DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Final Office Action is in response to the Amendment and Remarks filed 05/18/2026. Claims 1-20 are currently pending and considered herein.
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.
Claim 1 recites as follows, wherein the abstract elements are not emboldened:
A computer-implemented method for mental health (MH) risk prediction using passive sensing of wireless glucometer data, comprising: receiving mental health risk input signals for a subject, the mental health risk input signals including: glucometer data for the subject including at least one blood glucose value and a total number of blood glucose checks performed within a particular time interval, wherein the glucometer data is received via a wireless network, and demographic data for the subject entered by the subject via a computing device; inputting the mental health risk input signals into a machine learning (ML) system previously trained with mental health risk input signals for a plurality of subjects and mental health status data for the plurality of subjects; obtaining a prediction of mental health risk for the subject from the ML system; and transmitting a notification to a remote healthcare provider that the subject represents a mental health risk.
Independent claim 11 recites substantially similar limitations. The above limitations of “receiving mental health risk input signals for a subject, the mental health risk input signals including: glucometer data for the subject including at least one blood glucose value and a total number of blood glucose checks performed within a particular time interval, wherein the glucometer data is received, and demographic data for the subject entered by the subject; inputting the mental health risk input signals […] with mental health risk input signals for a plurality of subjects and mental health status data for the plurality of subjects; obtaining a prediction of mental health risk for the subject; and a notification to a healthcare provider that the subject represents a mental health risk,” as drafted, is a process that, under its broadest reasonable interpretation, is an abstract idea that covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than broadly reciting a “computer-implemented method” (claims 1-10) “system” (claims 11-20) and recitation of a generic computer device, wireless network, machine learning system and “wireless glucometer data,” and “signals,” nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the generic computer components and language and glucometer, a computer-implemented method or system, in the context of this claim, encompasses one skilled in the pertinent art to manually determine mental health risk based on a blood glucose data reading, history and demographics when observing a patient. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
Additionally, the claims recite the abstract idea of a form of organizing human activity including following rules or instructions, and broadly amounts to interactions between a physician observing and diagnosing her patients. The claims appear to monopolize the diagnostic techniques of the physician making clinical assessments using blood glucose data and other patient data. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of using glucometer data, sending signals to/from a generic computer device and training a machine learning system as a function of mental health risk input signals and history (a generic computer device, wireless network, machine learning system and “wireless glucometer data,” and “signals”). However, the use of a device output measurement and the machine learning system in these steps are recited at a high-level of generality (i.e., as a generic processor/server/storage/display performing a generic computer function of receiving inputs, analyzing the inputs, and displaying selected information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements when considered separately and as an ordered combination do not integrate the judicial exception/abstract idea into a “practical application” of the judicial exception because they do not impose any meaningful limit on practicing the judicial exception. The claim is thus directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a generic computer device, wireless network, machine learning system and “wireless glucometer data,” and “signals,” amounts to no more than mere instructions to apply the exception using a computer component, namely data communication and training models. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter. The dependent claims further limit the abstract idea, but do not overcome it. Claims 2 and 12 include “neural networks” and defines the machine learning system and comprises, like the machine learning it limits, mere instructions to apply the exception using a computer component. Claims 3 and 13 define glucometer data and limit the abstract idea. Claims 4 and 14 define demographic data. Claims 5 and 15 narrow the mental health status data including medications, assessments, insurance claims and interventions and merely limits the abstract the idea further. Claims 6-7 and 16-17 include additional training steps for a machine learning model, but does not amount to a technological improvement or practical application, and further limits the abstract idea. Claims 8-9 and 18-19 narrows the mental health input signals to include coaching data and further limits the abstract the idea. Claims 10 and 20 narrows the mental health risk signals and further limits the abstract idea. Thus, the additional limitations from dependent claims merely detail a type of data input or calculated from one source or another, and limits the abstract idea.
Therefore, the claims are not patent eligible.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-2, 4, 6, 10-12, 14, 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2023/0094344 A1 to Tara, hereinafter “Tara,” in view of U.S. 2024/0079145 A1 to Conward, hereinafter “Conward,” in view of U.S. 9,241,631 B2 to Valdes et al., hereinafter “Valdes” and further in view of U.S. 2014/0032195 A1 to Albisser et al., hereinafter “Albisser.”
Regarding claim 1, Tara discloses A computer-implemented method for mental health (MH) risk prediction using passive sensing of wireless glucometer data, comprising: receiving mental health risk input signals for a subject (See Tara at least at Abstract (collecting mental evidence nodes and other mental health data); Paras. [0046]-[0049] (“[C]ommunication of data and/or signals between any of the components.” Blood glucose sensor and data related to mental state of patient.) [0333]-[0338] (mental predictive model), [0368] (signals), [0391]; Figs. 1, 2, 5, 8, 9), the mental health risk input signals including: glucometer data for the subject including at least one blood glucose value (See id. at least at Paras. [0010]-[0011] (blood glucose sensor data), [0040], [0046]-[0049] (blood glucose sensor); Claim 1; Figs. 1, 2, 8, 10-12), and demographic data for the subject (See id. at least at Paras. [0032] (demographic factors of the patient), [0035], [0042], [0107] (demographic and socioeconomic and genotypical data)); and transmitting a notification to a remote healthcare provider that the subject represents a mental health risk (See id. at least at Paras. [0040]-[0041] (Risk prediction), [0048]-[0049] (Blood glucose sensor and sensor data related to a mental state of a patient), [0167]-[0169]), [0182] (An alert is sent when health score is declining), [0183]-[0185], [0296] (Score includes mental health), [0333]-[0334] (Mental predictive model); Figs. 10-12)).
Tara may not specifically describe but Conward teaches inputting the mental health risk input signals into a machine learning (ML) system previously trained with mental health risk input signals for a plurality of subjects and mental health status data for the plurality of subjects (See Conward at least at Abstract (machine learning model using patient-generated data and biometric data); Paras. [0007]-[0012], [0019]-[0022] (machine learning and health risks including mental health risks), [0068], [0105]-[0107], [0115], [0145]; Claim 12; Figs. 2-7); and obtaining a prediction of mental health risk for the subject from the ML system (See id. at least at Paras. [0012], [0019]-[0022] (machine learning and predictions), [0061]-[0062], [0073]-[0080] (predictions and diagnoses), [0105]-[0106]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara to incorporate the teachings of Conward and provide machine learning for various health inputs. Conward is directed to systems for deriving health indicators from various content, generating priorities using machine learning. Incorporating the machine learning techniques as in Conward with the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would thereby increase the applicability, utility, and efficacy of mental health risk detection using glucometer data.
The references may not specifically describe but Valdes teaches a total number of blood glucose checks performed within a particular time interval, wherein the glucometer data is received via a wireless network (See Valdes at least at Abstract (Continuous analyte (glucose) sensors.); Col. 12, ln. 22-51 (“[A]nalyte (e.g., glucose) from a biological sample produces a current flow at a working electrode of the device 120, with equal current provided by a counter electrode in a reference circuit. The current is converted in an analog section by a current to voltage converter to a voltage, which is inverted, level-shifted, and delivered to an A/D converter in the processor (see FIG. 4). As part of the calibration, the processor can set the analog gain via its control port. The A/D converter is preferably activated at one-second intervals.”); Claims 1 (“[R]eal-time glucose concentration values associated with the continuous glucose sensor on the user interface and a network server configured to generate a computer-displayable performance report that includes a graphical representation of measured glucose concentration values over time.”), 8, 9; Figs. 1, 3-5, 7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara and Conward to incorporate the teachings of Valdes and provide glucose checks per time interval. Valdes is directed to a continuous glucose sensor and recording data. Incorporating the continuous glucose sensor and measurements as in Valdes with the machine learning techniques as in Conward and the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would improve the glucometer data for assessing mental health patients.
The references may not specifically describe but Albisser teaches demographic data for the subject entered by the subject via a computing device (See Albisser at least at Para. [0030] (“Once installed, the database 14 and program 15 are personalized, if not already personalized by using the glucometer's user interface, with the patient's and his caregiver's demographic information. The patient 11 can then optionally enter additional recent SMBG values, lifestyle, and diabetes medication details.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara, Conward and Valdes to incorporate the teachings of Albisser and provide demographic and other data inputted by a user. Albisser is directed to a blood glucose meter and improving patient health. Incorporating the blood glucose monitoring techniques of Albisser with the continuous glucose sensor and measurements as in Valdes, the machine learning techniques as in Conward and the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would improve the data for determining mental health risks for a patient using a glucometer.
Regarding claim 2, Tara as modified by Conward, Valdes and Albisser teaches all the limitations of claim 1 and Conward further teaches wherein the ML system comprises a neural network (See Conward at least at Paras. [0019] (“[A] machine learning platform may process health data using one or more approaches including, but not limited to, neural networks, decision tree learning, deep learning, etc.”), [0039]-[0042]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara, Valdes and Albisser to incorporate the teachings of Conward and provide machine learning comprising neural networks. Conward is directed to systems for deriving health indicators from various content, generating priorities using machine learning. (See Conward at Paras. [0039]-[0042]). Incorporating the machine learning and neural network as in Conward with the blood glucose monitoring techniques of Albisser, the continuous glucose sensor and measurements as in Valdes and the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would improve the determinations for mental health risks for a patient using a glucometer.
Regarding claim 4, Tara as modified by Conward, Valdes and Albisser teaches all the limitations of claim 1 and Tara further discloses wherein the demographic data includes one or more of: age; body mass index (BMI); gender; race; diabetes status; and smoking status. (See id. at least at Abstract (smoking status); Paras. [0010]-[0011], [0032], [0051], [0053], [0309]).
Regarding claim 6, Tara as modified by Conward, Valdes and Albisser teaches all the limitations of claim 1 and Tara further discloses for each subject of a first set of subjects, collecting mental health risk input signals including: glucometer data including a glucose value, demographic data, and mental health status data value (See id. at least at Paras. [0010]-[0011], [0032], [0040], [0048], [0309]-[0311]; Claim 1; Figs. 1, 2). While Conward teaches creating a training set comprising the glucometer data, demographic data, and mental health status data for each subject of the first set of subjects; and training the ML system in a training stage using the training set to create an ML model (See Conward at least at Paras. [0039]-[0044], [0064]-[0068]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara, Valdes and Albisser to incorporate the teachings of Conward and provide particular training for machine learning models including different health inputs. Conward is directed to systems for deriving health indicators from various content, generating priorities using machine learning. (See Conward at Abstract). Incorporating the machine learning techniques as in Conward with the blood glucose monitoring techniques of Albisser, the continuous glucose sensor and measurements as in Valdes and the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would improve the determinations for mental health risks for a patient using a glucometer.
Regarding claim 10, Tara as modified by Conward, Valdes and Albisser teaches all the limitations of claim 1 and Tara further discloses wherein the mental health risk input signals further include event data relating to one or more of frequency, duration, interactivity, and consistency of interaction sessions associated with use by the subject of a mobile application or web portal (See Tara at least at Paras. [0049], [0125], [0334]-[0336]).
Regarding claim 11, claim 11 recites substantially the same limitations as included in independent claim 1 except for an input device and an output device. Thus, claim 11 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 1, above and wherein Tara further discloses an input device for receiving mental health risk input signals for a subject (See Tara at least at Abstract; Paras. [0036]-[0037]; [0271], Fig. 1 (102)) and an output device for providing a prediction of mental health risk for the subject output by the ML system (See id. at Para. [0372]-[0373]; Fig. 1 (124), (128); Fig. 14).
Regarding claims 12, 14, 16 and 20, claims 12, 14 and 16 and 20 recite substantially the same limitations as included in claims 2, 4, 6 and 10, respectively. Thus, claims 12, 14, 16 and 20 are rejected under the same grounds of rejection and for the same reasoning as applied to claims 2, 4, 6 and 10, above.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Tara, in view of Conward, in view of Valdes, in view of Albisser and further in view of U.S. 2008/0249386 A1 to Besterman et al., hereinafter “Besterman.”
Regarding claim 3, Tara as modified by Conward, Valdes and Albisser teaches all the limitations of claim 1. The references may not specifically describe but Besterman teaches wherein the glucometer data includes one or more of: a proportion of days with blood glucose checks; a minimum blood glucose value; a mean blood glucose; a maximum blood glucose value; a standard deviation of blood glucose values; a proportion of blood glucose values below 70 mg/dl; proportion of blood glucose values above 180 mg/dl; a proportion of blood glucose checks with wellness indicated; a proportion of blood glucose checks with unwell state indicated; a proportion of blood glucose checks without a reported feeling tag; and a proportion of blood glucose checks with exercise indicated (See Besterman at least at Paras. [0046]-[0052] (blood glucose checks and intervals), [0065] (blood glucose check intervals, values below 70 mg/dl, etc.), [0083]; Claim 22; Figs. 1-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara, Conward, Valdes and Albisser to incorporate the teachings of Besterman and provide certain blood glucose measurements and concentrations. Besterman is directed to improving management of patient physiologic status. Incorporating the physiological monitoring and blood glucose data as in Besterman with the machine learning techniques of Conward, the blood glucose monitoring techniques of Albisser, the continuous glucose sensor and measurements as in Valdes and the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would improve the determinations for risks for a patient using data from a glucometer and other health data.
Regarding claim 13, claim 13 recites substantially the same limitations as included in claims 3. Thus, claims 13 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 3, above.
Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Tara, in view of Conward, in view of Valdes, in view of Albisser and further in view of U.S. 2011/0223574 A1 to Crawford et al., hereinafter “Crawford.”
Regarding claim 8, Tara as modified by Conward, Valdes and Albisser teaches all the limitations of claim 1. The references may not specifically describe but Crawford teaches wherein the mental health risk input signals further include coaching data relating to contacts between the subject and a coach (See Crawford at least at Abstract (“The virtual coach works alongside assigned human coaches to assist participants.”); Paras. [0047], [0055], [0072]-[0073]; Claim 3; Figs. 1, 7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara, Conward, Valdes and Albisser to incorporate the teachings of Crawford and provide coaching and interaction records and tasks. Crawford is directed to a directed collaboration platform for online virtual coaching. Incorporating the coaching, contacts and assessment techniques as in Crawford with the machine learning techniques of Conward, the blood glucose monitoring techniques of Albisser, the continuous glucose sensor and measurements as in Valdes and the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would improve the experience and adherence to a patient using a glucometer.
Regarding claim 9, Tara as modified by Conward, Valdes and Albisser and Crawford teaches all the limitations of claim 8, and Crawford further teaches wherein the coaching data includes one or more of: a number of coaching alerts triggered; a number of successful coach-subject contacts (See Crawford at Abstract; Paras. [0072]-[0073]; Claim 3 (virtual coach and alerts and sending messages and encouragement), Claim 6; Figs. 1-7); a number of successful coach-subject contacts by phone; a number of attempted unsuccessful coach-subject contacts by phone; a number of successful coach-subject contacts by text (See id.); a number of attempted coach-subject unsuccessful contacts by text; a number of successful coach-subject contacts by email; a number of attempted unsuccessful coach-subject contacts by email; a number of successful coach-subject contacts by glucometer; a number of attempted unsuccessful contacts by glucometer; a number of coaching sessions where subjects took scheduled a future coaching session; and average minutes spent on a coaching alert interaction (See id.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara, Conward, Valdes and Albisser to incorporate the teachings of Crawford and provide coaching and interaction records and tasks. Crawford is directed to a directed collaboration platform for online virtual coaching. Incorporating the coaching, contacts and assessment techniques as in Crawford with the machine learning techniques of Conward, the blood glucose monitoring techniques of Albisser, the continuous glucose sensor and measurements as in Valdes and the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would improve the experience and adherence to a patient using a glucometer.
Regarding claims 18-19, claims 18-19 recite substantially the same limitations as included in claims 8-9, respectively. Thus, claims 18-19 are rejected under the same grounds of rejection and for the same reasoning as applied to claims 8-9, above.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Tara, in view of Conward, in view of Valdes, in view of Albisser and further in view of U.S. 2016/0019813 A1 to Mullen, hereinafter “Mullen.”
Regarding claim 5, Tara as modified by Conward, Valdes and Albisser teaches all the limitations of claim 1. Tara and Conward may not specifically describe but Mullen teaches wherein the mental health status data includes one or more of: mental health medications prescribed for one or more of the plurality of subjects; mental health assessments made for one or more of the plurality of subjects; mental health insurance claims reported for one or more of the plurality of subjects; and mental health interventions provided for one or more of the plurality of subjects. (See Mullen at least at Paras. [0004], [0027]-[0029], [0038],[0052]-[0060] (mental health history inputs, interventions, medications, goals, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara, Conward, Valdes and Albisser to incorporate the teachings of Mullen and provide inputs including medications, assessments and lifestyle choices. Mullen is directed systems for monitoring and treating individuals with sensory processing issues. Incorporating the mental health inputs and interventions as in Mullen with the machine learning techniques of Conward, the blood glucose monitoring techniques of Albisser, the continuous glucose sensor and measurements as in Valdes and the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would improve mental health assessments for a patient using a glucometer.
Regarding claim 15, claim 15 recites substantially the same limitations as included in claims 5. Thus, claims 15 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 5, above.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tara, in view of Conward, in view of Valdes, in view of Albisser and further in view of U.S. 2021/0287131 A1 to Bhide et al., hereinafter “Bhide.”
Regarding claim 7, Tara as modified by Conward, Valdes and Albisser teaches all the limitations of claim 6. Tara further discloses for each subject of a second set of subjects, collecting mental health risk input signals including: glucometer data including a glucose value, demographic data, and mental health status data (See Tara. at least at Paras. [0010]-[0011], [0032], [0040], [0048], [0309]-[0311]; Claim 1; Figs. 1, 2). While Conward teaches creating a validation set comprising the glucometer data, demographic data, and mental health status data for each subject of the second set of subjects; validating the ML model in a validation stage using the validation set; (See Conward at least at Paras. [0019], [0028], [0040]-[0041], [0046], [0052], [0055]; Figs. 1, 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara, Valdes and Albisser to incorporate the teachings of Conward and provide machine learning validation for various health inputs. Conward is directed to systems for deriving health indicators from various content, generating priorities using machine learning. Incorporating the machine learning techniques as in Conward with the blood glucose monitoring techniques of Albisser, the continuous glucose sensor and measurements as in Valdes and the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would improve mental health assessments for a patient using a glucometer.
Tara as modified by Conward, Valdes and Albisser may not specifically describe but Bhide teaches updating the ML model in response to one or more validation errors (See Bhide at least at Paras. [0034]-[0040], [0048]-[0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Tara, Conward, Valdes and Albisser to incorporate the teachings of Bhide and provide an updated machine learning model in response to errors. Bhide is directed to machine learning model accuracy fairness. Incorporating the updated machine learning model validation inputs as in Bhide the machine learning techniques as in Conward with the blood glucose monitoring techniques of Albisser, the continuous glucose sensor and measurements as in Valdes and the methods including multiple sensors for data related to a physical or mental health of a patient as in Tara would improve mental health assessments for a patient.
Regarding claim 17, claim 17 recites substantially the same limitations as included in claims 7. Thus, claims 17 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 7, above.
Response to Arguments
Applicant’s remarks filed April 18, 2026 have been fully considered, but they are not entirely persuasive. The following explains why:
Applicant’s arguments pertaining to subject matter eligibility are not persuasive. The claims have been addressed with regard to the updated 35 U.S.C. §101 rejection discussed above, and considered under relevant sections of the MPEP. The arguments at pages 9-10 of Applicant’s Remarks are not persuasive. At page 9 the Examiner disagrees that there is not an abstract idea, that there is any practical application thereof or there is a technological improvement in the claims. The claim limitations recite “glucometer data,” and those data are used by computers as a tool to employ the abstract idea of making determinations about a patient’s mental health risk. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. That it may be tedious or laborious to perform analyses in the mind or manually is not of consequence in the eligibility analysis. The current claims are included as a mental process(es) for the judicial exception as well as organizing human activity as following rules or instructions. There is no clear technological improvement that is described in the claims for the underlying computer technology. The Examiner disagrees at Page 10 with the conclusory statement of a generic improvement to telehealth. This is not recited by the claims and the underlying telehealth computer technology is not being improved, either in the claims or in the Specification. For at least these reasons and those stated above, the claims are not patent eligible.
Applicant’s arguments pertaining to prior art rejections are not persuasive. The amended claims have been addressed with regard to the 35 U.S.C. §103 rejection discussed above. The arguments pertaining to prior art references of the Applicant’s Remarks at Page 11 are not persuasive and moot in light of at least new references Valdes and Albisser. Thus, the cited prior art reads on the broad claim limitations and are in the same field of endeavor, namely, analyte or glucose sensors and related mental health and other health determinations. Therefore, the claims are rejected.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM T. MONTICELLO whose telephone number is (313)446-4871. The examiner can normally be reached M-Th; 08:30-18:30 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FONYA LONG can be reached at (571) 270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WILLIAM T. MONTICELLO/Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
Prosecution Insights