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 .
DETAILED ACTION
Response to Amendment
The present Office Action is in response to the Request for Continued Examination dated 02 March 2026.
In the amendment dated 02 March 2026, the following has occurred: Claims 1, 3, 24, 25, 28, 29, and 31-37 have been amended.
Claims 1, 3, 8, and 21-37 are pending.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02 March 2026has been entered.
Priority
This application claims priority to U.S. Provisional Patent Application No. 63/421,935 dated 02 November 2022.
Claim Objections
Claims 21-27 are objected to because changes to the number were not marked up and each of the claim have an incorrect status identifier. The Examiner has proceeded with examination presuming the amendments and status identifier are correct.
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 of this title, 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, 8, 22-28, 30, and 32-37 is/are rejected under 35 U.S.C. § 103 as being unpatentable over T et al. (U.S. Pre-Grant Patent Publication No. 2019/0198169).
REGARDING CLAIM 1
T teaches the claimed system for training a neural network configured for determining an action item associated with a group health care plan comprising:
a […] deep neural network including an input layer, an output layer, and a plurality of interior layers connecting the input layer to the output layer; and [Fig. 5, Para. 0059 teaches a DNN having input, hidden (interior), and output layers.]
a computing device configured to train the deep neural network (DNN), wherein training the deep neural network further comprises: [Para. 0198 teaches that the functionality is implemented via a smartphone.]
collecting a plurality of […] data from a user interface of a mobile application, […]; [Para. 0039, 0040, 0084 teaches that a patient interacts with a patient healthcare interaction device to input symptoms. Para. 0198 teaches that the patient healthcare interaction device is a smartphone which necessarily has an interface. Fig. 7, Para. 0086 teaches that the data analysis is performed on the device (i.e., is an application).]
inputting the plurality of […] data to the deep neural network; [Para. 0041, 0042 teaches that a “digital twin” model is generated based on the received information, thus the data is inputted into the model. Para. 0059 teaches that the model is a deep neural network.]
outputting, based on the plurality of data […], a plurality of action items, wherein each action item of the plurality of action items includes a success metric; [Para. 0041, 0068 teaches that the digital twin model determines treatment plans (a plurality of action items) and associated success rates (success metrics), which are displayed on a user interface.]
collecting, using the user interface, a plurality of outcome data, wherein each datum of the plurality of outcome data corresponds to an action item of the plurality of action items; and [Fig. 12, Para. 0132 teaches that various health tracking information (outcome data having a datum) based on the selected treatment (action item) is received.]
retraining the deep neural network using the plurality of outcome data. [Fig. 12, Para. 0132 teaches that the digital twin model is updated (interpreted as retrained) with the health tracking information.]
T may not explicitly teach
a language processing deep neural network
user interaction data […],
wherein the plurality of user interaction data further comprises a plurality of data representing time spent by users in the mobile application;
user interaction
representing time spent by users in the mobile application
However, the limitation claims information/labels that constitute nonfunctional descriptive information that is/are not functionally involved in the recited system. The function described by the system would be performed the same regardless of whether the claimed information/labels was substituted with nothing. Because T teaches using a DNN, a receiving patient data, inputting the patient data into the DNN, and displaying information based on received data, substituting the information/labels of the claimed invention for the information/labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have substituted the information/labels applied to the received patient data of the prior art with any other information/labels because the results would have been predictable. MPEP 2112.01, Section III (see also In re Ngai, Ex Parte Breslow). The Examiner notes that there is no functionality associated with the DNN being labeled “a language processing” DNN. Nor is there any indication as to how the plurality of data representing time spent by users in the mobile application would affect the outcome of the claim and thus the type of data is nonfunctional.
REGARDING CLAIM 8
T teaches the claimed system for training a neural network configured for determining an action item associated with a group health care plan of Claim 1. T may not explicitly teach
further comprising inputting data associated with a group health care plan into the DNN.
However, the limitation claims information/labels that constitute nonfunctional descriptive information that is/are not functionally involved in the recited system. The function described by the system would be performed the same regardless of whether the claimed information/labels was substituted with nothing. Because T teaches inputting the patient data into a DNN, substituting the information/labels of the claimed invention for the information/labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have substituted the information/labels applied to the data inputted into the DNN of the prior art with any other information/labels because the results would have been predictable. MPEP 2112.01, Section III (see also In re Ngai, Ex Parte Breslow). The Examiner notes that there is no indication how the inputted data would affect the outcome of the claim and thus the type of data is nonfunctional.
REGARDING CLAIMS 22-27
T teaches the claimed system for training a neural network configured for determining an action item associated with a group health care plan of Claim 1. T may not explicitly teach
Claim 22: wherein the data associated with the group health care plan includes an amount of time a member of the group health care plan has been active.
Claim 23: wherein the data associated with the group health care plan includes at least one identification of at least one member who has populated an electronic medical record (EMR).
Claim 24: wherein the data associated with the group health care plan includes a number of members in the group health care plan.
Claim 25: wherein the data associated with the group health care plan includes at least one health status of at least one member of the group health care plan.
Claim 26: wherein the data associated with the group health care plan includes a number of active members of the group health care plan.
Claim 27: wherein the data associated with the group health care plan includes a number of active members of the group health care plan that have scheduled callbacks with physicians.
However, as per Claim 8, these limitations claim information/labels that constitute nonfunctional descriptive information that is/are not functionally involved in the recited system. The function described by the system would be performed the same regardless of whether the claimed information/labels was substituted with nothing. Because T teaches inputting the patient data into a DNN, substituting the information/labels of the claimed invention for the information/labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have substituted the information/labels applied to the data inputted into the DNN of the prior art with any other information/labels because the results would have been predictable. MPEP 2112.01, Section III (see also In re Ngai, Ex Parte Breslow). The Examiner notes that there is no indication how the inputted data would affect the outcome of the claim and thus the type of data is nonfunctional.
REGARDING CLAIM(S) 28
Claim(s) 28 is/are analogous to Claim(s) 1, thus Claim(s) 28 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
REGARDING CLAIM(S) 30 AND 32-37
Claim(s) 30 and 32-37 is/are analogous to Claim(s) 8 and 21-36 thus Claim(s) 30 and 32-37 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 8 and 21-36.
Claim(s) 3, 21, 29, and 31 is/are rejected under 35 U.S.C. § 103 as being unpatentable over T et al. (U.S. Pre-Grant Patent Publication No. 2019/0198169) in view of Pellinat (U.S. Pre-Grant Patent Publication No. 2019/0198169).
REGARDING CLAIM 3
T teaches the claimed system for training a neural network configured for determining an action item associated with a group health care plan of Claim 1. T may not explicitly teach
wherein the plurality of action items includes a plurality of action items ranked according to a plurality of success metrics of the plurality of action items.
Pellinat at Para. 0021, 0023, 0031, 0047 teaches that it was known in the art of computerized healthcare, at the time of filing, to rank clinical treatment option taking into account likelihood of success
wherein the plurality of action items includes a plurality of action items ranked according to a plurality of success metrics of the plurality of action items. [Pellinat at Para. 0021, 0023, 0031, 0047 teaches ranking clinical treatment options (the treatment plans of T; a ranked plurality of action items) where one of the factors is likelihood of success.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, at the time of filing, to modify the deep neural network training of T to rank clinical treatment option taking into account likelihood of success as taught by Pellinat, with the motivation of improving patient confidence in their decision making (see Pellinat at Para. 0024).
REGARDING CLAIM 21
T teaches the claimed system for training a neural network configured for determining an action item associated with a group health care plan of Claim 1. T may not explicitly teach
wherein outputting the plurality of action items further comprises ranking the plurality of action items by importance.
Pellinat at Para. 0021, 0023, 0031, 0047 teaches that it was known in the art of computerized healthcare, at the time of filing, to rank clinical treatment option taking into importance
wherein outputting the plurality of action items further comprises ranking the plurality of action items by importance. [Pellinat at Para. 0021, 0023 teaches ranking clinical treatment options (the treatment plans of T) where one of the factors is importance.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, at the time of filing, to modify the deep neural network training of T to rank clinical treatment option taking into importance as taught by Pellinat, with the motivation of improving patient confidence in their decision making (see Pellinat at Para. 0024).
REGARDING CLAIM(S) 29 AND 31
Claim(s) 29 and 31 is/are analogous to Claim(s) 3 and 21, thus Claim(s) 29 and 31 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3 and 21.
Response to Arguments
Claim Objections
Regarding the rejection of Claims 1, 27 and 30, the Applicant has amended the claims to overcome the previous bases od objection.
Rejection under 35 U.S.C. § 112
Regarding the indefiniteness rejection of Claim 31, the Applicant has amended the claim to overcome the basis of rejection.
Rejection under 35 U.S.C. § 103
Regarding the rejection of Claims 1 and 28, the Examiner has considered Applicant’s arguments; however, these arguments are not persuasive. Applicant argues: “The Office has not asserted that T teaches, suggests, or motivates that the DNN is a language processing DNN. Applicant respectfully asserts that T contains no such teaching, suggestion, or motivation.” The Examiner has found that the new feature of labeling the Deep Neural Network as being “a language processing” DNN to be obvious in view of T. There is no claimed functionality associated with the DNN being “a language processing” DNN and thus this represents a nonfunctional label. Because T teaches a DNN, labeling the DNN as “a language processing” DNN would have been obvious. The Examiner notes that should the claim be amended to make “a language processing deep neural network” functional, Chen (cited in the PTO-892) appears to teach this feature.
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Chen et al. (U.S. Pre-Grant Patent Publication No. 2025/0131184) discloses a system for analyzing medical record data using a deep neural network and long short-term models to extract textual data.
Smyth et al. (U.S. Pre-Grant Patent Publication No. 2023/0228867) which discloses an app that tracks a patient’s pre-appointment symptoms.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON S TIEDEMAN whose telephone number is (571)272-4594. The examiner can normally be reached 7:00am-4:00pm, off alternate Fridays.
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/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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