DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 19, 2026 has been entered.
Response to Amendment
In the amendment filed on January 19, 2026, the following has occurred: claim(s) 1, 29 have been amended. Now, claim(s) 1-4, 6, 8, 11-12, 22, 27, and 29-38 are pending.
Affidavit
The Examiner has reviewed the Affidavit, “Subject Matter Eligibility Declaration (SMED) under 37 C.F.R. § 1.132” from January 19, 2026. The Examiner has reviewed the Affidavit in light of the newly amended claims. The Examiner does not acknowledge that the Affidavit overcomes the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s) as described below in the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s) below, and further described in the Response to Argument(s) section below, as the Applicant’s Remarks point to the Affidavit.
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.
Claim(s) 1-4, 6, 8, 11-12, 22, 27, and 29-38 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-4, 6, 8, 11-12, 22, 27: Step 2A Prong One
Claim 1 recite(s)
consulting, a user profile associated with at least a first device and comprising medical history data, biometric data, and insurance information associated with a user corresponding to the user profile;
processing the symptom data and at least some data from the user profile at a certain time to determine at least one presence of a medical diagnosis;
generating, a likelihood of a medical diagnosis in real time with respect to the certain time based on the symptom data and at least some data from the user profile including the medical history data;
comparing the likelihood of the medical diagnosis to a predetermined value indicative of a presence of the medical diagnosis;
determining, in real time with receiving the symptom data, the medical diagnosis is present based on the likelihood of the medical diagnosis satisfying the predetermined value;
determining to output one or more medications corresponding to certain medical diagnoses;
determining, using the medical history data in the user profile, the medical diagnosis, and a database associating a plurality of medical diagnoses with the insurance information associated with the user, a medication from a plurality of medications for treating the medical diagnosis;
automatically authorizing submission of the medication to a supplier;
causing shipment of the medication to a location identified in the user profile, to treat the medical diagnosis, and
providing treatment to the user corresponding to the medical diagnosis
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the “a computing device”, “at least a first device”, “the first device comprising a sensor” language, the “consulting” function in the context of this claim encompasses a person following instructions to consult a user profile associated with a generic computer component. Similarly, but for the “at least one first machine learning algorithm” language, the “processing” function in the context of this claim encompasses a person following instructions to make a determination on the symptom data and at least some data from the user profile. Similarly, but for the “at least one first machine learning algorithm” language, the “generating” function in the context of this claim encompasses a person following instructions to determine a likelihood of a medical diagnosis. Similarly, the “comparing” function in the context of this claim encompasses a person following instructions to compare the likelihood of the medical diagnosis to a predetermined value indicative of a presence of the medical diagnosis. Similarly, but for the “at least one second machine learning algorithm” language, the “determining” functions in the context of this claim encompasses a person following instructions to determine the presence of the medical diagnosis, determine to output one or more medications and determining a medication from a plurality of medications. Similarly, the “authorizing” function in the context of this claim encompasses a person following instructions to authorize submission of the medication to a supplier. Similarly, the “causing” function in the context of this claim encompasses a person following instructions to begin the process of sending medication to a location identified in the user profile. Finally, the “providing” function in the context of this claim encompasses a person following instructions to providing the medication to the user. These steps could be accomplished by a person following instructions to determine and orchestrate delivery of medication to another person, such steps encompass Certain Methods of Organizing Human Activity.
Claims 2-4, 6, 8, 11-12, 22, 27 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, but for the recitation of generic computer components, claims 2-4 describe sending information to another person. Similarly, but for the recitation of generic computer components, claim 6 describes scheduling an in-person appointment for a user. Similarly, but for the recitation of generic computer components, claims 8, 22, 27 describe displaying information to the user. Similarly, but for the recitation of generic computer components, claim 11, describes sending information to an insurance system. Finally, but for the recitation of generic computer components, claim 12 describes sending information to a payment system. These claims also encompass Certain Methods of Organizing Human Activity for the reasons set forth above.
Claims 1-4, 6, 8, 11-12, 22, 27: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, insignificant extra-solution activity, and generally linking the abstract idea to a
technical environment.
Claims 1-4, 6, 8, 11-12, 22, 27, directly or indirectly, recite the following generic computer components configured to implement the abstract idea: "a computing device", "a first device", "the first device comprising a sensor", "at least one first machine learning algorithm", "at least one second machine learning algorithm". The written description discloses that the recited computer components encompass generic computer components including, "Referring now to FIG. 11, an exemplary diagnostic system is illustrated in accordance with the present disclosure. Diagnostic system 1030 may include user device 1032, sensor device 1034, patch device 1036, saliva device 1038, tissue device 1040, blood device 1042 and/or computing device 1044, which may be a remote computing device. Sensor device 1034 and computing device 1044 may be the same as or similar to sensor device 104 and computing device 102, respectively, described above with respect to FIG. 1. Sensor device 1034 may be a wearable device (e.g., smart watch) and/or may include one or more photoplethysmography (PPG) sensors and/or accelerometers. User device 1032 may be the same or similar to user device 302 described above with respect to FIG. 3." (See Specification in Paragraph [0108])
Additionally, the claim recites “receiving symptom data from the first device, the symptom data generated at a certain time by the sensor and indicative of a physiological condition of the user;” at a high degree of generality, amount no more than receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). As set forth in MPEP 2106.05(d)(II), computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claims recite "at least one first machine learning algorithm trained to determine at least one presence of a medical diagnosis", "determining at least on second machine learning algorithm trained to output one or more medications corresponding to certain medical diagnoses ", "without human intervention," and "the at least one second machine learning algorithm" at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words "apply it" to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words "apply it" or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claim 22 recites a healthcare virtual avatar. The healthcare virtual avatar is recited at a high level of generality (i.e., as a generic computer element performing a generic computer functions of collecting, analyzing, and transmitting data) [Spec Pg. 21-23] such that they amount to no more than mere instructions to apply the exception using a generic computer component. Generic computer components recited as performing generic computer functions that are well- understood, routine, and conventional activities amount to no more than implementing the abstract idea with a computerized system.
Claims 1-4, 6, 8, 11-12, 22, 27: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 ("mere recitation of generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.")
Receiving and transmitting data over a network (i.e., receiving and communicating data or signals) has been recognized as well-understood, routine, and conventional activity of a general-purpose computer (see MPEP2106.05(d)) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
Claims 29-38 recite the same functions as claims 1-4, 6, 8, 11-12, and 27 but in method form, and claim 29 lacks the step of "causing shipment of the medication to a location identified in the user profile, to treat the medical diagnosis" in claim 1.
Accordingly, claims 1-4, 6, 8, 11-12, 27, and 29-38 are directed to an abstract idea
without significantly more. Therefore claims 1-4, 6, 8, 11-12, 27, and 29-38 are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6, 8, 11-12 and 29-36 are rejected under 35 U.S.C. 103 as being unpatentable over Bharmi (U.S. Patent Pre-Grant Publication No. 2021/0020294) in view of McNamar (U.S. Patent Pre-Grant Publication No. 2008/0201172) in further view of Yered (U.S. Patent Pre-Grant Publication No. 2007/0050209).
As per independent claim 1, Bharmi discloses a method for treating a medical condition detected by an unsupervised automated medical diagnosis system, the method comprising:
consulting, by a computing device (See Paragraph [0140]: The one or more processors may be implemented by a server computing device or remote device), a user profile associated with at least a first device (See Paragraphs [0153], [0185]: Devices encoded with a patient ID, which is interpreted as a user profile associated with a device as the BGA test device will send a new data upload request (with the patient ID and test results) to a server, database or other computing device of a medical network) and comprising medical history data, biometric data, and insurance information associated with a user corresponding to the user profile (See Paragraphs [0132], [0302]: The processor collects historical data related to the patient, this includes age, height, weight, medications, medical history, etc., and when combined with McNamar's disclosure of insurance coverage, the combination of Bharmi/McNamar encompasses the claimed portion, and the Examiner is interpreting the information collected from the patient data entry device to encompass biometric data (Paragraph [0132]) as audio data can be collected), the first device comprising a sensor (See Paragraph [0151]: The healthcare system is configured to receive data from a variety of external and implantable sources including, active IMDs capable of delivering therapy to a patient, passive IMDs or sensors, and wearable sensors);
receiving symptom data from the first device (See Paragraph [0280]: The processor analyzes the information obtained and determined by utilizing a application-specific model to assign a health risk index to the patient's present condition), the symptom data generated at a certain time (See Paragraph [0132]: The position tracking device may monitor and collect, as BRM data, movement information, such as a number of steps or distance traveled in a select period of time, a rate of speed, a level of exercise and the like, which the Examiner is interpreting a select period of time to encompass a certain time) by the sensor and indicative of a physiological condition of the user (See Paragraphs [0154]-[0155]: Collecting BGA data from the BGA device, which is interpreted as symptom data and the first device, respectively, and that the BGA data is body generated analyte data, which is interpreted as physiological data);
processing the symptom data and at least some data from the user profile including the medical history data using at least one first machine learning algorithm trained to determine at least one presence of a medical diagnosis (See Fig. 8D and Paragraphs [0114], [0189], [0237]-[0239], [0280], [0291]-[0292], [0318]: The process of Fig. 8D may utilize functional information about the patient, such as activity, fatigue symptoms, quality of life, and the diagnosis of the patient can be used to assign a health risk index to the patient, which the Examiner is interpreting fatigue symptoms to encompass symptom data (Paragraph [0238]), the Examiner is interpreting the patient’s quality of life to encompass at least some data from the user profile, and the application specific model may be implemented in various manners, as described herein, including but not limited to lookup tables, decision trees, machine learning algorithms to encompass at least one first machine learning algorithm trained, and the Examiner is interpreting the processor updates a patient medical record with the BGA Index and BGA data to encompass the user profile including the medical history data ([0189]));
generating, without human intervention, a likelihood of a medical diagnosis in real time with respect to the certain time based on the symptom data and at least some data from the user profile (See Paragraphs [0141], [0359]: The analysis of the serum albumin and generation of the diagnosis and treatment recommendation are performed in real-time, namely while the patient is experiencing a certain malnutrition state, not to exceed 24 hours from the time the BGA was collected, which the Examiner is interpreting to encompass in real time with respect to the certain time based on the symptom data and at least some data from the user profile as the BGA data may be previously acquired and stored in the patient's medical record) including the medical history data and using the at least one first machine learning algorithm (See Paragraphs [0139], [0318], [0321]: Numerous variables are utilized by the risk score algorithm in determining the probability a patient will experience heart failure within the predetermined interval of time, the variables include historical data, pulmonary artery pressure, heart rate, medication usage, and the like, which the Examiner is interpreting the probability a patient will experience heart failure to encompass a likelihood of a medical diagnosis, and the Examiner is interpreting a risk category algorithm to encompass the at least one first machine learning algorithm);
comparing the likelihood of the medical diagnosis to a predetermined value indicative of a presence of the medical diagnosis (See Paragraphs [0291]-[0292]: The processor determines whether the health risk index exceeds a threshold, the health risk index exceeds the health threshold, the processor generates a treatment notification based on the diagnosis, which the Examiner is interpreting the health threshold to encompass a predetermined value and determines whether the health risk index exceeds a threshold to encompass comparing the likelihood of the medical diagnosis);
determining, in real time with receiving the symptom data, the medical diagnosis is present based on the likelihood of the medical diagnosis satisfying the predetermined value (See Paragraphs [0291]-[0292]: The processor determines whether the health risk index exceeds a threshold, the health risk index exceeds the health threshold, the processor generates a treatment notification based on the diagnosis, which the Examiner is interpreting the health risk index exceeds a threshold to encompass satisfying the predetermined value, and the BGA data may be obtained in real time (Paragraph [0359]));
determining at least one second machine learning algorithm trained to output one or more medications corresponding to certain medical diagnoses (See Paragraphs [0207]-[0210], [0222]: The IMD data, BRM data, and BGA data in connection with one or more application specific models to generate a treatment diagnosis, the processor identifies a treatment diagnosis and treatment notification to be provided in connection with the health risk index, which the Examiner is interpreting the treatment notification to encompass output one or more medications corresponding to certain medical diagnoses, and one or more application specific models to encompass at least one second machine learning algorithm);
determining, by the at least one second machine learning algorithm and without human intervention, using the medical history data in the user profile, the medical diagnosis, and a database associating a plurality of medical diagnoses with the insurance information associated with the user, a medication from a plurality of medications for treating the medical diagnosis (See Fig. 8D and Paragraphs [0114], [0237]- [0239], [0280], [0291]-[0292], [0301]-[0302], [0318]: The process of Fig. 8D may utilize functional information about the patient, such as activity, fatigue symptoms, quality of life, and the diagnosis of the patient can be used to assign a health risk index to the patient, which the Examiner is interpreting fatigue symptoms to encompass symptom data and the application specific model may be implemented in various manners, as described herein, including but not limited to lookup tables, decision trees, machine learning algorithms to encompass at least one second machine learning algorithm when combined with McNamar's disclosure of insurance information, and the Examiner is interpreting the prescription information can include the date when medication was altered and the change in medical usage, or dosage, or type of medication to encompass determining a medication from a plurality of medications as the invention of Bharmi is knowledgeable of a plurality of medications ([0301]-[0302])); and
providing treatment to the user corresponding to the medical diagnosis (See Paragraphs [0229]-[0232]: The treatment recommendation may recommend a change in diuretic dosage for the patient that is tailored to lower a long term upward PAP trend, which the Examiner is interpreting the treatment recommendation may recommend a change in diuretic dosage to encompass the claimed portion.)
While Bharmi discloses the method as described above, Bharmi may not explicitly teach associating a plurality of medical diagnoses with the insurance information associated with the user.
McNamar teaches a method for associating a plurality of medical diagnoses with the insurance information associated with the user (See Paragraphs [0151]-[0152]: A potential diagnosis is identified based on the algorithm calculation results, an insurance carrier associated with the patient and insurance coverage of that insurance carrier for one or more treatments for the potential diagnosis are determined, which the Examiner is interpreting to encompass the claimed portion when combined with Bharmi.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Bharmi to include associating a plurality of medical diagnoses with the insurance information associated with the user as taught by McNamar. One of ordinary skill in the art at the time the invention was made would have found it obvious to include the method, system, and computer software for using xbrl medical record for diagnosis, treatment, and insurance coverage as taught by McNamar with the methods, devices, and systems for holistic integrated healthcare patient management taught by Bharmi with the motivation of reducing medical errors and costs (See McNamar in Paragraphs [0173]-[0l 76]).
While Bharmi/McNamar discloses the method as described above, Bharmi/McNamar may not explicitly teach automatically authorizing, without human intervention, submission of the medication to a supplier; and
causing shipment of the medication to a location identified in the user profile to treat the medical diagnosis.
Yered teaches a method for automatically authorizing, without human intervention, submission of the medication to a supplier (See Paragraphs [0017]-[0018], [0096]-[0097]: The prescription claims processing center may then route the prescription request to the selected prescription service provider for fulfillment of the prescription, which the Examiner is interpreting the selected prescription service provider to encompass a supplier); and
causing shipment of the medication to a location identified in the user profile to treat the medical diagnosis (See Paragraphs [0098]-[0099]: The prescription claims processing center may then arrange for the filled prescription to be delivered to the geographical location of the customer within a desired time frame.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Bharmi/McNamar to include automatically authorizing, without human intervention, submission of the medication to a supplier, and causing shipment of the medication to a location identified in the user profile to treat the medical diagnosis as taught by Yered. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Bharmi/McNamar with Yered with the motivation of providing convenience and expedited processing and delivery of prescription services (See Background of Yered in Paragraph [0006]).
Claim(s) 29 mirrors claim 1 only within a different statutory category, and is rejected for the same reason as claim 1.
As per claim 2, Bharmi/McNamar/Yered discloses the method of claim 1 as described above. Bharmi further teaches further comprising sending the symptom data, the user profile the medical diagnosis and the medication to a physician or care-giver identified in the user profile (See Paragraph [0214]: The diagnosis and treatment recommendation may be provided to the patient's physician, which the Examiner is interpreting the diagnosis and treatment recommendation to encompass the medical diagnosis and the prescription, the patient's physician as a physician identified in the user profile, and the data and user profile is encompassed by Paragraph [0297] that a physician may designate a patient medical record to be analyzed to encompass symptom data and user profile.)
Claim(s) 30 mirrors claim 2 only within a different statutory category, and is rejected for the same reason as claim 2.
As per claim 3, Bharmi/McNamar/Yered discloses the method of claim 1 as described above. Bharmi further teaches further comprising, after administration of the medication, receiving follow-up data from the first device regarding efficacy of the medication (See Paragraph [0293]: The process affords a real-time analysis, albeit over several days, indicating whether the prescription change caused a positive impact on a primary related physiologic characteristic.)
Claim(s) 31 mirrors claim 3 only within a different statutory category, and is rejected for the same reason as claim 3.
As per claim 4, Bharmi/McNamar/Yered discloses the method of claims 1 and 3 as described above. Bharmi further teaches further comprising sending the symptom data, the follow-up data, the user profile, the medical diagnosis and the medication to a physician or care-giver identified in the user profile (See Paragraph [0214]: The diagnosis and treatment recommendation may be provided to the patient's physician, which the Examiner is interpreting the diagnosis and treatment recommendation to encompass the medical diagnosis and the prescription, the patient's physician as a physician identified in the user profile, and the symptom data and user profile is encompassed by Paragraph [0297] that a physician may designate a patient medical record to be analyzed to encompass symptom data and user profile.)
Claim(s) 32 mirrors claim 4 only within a different statutory category, and is rejected for the same reason as claim 4.
As per claim 6, Bharmi/McNamar/Yered discloses the method of claims 1 and 3-4 as described above. Bharmi further teaches further comprising, if the follow-up data indicates persistence of the medical condition, scheduling an in-person appointment for the user with the physician or care-giver identified in the user profile (See Paragraph [0317]: A high risk patient may be given priority, such that a high risk patient must be scheduled within a predetermined period, which the Examiner is interpreting to encompass the claimed portion.)
Claim(s) 33 mirrors claim 6 only within a different statutory category, and is rejected for the same reason as claim 6.
As per claim 8, Bharmi/McNamar/Yered discloses the method of claim 1 as described above. Bharmi further teaches further comprising presenting the medical diagnosis and the medication on the first device for display to the user (See Paragraph [0143]: The communication may be presented in various formats such as to display patient information, messages, user directions and the like.)
Claim(s) 34 mirrors claim 8 only within a different statutory category, and is rejected for the same reason as claim 8.
As per claim 11, Bharmi/McNamar/Yered discloses the method of claim 1 as described above. Bharmi may not explicitly teach further comprising: sending information indicative of the medical diagnosis to an insurance system; and
sending medical premium information to the insurance system.
McNamar teaches a method further comprising: sending information indicative of the medical diagnosis to an insurance system (See Paragraphs [0150]-[0151], [0167]: Diagnoses and costs can be communicated to an insurance provider); and
sending medical premium information to the insurance system (See Paragraphs [0150]-[0151], [0167]: Diagnoses and costs can be communicated to an insurance provider.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Bharmi to include sending information indicative of the medical diagnosis to an insurance system, and sending medical premium information to the insurance system as taught by McNamar. One of ordinary skill in the art at the time the invention was made would have found it obvious to include the method, system, and computer software for using xbrl medical record for diagnosis, treatment, and insurance coverage as taught by McNamar with the methods, devices, and systems for holistic integrated healthcare patient management taught by Bharmi with the motivation of reducing medical errors and costs (See McNamar in Paragraphs [0173]- [0176]).
Claim(s) 35 mirrors claim 11 only within a different statutory category, and is rejected for the same reason as claim 11.
As per claim 12, Bharmi/McNamar/Yered discloses the method of claim 1 as described above. Bharmi may not explicitly teach further comprising sending payment information to a payment system, wherein the payment information is encrypted.
McNamar teaches a method further comprising sending payment information to a payment system, wherein the payment information is encrypted (See Paragraphs [0111], [0125]- [0127]: McNamar teaches sending payment data and that data may be encrypted.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Bharmi to include sending payment information to a payment system, wherein the payment information is encrypted as taught by McNamar. One of ordinary skill in the art at the time the invention was made would have found it obvious to include the method, system, and computer software for using xbrl medical record for diagnosis, treatment, and insurance coverage as taught by McNamar with the methods, devices, and systems for holistic integrated healthcare patient management taught by Bharmi with the motivation of reducing medical errors and costs (See McNamar in Paragraphs [0173]-[0176]).
Claim(s) 36 mirrors claim 12 only within a different statutory category, and is rejected for the same reason as claim 12.
Claims 22, 27, and 37-38 are rejected under 35 U.S.C. 103 as being unpatentable over Bharmi (U.S. Patent Pre-Grant Publication No. 2021/0020294) in view of McNamar (U.S. Patent Pre-Grant Publication No. 2008/0201172) in view of Yered (U.S. Patent Pre- Grant Publication No. 2007/0050209) in further view of Roh (U.S. Patent Pre-Grant Publication No. 2021/0319914).
As per claim 22, Bharmi/McNamar/Yered discloses the method of claim 1 as described above. Bharmi/McNamar/Yered may not explicitly teach further comprising: causing the first device to present a healthcare virtual avatar; and causing the healthcare virtual avatar to interact with the user to obtain symptom data from the user.
Roh teaches a method further comprising: causing the first device to present a healthcare virtual avatar (See Paragraph [0061]: Creating a virtual reality experience for physical examinations and a physician is able to generate a hologram (See Paragraph [0015])); and causing the healthcare virtual avatar to interact with the user to obtain symptom data from the user (See Paragraph [0067]: Performing a medical examination between the provider and patient within the virtual reality environment including output of questions.)
One of ordinary skill in the art at the time the invention was made would have found it obvious to include receiving from the first device further comprises presenting a healthcare virtual avatar corresponding to the user profile associated with the user and the first device and causing the healthcare virtual avatar to interact with the user to obtain symptom data from the user as taught by Roh with the methods, devices, and systems for holistic integrated healthcare patient management taught by Bharmi/McNamar/Yered with the motivation of improving patient experience (See Roh in Paragraphs [0003]).
Claim(s) 37 mirrors claim 22 only within a different statutory category, and is rejected for the same reason as claim 22.
As per claim 27, Bharmi/McNamar/Yered discloses the method of claim 1 and Bharmi/McNamar/Yered/Roh discloses the method of claim 22 as described above. Bharmi may not explicitly teach wherein the symptom data comprises at least one of: audio data indicative of speech, physiological data, body examination data, a body structure evaluation, body secretion data, data indicative of biological or cell structure, and/or intelligent marker data indicative of a cancerous cell, bacteria, or a virus.
Roh discloses a method wherein the symptom data comprises at least one of: audio data indicative of speech, physiological data, body examination data, a body structure evaluation, body secretion data, data indicative of biological or cell structure, and/or intelligent marker data indicative of a cancerous cell, bacteria, or a virus (See Paragraph [0047]: Roh teaches collecting data including audio data.)
Obviousness for combining the teachings of Bharmi and Roh is discussed in claim 22 above and is incorporated herein.
Claim(s) 38 mirrors claim 27 only within a different statutory category, and is rejected for the same reason as claim 27.
Response to Arguments
In the Remarks filed on January 19, 2026, the Applicant argues that the newly amended and/or added claims overcome the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s). The Examiner does not acknowledge that the newly added and/or amended claims overcome the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s).
The Applicant argues that:
(1) in the accompanying declaration by Dr. Jacques Seguin submitted herewith, one of ordinary skill in the art at the time of filing the instant application would have interpreted the specification and claimed invention to describe significantly more than an abstract idea as well as a practical application of any such abstract idea. The Office action alleges that the judicial exception is not integrated into a practical application because the remaining elements amount to no more than a general purpose computer. Final Office action, page 6. However, the Deputy Commissioner for Patents, Charles Kim, explained in the memo dated August 4, 2025, titled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, and explicitly directed to art unit 3600 (referred to hereinafter as the "Kim Memo"), that "[w]hile an additional limitation (or combination) that merely applies the judicial exception on a generic computer may not render a claim eligible on its own, an additional limitation (or combination) that meaningfully limits the judicial exception can render it eligible." Deputy Commissioner Charles Kim continued on to explain that examiners can conclude that "claims are eligible in Step 2A Prong Two by finding that a claim reflects an improvement to the functioning of a computer or to another technology or technical field, integrating a recited judicial exception into a practical application of the exception." Kim Memo, page 4. The instant claims recite additional limitations and combinations of limitations that apply any such judicial exception in a manner that meaningfully limits such judicial exception and results in a practical application of such judicial exception. As explained in the accompanying declaration by Dr. Jacques Seguin, "[t]he currently pending claims describe an invention that would have been understood by one of ordinary skill in the art at the time of filing the instant application as being an improvement on the current technology thereby integrating any judicial exception in the claim into a practical application." Dr. Seguin opined that "at the time of filing the instant application, there did not exist a device or system (e.g., software platform) capable of processing symptom data and data from a user profile (e.g., medical history data) using a machine learning algorithm to determine a presence of a medical diagnosis and also capable of processing the medical diagnosis and information from the user profile (e.g., insurance information and medical history data) to determine a medication from a plurality of medications for treating the diagnosis, let alone either automatically authorize submission of a request for the medication, cause shipment of the medication to the user, and provide the treatment to the user." See Seguin Decl. ¶ 4. As explained by Dr. Seguin, the claims explicitly recite the features which support this improved functionality. See Seguin Decl. ¶ 15. In his declaration, Dr. Seguin explained that one of ordinary skill in the art at the time of filing the present application would interpret the claimed invention to be an improvement of the telehealth system and wearable devices or smart sensor systems that existed at the time of filing the present application as the claimed invention would have understood the cost savings involved with employing AI tool in diagnosis and treatment. See Seguin Decl. ¶ 16. Further, one of ordinary skill in the art at the time of filing the present application would further interpret the claimed invention to be an improvement over current wearables or smart sensors systems because of the ability to automatically determine a presence of a medical diagnosis based on symptom data and medical history data or other relevant user data and automatically determine a medication and cause shipment of the medication. See Seguin Decl. ¶ 17;
(2) the Office action further alleges that 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" and reasons that the claims do not "include additional elements that are sufficient to amount to significantly more" because "the additional elements are recited at a high level generality, and the written description indicates that these elements are generic computer components." See Final Office action, pages 6-7. As explained in the accompanying declaration by Dr. Jacques Seguin "one of ordinary skill in the art at the time of filing the present application would have understood that the unconventional arrangement of additional elements amounts to an inventive concept." See Seguin ¶ 18. Dr. Seguin opined that one of ordinary skill in the art at the time of filing the instant application would have would have understood that "the additional elements such as the user profile and the database associating a plurality of medical diagnoses with the insurance information associated with the user results in an unconventional arrangement of elements that amounts to an inventive concept in facilitating the generating of the likelihood of a medical diagnosis using a first machine learning algorithm and determining a medication for treatment of the medical diagnosis using a second machine learning algorithm." See Seguin Decl. ¶ 20. For example, Dr. Seguin explained that specification of the instant application details the unique functionality and role the "user profile" plays in facilitating a determination of a medical diagnosis, determining a medication corresponding to the medical diagnosis, and determining a location for shipment of the medication. Additionally, the speciation describes databases of medication and information such as insurance information which supports the ultimate determination of the medication for treating the medical diagnosis. See Seguin Decl. ¶ 19. For the foregoing reasons, Dr. Seguin provided that "it is my opinion that one of ordinary skill in the art at the time of filing the instant application would have interpreted the specification and claimed invention to describe significantly more than an abstract idea as well as a practical application of any such abstract idea." See Seguin Decl. ¶ 21. Regarding the role of 35 U.S.C. §101 more generally, Director Squires recently joined the Appeals Review Panel (ARP) in the case of Ex Parte Desjardins. The ARP was clear in its directive with respect to Section 101 that"§§ 102, 103 and 112 are the traditional and appropriate tools to limit patent protection to its proper scope" and that "[t]hese statutory provisions should be the focus of examination." See Ex Parte Desjardins, page 10. Consistent with this reasoning, the Kim Memo provides that "Examiners are reminded that if it is a 'close call' as to whether a claim is eligible, they should only make a rejection when it is more likely than not (i.e., more than 50%) that the claim is ineligible under 35 U.S.C. 101." For these reasons, Applicant submits that the pending claims are patent eligible pursuant to 35 U.S.C. § 101. Accordingly, Applicant submits that the pending claims are in condition for allowance;
(3) Applicant also respectfully notes that the beginning the Fiscal Year 2026, the USPTO is augmenting its existing quality review programs "by prioritizing statistically identified, high-deviation areas for strategic reviews." See USPTO press release on November 21, 2025, titled "USPTO Launches Data-Driven Quality Initiative to Address Areas of Highest Deviation." The initiative will be a "new, data-driven quality initiative that strategically focuses our resources on examination areas showing the greatest statistical deviation." As shown in the attached printout from Juristat, Inc., Examiner Bennett Erickson's allowance rate is around 15% with an allowance rate of around 5% in 2023, 20% in 2024, and 16% in 2025, while Supervisory Patent Examiner Robert Morgan's allowance rate is around 19% with an allowance rate at 0% over the last 5 years. These allowance rates are high deviations from the allowance rate of Art Unit 3683 of around 34%. As such, Applicant requests further USPTO review of this application;
(4) neither Bharmi, McNamar, nor Yered teaches or suggests, inter alia, "consult[]... a user profile associated with at least a first device and comprising medical history data, biometric data, and insurance information associated with a user corresponding to the user profile," "process[] the symptom data and at least some data from the user profile including the medical history data using at least one machine learning algorithm trained to determine at least one presence of a medical diagnosis," "determine[], by the at least one second machine learning algorithm and without human intervention, using the medical history data in the user profile, the medical diagnosis, and a database associating a plurality of medical diagnoses with the insurance information associated with the user, a medication from a plurality of medications for treating the medical diagnosis," and "caus[e] shipment of the medication to a location identified in the user profile, to treat the medical diagnosis," as recited by amended claim 1. With respect to the claim 1 limitation, "user profile associated with at least a first device," the Final Office action at page 9 cited paragraph [0153] of Bharmi for support and provided "Devices encoded with a patient ID, which is interpreted as a user profile associated with a device." For the claim 1 limitation "user profile ... comprising medical history data, biometric data, and insurance information associated with a user corresponding to the user profile," the Office action relies on paragraph [0132] and [0302] of Bharmi. With respect to paragraph [0153] of Bharmi, which the Examiner relies upon to teach a user profile, as recited by claim 1, paragraph [0153] mentions only that "[t]he devices (BGA test device, IMD, local external device, MP device) include an encoder and register to store encoded patient IDs, IMD data and BGA data" and that "[t]he IMDs, BGA test devices, local external devices, servers and MP devices are provided with one or more decoders that are configured to or decode patient IDs, IMD data and BGA data." Even under the broadest reasonable interpretation, a device encoded with a patient ID is entirely different from a user profile that comprises medical history data, biometric data, and insurance information and that is associated with at least a first device, as recited by claim 1. Regarding paragraphs [0132] and [0302] of Bharmi, which the Examiner relies upon to teach a user profile "comprising medical history data, biometric data, and insurance information associated with a user corresponding to the user profile," neither paragraph teaches such a user profile. Paragraph [0132] describes an electronic device that includes a user interface that receives patient data that is entered by the patient and patient data in connection with actions/decisions by the patient. In other words, paragraph [0132] describes a device that collects patient data or information, which is entirely different than the claimed user profile. Turning to paragraph [0302], this paragraph describes a processor that collects data including historical data age, height, weight, medications, changes in medications, previous medical procedures, diagnosed medical conditions, medical history, prior health readings such as blood pressure, PAP data, EKG readings, and test results. The one or more processor may receive such information and data from a local memory, a remote processor, a remote memory, the cloud, or from more than one of the local memory, remote processor, remote memory, the cloud, or the like. However, a processor that stores information from a patient by no means is "a user profile... comprising medical history data, biometric data, and insurance information associated with a user corresponding to the user profile" that is also associated with at least a first device comprising a sensor;
(5) with respect to the claimed user profile comprising "insurance information," the Office action relies on "McNamar's disclosure of insurance coverage." McNamar describes a healthcare management system that updates a patient's electronic health record and facilitates insurance payments by providing the insurance coverage to a patient. However, the Final Office action provides no reason or motivation to combine a processor in Bharmi that collects historical data relating to the patient and the system McNamar for insurance carrier payments Further, neither Bharmi, McNamar, nor Yered teach or suggest the at least one first machine learning algorithm and the at least one second machine learning algorithm that generate and determine outputs "without human intervention." Specifically, amended claim 1 recites "generating, without human intervention, a likelihood of a medical diagnosis in real time with respect to the certain time based on the symptom data and at least some data from the user profile including the medical history data and using the at least one first machine learning algorithm," as well as "determining, by the at least one second machine learning algorithm and without human intervention, using the medical history data in the user profile, the medical diagnosis, and a database associating a plurality of medical diagnoses with the insurance information associated with the user, a medication from a plurality of medications for treating the medical diagnosis." However, at no point does Bharmi teach or suggest "processing the symptom data and at least some data from the user profile including the medical history data using at least one first machine learning algorithm trained to determine at least one presence of a medical diagnosis," as recited by amended claim 1. Bharmi is entirely devoid of any machine learning model that processes both symptom data and some data from a user profile including medical history data. As Bharmi is devoid of any teaching of such a user profile, Bharmi could not possibly teach a machine learning model that processes data from the user profile including the medical history data. Further, while Bharmi briefly mentions the term "machine learning algorithm" Bharmi offers no explanation the machine learning algorithm being trained to determine at least one presence of a medical diagnosis or any explanation of what data is input into and processed by the machine learning algorithm or output from the machine learning algorithm. Regarding "determining, by the at least one second machine learning algorithm and without human intervention" of claim 1, the Office action once again looks to FIG. 8D and paragraphs [0014], [0237]-[0239], [0280], [0291]-[0292], [0318] for this teaching, which are the exact same citations relied on to teach the "at least one first machine learning algorithm." Indeed, the cited support for teaching the at least one first machine learning model and the at least one second machine learning model is identical- "See Fig. 8D and Paragraphs [0114], [0237]-[0239], [0280], [0291]-[0292], [0318]: The process of Fig. 8D may utilize functional information about the patient, such as activity, fatigue symptoms, quality of life, and the diagnosis of the patient can be used to assign a health risk index to the patient, which the Examiner is interpreting as fatigue symptoms to encompass symptom data and the application specific model may be implemented in various manners, as described herein, including but not limited to lookup tables, decision trees, machine learning algorithms..." (See Office action, pages 9-11). However, at no point does Bharmi teach or suggest using an additional machine learning model, which is different from the at least one first machine learning model, for "determining, by the at least one second machine learning algorithm and without human intervention, using the medical history data in the user profile, the medical diagnosis, and a database associating a plurality of medical diagnoses with the insurance information associated with the user, a medication from a plurality of medications for treating the medical diagnosis" and further does not teach that each machine learning algorithm processes respective information without human intervention to generate a presence of a medical diagnosis and a medication for treating the medical diagnosis, respectively. Merely copying and pasting the same citations used for the at least one first machine learning model from pages 9-10 of the Office action fails to support a teaching of using at least one second machine learning algorithm to determine a medication from a plurality of medications for treating the medical diagnosis using (1) medical history data in the user profile, (2) the medical diagnosis, and (3) a database associating a plurality of medical diagnoses with the insurance information associated with the user;
(6) finally, to teach "causing shipment of the medication to a location identified in the user profile, to treat the medical diagnosis," the Office action acknowledges that both Bharmi and McNamar fail to teach this limitation and relies on Yered for this teaching. Specifically, the Office action looks to paragraphs [0098]-[0099] and suggest that "[t]he prescription claims processing center may then arrange for the filled prescription to be delivered." However, the Office action points to no teaching in Yered and Yered is silent with respect to any teaching of a shipping location identified in the user profile and only suggests that "[i]t would have been obvious to one of ordinary skill ... to modify the method of Bharmi/McNamar to include... causing shipment to the medication to a location identified in the user profile, despite needing three separate references to cobble together support for such a user profile. The Office action's conclusory declaration "that it would have been obvious" to include such a location in the user profile together with the numerous Examiner interpretations and copy and past rejections for entirely different claim components (i.e., at least one first machine learning model and at least second machine learning model) demonstrate the lengths at which each reference of the obviousness rejection has been stretched and contorted with the aid of impermissible hindsight to arrive at the limitations in claim 1. For the foregoing reasons, Applicant respectfully submits that it would not have been obvious to modify or combine Bharmi, McNamar, or Yeder to arrive at the inventions claimed in independent claim 1. Accordingly, Applicant respectfully requests that the obviousness rejection with respect to claim 1 be withdrawn. Claims 2-4, 6, 8, 11-12, 22, 27, and 29-38 are allowable at least by virtue of their respective dependencies from an allowable independent claim, in addition to their further patentable recitations. While differing in scope, amended independent claim 29 is allowable for similar and/or analogous reasons as set forth above with respect to claim 1. Claims 30-38 are allowable at least by virtue of their respective dependencies from an allowable independent claim, in addition to their further patentable recitations. Accordingly, Applicant respectfully requests that the obviousness rejections be withdrawn.
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner does not acknowledge that the “Kim Memo” applies to the Applicant’s newly amended claims as the Applicant’s newly amended claims do not reflect a clear improvement to a technology or to computer functionality. The Applicant’s newly amended claims are similar to “iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48” (See MPEP 2106.05(a)(II)) which the courts have indicated may not be sufficient to show an improvement to technology. The Examiner maintains that the Applicant’s claims as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. The Examiner maintains that the Applicant’s additional elements amount to no more than general purpose computer components programmed to perform the abstract ideas, insignificant extra-solution activity, and generally linking the abstract idea to a technical environment. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (2), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner does not acknowledge that the Applicant’s additional elements amount to no more than general purpose computer components programmed to perform the abstract ideas, insignificant extra-solution activity, and generally linking the abstract idea to a
technical environment. The Examiner does not acknowledge that utilizing a “user profile” and “database associating a plurality of medical diagnoses with the insurance information associated with the user” is an unconventional arrangement of elements that would amount to an inventive concept as these elements are similar to gathering data to make a decision, which the additional elements of “a first machine learning algorithm” and “a second machine learning algorithm” use the information to make decisions. The Examiner maintains that querying a database does not integrate a judicial exception into a practical application. The Examiner maintains that the decision to reject the newly amended claims under 35 U.S.C. 101 was not a “close call”. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (3), the Examiner does not find the Applicant’s argument(s) persuasive. Juristat, Inc. is an artificial intelligence analytics platform. Examiner Erickson examines each case on a case-by-case basis, and examines each case based on the present claims. The Examiner does not make decisions on allowability based on statistics, but on the patentability of the present claims. The Examiner maintains that the Applicant’s claims are rejected under 35 U.S.C. 101. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (4), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the combination of Bharmi (U.S. Patent Pre-Grant Publication No. 2021/0020294) in view of McNamar (U.S. Patent Pre-Grant Publication No. 2008/0201172) in further view of Yered (U.S. Patent Pre-Grant Publication No. 2007/0050209) encompasses the newly amended claimed portions as described above in the 35 U.S.C. 103 rejection(s). The “user profile” given the broadest reasonable interpretation is a collection of information that possesses an identification that the information belongs to a certain person. Bharmi in Paragraph [0185] further describes “For example, each time a BGA test device analyzes a blood sample or other fluid sample from the patient, the BGA test device will send a new data upload request (with the patient ID and test results) to a server, database or other computing device of a medical network.” The patient ID of Bharmi links the collection of data to a certain patient, and the patient ID is encoded to a certain device. The Examiner maintains that Bharmi in Paragraphs [0132], [0302] teaches “comprising medical history data, biometric data, and insurance information associated with a user corresponding to the user profile,” when combined with the teachings of McNamar as described above in the 35 U.S.C. 103 rejection(s). The Applicant’s use of the “user profile” is to link data to a certain person (Specification in Paragraphs [0085]-[0088]). Additionally, Bharmi teaches “the first device comprising a sensor” in Paragraph [0151]. The 35 U.S.C. 103 rejection(s) stand.
In response to argument (5), the Examiner does not find the Applicant’s argument(s) persuasive. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Bharmi to include associating a plurality of medical diagnoses with the insurance information associated with the user as taught by McNamar. One of ordinary skill in the art at the time the invention was made would have found it obvious to include the method, system, and computer software for using xbrl medical record for diagnosis, treatment, and insurance coverage as taught by McNamar with the methods, devices, and systems for holistic integrated healthcare patient management taught by Bharmi with the motivation of reducing medical errors and costs (See McNamar in Paragraphs [0173]-[0176]). Bharmi in Paragraphs [0141], [0210], [0222], [0318], [0321], [0359] teaches “the at least one first machine learning algorithm and the at least one second machine learning algorithm that generate and determine outputs ‘without human intervention’”. Bharmi teaches “processing the symptom data and at least some data from the user profile including the medical history data using at least one first machine learning algorithm trained to determine at least one presence of a medical diagnosis” in Fig. 8D and Paragraphs [0114], [0189], [0237]-[0239], [0280], [0291]-[0292], [0318] as the process of Fig. 8D may utilize functional information about the patient, such as activity, fatigue symptoms, quality of life, and the diagnosis of the patient can be used to assign a health risk index to the patient. Further, the ASM may be implemented as at least one of a threshold-based algorithm, template correlation algorithm, lookup table, decision tree, or machine learning algorithm (Bharmi Paragraph [0016]). Bharmi in Paragraphs 0141], [0359] teaches that the analysis of the serum albumin and generation of the diagnosis and treatment recommendation are performed in real-time, namely while the patient is experiencing a certain malnutrition state, not to exceed 24 hours from the time the BGA was collected, which the Examiner is interpreting to encompass in real time with respect to the certain time based on the symptom data and at least some data from the user profile as the BGA data may be previously acquired and stored in the patient's medical record. As previously responded to in the Final Rejection mailed on February 28, 2025 on p. 36 “Bharmi may not explicitly recite a ‘user profile’, but the Examiner has interpreted the Applicant’s recitation of ‘user profile’ as an identifier of a certain patient (Specification [0061]-[0063], [0075], [0077], [0082]) as this appears to be the function of the ‘user profile’ in the claimed invention.”. Bharmi describes in Paragraph [0033]: “The system further includes a processor configured to implement the program instructions to: apply an application-specific model (ASM) to the medical data collection to determine a diagnosis and a treatment notification based on the diagnosis; wherein the ASM is implemented as at least one of a threshold-based algorithm, template correlation algorithm, lookup table, decision tree, or machine learning algorithm.”, which the Examiner is interpreting to explain that “the machine learning algorithm being trained to determine at least one presence of a medical diagnosis or any explanation of what data is input into and processed by the machine learning algorithm or output from the machine learning algorithm”. The Examiner maintains that the Bharmi’s disclosure of “at least one of a threshold-based algorithm, template correlation algorithm, lookup table, decision tree, or machine learning algorithm” ([0033]) teaches that more than one machine learning algorithm can be utilized, and further in Paragraph [0139] a risk score algorithm may be used to determine a risk range or risk category a patient falls into with the range or risk category illustrated by color, bars, and the like, which the Examiner is interpreting is different from the ASM as the risk score algorithm is relied upon to teach the first machine learning algorithm and the ASM to teach the second machine learning algorithm. The 35 U.S.C. 103 rejection(s) stand.
In response to argument (6), the Examiner does not find the Applicant’s argument(s) persuasive. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). Yered discloses in Paragraphs [0098]-[0099]: “When the selected prescription service provider receives the prescription request, the selected prescription service provider may fulfill the prescription. The prescription claims processing center may then arrange for the filled prescription to be delivered to the geographical location of the customer within a desired time frame. Alternatively, the selected prescription service provider may offer their own delivery service that picks up the filled prescription and delivers it to the customer's geographic location within the desired time frame. In both cases, the desired time frame may include expedited same-day delivery on the same day the request was submitted.” and “FIG. 5 conceptually illustrates an overview of a flow of operations that may occur, in one embodiment of a system and method for providing expedited processing and delivery of prescription services. In initial act 510, a request for a prescription may be submitted to a centralized prescription claims processing center 500. The prescription request may be a request for a prescription drug (i.e. a drug that needs a prescription from a medical professional). Alternatively, the prescription request may be a request for a written prescription order for a prescription drug.” The Final Rejection mailed on February 28, 2025 pointed to Paragraphs [0017]-[0018], [0096]-[0099] to teach “automatically authorizing, without human intervention, submission of the medication to a supplier” and “causing shipment of the medication to a location identified in the user profile to treat the medical diagnosis”. The Applicant’s Specification describes the “shipment” in Paragraph [0163], but does not describe how the medication is shipped, only that the medication system could, in some instances, “manage user requests for medication shipment and/or refills” and “The medication system may also automatically facilitate shipments of medication re-fills and/or perform any other automated processes with respect to user medication.” (Specification in Paragraph [0163]). The Examiner maintains that the “geographic location of the customer” (Yered in Paragraph [0007]) encompasses “a shipping location”. The Examiner maintains that the combination of Bharmi (U.S. Patent Pre-Grant Publication No. 2021/0020294) in view of McNamar (U.S. Patent Pre-Grant Publication No. 2008/0201172) in further view of Yered (U.S. Patent Pre-Grant Publication No. 2007/0050209) teaches claims 1-4, 6, 8, 11-12 and 29-36. The 35 U.S.C. 103 rejection(s) stand.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Muse et al. (U.S. Patent Pre-Grant Publication No. 2021/0241869), describes systems and methods are configured for maintaining identifying data of a first user in association with a personal device of the first user; maintaining a rolling recording of medical data generated at the personal device of the first user, wherein the rolling recording encompasses data generated during a defined duration of time; receiving a request to access the medical data from a second device, wherein the request comprises authorization credentials associated with the second device.
Ohnemus et al. (U.S. Patent Publication No. 2021/0241869), describes a system and method are disclosed for computing a Health Score, and health data and extrinsic data are received that are parameters for computation of the Health Score.
Tarar et al. (“Wearable Skin Sensors and Their Challenges: A Review of Transdermal, Optical, and Mechanical Sensors”), describes three different biosensing modalities that are commonly used along with the challenges faced in their implementation for detection.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bennett S Erickson whose telephone number is (571)270-3690. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Bennett Stephen Erickson/ Primary Examiner, Art Unit 3683