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 .
Status of Application
This action is in reply to the correspondence received through September 16, 2025.
Claims 1-9 are pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. However, applicant cannot rely upon the certified copy of the foreign priority application to overcome some intervening prior art rejections because a translation of said application has not been made of record in accordance with 37 C.F.R. § 1.55. See M.P.E.P. §§ 215 and 216.
Information Disclosure Statement
The information disclosure statement submitted September 16, 2025, and its contents have been considered.
Claim Rejections - 35 U.S.C. § 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 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 1-3 and 7-9 are rejected under AIA 35 U.S.C. § 103 as being unpatentable over Molapo et al. (U.S. Pub. No. 2021/0366585 A1) (hereinafter “Molapo”) in view of Lauriola et al. (“The role of personality in positively and negatively framed risky health decisions.” Personality and individual differences 38.1 (2005): 45-59) (hereinafter “Lauriola”).
Claims 1 and 9: Molapo, as shown, discloses the following limitations:
a first information terminal owned by at least a patient (see at least ¶ [0073]: implementations of the invention may include a computer system/server 12 of FIG. 1 in which one or more of the program modules 42 are configured to perform (or cause the computer system/server 12 to perform) one of more functions for the treatment adherence 96 of FIG. 3; see also at least ¶ [0067]: referring now to FIG. 2, illustrative cloud computing environment 50 is depicted. As shown, cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or automobile computer system 54N may communicate; see also at least ¶¶ [0074]), the first information terminal including at least:
a first processor configured to execute an application that manages information sharing and communication (see at least ¶ [0037]: these computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks; see also at least ¶¶ [0038] and [0067]); and
a first data communication module configured to perform data communication with the second information terminal (see at least ¶¶ [0037]-[0038] and [0067] and the analysis above; see also at least ¶ [0073]: the one or more of the program modules 42 may be configured to: a) associate a prescribed treatment to a user using a computing device of the user; b) load the prescribed treatment into a database; c) take an image of the user using their computing device; d) send a message to the user through their computing device showing an image transformation into a personalized visualization using deep generative machine learning models in view of treatment adherence and/or nonadherence as a preventative measure/education when treatment commences or as part of treatment counselling; e) capture treatment adherence by the user using the computing device; f) analyze the captured image to determine treatment adherence; g) send messages which include personalized visualizations showing consequences of treatment nonadherence to the user through the computing device if treatment nonadherence and/or untimely; and h) if nonadherence of treatment continues, send more messages to the user and/or sending messages to a healthcare provider who prescribed the treatment); and
a second information terminal owned by a medical institution (see at least ¶ [0073]; see also at least ¶ [0074]: FIG. 4 shows a block diagram of an exemplary environment 400 in accordance with aspects of the invention. In embodiments, the environment 400 includes network 410, healthcare server 420, database 440 and computing device 450. In embodiments, the healthcare server 420 comprises a treatment adherence module 430, which comprises one or more program modules such as program modules 42 described with respect to FIG. 1. The healthcare server 420 may include additional or fewer modules than those shown in FIG. 4; see also at least ¶¶ [0059] and [0061]), the second information terminal including at least:
a second processor configured to execute the application (see at least ¶ [0037]: these computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks; see also at least ¶¶ [0059] and [0061]); and
a second data communication module configured to perform data communication with the first information terminal (see at least ¶¶ [0037]-[0038], [0059], and [0061] and the analysis above; see also at least ¶ [0073]: the one or more of the program modules 42 may be configured to: a) associate a prescribed treatment to a user using a computing device of the user; b) load the prescribed treatment into a database; c) take an image of the user using their computing device; d) send a message to the user through their computing device showing an image transformation into a personalized visualization using deep generative machine learning models in view of treatment adherence and/or nonadherence as a preventative measure/education when treatment commences or as part of treatment counselling; e) capture treatment adherence by the user using the computing device; f) analyze the captured image to determine treatment adherence; g) send messages which include personalized visualizations showing consequences of treatment nonadherence to the user through the computing device if treatment nonadherence and/or untimely; and h) if nonadherence of treatment continues, send more messages to the user and/or sending messages to a healthcare provider who prescribed the treatment),
wherein the first processor is configured to set a specific function for prompting a behavior modification related to disease treatment of the patient in the application […] executed by the application (see at least ¶ [0073]; see also at least ¶ [0075]: the computing device 450 associates a prescribed treatment, captures treatment adherence and receives messages containing personalized visualizations in view of treatment adherence and/or nonadherence; see also at least ¶ [0080]: when the deep generative machine learning models receive new images from the user, i.e., previously unseen data, the deep generative machine learning models automatically generate personalized visualizations illustrating a hypothetical image or other illustration with respect to the particulars of the user's case in view of the training data. Examples of personalized visualizations include a virtual pictorial and/or animated representation of an organ system or body part, and/or a photograph of one or more elements of the user. In one example, the personalized visualization is a photographic ‘selfie’ which now shows a malady resulting from treatment nonadherence; see also at least ¶ [0081]: the virtual pictorial includes computer enhanced items including a negative change in condition or other malady to show consequences for treatment nonadherence. Alternatively, for treatment adherence, virtual pictorial includes computer enhanced items showing improvement in the condition. For example, a reduction in an area of a particular skin malady in view of treatment adherence. In this way, the treatment adherence module 430 includes an image visualization component which generates augmented images of a typical user —including both current and future implications—with expected or potential physical, medical and operational changes due to pharmaceutical treatment nonadherence. In embodiments, the treatment adherence module 430 includes association component that relates images of users with diseases at various stages; see also at least ¶¶ [0024], [0085], and [0111]).
Molapo does not explicitly disclose, but Lauriola, as shown, teaches set[ting] a specific function for prompting a behavior modification based on a result of a patient's personality diagnosis test (see at least p. 47: “A goal-framing effect is found when the appeal of a persuasive message (e.g., avoiding high-fat foods) differs depending on whether the message highlights the benefit of attaining a goal (i.e., if one succeeds in avoiding high-fat food, then he or she will reduce the chance of a later CHD) or the detriment of not attaining a goal (i.e., if one fails to avoid high-fat foods, then he or she will not reduce the chance of a later CHD)”; see also at least p. 52: “The health behavior being framed was either increasing the consumption of established healthy foods (promotion focus) or decreasing the consumption of unhealthy foods (prevention focus). The messages were presented either describing the positive consequences of attaining the goal (positive valence) or describing the negative consequences of not attaining the goal (negative valence). The extent to which research participants were persuaded by different health messages was assessed by measuring: how much they think the described person should comply with the behavior, respondents’ willingness to monitor the described person, and respondents’ willingness to comply with the described behavior themselves if they were in that condition”; see also at least p. 53: “Overall, individual differences in personality and health-related tendencies accounted for 22% of the message appeal variance in the prevention focus condition and for only 6% of the variance in the promotion focus condition”; see also at least p. 57: “We suggest that future health researchers consider the usefulness of incorporating basic decision variables, personality characteristics and specific health-related attitudes in order to gain a more complete understanding of health risky decisions”; see also at least p. 56).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the consideration of personality factors in health decision messaging taught by Lauriola with the treatment adherence systems disclosed by Molapo, because Lauriola advises at p. 57 that “researchers consider the usefulness of incorporating basic decision variables, personality characteristics and specific health-related attitudes in order to gain a more complete understanding of health risky decisions.” See M.P.E.P. § 2143(I)(G).
Moreover, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the treatment adherence systems disclosed by Molapo with the consideration of personality factors in health decision messaging taught by Lauriola, because the claimed invention is merely a combination of old elements (the treatment adherence systems disclosed by Molapo and the consideration of personality factors in health decision messaging taught by Lauriola), in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See M.P.E.P. § 2143(I)(A).
Claim 2: The combination of Molapo and Lauriola teaches the limitations as shown in the rejections above. Further, Molapo, as shown, discloses the following limitations:
wherein the first information terminal further includes a first display (see at least ¶ [0075]: the computing device 450 may be a mobile computing device, e.g., smartphone, smartwatch, tablet, laptop etc., and includes one or more components of the computer system 12 of FIG. 1. The computing device 450 associates a prescribed treatment, captures treatment adherence and receives messages containing personalized visualizations in view of treatment adherence and/or nonadherence. In embodiments, the computing device 450 includes a display device 460 for displaying the messages and an input device 470 for associating the prescribed treatment and capturing treatment adherence. In one example, the input device 470 is a camera and/or a keyboard or other input device; see also at least ¶ [0086]),
wherein the second processor is configured to select a first image showing a current image of a patient's disease and a second image showing a predictive image of the disease treatment based on a status of the disease treatment of the patient, and transmit the selected first and second images to the first information terminal (see at least ¶¶ [0037]-[0038], [0059], and [0061]; see also at least ¶ [0073]: the one or more of the program modules 42 may be configured to: a) associate a prescribed treatment to a user using a computing device of the user; b) load the prescribed treatment into a database; c) take an image of the user using their computing device; d) send a message to the user through their computing device showing an image transformation into a personalized visualization using deep generative machine learning models in view of treatment adherence and/or nonadherence as a preventative measure/education when treatment commences or as part of treatment counselling; e) capture treatment adherence by the user using the computing device; f) analyze the captured image to determine treatment adherence; g) send messages which include personalized visualizations showing consequences of treatment nonadherence to the user through the computing device if treatment nonadherence and/or untimely; and h) if nonadherence of treatment continues, send more messages to the user and/or sending messages to a healthcare provider who prescribed the treatment; see also at least ¶ [0111]), and
wherein the first processor is configured, as part of the specific function, to display the first image and the second image which are selected by the second processor on the first display (see at least ¶ [0111]: FIG. 7 illustrates an image 700 of a user 705 with a medical condition 710, e.g., fever. In embodiments, capturing of the image occurs prior to treatment commencing. FIGS. 8A and 8B illustrate personalized visualizations in view of adherence or nonadherence to the prescribed treatment. In FIG. 8A, the user 705 adheres to the prescribed treatment. Accordingly, the adherence personalized visualization 800 a illustrates the beneficial results of adherence to the prescribed treatment. In embodiments, the treatment adherence module 430 indicates beneficial results by removing the medical condition 710. Alternatively, in FIG. 8B, the user 705 does not adhere to the prescribed treatment. Accordingly, the nonadherence personalized visualization 800 b illustrates the consequences of nonadherence to the prescribed treatment. In embodiments, the user not only still has the medical condition 710, but now has a new medical condition 810 resulting from the nonadherence to the prescribed treatment. In embodiments, the treatment adherence module 430 generates the personalized visualizations 800 a and 800 b prior to the user commencing treatment and/or after the user commences treatment; see also at least ¶ [0073]).
Claim 3: The combination of Molapo and Lauriola teaches the limitations as shown in the rejections above. Further, Molapo, as shown, discloses the following limitations:
wherein the second processor is configured to reselect the first and second images based on a progress in the patient's disease treatment, and transmit the reselected first and second images to the first information terminal (see at least ¶ [0106]: at step 665, the treatment adherence module 430 continues to monitor treatment adherence and/or nonadherence by the user. In embodiments, and as described with respect to FIG. 4, this includes the treatment adherence module 430 repeating the steps of 605-660 depending on adherence or nonadherence to the prescribed treatment; see at least ¶¶ [0037]-[0038], [0059], and [0061]; see also at least ¶ [0073]: the one or more of the program modules 42 may be configured to: a) associate a prescribed treatment to a user using a computing device of the user; b) load the prescribed treatment into a database; c) take an image of the user using their computing device; d) send a message to the user through their computing device showing an image transformation into a personalized visualization using deep generative machine learning models in view of treatment adherence and/or nonadherence as a preventative measure/education when treatment commences or as part of treatment counselling; e) capture treatment adherence by the user using the computing device; f) analyze the captured image to determine treatment adherence; g) send messages which include personalized visualizations showing consequences of treatment nonadherence to the user through the computing device if treatment nonadherence and/or untimely; and h) if nonadherence of treatment continues, send more messages to the user and/or sending messages to a healthcare provider who prescribed the treatment; see also at least ¶¶ [0109] and [0111]), and
wherein the first processor is configured, as part of the specific function, to display the reselected first and second images on the first display (see at least ¶ [0111]: FIG. 7 illustrates an image 700 of a user 705 with a medical condition 710, e.g., fever. In embodiments, capturing of the image occurs prior to treatment commencing. FIGS. 8A and 8B illustrate personalized visualizations in view of adherence or nonadherence to the prescribed treatment. In FIG. 8A, the user 705 adheres to the prescribed treatment. Accordingly, the adherence personalized visualization 800 a illustrates the beneficial results of adherence to the prescribed treatment. In embodiments, the treatment adherence module 430 indicates beneficial results by removing the medical condition 710. Alternatively, in FIG. 8B, the user 705 does not adhere to the prescribed treatment. Accordingly, the nonadherence personalized visualization 800 b illustrates the consequences of nonadherence to the prescribed treatment. In embodiments, the user not only still has the medical condition 710, but now has a new medical condition 810 resulting from the nonadherence to the prescribed treatment. In embodiments, the treatment adherence module 430 generates the personalized visualizations 800 a and 800 b prior to the user commencing treatment and/or after the user commences treatment; see also at least ¶¶ [0073], [0106], and [0109]).
Claim 7: The combination of Molapo and Lauriola teaches the limitations as shown in the rejections above. Further, Molapo, as shown, discloses the following limitations:
wherein the second image is an image showing a predicted post-treatment field of view of the disease, if the patient continues treatment of the disease (see at least ¶ [0073]; see also at least ¶ [0075]: the computing device 450 associates a prescribed treatment, captures treatment adherence and receives messages containing personalized visualizations in view of treatment adherence and/or nonadherence; see also at least ¶ [0080]: when the deep generative machine learning models receive new images from the user, i.e., previously unseen data, the deep generative machine learning models automatically generate personalized visualizations illustrating a hypothetical image or other illustration with respect to the particulars of the user's case in view of the training data. Examples of personalized visualizations include a virtual pictorial and/or animated representation of an organ system or body part, and/or a photograph of one or more elements of the user. In one example, the personalized visualization is a photographic ‘selfie’ which now shows a malady resulting from treatment nonadherence; see also at least ¶ [0081]: the virtual pictorial includes computer enhanced items including a negative change in condition or other malady to show consequences for treatment nonadherence. Alternatively, for treatment adherence, virtual pictorial includes computer enhanced items showing improvement in the condition. For example, a reduction in an area of a particular skin malady in view of treatment adherence. In this way, the treatment adherence module 430 includes an image visualization component which generates augmented images of a typical user —including both current and future implications—with expected or potential physical, medical and operational changes due to pharmaceutical treatment nonadherence. In embodiments, the treatment adherence module 430 includes association component that relates images of users with diseases at various stages; see also at least ¶¶ [0024], [0085], and [0111]).
Claim 8: The combination of Molapo and Lauriola teaches the limitations as shown in the rejections above. Further, Molapo, as shown, discloses the following limitations:
wherein the second image is an image showing a predicted post-deterioration field of view of the disease if the patient does not continue the treatment of the disease (see at least ¶ [0073]; see also at least ¶ [0075]: the computing device 450 associates a prescribed treatment, captures treatment adherence and receives messages containing personalized visualizations in view of treatment adherence and/or nonadherence; see also at least ¶ [0080]: when the deep generative machine learning models receive new images from the user, i.e., previously unseen data, the deep generative machine learning models automatically generate personalized visualizations illustrating a hypothetical image or other illustration with respect to the particulars of the user's case in view of the training data. Examples of personalized visualizations include a virtual pictorial and/or animated representation of an organ system or body part, and/or a photograph of one or more elements of the user. In one example, the personalized visualization is a photographic ‘selfie’ which now shows a malady resulting from treatment nonadherence; see also at least ¶ [0081]: the virtual pictorial includes computer enhanced items including a negative change in condition or other malady to show consequences for treatment nonadherence. Alternatively, for treatment adherence, virtual pictorial includes computer enhanced items showing improvement in the condition. For example, a reduction in an area of a particular skin malady in view of treatment adherence. In this way, the treatment adherence module 430 includes an image visualization component which generates augmented images of a typical user —including both current and future implications—with expected or potential physical, medical and operational changes due to pharmaceutical treatment nonadherence. In embodiments, the treatment adherence module 430 includes association component that relates images of users with diseases at various stages; see also at least ¶¶ [0024], [0085], and [0111]).
Claims 4 and 5 are rejected under AIA 35 U.S.C. § 103 as being unpatentable over Molapo et al. (U.S. Pub. No. 2021/0366585 A1) (hereinafter “Molapo”) in view of Lauriola et al. (“The role of personality in positively and negatively framed risky health decisions.” Personality and individual differences 38.1 (2005): 45-59) (hereinafter “Lauriola”) and further in view of Wong et al. (U.S. Pat. No. 10,037,820 B2) (hereinafter “Wong”).
Claim 4: The combination of Molapo and Lauriola teaches the limitations as shown in the rejections above. Further, Molapo, as shown, discloses the following limitations:
wherein the first information terminal further includes an input interface (see at least ¶ [0075]: the computing device 450 includes a display device 460 for displaying the messages and an input device 470 for associating the prescribed treatment and capturing treatment adherence. In one example, the input device 470 is a camera and/or a keyboard or other input device).
Molapo does not explicitly disclose, but Wong, as shown, teaches the following limitations:
wherein the first processor is configured, as part of the specific function, to display a medication history table of a medication for the patient on the first display, and update the medication history table in response to an operation on the input interface by the patient who has taken the medication (see at least [12:25-38]: the second component of the timeline functionality is a filter 72 embedded within the timeline that allows a user to filter content based on search parameters, such as conditions, procedures, medications, diet, activities, symptom tags and any other types of notes the user has made. Once users submit a search query, they are provided with a list of results based on the search criteria. Upon clicking on a listed result, specific points in the timeline are highlighted to indicate exact dates and times. Users may then scroll to a specific date and time to review the desired content. In one embodiment, once the users have finished their filtered search, they may select “Done” in the list of results to default back to the original screen; see also at least [17:38-51]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the health information management techniques taught by Wong with the treatment adherence systems disclosed by Molapo (as modified by Lauriola), because Wong teaches at [1:43-50] that its techniques guide “health and medical-related decision-making by aggregating critical information about the past, present, and future in a personalized, engaging, and interactive way.” See M.P.E.P. § 2143(I)(G).
Moreover, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the health information management techniques taught by Wong with the treatment adherence systems disclosed by Molapo (as modified by Lauriola), because the claimed invention is merely a combination of old elements (the health information management techniques taught by Wong, consideration of personality factors in health decision messaging taught by Lauriola, and the treatment adherence systems disclosed by Molapo), in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See M.P.E.P. § 2143(I)(A).
Claim 5: The combination of Molapo, Lauriola, and Wong teaches the limitations as shown in the rejections above.
Molapo does not explicitly disclose, but Wong, as shown, teaches the following limitations:
wherein the medication history table includes at least one of a next hospital visit date and a scheduled end date of the disease treatment (see at least [11:55-67]: the ticker bar 70 may be located anywhere on the display. For example, it may span the length of the screen just below the header but above the main digital anatomical medical avatar. The timeline's starting point is the user's current age and last input. From there, a user may scroll to another section of the timeline where an input was made. The inputs can be of any variety, such as, but not limited to, notes made by the user about symptoms on a specific date and time, a Continuity of Care Document which was input by the user or a third party, and any medication or procedure updates made by either the user or a third party. A timeline icon allows users to hide or show the timeline depending on their preference; see also at least [12:25-48]: the second component of the timeline functionality is a filter 72 embedded within the timeline that allows a user to filter content based on search parameters, such as conditions, procedures, medications, diet, activities, symptom tags and any other types of notes the user has made. Once users submit a search query, they are provided with a list of results based on the search criteria. Upon clicking on a listed result, specific points in the timeline are highlighted to indicate exact dates and times. Users may then scroll to a specific date and time to review the desired content. In one embodiment, once the users have finished their filtered search, they may select “Done” in the list of results to default back to the original screen; see also at least [12:63-13:3]).
The rationales to modify/combine the teachings of Molapo and Lauriola to include the teachings of Wong are presented above regarding claim 4 and incorporated herein.
Claims 6 is rejected under AIA 35 U.S.C. § 103 as being unpatentable over Molapo et al. (U.S. Pub. No. 2021/0366585 A1) (hereinafter “Molapo”) in view of Lauriola et al. (“The role of personality in positively and negatively framed risky health decisions.” Personality and individual differences 38.1 (2005): 45-59) (hereinafter “Lauriola”) and further in view of Pahwa et al. (U.S. Pub. No. 2016/0019360 A1) (hereinafter “Pahwa”).
Claim 6: The combination of Molapo and Lauriola teaches the limitations as shown in the rejections above.
Molapo does not explicitly disclose, but Pahwa, as shown, teaches the following limitations:
a third information terminal owned by a different patient and configured to perform data communication with the second information terminal and to use the application (see at least ¶ [0276]: expanded view 1005 can further include summaries of the user's weight data, such as a sliding scale 1140 indicating the user's weight relative to a range of weights and a graph 1141 tracking the user's weight throughout the day, week, month, or year. Interface 1100 can further include button 1104 that can cause user device 610 to display options for sharing some or all of their wellness data using any desired communication medium, such as text message, email, social media provider, or the like. In these examples, the wellness data can be encrypted and sent from the user device 610 directly to the user device of the recipient (rather than from user server 614), where the wellness data can be decrypted; see at least ¶ [0252]: the shared wellness or non-wellness data can be pushed to the user device (e.g., user device 622 or 624) of the authorized other user),
wherein the first processor is configured to execute, as the specific function, the information sharing and the communication using the application with the third information terminal, when a first approval operation by the patient to approve the different patient and a second approval operation by the different patient to approve the patient are detected (see at least ¶ [0251]: as discussed above, a user's wellness or non-wellness data can be stored in user database 616 and can be shared with other users with the owning user's authorization. The other users can be any type of user, such as a friend, family member, caregiver, physician, social media provider, or the like. Different types and levels of authorization can be granted for the wellness or non-wellness data contained in wellness database 616; see also at least ¶ [0252]: the identification can include a username, legal name, contact information, or any other identifier or credential for the other user, along with a level of access, such as access to all of the wellness or non-wellness data or a subset of the wellness or non-wellness data. In some examples, the authorized other user can be grouped into categories of users (e.g., family, friends, other, etc.), where each category is associated with a particular set of wellness data types that those authorized other users are allowed to view. For example, users in the family category can be allowed to view all types of wellness data, while users in the friend category can only view activity data; see also at least ¶ [0293]: a request to view a second plurality of partitions of a second user can be received. The second user can be a user that has authorized the first user to view their wellness data as described above. In some examples, the request can include a user input to scroll the display of the first plurality of partitions displayed at block 2102 and can be received by the user device in the form of rotation of a mouse wheel, an arrow key on a keyboard, a touch and/or swipe on a touch sensitive display, or the like).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the health information management techniques taught by Pahwa with the treatment adherence systems disclosed by Molapo (as modified by Lauriola), because Pahwa teaches at ¶ [0561] that “the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to deliver targeted content that is of greater interest to the user. Accordingly, use of such personal information data enables calculated control of the delivered content.” See M.P.E.P. § 2143(I)(G).
Moreover, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the health information management techniques taught by Pahwa with the treatment adherence systems disclosed by Molapo (as modified by Lauriola), because the claimed invention is merely a combination of old elements (the health information management techniques taught by Pahwa, consideration of personality factors in health decision messaging taught by Lauriola, and the treatment adherence systems disclosed by Molapo), in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See M.P.E.P. § 2143(I)(A).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. The following references have been cited to further show the state of the art with respect to supporting behavior modification.
Gorzelniak et al. (U.S. Pub. No. 2018/0358129 A1) (educating a user about a condition of interest);
Joireman et al. (“Promotion orientation explains why future-oriented people exercise and eat healthy: Evidence from the two-factor consideration of future consequences-14 scale.” Personality and Social Psychology Bulletin 38.10 (2012): 1272-1287).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christopher Tokarczyk, whose telephone number is 571-272-9594. The examiner can normally be reached Monday-Thursday between 6:00 AM and 4:00 PM Eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid, can be reached at 571-270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER B TOKARCZYK/Primary Examiner, Art Unit 3687
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