processor is implemented by the artificial intelligence model, moderator engine, the data acquisition engine is configured for data connection with one or more applications executing on a computing device, and the graphical user interface configured for display on a computing system of the patient, the graphical user interface including a region for displaying the recommendation for the patient in real-time dependent claims 2-26 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 2-26 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 20-26 are rejected under 35 U.S.C. 103 as being unpatentable over Bitran (US-20170039344-A1)[hereinafter Bitran], in view of Capell (US-20180085630-A1)[hereinafter Capell], in view of Pauley et al. (US-20210104173-A1)[hereinafter Pauley].
As per Claim 1, Bitran discloses a method for operating a patient guidance system in paragraphs [0003] and [0015] and [0047] (a method for operating a computing system (referring to the patient guidance system) that includes a health recommender (Examiner notes that the health recommender provides guidance to the patient in regards to treating a health condition)), comprising: operating a data acquisition engine to receive multiple input data streams, wherein the multiple input data streams include a current profile for the patient that specifies personal preferences of the patient, a stream of current medical data for a patient, a stream of current situational data for the patient, and one or more streams of current environmental characterization data relevant to the patient in paragraphs [0017] and [0019-0020] and [0022-0023] (operating a personal assistant interpretation engine (synonymous to a data acquisition) receives user data for the user profile which includes inferred data (referring to the personal preferences of the patient), medical data, 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)), wherein the stream of current medical data conveys a current health condition of the patient in paragraphs [0017] and [0019-0020] and [0022-0023] (wherein the medical data includes the user's electronic medical record which consists of the preexisting medical conditions of the user); operating an artificial intelligence model to automatically generate a recommendation for the patient in real-time based on the multiple input data streams in paragraphs [0035] and [0037] (operating a Bayesian machine-learning algorithm (referring to the artificial intelligence model) determines 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);
Bitran discloses the user profile including user settings, but does not disclose the profile including a desired coaching intensity profile setting that specifies an intensity level at which the patient desires to receive coaching. However, Capell discloses wherein the current profile for the patient includes a desired coaching intensity profile setting that specifies an intensity level at which the patient desires to receive coaching from the patient guidance system in paragraphs [0023] and [0028] and [0059] and Figure 2B (the user preferences (synonymous to the current profile for the patient) that includes a desired fitness level (synonymous to a desired coaching intensity profile setting) that specifies an intensity level at which the patient desires to receive coaching from the health system (synonymous to the patient guidance system) (Examiner notes that Figure 2B shows that the data entry field for the fitness level is a drop down box indicating the user to select a fitness intensity level of beginner, intermediate, or advanced)) , apply a probabilistic confidence assessment to determine a confidence level that the recommendation for the patient is compatible with the current profile for the patient including the desired coaching intensity profile setting in paragraphs [0038-0048] and [0059] and [0071] (apply a formula (synonymous to a probabilistic confidence assessment) to determine a weight (synonymous to a confidence level) that the automated recommended workout for the patient is compatible with the user preferences including the desired fitness intensity level).
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 operating a patient guidance system, as disclosed by Bitran, to be combined with the current profile for the patient including a desired coaching intensity profile setting that specifies an intensity level at which the patient desires to receive coaching and applying a probabilistic confidence assessment to determine a confidence level that the recommendation is compatible with the current profile for the patient including the desired coaching intensity profile setting, as disclosed by Capell, for the purpose of automatically making an appropriate recommendation based on the individuals healthy and unhealthy habits [0002-0004].
The combination of Bitran and Capell discloses applying a formula to determine a value that the recommended workout for a patient is compatible with the desired coaching intensity profile setting, but does not disclose the moderator engine being operated to perform the function. However, Pauley discloses operating a moderator engine to apply a probabilistic confidence assessment to determine a confidence level that the recommendation for the patient as automatically generated by the artificial intelligence model is compatible with the current profile for the patient including the desired coaching intensity profile setting in paragraphs [0005] and [0078] and [0084-0085] and [0140] and [0155] and [0178] and [0180] and [0311] and [0325-0326] (operating a prediction engine (synonymous to a moderator engine) to apply a glucose estimation algorithm (synonymous to a probabilistic confidence assessment) to determine a confidence score (synonymous to a confidence level) that the recommendation for the user as automatically generated by the decision logic (synonymous to the artificial intelligence model) is compatible with the patient's GluScore in the user profile for the patient that includes goal settings that personalizes a healthy coaching experience (Examiner notes that the goal settings that personalize a healthy coaching experience is synonymous to the desired coaching intensity profile setting, wherein the goal settings include the GluScore)); operating an output processor to convey the recommendation for the patient as automatically generated by the artificial intelligence model to the patient when the confidence level as determined by the moderator engine meets or exceeds a specified confidence level threshold value in paragraphs [0005] and [0010] and [0092] and [0155-0157] and [0178] and [0180] (operating an output engine (synonymous to an output processor) to convey the recommendation for the user as automatically generated by the decision logic to the user when the confidence score as determined by the prediction engine meets a threshold confidence (synonymous to a specified confidence level threshold value)); and operating the output processor to not convey the recommendation for the patient as automatically generated by the artificial intelligence model to the patient when the confidence level as determined by the moderator engine does not meet or exceed the specified confidence level threshold value so as to avoid unnecessary use of a data communication network over which the patient guidance system receives and transmits data in paragraphs [0005] and [0010] and [0092] and [0153-0157] and [0536] and Figure 8 (operating an output engine to not transmit the recommendation for the user as automatically generated by the decision logic to the user when the confidence score does not meet the threshold confidence so as to perform embodiments, events, or acts concurrently through multi-threaded processing instead of sequentially (Examiner notes that performing the embodiments, acts, or events of the invention concurrently instead of sequentially avoids unnecessary use of data communication network over which the personalized health coaching system (synonymous to the patient guidance system) receives and transmits data)).
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 operating a patient guidance system, as disclosed by Bitran and Capell, to be combined with operating a moderator engine to apply a probabilistic confidence assessment to determine a confidence level that the recommendation is compatible with the current profile for the patient including the desired coaching intensity profile setting, operating an output processor to convey the recommendation when the confidence level meets or exceeds a threshold value, and operating the output processor to not convey the recommendation when the confidence level does not meet or exceed the threshold value, as disclosed by Pauley, for the purpose of assisting people to improve their health through health and lifestyle coaching [0002-0004].
As per Claim 2, Bitran, Capell, and Pauley disclose the method for operating the patient guidance system 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, Capell, and Pauley disclose the method for operating the patient guidance system 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 paragraphs [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, Capell, and Pauley disclose the method for operating the patient guidance system 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, Capell, and Pauley disclose the method for operating the patient guidance system 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 paragraphs [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, Capell, and Pauley disclose the method for operating the patient guidance system 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 paragraphs [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, Capell, and Pauley disclose the method for operating the patient guidance system 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, Capell, and Pauley disclose the method for operating the patient guidance system 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, Capell, and Pauley disclose the method for operating the patient guidance system 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, Capell, and Pauley disclose the method for operating the patient guidance system 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 paragraphs [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, Capell, and Pauley disclose the method for operating the patient guidance system 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, Capell, and Pauley disclose the method for operating the patient guidance system 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, Capell, and Pauley disclose the method for operating the patient guidance system as recited in claim 1, Bitran also discloses wherein the artificial intelligence model is trained based on case data for a population of patients 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 20, Bitran, Capell, and Pauley disclose the method for operating the patient guidance system as recited in claim 1, 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 paragraphs [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)).
As per Claim 21, Bitran, Capell, and Pauley disclose the method for operating the patient guidance system as recited in claim 1, Bitran also discloses wherein the data acquisition engine is configured for data connection with one or more applications executing on a computing device of the patient, wherein the one or more applications provide one or more of the multiple input data streams to the data acquisition engine 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, Capell, and Pauley disclose the method for operating the patient guidance system as recited in claim 1, Bitran also discloses further comprising: a graphical user interface configured for display 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, Capell, and Pauley disclose the method for operating the patient guidance system as recited in claim 22, Bitran also discloses wherein the region provides for bi-directional communication between the 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, Capell, and Pauley disclose the method for operating the patient guidance system as recited in claim 1, Bitran also discloses wherein the artificial intelligence model is configured 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, Capell, and Pauley disclose the method for operating the patient guidance system as recited in claim 1, Bitran also discloses wherein the artificial intelligence model is configured 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, Capell, and Pauley disclose the method for operating the patient guidance system as recited in claim 1, Bitran also discloses wherein the artificial intelligence model is configured 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 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Bitran (US-20170039344-A1)[hereinafter Bitran], in view of Capell (US-20180085630-A1)[hereinafter Capell], in view of Pauley et al. (US-20210104173-A1)[hereinafter Pauley], in view of Aranke (US-11830623-B1)[hereinafter Aranke].
As per Claim 4, Bitran, Capell, and Pauley disclose the method for operating the patient guidance system as recited in claim 3.
Bitran, Capell, and Pauley 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 operating a patient guidance system, as disclosed by Bitran, Capell, and Pauley, to be combined with the current medical data for the patient includes a current body weight and current body measurements, 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, Capell, and Pauley disclose the method for operating the patient guidance system as recited in claim 1.
Bitran, Capell, and Pauley do not disclose the following limitations. However, Aranke discloses further comprising: wherein the moderator engine is configured to convey the recommendation for the patient as automatically generated by the artificial intelligence model to a human moderator in column 2 lines 17-23 and column 16 line 8-59 and column 19 line 23-column 20 line 29 (the machine learning engine (referring to the moderator engine) approves and publishes the machine learning models (synonymous to the artificial intelligence model) that generate the real-time recommendations of actionable intervention for the patient to the remotely located reviewers (synonymous to a human moderator)).
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 operating a patient guidance system, as disclosed by Bitran, Capell, and Pauley, to be combined with the moderator engine to convey the recommendation to a human moderator, 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, Capell, and Pauley disclose the method for operating the patient guidance system as recited in claim 1.
Bitran, Capell, and Pauley do not disclose the following limitations. However, Aranke discloses wherein the moderator engine is configured to provide feedback into the artificial intelligence model 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)).
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 operating a patient guidance system, as disclosed by Bitran, Capell, and Pauley, to be combined with the moderator engine provides feedback into the artificial intelligence model, as disclosed by Aranke, for the purpose of providing a way to improve managing health conditions [column 1 lines 18-52].
Claims 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Bitran (US-20170039344-A1)[hereinafter Bitran], in view of Capell (US-20180085630-A1)[hereinafter Capell], in view of Pauley et al. (US-20210104173-A1)[hereinafter Pauley], in view of Leonard (US-20160321415-A1)[hereinafter Leonard].
As per Claim 15, Bitran, Capell, and Pauley disclose the method for operating the patient guidance system as recited in claim 1.
Bitran, Capell, and Pauley do not disclose the following limitations. However, Leonard discloses further comprising: a natural language processor configured to support bi-directional communication between the patient guidance system and the patient without human intervention 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 operating a patient guidance system, as disclosed by Bitran, Capell, and Pauley, to be combined with a natural language processor used to support bi-directional communication, 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, Capell, Pauley, and Leonard disclose the method for operating the patient guidance system as recited in claim 15.
Bitran, Capell, and Pauley 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 operating a patient guidance system, as disclosed by Bitran, Capell, and Pauley, to be combined with the recommendation is 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, Capell, Pauley, and Leonard disclose the method for operating the patient guidance system as recited in claim 15.
Bitran, Capell, and Pauley 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 operating a patient guidance system, as disclosed by Bitran, Capell, and Pauley, to be combined with the natural language processor is 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].
Response to Arguments
Applicant’s arguments, see Page 8, “Objections to Claims”, filed 10/08/2025, with respect to claim 19 have been fully considered and are persuasive. The claim objection of claim 19 have been withdrawn.
Applicant’s arguments, see Page 8, “Claim Interpretation”, filed 10/08/2025, with respect to claims 1-26 have been fully considered and are persuasive. The claim interpretation of claims 1-26 have been withdrawn.
Applicant understands the examiner interpretations of “a data acquisition engine” in claims 1-26 and “a moderator engine” in claims 18-20.
Applicant's arguments, see Pages 9-11, “Rejections under 35 U.S.C. 101”, filed 10/08/2025 with respect to claims 1-26 have been fully considered but they are not persuasive.
Applicant argues that amended claim 1 does not recite a judicial exception, specifically, methods of organizing human activity for managing personal behavior or relationships or interactions between people. Examiner respectfully disagrees. The amended claim limitations are directed to receive multiple input data streams, generating a recommendation, applying a probabilistic confidence assessment to determine a confidence level that the recommendation is compatible with the patient profile, conveying the recommendation if the confidence level meets or exceeds a threshold, and not conveying the recommendation if the confidence level does not meet or exceed the threshold. The limitations merely recite receiving multiple input data streams, generating a recommendation, applying an assessment to determine if the recommendation is compatible with the patient profile and conveys the recommendation to the patient, 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 amended claim 1 recites an integration into a practical application using an improvement to the operation of the patient guidance system and its associated data communication network over which the patient guidance system receives and transmits data. Examiner respectfully disagrees. The claims do not recite an improvement to the technology of operating the patient guidance system and its associated data communication network. The claims merely recite receive multiple input data streams, generating a recommendation, applying a probabilistic confidence assessment to determine a confidence level that the recommendation is compatible with the patient profile, conveying the recommendation if the confidence level meets or exceeds a threshold, and not conveying the recommendation if the confidence level does not meet or exceed the threshold, which are a part of the abstract idea. An improvement to the abstract ideas of receive multiple input data streams, generating a recommendation, applying a probabilistic confidence assessment to determine a confidence level that the recommendation is compatible with the patient profile, conveying the recommendation if the confidence level meets or exceeds a threshold, and not conveying the recommendation if the confidence level does not meet or exceed the threshold 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 receive multiple input data streams, generating a recommendation, applying a probabilistic confidence assessment to determine a confidence level that the recommendation is compatible with the patient profile, conveying the recommendation if the confidence level meets or exceeds a threshold, and not conveying the recommendation if the confidence level does not meet or exceed the threshold. 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.
Applicant's arguments, see Pages 11-12, “Double Patenting”, filed 10/08/2025 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. 18/307,757, 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 12-14, “Rejections under 35 U.S.C. 102” and “Rejections under 35 U.S.C. 103”, filed 10/08/2025 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 does not teach or suggest each and every feature of amended claim 1. Examiner finds this persuasive. Therefore, the rejection of 04/08/2025 has been withdrawn. However, upon further consideration a new grounds of rejection is made over Bitran, in view of Capell, in view of Pauley. As per the rejections of Claims 4 and 15-20, Applicant argues that the dependent claims are patentable for the same reasons as its independent claim. Examiner 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.
Francois (US 20200185100 A1) teaches a system and program that guides a patient in health tracking and management
BUSSMANN et al. (US 20220319720 A1) teaches systems and methods for managing health care by gathering, processing, and sharing medical information.
Siddique, S. “Machine Learning in Healthcare Communication” teaches how machine learning/ artificial intelligence is beneficial in healthcare communication.
Deepjyoti Roy “A survey on personalized health recommender systems for diverse healthcare applications” teaches an effective health recommender system.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.N.W./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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