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 .
Formal Matters
Applicant's response, filed 16 March 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1-20 are currently pending and have been examined.
Claims 1-20 have been rejected.
Priority
The instant application claims the benefit of priority under 35 U.S.C 119(e) or under 35 U.S.C. § 120, 121, or 365(c). Accordingly, the effective filing date for the instant application is 01 November 2019 claiming benefit to Provisional Application 62/930,364.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-20 are drawn to a method, which is a statutory category of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method in part performing the steps of receiving a plurality of responses to a plurality of screening questions, wherein each screening question is associated with a symptomatic indicator of a plurality of symptomatic indicators; determining, for each symptomatic indicator of the plurality of symptomatic indicators, based on the response to each of the plurality of screening questions, a score; receiving one or more signals representing one or more of symptoms of a user, activities of the user, diet of the user, or vital measurements of the user; and generating, based on the score for each symptomatic indicator of the plurality of symptomatic indicators and the one or more signals, a profile comprising a possible diagnosis of a health issue related to as least one symptomatic indicator of the plurality of symptomatic indicators; verifying, using a [model], the possible diagnosis of the health issue based on the score for each symptomatic indicator of the plurality of symptomatic indicators and the one or more signals.
Independent claim 15 recites a method in part performing the steps of receiving a plurality of responses to a plurality of screening questions; determining, for each response of the plurality of responses, a score; receiving one or more signals representing one or more of symptoms of a user, activities of a user, diet of the user, or vital measurements of the user; generating, based on the score for each response of the plurality of responses and the one or more signals, a profile comprising a possible diagnosis of a health issue related to at least on symptomatic indicator of a plurality of symptomatic indicators; comparing the profile to a plurality of profiles, wherein each profile of the plurality of profiles is associated with a respective user of a plurality of users and each profile of the plurality of profiles is associated with the possible diagnosis of the health issue; and determining, using a [model], based on comparing the profile to the plurality of profiles, a target area, wherein the target area is associated with a symptomatic indicator and possible diagnosis of the health issue.
Independent claim 19 recites a method in part performing the steps of determining first user data associated with a plurality of medical conditions; determining second user data associated with the plurality of medical conditions, wherein the second user data comprises a plurality of user profiles each labeled as being indicative or not indicative of at least one of the plurality of medical conditions; [generating a predictive model] to at least verify a possible diagnosis of a medical issue of the plurality of medical conditions based on a profile comprising a score for a plurality of symptom indicators; testing, based on a second portion of the second user data, the predictive model; and outputting, based on the testing, the predictive model.
These steps of independent claims 1, 15, and 19 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 (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 similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982) – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping). Examiner notes that displaying and receiving data generally may be considered a part of the abstract idea. Examiner has not treated the technological hardware of a display or a user input device as a part of the abstract idea, the actions associated with displaying and selecting/receiving an input are considered a part of the abstract idea — “We have recognized that "information as such is an intangible" and that collecting, analyzing, and displaying that information, without more, is an abstract idea. Elec. Power Grp. , 830 F.3d at 1353-54 ; see also id. at 1355 (noting claim requirement of " ‘displaying concurrent visualization” of two or more types of information" was insufficient to confer patent eligibility) Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1344 (Fed. Cir. 2018); see MPEP § 2106.04(a)(2)(I1)(C)).
Dependent claim 2 recites, in part, receiving a device identifier of the [user device] and a user identifier associated with the user; authenticating, based on the device identifier, the user device; and authenticating, based on the user identifier, the user.
Dependent claim 3 recites, in part, encrypting at least one of, a device identifier, a user identifier, or the score for each response of the plurality of responses.
Dependent claim 4 recites, in part, the method of claim 1 further comprising determining, based on at least one score for the symptomatic indicator of the plurality of symptomatic indicators, an alert condition for the user, wherein the alert condition comprises an indication that the user is at risk for an issue associated with the symptomatic indicator.
Dependent claim 5 recites, in part, wherein determining, based on the at least one score for the symptomatic indicator of the plurality of symptomatic indicators, the alert condition further comprises determining, based on the score satisfying a threshold, the alert condition.
Dependent claim 6 recites, in part, the method of claim 5, further comprising: determining a clinician associated with a type of the alert condition.
Dependent claim 12 recites, in part, wherein the plurality of screening questions are associated with two or more of occupational and regional exposure, military service history, somatic symptoms, physical injury, illness, pain, post-traumatic stress disorder (PTSD) symptoms, behavior, depression symptoms, and social interactions
Dependent claim 13 recites, in part, wherein determining, for each symptomatic indicator of the plurality of symptomatic indicators, based on the response to each of the plurality of screening questions, the score comprises: determining, based on the symptomatic indicator, a scale; and scaling, based on the scale, the response to each of the plurality of screening questions, wherein the scaled response represents the score.
Dependent claim 14 recites, in part, determining, for each of a plurality of user, a dataset comprising a score for each symptomatic indicator of the plurality of symptomatic indicators and one of, an indication of a possible diagnosis of a health issue related to the symptomatic indicator or an indication of no likely diagnosis of the health issue related to the symptomatic indicator; determining, based on the dataset, a training dataset.
Dependent claim 20 recites, in part, wherein the plurality of features for the predictive model comprise one or more pieces of clinical data or user health data.
Each of these steps of the preceding dependent claims 2-6, 12-14, and 20 only serve to further limit or specify the features of independent claims 1 or 19 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements already analyzed in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 1, 7, 8, 15, 17, and 18 recite a user device with an interface wherein the user device comprises one or more of, a mobile phone, a tablet computer, a laptop computer, or a desktop computer and the interface comprises one or more of an accelerometer, a pedometer, a geographical position sensing (GPC) module, an oximeter, or a tactile sensor. The specification defines the user device with an interface wherein the user device comprises one or more of, a mobile phone, a tablet computer, a laptop computer, or a desktop computer and the interface comprises one or more of an accelerometer, a pedometer, a geographical position sensing (GPC) module, an oximeter, or a tactile sensor as any suitable means known to one of ordinary skill in the art (Detailed Description in ¶ 0013-15). The use of a user device serves as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claim 1 recites storing a profile. Claim 2 recites storing, based on authenticating the user device and the user, the profile. The use of a storing a profile only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”) AND serves as extra solution activities incidental to the primary process that is merely a nominal or tangential addition to the claim (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
Claim 1 recites receiving, at the user device and from a user interface associated with the user device, one or more signals. As established above, the user device is considered applying the abstract idea to a computer. Furthermore, the limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Claim 2 recites establishing a communication session between the user device and a computing device system. The use of a establishing a communication session between the user device and a computing device system only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”) AND serves as extra solution activities incidental to the primary process that is merely a nominal or tangential addition to the claim (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
Claim 11 recites wherein presenting, via the user device, the plurality of screening questions comprises presenting the plurality of screening questions via an application running on the user device. Claim 16 recites causing display of the target area. The limitations are only recited as a tool which only serves as display/output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to post-solution output on a well-known display device) and is therefore not a practical application of the recited judicial exception.
Claim 6 recites sending a message associated with the alert condition to the clinician. Claim 9 recites sending, based on at least one score of a plurality of scores satisfying a threshold, a notification. Claim 10 recites sending, based on a signal of the one or more signals satisfying a threshold, a notification. The limitations are only recited as a tool which only serves as display/output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to post-solution output on a well-known display device) and is therefore not a practical application of the recited judicial exception.
Claim 1 recites verifying, using a machine learning mode, the possible diagnosis of the health issue. Claims 15 recites determining, using a machine learning model… a target area. Claim 14 recites a training, based on the training dataset, a machine learning module. Claim 19 recites a training, based on a first portion of the second user data, the predictive model according to the plurality of features. The specification provides a list of possible generic model types that may be used and provides no details regarding the algorithm, stating “a machine learning approach such as discriminant analysis; decision tree; a nearest neighbor (NN) algorithm (e.g., k-NN models, replicator NN models, etc.); statistical algorithm (e.g., Bayesian networks, etc.); clustering algorithm (e.g., k-means, mean-shift, etc.); neural networks (e.g., reservoir networks, artificial neural networks, etc.); support vector machines (SVMs); logistic regression algorithms; linear regression algorithms; Markov models or chains; principal component analysis (PCA) (e.g., for linear models); multi-layer perceptron (MLP) ANNs (e.g., for non-linear models); replicating reservoir networks (e.g., for non-linear models, typically for time series); random forest classification; a combination thereof and/or the like” (Detailed Description in ¶ 0073). The use of a training, a machine learning module, in this case to determine a likelihood that another user will have a diagnosis of an issue related to the symptomatic indicator based on the score for the symptomatic indicator, only recites the training a machine learning module as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claims 1, 7, 8, 15, 17, and 18 recite a user device with an interface wherein the user device comprises one or more of, a mobile phone, a tablet computer, a laptop computer, or a desktop computer and the interface comprises one or more of an accelerometer, a pedometer, a geographical position sensing (GPC) module, an oximeter, or a tactile sensor. Claim 1 recites verifying, using a machine learning mode, the possible diagnosis of the health issue. Claims 15 recites determining, using a machine learning model… a target area. Claim 14 recites a training, based on the training dataset, a machine learning module. Claim 19 recites a training, based on a first portion of the second user data, the predictive model according to the plurality of features.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of 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 do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claim 1 recites storing a profile. Claim 2 recites storing, based on authenticating the user device and the user, the profile. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when 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)).
Claim 1 recites receiving, at the user device and from a user interface associated with the user device, one or more signals. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when 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) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 2 recites establishing a communication session between the user device and a computing device system. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when 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) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 11 recites wherein presenting, via the user device, the plurality of screening questions comprises presenting the plurality of screening questions via an application running on the user device. Claim 16 recites causing display of the target area. The courts have decided that presenting generated data as well-understood, routine, conventional activity when 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) other types of activities example iv. presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
Claim 6 recites sending a message associated with the alert condition to the clinician. Claim 9 recites sending, based on at least one score of a plurality of scores satisfying a threshold, a notification. Claim 10 recites sending, based on a signal of the one or more signals satisfying a threshold, a notification. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when 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) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
For the reasons stated, these claims fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-15 and 17-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Moturu et al. (US Patent App Pub No 2018/0096738)[hereinafter Moturu] as evidenced by Madan et al. (US Patent App Pub No 2014/0052474)[incorporated by reference in Moturu – hereinafter Madan].
Claim 1 is rejected because Moturu teaches on all elements of the claim:
method comprising is taught in the Detailed Description in ¶ 0052, ¶ 0045, ¶ 0105, ¶ 0111-112, and in the Figures at fig. 1B (teaching on a method for determining a patient therapy plan from patient survey and sensor data);
receiving, at a user device, a plurality of responses to a plurality of screening questions, wherein each screening question is associated with a symptomatic indicator of a plurality of symptomatic indicators is taught in the Detailed Description in ¶ 0052, ¶ 0055-56, ¶ 0047, ¶ 0024, ¶ 0150, and in the Figures at fig. 1B reference character S115 (survey response dataset - ref char S120 in alternative figures) (teaching on receiving survey response to a list of questions (treated as synonymous to responses to screening questions) wherein the questions may be associated with a symptom from a patient mobile phone);
determining, for each symptomatic indicator of the plurality of symptomatic indicators, based on a response to each of the plurality of screening questions, a score is taught in the Detailed Description in ¶ 0104-105 (teaching on generating a score for the survey response used to indicate/analyze the user's symptomatic progress);
receiving, from an interface associated with the user device, one or more signals representing one or more symptoms of a user, activities of the user, diet of the user, or vital measurements of the users is taught in the Detailed Description in ¶ 0045-47 and in the Figures at fig. 1B reference character S115 (teaching on receiving a supplementary data from a plurality of user sensors (treated as synonymous to a signal) including the physical activity of the users collected from location and movement data and vital sign data from the user device);
generating, based on the score for each symptomatic indicator of the plurality of symptomatic indicators and the one or more signals, a profile comprising a possible diagnosis of a health issue related to at least one symptomatic indicator of the plurality of symptomatic indicators is taught in the Detailed Description in ¶ 0105-106, ¶ 0111-112, ¶ 0115 and in the Figures at fig. 1B reference character S146 (teaching on generating a second score associating the symptomatic progress with the supplementary data and determining a patient health state classification from the plurality of scores wherein the scoring utilizes health condition types associated with each disease state);
verifying, using a machine learning model, the possible diagnosis of the health issue based on the score for each symptomatic indicator of the plurality of symptomatic indicators and the one or more signals; and is taught in the Detailed Description in ¶ 0081-82, ¶ 0096-98, ¶ 0103-105, and ¶ 0115 (teaching on generating a machine learning score prediction model (treated as synonymous to profile comparisons) for determining a disease state scores for a user from other users as further evidenced by Madan in ¶ 0082-83, ¶ 0087, and ¶ 0092-93 wherein the model utilizes health condition types associated with each disease state);
storing the profile is taught in the Detailed Description in ¶ 0147-148 (teaching on saving the patient and corresponding health state classification in a dataset).
As per claim 2, Moturu discloses all of the limitations of claim 1. Moturu also discloses the following:
the method of claim 1, wherein storing the profile comprises: establishing a communication session between the user device and a computing device system is taught in the Detailed Description in ¶ 0150 and ¶ 0152 (teaching on the user device and a central computer in communication over a network for sending user data);
receiving, by the computing device system, a device identifier of the user device and a user identifier associated with the user; authenticating, based on the device identifier, the user device; authenticating, based on the user identifier, the user; and storing, based on authenticating the user device and the user, the profile is taught in the Detailed Description in ¶ 0150 and ¶ 0152 (teaching on the central computer receiving authorization to collect user data collected from the user device associated with the user wherein the user is associated with a user account/profile).
As per claim 3, Moturu discloses all of the limitations of claim 1. Moturu also discloses the following:
the method of claim 1, further comprising encrypting at least one of, a device identifier, a user identifier, or the score for each response of the plurality of responses is taught in the Detailed Description in ¶ 0150 and ¶ 0152 (teaching on encrypting the user data and user authentication data).
As per claim 4, Moturu discloses all of the limitations of claim 1. Moturu also discloses the following:
the method of claim 1 further comprising determining, based on at least one score for the symptomatic indicator of the plurality of symptomatic indicators, an alert condition for the user wherein the alert condition comprises an indication that the user is at risk for an issue associated with the symptomatic indicator is taught in the Detailed Description in ¶ 0105 and ¶ 0137-138 (teaching on sending a notification to the user's device if the patient therapy plan needs to be changed (treated as synonymous to an indication the user is at risk) due to, in part, the score derived from the survey responses).
As per claim 5, Moturu discloses all of the limitations of claim 4. Moturu also discloses the following:
the method of claim 4, wherein determining, based on the at least one score for the symptomatic indicator of the plurality of symptomatic indicators, the alert condition further comprises determining, based on the score satisfying a threshold, the alert condition is taught in the Detailed Description in ¶ 0105 and ¶ 0137-138 (teaching on the notification for the patient therapy plan change being based on the score exceeding a threshold value).
As per claim 6, Moturu discloses all of the limitations of claim 5. Moturu also discloses the following:
the method of claim 5, further comprising: determining a clinician associated with a type of the alert condition; and is taught in the Detailed Description in ¶ 0105, ¶ 0124, and ¶ 0137-138 (teaching on prompting a care provider associated with the user to approve of the patient therapy plan change when a threshold score is exceeded prompting the change);
sending a message associated with the alert condition to the clinician is taught in the Detailed Description in ¶ 0105, ¶ 0124, and ¶ 0137-138 (teaching on sending a notification to the care provider's device if the patient therapy plan needs to be changed (treated as synonymous to an indication the user is at risk) due to, in part, the score derived from the survey responses).
As per claim 7, Moturu discloses all of the limitations of claim 1. Moturu also discloses the following:
the method of claim 1, wherein the user device comprises one or more of, a mobile phone, a tablet computer, a laptop computer, or a desktop computer is taught in the Detailed Description in ¶ 0128 (teaching on the user device including a mobile computing device, a tablet, personal computer, cardiovascular device, biosignal detector head - mounted wearable computing device, wrist - mounted wearable com putting device, etc.).
As per claim 8, Moturu discloses all of the limitations of claim 1. Moturu also discloses the following:
the method of claim 1, wherein the interface comprises one or more of an accelerometer, a pedometer, a geographical position sensing (GPC) module, an oximeter, or a tactile sensor is taught in the Detailed Description in ¶ 0045-47 and in ¶ 0071 (teaching on the user device including a GPS sensors, accelerometers, gyroscopes, M7 chips, M8 chips for collecting data).
As per claim 9, Moturu discloses all of the limitations of claim 1. Moturu also discloses the following:
the method of claim 1, further comprising sending, based on at least one score of a plurality of scores satisfying a threshold, a notification is taught in the Detailed Description in ¶ 0052, ¶ 0061, and in ¶ 0128 (teaching on the survey being presented on an application executing on the user's mobile computing device).
As per claim 10, Moturu discloses all of the limitations of claim 1. Moturu also discloses the following:
the method of claim 1, further comprising sending, based on a signal of the one or more signals satisfying a threshold, a notification is taught in the Detailed Description in ¶ 0105-106, ¶ 0111-112, and ¶ 0137-138 (teaching on sending a notification to the user's device if the patient therapy plan needs to be changed (treated as synonymous to an indication the user is at risk) due to, in part, the scores derived from the supplementary data).
As per claim 11, Moturu discloses all of the limitations of claim 1. Moturu also discloses the following:
the method of claim 1, wherein presenting, via the user device, the plurality of screening questions comprises presenting the plurality of screening questions via an application running on the user device is taught in the Detailed Description in ¶ 0052, ¶ 0061, and in ¶ 0128 ( teaching on the survey being presented on an application executing on the display of a user's mobile computing device).
As per claim 12, Moturu discloses all of the limitations of claim 1. Moturu also discloses the following:
the method of claim 1, wherein the plurality of screening questions are associated with two or more of occupational and regional exposure, military service history, somatic symptoms, physical injury, illness, pain, post-traumatic stress disorder (PTSD) symptoms, behavior, depression symptoms, and social interactions is taught in the Detailed Description in ¶ 0058-59 (teaching on the survey questions being related to social contact, mood (treated as synonymous to depression symptoms), and psychiatric symptoms).
As per claim 13, Moturu discloses all of the limitations of claim 1. Moturu also discloses the following:
the method of claim 1, wherein determining, for each symptomatic indicator of the plurality of symptomatic indicators, based on the response to each of the plurality of screening questions, the score comprises: determining, based on the symptomatic indicator, a scale; and scaling, based on the scale, the response to each of the plurality of screening questions, wherein the scaled response represents the score is taught in the Detailed Description in ¶ 0052, ¶ 0055-56, and ¶ 0104-105 (teaching on generating a score for the survey response used to indicate/analyze the user's symptomatic progress).
As per claim 14, Moturu discloses all of the limitations of claim 1. Moturu also discloses the following:
the method of claim 1, further comprising: determining, for each of a plurality of user, a dataset comprising a score for each symptomatic indicator of the plurality of symptomatic indicators and one of, an indication of the possible diagnosis of the health issue related to the symptomatic indicator or an indication of no likely diagnosis of the health issue related to the symptomatic indicator; determining, based on the dataset, a training dataset; and training, based on the training dataset, a machine learning module to determine a likelihood that another user will have a diagnosis of an issue related to the symptomatic indicator based on the score for the symptomatic indicator is taught in the Detailed Description in ¶ 0052, ¶ 0055-56, ¶ 0098, and ¶ 0103-105 (teaching on generating training data for a machine learning model from the scores associated with a user's symptomatic progress and condition state (treated as synonymous to a diagnosis) as further evidenced by Madan in ¶ 0082-83, ¶ 0087, and ¶ 0092-93);
training, based on the training dataset, a machine learning module to determine a likelihood that another user will have a diagnosis of an issue related to the symptomatic indicator based on the score for the symptomatic indicator is taught in the Detailed Description in ¶ 0096-98 and ¶ 0103-105 (teaching on generating/training a machine learning score prediction model (treated as synonymous to profile comparisons) for determining scores for a user from other users as further evidenced by Madan in ¶ 0082-83, ¶ 0087, and ¶ 0092-93).
Claim 15 is rejected because Moturu teaches on all elements of the claim:
a method comprising is taught in the Detailed Description in ¶ 0052, ¶ 0045, ¶ 0105, ¶ 0111-112, and in the Figures at fig. 1B (teaching on a method for determining a patient therapy plan from patient survey and sensor data);
receiving, at a user device, a plurality of responses to a plurality of screening questions is taught in the Detailed Description in ¶ 0052, ¶ 0055-56, ¶ 0047, ¶ 0024, ¶ 0150, and in the Figures at fig. 1B reference character S115 (survey response dataset - ref char S120 in alternative figures) (teaching on receiving survey response to a list of questions (treated as synonymous to responses to screening questions) wherein the questions may be associated with a symptom from a patient mobile phone);
determining, for each response of the plurality of responses, a score is taught in the Detailed Description in ¶ 0104-105 (teaching on generating a score for the survey response used to indicate/analyze the user's symptomatic progress);
receiving, via an interface of the user device, one or more signals representing one or more of symptoms of a user, activities of the user, diet of the user, or vital measurements of the user is taught in the Detailed Description in ¶ 0045-47 and in the Figures at fig. 1B reference character S115 (teaching on receiving a supplementary data from a plurality of user sensors (treated as synonymous to a signal) including the physical activity of the users collected from location and movement data and vital sign data from the user device);
generating, based on the score for each response of the plurality of responses and the one or more signals, a profile comprising a possible diagnosis of a health issue related to at least one symptomatic indicator of the plurality of symptomatic indicators is taught in the Detailed Description in ¶ 0105-106, ¶ 0111-112, ¶ 0115, and in the Figures at fig. 1B reference character S146 (teaching on generating a second score associating the symptomatic progress with the supplementary data and determining a patient health state classification from the plurality of scores wherein the scoring model utilizes health condition types associated with each disease state);
comparing the profile to a plurality of profiles, wherein each profile of the plurality of profiles is associated with a respective user of a plurality of users and each profile of the plurality of profiles is associated with the possible diagnosis of the health issue; and is taught in the Detailed Description in ¶ 0081-82, ¶ 0096-98, ¶ 0103-105, and ¶ 0115 (teaching on generating a machine learning score prediction model (treated as synonymous to profile comparisons) for determining a disease state scores for a user from other users as further evidenced by Madan in ¶ 0082-83, ¶ 0087, and ¶ 0092-93 wherein the model utilizes health condition types associated with each disease state);
determining, using a machine learning model, based on comparing the profile to the plurality of profiles, a target area, wherein the target area is associated with a symptomatic indicator and possible diagnosis of the health issue is taught in the Detailed Description in ¶ 0091, ¶ 0096-98, and ¶ 0106 (teaching on tracking a user's location (treated as synonymous to a target area) as a feature of the user amongst the other associated symptomatic features to compare with a plurality of other users in a machine learning training set - here the example of prolonged time at home associated with other symptomatic indicators is used as an example wherein the model utilizes health condition types associated with each disease state).
As per claim 17, Moturu discloses all of the limitations of claim 15. Moturu also discloses the following:
the method of claim 15, wherein the user device comprises one or more of, a mobile phone, a tablet computer, a laptop computer, or a desktop computer is taught in the Detailed Description in ¶ 0128 (teaching on the user device including a mobile computing device, a tablet, personal computer, cardiovascular device, biosignal detector head - mounted wearable computing device, wrist - mounted wearable com putting device, etc.).
As per claim 18, Moturu discloses all of the limitations of claim 15. Moturu also discloses the following:
the method of claim 15, wherein the interface comprises one or more of an accelerometer, a pedometer, a geographical position sensing (GPC) module, an oximeter, or a tactile sensor is taught in the Detailed Description in ¶ 0045-47 and in ¶ 0071 (teaching on the user device including a GPS sensors, accelerometers, gyroscopes, M7 chips, M8 chips for collecting data).
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.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Moturu et al. (US Patent App Pub No 2018/0096738)[hereinafter Moturu] in view of Hwang (US Patent App Pub No 20210005321)[hereinafter Hwang].
As per claim 16, Moturu discloses all of the limitations of claim 15. Moturu fails to teach the following; Hwang, however, does disclose:
the method of claim 15 further comprising, causing display of the target area is taught in the Detailed Description in ¶ 0033 and ¶ 0073 (teaching on displaying a geographical region associated with a particular patient risk score).
One of ordinary skill in the art would display the location of the associated symptomatic score of Hwang with the symptom risk score analysis of Moturu with the motivation of allowing a user to visualize the predicative modeling outcome and related risk features (Hwang in the Detailed Description in ¶ 0073). Furthermore, one of ordinary skill in the art would have recognized that applying the known technique of Hwang would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of displaying the risk score side by side with the location of the patient of Hwang to the risk score determination and notification of Moturu would have yielded predictable results of displaying the relevant information to the user to identify a risk and a patient location known in the art.
Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Moturu et al. (US Patent App Pub No 2018/0096738)[hereinafter Moturu] in view of Hong et al. (US Patent App Pub No 2019/0259499)[hereinafter Hong].
As per claim 19, Moturu teaches on the following limitations of the claim:
a method comprising: is taught in the Detailed Description in ¶ 0052, ¶ 0045, ¶ 0096-98, ¶ 0105, ¶ 0111-112, and in the Figures at fig. 1B (teaching on a machine learning method for determining a patient therapy plan from patient survey and sensor data);
determining first user data associated with a plurality of medical conditions; determining second user data associated with the plurality of medical conditions, wherein the second user data comprises a plurality of user profiles each labeled as being indicative or not indicative of at least one of the plurality of medical conditions is taught in the Detailed Description in ¶ 0045-47, ¶ 0052, ¶ 0055-56, ¶ 0024, and ¶ 0150 (teaching on receiving clinical data from a plurality of users associated with a medical condition wherein the data is indicative (or necessarily not indicative) of a condition progression/state);
determining, based on the first user data and the second user data, a plurality of features for a predictive model is taught in the Detailed Description in ¶ 0091, ¶ 0096-98, and ¶ 0106 (teaching on determining asymptomatic feature data (treated as synonymous to user health OR clinical data) to compare with a plurality of other users in a machine learning training set - here the example of prolonged time at home associated with other symptomatic indicators is used as an example);
training, based on a first portion of the second user data, the predictive model according to the plurality of features to at least verify a possible diagnosis of a medical issue of the plurality of medical conditions based on a profile comprising a score for a plurality of symptomatic indicators is taught in the Detailed Description in ¶ 0081-82, ¶ 0096-98, ¶ 0103-105, and ¶ 0115 (teaching on generating a machine learning score prediction model (treated as synonymous to profile comparisons) for determining a disease state scores for a user from other users as further evidenced by Madan in ¶ 0082-83, ¶ 0087, and ¶ 0092-93 wherein the model utilizes health condition types associated with each disease state);.
Moturu fails to teach the following limitation of claim 19; Hong, however, does teach the following:
testing, based on a second portion of the second user data, the predictive model; and is taught in the Detailed Description in ¶ 0189, ¶ 0196, and ¶ 0211 (teaching on utilizing a test set of user feature data, the predictive model); -AND-
outputting, based on the testing, the predictive model is taught in the Detailed Description in ¶ 0189, ¶ 0196, and ¶ 0211 (teaching on providing the predictive model to a clinician once the model has been validated).
One of ordinary skill in the art would have recognized that applying the known technique of Hong would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of testing the model of Hong to the machine learning teachings of Moturu would have yielded predictable results of testing the generating model to ensure model accuracy known in the art.
As per claim 20, the combination of Moturu and Hong discloses all of the limitations of claim 19. Moturu also discloses the following:
the method of claim 19, wherein the plurality of features for the predictive model comprise one or more pieces of clinical data or user health data is taught in the Detailed Description in ¶ 0091, ¶ 0096-98, and ¶ 0106 (teaching on tracking users' symptomatic feature data (treated as synonymous to user health OR clinical data) to compare with a plurality of other users in a machine learning training set - here the example of prolonged time at home associated with other symptomatic indicators is used as an example).
Response to Arguments
Applicant's arguments filed 16 March 2026 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserted that "certain methods of organizing human activity" may only be selected from the provided groupings and that the instant claims do not fit into the enumerated categories – notably the sub-grouping "managing personal behavior or relationships or interactions between people" include social activities, teaching, and following rules or instructions. Examiner disagreed. The human interaction subgroup “managing personal behavior or relationships or interactions between people” would include a diagnostic medicine interaction similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). It is important to note that the text within the parentheses stating social activities, teaching, and following rules or instructions are provided as examples and not an exclusive listing and that the October 2019 Update: Subject Matter Eligibility on p. 5 stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping.
Next, Applicant asserts that the instant claims recite a practical application via an improvement to computers similar to Example 40. Examiner is not persuaded. Example 40 is directed towards analyzing computer network signals, not patient data in signal form. The processing of the instant signals has no bearing on the operational improvement of computers or corresponding hardware such as a network.
Next, Applicant asserts that outputting a predictive model would be more than organizing human activity. Examiner disagrees. Determining a model for diagnosing or verifying a patient diagnosis and communicating said model would be a method of organizing human activity.
Next, Applicant asserts the claims 19-20 recite a practical application via an improvement to technology or a technical field. An improvement to the abstract idea or medical triage does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) 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. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). Merely adding generic computer components to perform the method is not sufficient.
Applicant's arguments filed 16 March 2026 with respect to 35 USC § 102 with regards to claim 1 have been fully considered and are not persuasive. Applicant asserts that the Moturu teaches on utilizing machine learning for “therapeutic intervention prediction” rather than verifying the possible diagnosis recited in claim 1. Moturu teaches on determining a disease state scores (see specifically ¶ 0103) which is nearly identical to the claim language “verifying, using a machine learning model, the possible diagnosis of the health issue based on the score for”. While Moturu anticipates utilizing said score to ultimately assign a therapeutic approach, the prior art teaching a narrower embodiment of the general embodiment of the instant claims does not preclude the prior art from being applied.
Applicant's arguments filed 16 March 2026 with respect to 35 USC § 103 with regards to claim 16 have been fully considered but are not persuasive with regards to the motivation to combine Moturu and Hwang. Examiner notes that while Applicant has outlined Examiner’s failure to establish “why” a person of ordinary skill in the art would be motivated to combine Moturu with Hwang (Examiner notes that the motivation is applying a known technique with a predictable result), Applicant has failed to establish the unpredictable result of the combination as required under MPEP § 2145 wherein the burden shifts to the applicant to come forward with arguments and/or evidence to rebut the prima facie case. See, e.g., In re Dillon, 919 F.2d 688, 692, 16 USPQ2d 1897, 1901 (Fed. Cir. 1990) (en banc). Applicant has offered no secondary considerations more than a mere assertion that Examiner has failed to meet her burden. This is unpersuasive.
Applicant's arguments filed 16 March 2026 with respect to 35 USC § 103 with regards to claims 19 and 20. This argument is directed to the same point made for the 35 USC 102 rejection above. Examiner sustains the response noted there.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857