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 .
Election/Restrictions
Claims 5-8, 13-14 & 16 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on June 17, 2025.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 6, 10-12, 15, 18 & 20-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bettencourt-Silva et al. (US 2019/0392924) in view of Stevens et al. (US 2018/0108440).
Bettencourt-Silva et al. discloses:
1.
A system comprising:
E.G. via the disclosed computing systems 50 {[0050] & (Fig 1)}.
a plurality of assessment data stores, wherein individual assessment data stores are configured to maintain information related to machine learned algorithms for conducting an assessment;
E.G. via the disclosed program modules 42 that are stored in the system memory and are used to carry out the functions of the invention including medical actions for patient assessments based on different data sources {[0015], [0052] & (Fig 1)}.
a plurality of user profile data stores configured to maintain at least historical information related to previously conducted user assessments;
E.G. via the disclosed historical data from different data sources, such as the functional block/modules/components 400 specifically directed towards said historical patient data 422 {[0015], [0067]-[0068] & (Fig 4)}.
and a processor in communication with the data store, wherein the processor is configured with specific computer-executable instructions to perform operations comprising:
E.G. via the disclosed computer system server 12 further comprising processing units 16 {[0048] & (Fig 1)}.
obtaining a set of passive and active inputs corresponding to user interactions with one or more devices;
E.G. via the disclosed data associated with one or more actions for a patient and the heterogeneous historical input data used by the machine learning mechanism to build a model ([0015] & [0017])
processing the set of passive and active inputs according to machine learned algorithm configured to generate a cognitive assessment;
E.G. via the disclosed data determining a health state, comprising a mental state of the user ([0017] & [0021]).
processing at least one of the set of passive and active inputs and an additional assessment according to machine learned algorithm configured to generate an emotional assessment;
E.G. via the disclosed data determining a health state, comprising the emotional state of the one or more users ([0017] & [0021]).
processing at least one of the set of passive and active inputs and an additional assessment according to machine learned algorithm configured to generate a physical assessment;
E.G. via the disclosed data determining a health state, comprising the physical stability of the one or more users ([0017] & [0021]).
processing at least one of the set of passive and active inputs and an additional assessment according to machine learned algorithm configured to generate a social assessment;
E.G. via the disclosed data determining a health state, comprising the activities of daily living (ADL) such as shopping, visiting friends and family, etc. ([0017] & [0022]).
processing two or more of the cognitive, emotional, physical and social assessments
E.G. via the machine learning mechanism utilizing disclosed historical input data comprising non-medical data such as social factors, behavior patterns, etc. and feedback information comprising one or more medical conditions such as a health state, mental health state, physical health state, etc. ([0017] & [0021]), wherein the machine learning mechanism may execute flow and/or block diagrams, which define the above steps associated with the disclosed data, substantially concurrently [0100].
storing the generated cognitive, emotional, physical and social assessments; and generating at least one processing result corresponding to at least one of the generated cognitive, emotional, physical and social assessment.
E.G. via collecting feedback associated with the historical data from the different sources (i.e. the disclosed mental, emotional states, physical stability, and ADL data) via the machine learning operation ([0015] & [0019]).
automatically generating and executing corrective actions or notifications based on the results of a comparative analysis with the historical information related to previously conducted user assessments.
E.G. via the disclosed generated advice by the processor which dynamically build a recommendation model for recommending one or more medical actions based on collected historical data from other patients, feedback data, a confidence score, domain knowledge, and/or past history in order to refine, update, adjust and/or modify according to said data {[0087]-[0089] & (Fig 6)}.
Bettencourt-Silvia et al. discloses the claimed invention having a computing system utilizing disclosed historical data from different data sources, i.e. data associated with one or more actions for a patient and the heterogeneous historical input data used by the machine learning mechanism ([0015] & [0017]), to build a model used to provide a recommendation of one or more useful medical actions based on program products that may operate and execute functionality substantially currently ([0087]-[0089] & [0100]), except wherein said execution is explicitly processing said recommendation in parallel. The examiner notes that a program product that executes operations in a concurrent-manner provides a parallel-processing element.
Stevens et al. teaches that it is known to use a system and a method for medical diagnosis and biomarker identification and machine learning module that further includes one or more neural networks [0020], wherein the neural network processes information in parallel [0071].
Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the system, method and computer-readable medium as taught by Bettencourt-Silvia et al. with the use of a machine learning module that processes data in a parallel-manner as taught by Stevens et al., since such a modification would provide the predictable results pertaining to effectively operating a machine learning module designed to utilize historical health information of a patient {Stevens, abstract & [0071]} so as to further provide the processing of the machine learning mechanism utilizing disclosed historical input data comprising non-medical data such as social factors, behavior patterns, etc. and feedback information comprising one or more medical conditions such as a health state, mental health state, physical health state, etc. in substantially concurrent- manner (Bettencourt-Silvia, [0017], [0021] & [0100]).
2.
The system of claim 1, wherein the operations further comprises processing the cognitive, emotional, physical and social assessments in parallel.
E.G. via the machine learning mechanism utilizing disclosed historical input data comprising non-medical data such as social factors, behavior patterns, etc. and feedback information comprising one or more medical conditions such as a health state, mental health state, physical health state, etc. (Bettencourt-Silvia, [0017] & [0021]), wherein the machine learning mechanism may execute flow and/or block diagrams, which define the above steps associated with the disclosed data, substantially concurrently [Bettencourt-Silvia, 0100].
AND
E.G. via the disclosed system and a method for medical diagnosis and biomarker identification and machine learning module that further includes one or more neural networks [Stevens, 0020], wherein the neural network processes information in parallel [Stevens, 0071].
3.
The system of Claim 1, wherein the processing result comprises at least one notification indicative of the generated cognitive, emotional, physical and social assessments.
E.G. via the disclosed data that is either displayed via the display 14 and/or the one or more computing devices 54 { Bettencourt-Silvia, ([0017], [0021]-[0022], [0054] & (Fig 2)}.
4.
The system of Claim 1, wherein the operations further comprises generating at least one additional interaction based on the generated cognitive, emotional, physical and social assessments.
E.G. via the disclosed historical data used by a machine learning operation (Bettencourt-Silvia, [0021]-[0022]), wherein said data may further be used by a device layer 55 of a cloud computing network environment 50 which further includes physical and/or virtual devices that allow for intercommunication, collection and dissemination of data { Bettencourt-Silvia, [0056] & (Fig 3)}, via the further use of the computing system (Bettencourt-Silvia, [0068]-[0071]).
6.
A computer-implemented method comprising: obtaining a set of passive and active inputs corresponding to user interactions with one or more devices;
E.G. via the disclosed method for intelligent recommendation of useful medical actions based on feedback data [0004].
processing the set of passive and active inputs and an additional assessment in parallel according to a plurality of machine learned algorithms configured to generate a set of individual assessments;
E.G. via the disclosed historical data from different data sources, such as the functional block/modules/components 400 specifically directed towards said historical patient data 422 {Bettencourt-Silvia, [0015], [0067]-[0068] & (Fig 4)}.AND
E.G. via the disclosed system and a method for medical diagnosis and biomarker identification and machine learning module that further includes one or more neural networks [Stevens, 0020], wherein the neural network processes information in parallel [Stevens, 0071].
and generating at least one processing result corresponding to the set of individual assessments.
E.G. via the disclosed data determining a health state, comprising a mental, emotional, physical and/or social state of the user (Bettencourt-Silvia [0017] & [0021]-[0022]);
and automatically generating and executing corrective actions or notifications based on the results of a comparative analysis with historical assessment information.
E.G. via the disclosed generated advice by the processor which dynamically build a recommendation model for recommending one or more medical actions based on collected historical data from other patients, feedback data, a confidence score, domain knowledge, and/or past history in order to refine, update, adjust and/or modify according to said data { Bettencourt-Silvia [0087]-[0089] & (Fig 6)}.
10.
The computer-implemented method of Claim 6, wherein at least one assessment is dependent on an assessment, the method further comprising processing the plurality of assessment in an ordered manner based on dependencies.
E.G. via the disclosed computing system 400 that utilizes an intelligent medical advice recommendation system 410 including a patient clustering component 430 and a usefulness evaluation component 440, in which each element is in association/communication with historical patient data and feedback data [Bettencourt-Silvia (0068]-[0071]).
11.
The computer-implemented method of Claim 6, wherein generating the at least one processing result comprises generating at least one notification indicative of at least one of generated cognitive, emotional, physical social, or diet assessments.
E.G. via collecting feedback associated with the historical data from the different sources (i.e. the disclosed mental, emotional states, physical stability, and ADL data) via the machine learning operation (Bettencourt-Silvia, [0015], [0019] & [0021]-[0022]).
12.
The computer-implemented method of Claim 6, wherein generating the processing result comprises generating at least one additional interaction based on the set assessment.
E.G. via the disclosed historical data used by a machine learning operation (Bettencourt-Silvia, [0021]-[0022]), wherein said data may further be used by a device layer 55 of a cloud computing network environment 50 which further includes physical and/or virtual devices that allow for intercommunication, collection and dissemination of data { Bettencourt-Silvia, [0056] & (Fig 3)}, via the further use of the computing system (Bettencourt-Silvia, [0068]-[0071]).
15.
A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, configure the processor to perform operations comprising:
E.G. via the disclosed computing systems being configured as software ‘rendered’ [0012].
obtaining a set of passive and active inputs corresponding to user interactions with one or more devices;
E.G. via the disclosed historical data from different data sources, such as the functional block/modules/components 400 specifically directed towards said historical patient data 422 {Bettencourt-Silvia, [0015], [0067]-[0068] & (Fig 4)}.
processing at least one of the set of passive and active inputs and an additional assessment according to a first machine learned algorithm configured to generate a first assessment;
E.G. via the disclosed data determining a health state, comprising a mental state of the user (Bettencourt-Silvia, [0017] & [0021]).
processing at least one of the set of passive and active inputs and an additional assessment according to a second machine learned algorithm configured to generate a second assessment, wherein the first and second assessments form a set of individual assessments;
E.G. via the disclosed data determining a health state, comprising the activities of daily living (ADL) such as shopping, visiting friends and family, etc. (Bettencourt-Silvia, [0017] & [0022]).
and generating at least one processing result corresponding to the set of individual assessments.
E.G. via the disclosed data determining a health state, comprising a mental, emotional, physical and/or social state of the user (Bettencourt-Silvia, [0017] & [0021]-[0022]).
18.
The non-transitory computer-readable medium of Claim 15, wherein the set of passive and active inputs corresponds to a plurality of devices, wherein individual devices are configured to provide at least one of an active or passive input.
E.G. via the disclosed cloud computing environment 50 comprising a plurality of computing devices 54A-N that provide that allow for intercommunication, collection and dissemination of data {Bettencourt-Silvia, [0054], [0056] & (Fig 3)}
20.
The non-transitory computer-readable medium of Claim 15, wherein at least one assessment is dependent on an assessment, the method further comprising processing the first and second assessment in an ordered manner based on dependencies.
E.G. via the disclosed computing system 400 that utilizes an intelligent medical advice recommendation system 410 including a patient clustering component 430 and a usefulness evaluation component 440, in which each element is in association/communication with historical patient data and feedback data (Bettencourt-Silvia, [0068]-[0071]).
21.
The system of claim 1, wherein passive inputs comprise information associated with or derived from the user’s utilization of mobile devices, interaction with computing devices, interaction with vehicles, and/or use of social media.
E.G. (Bettencourt-Silvia, [0018]).
22.
The system of claim 1, wherein passive inputs comprise information associated with a mobile device, the information comprising at least one of microphone, information, speaker output, motion sensor output, interactive control output, location-based information and/or image data related to user interaction.
E.G. (Bettencourt-Silvia, [0018]).
Response to Arguments
Applicant’s arguments, filed November 10, 2025, with respect to the 35 U.S.C. 101, i.e. the invention being directed to a judicial exemption without significantly more, have been fully considered and are persuasive and have been withdrawn.
Applicant’s arguments with respect to claim(s) 1, 3-4, 6-8, 10-12, 15, 18 & 20 under 35 U.S.C. 12(a)(2) have been considered but are moot because the new ground of rejection does not rely on the combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see the above action.
Applicant's arguments filed November 10, 2025 have been fully considered but they are not persuasive. The applicant argues that the previous office action did not establish a prima facia case of obviousness of claim 1 over the alleged combination of Bettencourt-Silvia in view of Stevens because the cited references fail to teach of suggest all elements of claim 1 as amended.
The examiner disagrees and further points out that In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, it would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the system, method and computer-readable medium as taught by Bettencourt-Silvia et al. with the use of a machine learning module that processes data in a parallel-manner as taught by Stevens et al., since such a modification would provide the predictable results pertaining to effectively operating a machine learning module designed to utilize historical health information of a patient {Stevens, abstract & [0071]} so as to further provide the processing of the machine learning mechanism utilizing disclosed historical input data comprising non-medical data such as social factors, behavior patterns, etc. and feedback information comprising one or more medical conditions such as a health state, mental health state, physical health state, etc. in substantially concurrent- manner (Bettencourt-Silvia, [0017], [0021] & [0100]). The examiner further notes that the disclosed data associated with one or more actions for a patient and the heterogeneous historical input data used by the machine learning mechanism to build a model of Bettencourt-Silvia ([0015] & [0017]) are interpreted as being the claimed passive and active inputs, which are processed and executed via a machine learning mechanism in a substantially concurrently-manner [0100], therefore providing said passive and active inputs coordinated in a manner required by the claimed invention, contrary to what is argued by the applicant.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICOLE F JOHNSON whose telephone number is (571)270-5040. The examiner can normally be reached Monday-Friday 8:00am-5:00pm 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, David Hamaoui can be reached at 571-270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICOLE F JOHNSON/Primary Examiner, Art Unit 3796