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 .
Response to Amendment
In light of the amendments, the claims are rejected under 35 U.S.C. 101.
In light of the amendments, the claims are rejected under 35 U.S.C. 103.
Notice to Applicant
In the amendment dated 07/07/2026, the following has occurred: claims 1 and 21 have been amended; claims 2-4, 6-7, 10-11, and 17-20 have been canceled; claims 5, 8-9, and 12-16 remain unchanged; and no new claims have been added.
Claims 1, 5, 8-9, 12-16, and 21 are pending.
Effective Filing Date: 01/02/2022
Response to Arguments
35 U.S.C. 101 Rejections:
Applicant argues that the amended claims overcome the previous 101 claim rejections. Applicant argues the two points below. Examiner however respectfully disagrees that these amendments overcome the previous 101 rejections.
Argument 1
Applicant argues in view of Enfish and McRO and states that the claims are not directed to generic health recommendations, rather the claims recite a specific technical pipeline. The specific combination of technical elements cannot be fairly characterized as merely “a person recommending activities to improve a patient’s health”. Enfish and McRO involve technical improvements whereas the present claims does not present such an improvement. For example, the aspects of providing content to a user based on their data using technology does not provide an improvement apart from using technology in the manner which it was intended. For example, the usage of the computing components in the claims is done so in an “apply it” manner whereas the providing of the content in a prioritized list is being included as an additional element, though it is deemed as a well-understood one.
Applicant states that a human analog cannot realistically perform real-time PPG measurement from a wearable device. Examiner however respectfully disagrees as the claims do not reflect this statement, they only recite reception of data measured by a wearable device and then a determination is made based on that data. The wearable device is not actively receiving and sending this data in the claims.
Argument 2
Applicant argues that there is a specific technical problem of some applications for recommending content may fail to identify and recommend relevant content that is most effective in improving the health and wellness of the user. The claims supposedly provide the improvement to this problem. Examiner however would like to point out that the solution to the above problem in the claimed invention involves ranking the content and then providing the content to the individual. Thus, the improvement here is an improvement to what data is being outputted, though it also is not guaranteed that the content which is provided will elicit a specific percentage improvement in wellness of a user.
Applicant points to other court cases and states that these cases improve the functioning of the content recommendation system by ensuring users receive content that is more effective. The improvement to providing data is to provide specific data with the intent of producing the most improvement to wellness, therefore the improvement here is directed to an improvement in the abstract idea involving providing content to a user.
Lastly, Applicant states that the statement of a merely application of the abstract idea using generic computer components is an oversimplification in view of the amended claims. Applicant states that the claims do not merely invoke machine learning. Examiner however respectfully disagrees based on the current construction of the claims. The computing components in the claims are recited in combination with the functional steps of the claims where these components could be used to replace a human performing the claimed steps. Furthermore, the recitation of the trained model is also done so in an apply it manner where a model is being used which is a trained machine learning model.
35 U.S.C. 103 Rejections:
Applicant argues with respect to the previously-cited Brust and Catani references and independent claims, however these claims rely on the Aimone et al. reference in view of the amendments to the claims.
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, 5, 8-9, 12-16, and 21 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. Claims 1, 5, 8-9, and 12-16 are drawn to a system and claim 21 is drawn to a system, each of which is within the four statutory categories. Claims 1, 5, 8-9, 12-16, and 21 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites a system for content recommendation comprising:
a) a wearable device configured to measure photoplethysmogram (PPG) data from a user using one or more light-emitting components and one or more light-receiving components,
b) a user device communicatively coupled with the wearable device, and
c) one or more processors communicatively coupled with the wearable device or the user device, the one or more processors configured to:
1) receive physiological data measured from the user by the wearable device;
2) receive, via d) a graphical user interface of the user device, classifier data indicating a type of activity in which the user engaged;
3) determine a pattern between the physiological data and the classifier data indicating the type of activity in which the user engaged, wherein the pattern indicates a relationship between the type of activity in which the user engaged and an impact on the physiological data;
4) select, based at least in part on e) a machine learning model trained to identify content for regulating the physiological data based on the pattern between the physiological data and the classifier data, a set of content for the user, wherein each content of the set of content corresponds to a relative effectiveness for regulating the physiological data in response to the type of activity in which the user engaged;
5) score each content of the set of content based at least in part on a respective effectiveness of each content for regulating a value of the physiological data;
6) assign a rank to each content of the set of content based at least in part on the pattern between the physiological data and the classifier data, wherein the rank assigned to each content is further based at least in part on a respective score that is indicative of the respective effectiveness of each content for regulating the physiological data as compared to other content;
7) order a list of content provided to the user via the graphical user interface of the user device based at least in part on assigning the rank to each content of the set of content;
8) determine that the first content in the set of content was previously selected, via the graphical user interface, by the user;
9) move the first content to a position on the graphical user interface higher in the list of content than second content in the set of content based at least in part on the first content being previously selected by the user via the graphical user interface; and
10) transmit a signal to cause the graphical user interface of the user device running an application to display the list of content, the list of content comprising the first content higher in the list of content than the second content.
Claim 1 recites, in part, performing the steps of 1) receive physiological data measured from the user by the wearable device, 2) receive classifier data indicating a type of activity in which the user engaged, 3) determine a pattern between the physiological data and the classifier data indicating the type of activity in which the user engaged, wherein the pattern indicates a relationship between the type of activity in which the user engaged and an impact on the physiological data, 4) select, based at least in part on a model, a set of content for the user, wherein each content of the set of content corresponds to a relative effectiveness for regulating the physiological data in response to the type of activity in which the user engaged, 5) score each content of the set of content based at least in part on a respective effectiveness of each content for regulating a value of the physiological data, 6) assign a rank to each content of the set of content based at least in part on the pattern between the physiological data and the classifier data, wherein the rank assigned to each content is further based at least in part on a respective score that is indicative of the respective effectiveness of each content for regulating the physiological data as compared to other content, 7) order a list of content provided to the user based at least in part on assigning the rank to each content of the set of content, and 8) determine that the first content in the set of content was previously selected, via the graphical user interface, by the user. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim reflects a person recommending activities to improve a patient’s health.
Claim 21 recites a system for regulating physiological data of a user, comprising:
f) a finger worn wearable ring device comprising one or more light-emitting components and one or more light-receiving components,
g) a user device communicatively coupled with the finger worn wearable ring device and comprising a graphical user interface, and
h) one or more processors communicatively coupled with the finger worn wearable ring device or the user device, the one or more processors configured to:
11) measure the physiological data from the user via the one or more light-emitting components of the finger worn wearable ring device and the one or more light-receiving components of the finger worn wearable ring device;
12) calculate a Sleep Score, a Readiness Score, or both, for the user based at least in part on the physiological data measured by the finger worn wearable ring device;
13) receive, via the graphical user interface of the user device, one or more tags that indicate one or more activities in which the user engaged;
14) determine a pattern between the physiological data and the one or more tags, wherein the pattern indicates a relationship between the one or more activities in which the user engaged and an impact on the physiological data;
15) select, based at least in part on e) a machine learning model trained to identify content for regulating the physiological data based on the pattern between the physiological data and the one or more tags, a set of content for the user comprising at least first content and second content;
16) score each content of the set of content based at least in part on a respective effectiveness of each content for regulating a value of the physiological data;
17) assign a rank to each content of the set of content based at least in part on the pattern between the physiological data and the classifier data, wherein the rank assigned to each content is further based at least in part on a respective score that is indicative of the respective effectiveness of each content for regulating the physiological data as compared to other content;
18) order a list of content provided to the user via the graphical user interface of the user device based at least in part on assigning the rank to each content of the set of content;
19) determine that the first content in the list of content was previously selected, via the graphical user interface, by the user;
20) move the first content to a position on the graphical user interface higher in the list of content than the second content in the list of content based at least in part on the first content being previously selected by the user via the graphical user interface; and
21) cause the graphical user interface of the user device to display the Sleep Score or the Readiness Score, and to display, in a same viewing window, a visual indicator of the one or more tags, and the list of content comprising the first content higher in the list of content than the second content.
Claim 21 recites, in part, 11) measure the physiological data from the user via the one or more light-emitting components of the finger worn wearable ring device and the one or more light-receiving components of the finger worn wearable ring device, 12) calculate a Sleep Score, a Readiness Score, or both, for the user based at least in part on the physiological data measured by the finger worn wearable ring device, 13) receive one or more tags that indicate one or more activities in which the user engaged, 14) determine a pattern between the physiological data and the one or more tags, wherein the pattern indicates a relationship between the one or more activities in which the user engaged and an impact on the physiological data, 15) select, based at least in part on a model, a set of content for the user comprising at least first content and second content, 16) score each content of the set of content based at least in part on a respective effectiveness of each content for regulating a value of the physiological data, 17) assign a rank to each content of the set of content based at least in part on the pattern between the physiological data and the classifier data, wherein the rank assigned to each content is further based at least in part on a respective score that is indicative of the respective effectiveness of each content for regulating the physiological data as compared to other content, 18) order a list of content provided to the user based at least in part on assigning the rank to each content of the set of content, and 19) determine that the first content in the list of content was previously selected by the user. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim reflects a person recommending activities to improve a patient’s health.
Depending claims 5, 8-9, and 12-16 include all of the limitations of claim 1, and therefore likewise incorporate the above described abstract idea. Depending claim 5 adds the additional step of “determine previous content recommended to the user via the graphical user interface of the user device based at least in part on the machine learning model trained to identify content for regulating the physiological data”; claim 8 adds the additional steps of “generate a notification based at least in part on the first content” and “output the notification in the application and via the graphical user interface of the user device”; and claim 9 adds the additional step of “cause the graphical user interface of the user device running the application to display the first content within a duration after receiving the classifier data”. Additionally, the limitations of depending claims 12-16 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. Thus, depending claims 5, 8-9, and 12-16 are nonetheless directed towards fundamentally the same abstract idea as independent claim 1 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – a) a wearable device configured to measure photoplethysmogram (PPG) data from a user using one or more light-emitting components and one or more light-receiving components/a finger worn wearable wing device comprising one or more light-emitting components and one or more light-receiving components, b) a user device communicatively coupled with the wearable device, with a graphical user interface, c) one or processors communicatively coupled with the wearable device/finger worn wearable ring device or the user device, d) a graphical user interface of the user device, e) a machine learning model trained to identify content for regulating the physiological data based on the pattern between the physiological data and the classifier data/the one or more tags, f) a finger worn wearable ring device comprising one or more light-emitting components and one or more light-receiving components, g) a user device communicatively coupled with the finger worn wearable ring device and comprising a graphical user interface, and h) one or more processors communicatively coupled with the finger worn wearable ring device or the user device the claimed steps.
The claim also includes the additional element steps of 6) “move the first content to a position on the graphical user interface higher in a list of content than second content in the set of content based at least in part on the first content being previously selected by the user”, 7) “transmit a signal to cause the graphical user interface of the user device running an application to display the list of content, the list of content comprising the first content higher in the list of content than the second content”, 14) “move the first content to a position on the graphical user interface higher in the list of content than the second content in the list of content based at least in part on the first content being previously selected by the user”, and 15) “cause the graphical user interface of the user device to display the Sleep Score or the Readiness Score, and to display, in a same viewing window, a visual indicator of the one or more tags, and the list of content comprising the first content higher in the list of content than the second content”.
The a) wearable device, b) a user device communicatively coupled with the wearable device, with a graphical user interface, f) a finger worn wearable ring device comprising one or more light-emitting components and one or more light-receiving components, and g) a user device communicatively coupled with the finger worn wearable ring device and comprising a graphical user interface in these steps and the steps of 6) “move the first content to a position on the graphical user interface higher in a list of content than second content in the set of content based at least in part on the first content being previously selected by the user”, 7) “transmit a signal to cause the graphical user interface of the user device running an application to display the list of content, the list of content comprising the first content higher in the list of content than the second content”, 14) “move the first content to a position on the graphical user interface higher in the list of content than the second content in the list of content based at least in part on the first content being previously selected by the user”, and 15) “cause the graphical user interface of the user device to display the Sleep Score or the Readiness Score, and to display, in a same viewing window, a visual indicator of the one or more tags, and the list of content comprising the first content higher in the list of content than the second content” in these steps adds insignificant extra-solution activity to the abstract idea (such as recitation of a), b), f), and g) amounts to mere data gathering and recitation of 6), 7), 14), and 15) amounts to insignificant application, see MPEP 2106.05(g)).
The c) and h) one or processors communicatively coupled with the wearable device/finger worn wearable ring device or the user device and d) a graphical user interface of the user device in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification, paragraph [0186] where there are general components, see MPEP 2106.05(f)).
Lastly, the e) machine learning model trained to identify content for regulating the physiological data based on the pattern between the physiological data and the classifier data/the one or more tags in these steps is recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that it amounts to no more than mere instructions to apply the exception using generic computer components (such as a generic application of machine learning), see MPEP 2106.05(f).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
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 using a) a wearable device configured to measure photoplethysmogram (PPG) data from a user using one or more light-emitting components and one or more light-receiving components/a finger worn wearable wing device comprising one or more light-emitting components and one or more light-receiving components, b) a user device communicatively coupled with the wearable device, with a graphical user interface, c) one or processors communicatively coupled with the wearable device/finger worn wearable ring device or the user device, d) a graphical user interface of the user device, e) a machine learning model trained to identify content for regulating the physiological data based on the pattern between the physiological data and the classifier data/the one or more tags, f) a finger worn wearable ring device comprising one or more light-emitting components and one or more light-receiving components, g) a user device communicatively coupled with the finger worn wearable ring device and comprising a graphical user interface, and h) one or more processors communicatively coupled with the finger worn wearable ring device or the user device to perform the claimed steps and the additional step of 6) “move the first content to a position on the graphical user interface higher in a list of content than second content in the set of content based at least in part on the first content being previously selected by the user”, 7) “transmit a signal to cause the graphical user interface of the user device running an application to display the list of content, the list of content comprising the first content higher in the list of content than the second content”, 14) “move the first content to a position on the graphical user interface higher in the list of content than the second content in the list of content based at least in part on the first content being previously selected by the user”, and 15) “cause the graphical user interface of the user device to display the Sleep Score or the Readiness Score, and to display, in a same viewing window, a visual indicator of the one or more tags, and the list of content comprising the first content higher in the list of content than the second content” amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity) and mere instructions to apply the exception using generic computer components that do not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain method steps of organizing human activity. Specifically, MPEP 2106.05(d) and MPEP 2106.05(f) recite that the following limitations are not significantly more:
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); and
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
The a) wearable device, b) a user device communicatively coupled with the wearable device, with a graphical user interface, f) a finger worn wearable ring device comprising one or more light-emitting components and one or more light-receiving components, and g) a user device communicatively coupled with the finger worn wearable ring device and comprising a graphical user interface in these steps and the additional steps of 7) “transmit a signal to cause the graphical user interface of the user device running an application to display the list of content, the list of content comprising the first content higher in the list of content than the second content” and 15) “cause the graphical user interface of the user device to display the Sleep Score or the Readiness Score, and to display, in a same viewing window, a visual indicator of the one or more tags, and the list of content comprising the first content higher in the list of content than the second content” add insignificant extra-solution activity/pre-solution activity in the form of WURC activity to the abstract idea. The following is an example of a court decision demonstrating computer functions as well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives wearable device data and classifier data using a these devices, and transmits a signal of the recommendation data to an interface of an apparatus over a network, for example the Internet.
Furthermore, the current invention displays content on a display utilizing c) and h) one or processors communicatively coupled with the wearable device/finger worn wearable ring device or the user device and d) graphical user interface of the user device, thus these elements are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Additionally, the current invention selects content utilizing e) a machine learning model trained to identify content for regulating the physiological data based on the pattern between the physiological data and the classifier data/the one or more tags, thus this model is adding the words “apply it” with mere instructions to implement the abstract idea on a computer (such as a generic implementation of machine learning).
Lastly, the following State of the Art Publication demonstrates the well-understood, routine, and conventional nature of the additional elements: 6) “move the first content to a position on the graphical user interface higher in a list of content than second content in the set of content based at least in part on the first content being previously selected by the user” and 14) “move the first content to a position on the graphical user interface higher in the list of content than the second content in the list of content based at least in part on the first content being previously selected by the user”, e.g. see paragraph [0006] of U.S. 2012/0096404 to Matsumoto et al. where such a display based on frequency is known.
Mere instructions to apply an exception using generic computer components or insignificant extra-solution activity in the form of WURC activity cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1, 5, 8-9, 12-16, and 21 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 1, 5, 8, 12-14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2022/0095974 to Southern et al. in view of U.S. 2014/0223462 to Aimone et al.
As per claim 1, Southern et al. teaches a system for content recommendation comprising:
--a wearable device configured to measure photoplethysmogram (PPG) data from a user using one or more light-emitting components and one or more light-receiving components; (see: paragraph [0063] where there is an optical light sensor integrated into the smartwatch body where light is emitted and then returned. Also see: paragraph [0062] where the sensors can be located on a wearable smart ring. Also see: paragraph [0065] where PPG data is received from the device. There is a wearable device here configured to measure the PPG using light)
--a user device communicatively coupled with the wearable device; (see: paragraph [0016] where there can be a companion device for the wearable device in the form of a mobile phone (user device). Also see: paragraph [0104] where a variety of devices can be used in combination) and
--the one or more processors configured to:
--receive physiological data measured from the user by the wearable device; (see: paragraph [0011] where user attribute data is being received from one or more sensors. The sensors here can be in the wearable ring as stated above. Also see: paragraph [0015] where physiological data is being received as the attribute data. The physiological data here is used to determine a mental state as explained in paragraph [0024])
--receive, via a graphical user interface of the user device, classifier data indicating a type of activity in which the user engaged; (see: paragraph [0011] where user attribute data is being received from one or more sensors. The sensors here can be in the mobile phone (user device with a GUI) as stated above. Also see: paragraph [0015] where activity data is being received as the attribute data and paragraph [0029] where there is an activity level, exercise measure (classifier data indicating a type of activity), etc.)
--determine a pattern between the physiological data and the classifier data indicating the type of activity in which the user engaged, (see: paragraph [0029] where there is a determination of a correlation between the mental state (physiological data) of the user and the activity level, exercise measure (classifier), etc.) wherein the pattern indicates a relationship between the type of activity in which the user is engaged and an impact on the physiological data; (see: paragraph [0030] where the pattern here indicates a relationship between the types of activities and their potential improvement on the user’s mental state)
--select, based at least in part on a model to identify content for regulating the physiological data based on the pattern between the physiological data and the classifier data, a set of content for the user, (see: paragraph [0102] where a notification is selected and sent to the user in the form of a recommendation to do some exercise soon to feel happier. The model here is as simple as sending a recommendation to exercise when the user needs to feel happier. The set here consists of one notification) wherein each of the set of content corresponds to a relative effectiveness for regulating the physiological data in response to the type of activity in which the user engaged; (see: paragraph [0102] where the first content here is the notification recommendation and it corresponds to a respective effectiveness for regulating the physiological data. The physiological data is based on a type of activity in which the user is engaged in) and
--transmit a signal to cause the graphical user interface of the user device running an application to display the list of content (see: paragraph [0102] where a notification is transmitted to the user’s device to cause an application to display that notification/recommendation (a list of content)).
Southern et al. may not further, specifically teach:
1) --one or more processors communicatively coupled with the wearable device or the user device,
2) --select a set of content for the user (where the set includes multiple items);
3) --a machine learning model trained to identify content for regulating the physiological data;
4) --score each content of the set of content based at least in part on a respective effectiveness of each content for regulating a value of the physiological data;
5) --assign a rank to each content of the set of content based at least in part on the pattern between the physiological data and the classifier data, wherein the rank assigned to each content is further based at least in part on a respective score that is indicative of the respective effectiveness of each content for regulating the physiological data as compared to other content;
6) --order a list of content provided to the user via the graphical user interface of the user device based at least in part on assigning the rank to each content of the set of content;
7) --determine that first content in the set of content was previously selected by the user;
8) --move the first content to a position on the graphical user interface higher in the list of content than second content in the set of content based at least in part on the first content being previously selected by the user via the graphical user interface; and
9) --the list of content comprising the first content higher in the list of content than the second content.
Aimone et al. teaches:
1) --one or more processors communicatively coupled with the wearable device or the user device, (see: paragraphs [0125] and [0129] where there is a wearable device coupled to a processor)
2) --select a set of content for the user (where the set includes multiple items); (see: paragraph [0023] where there is selection of content for the user)
3) --a machine learning model trained to identify content for regulating the physiological data; (see: paragraph [0141] where there is learning of a user’s preference and modification of presentation of content based on the brain state. There system here is being trained to identify content and modify the presentation of content for regulating brain state data/physiological data. Also see: paragraph [0285] where there is mood management)
4) --score each content of the set of content based at least in part on a respective effectiveness of each content for regulating a value of the physiological data; (see: paragraph [0247] where there is ranking/scoring of content based on respectively associated brain states. Also see: FIG. 33)
5) --assign a rank to each content of the set of content based at least in part on the pattern between the physiological data and the classifier data, (see: paragraph [0247] where a rank is being assigned to each content and a list of content items is being presented to a user using the computer system here (classifier) and past brain state data (physiological data)) wherein the rank assigned to each content is further based at least in part on a respective score that is indicative of the respective effectiveness of each content for regulating the physiological data as compared to other content; (see: paragraph [0247] where the rank assigned to the content here is based on effectiveness for regulating brain state data compared to other content where happiness-producing content is being preferred)
6) --order a list of content provided to the user via the graphical user interface of the user device based at least in part on assigning the rank to each content of the set of content; (see: paragraph [0247] where the content is sorted/ranked into a list and provided to the user via display based on the ranking of the content)
7) --determine that first content in the set of content was previously selected, via the graphical user interface, by the user; (see: paragraph [0247] where there is a determination that a previous content was used to produce happiness by the interface here)
8) --move the first content to a position on the graphical user interface higher in the list of content than second content in the set of content based at least in part on the first content being previously selected by the user via the graphical user interface; (see: paragraph [0249] where there is weighting of content in the form of sorting videos by a ranking algorithm based on a user’s preferences. The sorting here would rank a content here based on a user’s preference higher than another content. The preferences here are previous selections as explained in paragraph [0247]) and
9) --the list of content comprising the first content higher in the list of content than the second content (see: paragraph [0249] where there is weighting of content in the form of sorting videos by a ranking algorithm based on a user’s preferences. Also see: paragraph [0247] where there is content preferentially shown and in a list, therefore the first content will be shown higher than the second content).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 1) one or more processors communicatively coupled with the wearable device or the user device, 2) select a set of content for the user, 3) a machine learning model trained to identify content for regulating the physiological data, 4) score each content of the set of content based at least in part on a respective effectiveness of each content for regulating a value of the physiological data, 5) assign a rank to each content of the set of content based at least in part on the pattern between the physiological data and the classifier data, wherein the rank assigned to each content is further based at least in part on a respective score that is indicative of the respective effectiveness of each content for regulating the physiological data as compared to other content, 6) order a list of content provided to the user via the graphical user interface of the user device based at least in part on assigning the rank to each content of the set of content, 7) determine that first content in the set of content was previously selected, via the graphical user interface, by the user, 8) move the first content to a position on the graphical user interface higher in the list of content than second content in the set of content based at least in part on the first content being previously selected by the user via the graphical user interface, and 9) the list of content comprising the first content higher in the list of content than the second content as taught by Aimone et al. in the system as taught by Southern et al. with the motivation(s) of improving the quality of the feedback and improving the user’s brain state (see: paragraphs [0281] and [0331] of Aimone et al.).
As per claim 5, Southern et al. and Aimone et al. in combination teaches the system of claim 1, see discussion of claim 1. Aimone et al. further teaches:
--determine previous content recommended to the user via the graphical user interface of the user device based at least in part on the machine learning model (see: paragraph [0249] where there is content being recommended using a machine learning model which rely on previous recommendations to adapt the content) trained to identify content for regulating the physiological data (see: paragraphs [0142] and [0147] where there is content for regulating a brain state and the system here learns based on the user’s preferences).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 8, Southern et al. and Aimone et al. in combination teaches the system of claim 1, see discussion of claim 1. Southern et al. further teaches:
--generate a notification based at least in part on the first content; (see: paragraph [0102] where a notification is being generated and then sent. The notification is the first content) and
--output the notification in the application and via the graphical user interface of the user device (see: paragraph [0102] where a notification is being generated and then sent. The notification is the first content. The user is receiving the notification on their device).
As per claim 12, Southern et al. and Aimone et al. in combination teaches the system of claim 1, see discussion of claim 1. Aimone et al. further teaches wherein the physiological data comprises heart rate data associated with the user, respiratory rate data associated with the user, sleep data associated with the user, activity data associated with the user, or any combination thereof (see: paragraph [0201] where there is heart rate data).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 13, Southern et al. and Aimone et al. in combination teaches the system of claim 1, see discussion of claim 1. Aimone et al. further teaches wherein the classifier data additionally comprises activity information indicating timing information indicating a timestamp of the activity in which the user engaged, location information indicating a locality of the activity in which the user engaged, or any combination thereof (see: paragraph [0018] where there is timing information indicating a timestamp of the brain state activity).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 14, Southern et al. and Aimone et al. in combination teaches the system of claim 1, see discussion of claim 1. Aimone et al. further teaches wherein the first content comprises multimedia content including audio content, video content, or any combination thereof (see: paragraph [0010] where the suggestion may include video content).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 16, Southern et al. and Aimone et al. in combination teaches the system of claim 1, see discussion of claim 1. Southern et al. further teaches wherein the wearable device comprises a finger worn wearable ring device (see: paragraph [0017] where there is a wearable smart ring).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2022/0095974 to Southern et al. in view of U.S. 2014/0223462 to Aimone et al. as applied to claim 1, further in view of U.S. 2016/0103921 to Brust et al.
As per claim 9, Southern et al. and Aimone et al. in combination teaches the system of claim 1, see discussion of claim 1. The combination may not further, specifically teach:
--cause the graphical user interface of the user device running the application to display the first content within a duration after receiving the classifier data.
Brust et al. teaches:
--cause the graphical user interface of the user device running the application to display the first content within a duration after receiving the classifier data (see: paragraph [0189] where there is a location of the user (classifier data) and the suggested content is selected after the classifier data is received. Also see: paragraph [0205] where information is being displayed. Thus, after the classifier data is received, the data is being displayed on the device. The phrase “within a duration of time” is broad enough to apply to this scenario).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to cause the graphical user interface of the user device running the application to display the first content within a duration after receiving the classifier data as taught by Brust et al. in the system as taught by Southern et al. and Catani et al. in combination with the motivation(s) of recommending and delivering content based on an environmental goal or determined conditions of human subjects (see: paragraph [0002] of Brust et al.).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2022/0095974 to Southern et al. in view of U.S. 2014/0223462 to Aimone et al. as applied to claim 1, further in view of U.S. 2021/0407684 to Pho et al.
As per claim 15, Southern et al. and Aimone et al. in combination teaches the method of claim 1, see discussion of claim 1. The combination may not further, specifically teach wherein the content comprises a recommendation to the user to maintain or adjust a readiness score of the user.
Pho et al. teaches:
--wherein the content comprises a recommendation to the user to maintain or adjust a readiness score of the user (see: paragraph [0043] where there is a readiness score. Also see: paragraph [0029] where recommendations to the user are being made. These include recommendations about preparing for an illness. The recommendations here can be considered as an adjustment towards the readiness metric).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the content comprises a recommendation to the user to maintain or adjust a readiness score of the user as taught by Pho et al. in the system as taught by Southern et al. and Aimone et al. in combination with the motivation(s) of brining more insight to users regarding their physical health (see: paragraph [0002] of Pho et al.).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2022/0095974 to Southern et al. in view of U.S. 2014/0223462 to Aimone et al. and further in view of U.S. 2022/0370757 to Altman et al.
As per claim 21, Southern et al. teaches a system for regulating physiological data of a user, comprising:
--a finger worn wearable ring device comprising one or more light-emitting components and one or more light-receiving components; (see: paragraph [0063] where there is an optical light sensor integrated into the smartwatch body where light is emitted and then returned. Also see: paragraph [0062] where the sensors can be located on a wearable smart ring. Also see: paragraph [0065] where PPG data is received from the device. There is a wearable device here configured to measure the PPG using light)
--a user device communicatively coupled with the finger worn wearable ring device and comprising a graphical user interface; (see: paragraph [0016] where there can be a companion device for the wearable device in the form of a mobile phone (user device with a GUI). Also see: paragraph [0104] where a variety of devices can be used in combination) and
--the one or more processors configured to:
--measure the physiological data from the user via the one or more light- emitting components of the finger worn wearable ring device and the one or more light-receiving components of the finger worn wearable ring device; (see: paragraph [0011] where user attribute data is being received from one or more sensors. The sensors here can be in the wearable ring as stated above. Also see: paragraph [0015] where physiological data is being received as the attribute data. The physiological data here is used to determine a mental state as explained in paragraph [0024]. Physiological measurements are being taken from the finger worn device here)
--calculate a Sleep Score, a Readiness Score, or both, for the user based at least in part on the physiological data measured by the finger worn wearable ring device; (see: paragraph [0011] where user attribute data is being received from one or more sensors. The sensors here can be in the wearable ring as stated above. Also see: paragraph [0015] where physiological data is being received as the attribute data. The physiological data here is used to determine a mental state as explained in paragraph [0024]. The mental state is the calculated, readiness score and it is based on the finger word device information)
--receive, via the graphical user interface of the user device, one or more tags that indicate one or more activities in which the user engaged; (see: paragraph [0011] where user attribute data is being received from one or more sensors. The sensors here can be in the mobile phone (user device with a GUI) as stated above. Also see: paragraph [0015] where activity data is being received as the attribute data and paragraph [0029] where there is an activity level, exercise measure (tag data indicating a type of activity), etc.)
--determine a pattern between the physiological data and the one or more tags, (see: paragraph [0029] where there is a determination of a correlation between the mental state (physiological data) of the user and the activity level, exercise measure (tags), etc.) wherein the pattern indicates a relationship between the one or more activities in which the user engaged and an impact on the physiological data; (see: paragraph [0030] where the pattern here indicates a relationship between the types of activities and their potential improvement on the user’s mental state)
--select, based at least in part on a model to identify content for regulating the physiological data based on the pattern between the physiological data and the one or more tags, a set of content for the user comprising at least first content; (see: paragraph [0102] where a notification is selected and sent to the user in the form of a recommendation to do some exercise soon to feel happier. The model here is as simple as sending a recommendation to exercise when the user needs to feel happier (first content). The list is of one item here) and
--cause the graphical user interface of the user device to display the first content (see: paragraph [0102] where a notification is transmitted to the user’s device to cause an application to display that recommendation (first content)).
Southern et al. may not further, specifically teach:
1) --one or more processors communicatively coupled with the finger worn wearable ring device or the user device;
2) --a machine learning model trained to identify content for regulating the physiological data;
3) --a set of content for the user comprising first content and second content;
4) --score each content of the set of content based at least in part on a respective effectiveness of each content for regulating a value of the physiological data;
5) --assign a rank to each content of the set of content based at least in part on the pattern between the physiological data and the classifier data, wherein the rank assigned to each content is further based at least in part on a respective score that is indicative of the respective effectiveness of each content for regulating the physiological data as compared to other content;
6) --order a list of content provided to the user via the graphical user interface of the user device based at least in part on assigning the rank to each content of the set of content;
7) --determine that the first content in the list of content was previously selected, via the graphical user interface, by the user;
8) --move the first content to a position on the graphical user interface higher in the list of content than the second content in the list of content based at least in part on the first content being previously selected by the user via the graphical user interface;
9) --cause the graphical user interface of the user device to display the Sleep Score or the Readiness Score, and
10) --to display, in a same viewing window, a visual indicator of the one or more tags, and the list of content comprising the first content higher in the list of content than the second content.
Aimone et al. teaches:
1) --one or more processors communicatively coupled with the finger worn wearable ring device or the user device; (see: paragraphs [0125] and [0129] where there is a wearable device coupled to a processor)
2) --a machine learning model trained to identify content for regulating the physiological data; (see: paragraph [0141] where there is learning of a user’s preference and modification of presentation of content based on the brain state. There system here is being trained to identify content and modify the presentation of content for regulating brain state data/physiological data. Also see: paragraph [0285] where there is mood management)
3) --a set of content for the user comprising first content and second content; (see: paragraph [0023] where there is selection of set of content for the user)
4) --score each content of the set of content based at least in part on a respective effectiveness of each content for regulating a value of the physiological data; (see: paragraph [0247] where there is ranking/scoring of content based on respectively associated brain states. Also see: FIG. 33)
5) --assign a rank to each content of the set of content based at least in part on the pattern between the physiological data and the classifier data, (see: paragraph [0247] where a rank is being assigned to each content and a list of content items is being presented to a user using the computer system here (classifier) and past brain state data (physiological data)) wherein the rank assigned to each content is further based at least in part on a respective score that is indicative of the respective effectiveness of each content for regulating the physiological data as compared to other content; (see: paragraph [0247] where the rank assigned to the content here is based on effectiveness for regulating brain state data compared to other content where happiness-producing content is being preferred)
6) --order a list of content provided to the user via the graphical user interface of the user device based at least in part on assigning the rank to each content of the set of content; (see: paragraph [0247] where the content is sorted/ranked into a list and provided to the user via display based on the ranking of the content)
7) --determine that the first content in the list of content was previously selected, via the graphical user interface, by the user; (see: paragraph [0247] where there is a determination that a previous content was used to produce happiness by the interface here)
8) --move the first content to a position on the graphical user interface higher in the list of content than the second content in the list of content based at least in part on the first content being previously selected by the user via the graphical user interface; (see: paragraph [0249] where there is weighting of content in the form of sorting videos by a ranking algorithm based on a user’s preferences. The sorting here would rank a content here based on a user’s preference higher than another content. The preferences here are previous selections as explained in paragraph [0247]) and
10) --to display, in a same viewing window, a visual indicator of the one or more tags, and the list of content comprising the first content higher in the list of content than the second content (see: paragraph [0249] where there is weighting of content in the form of sorting videos by a ranking algorithm based on a user’s preferences. Also see: paragraph [0247] where there is content preferentially shown and in a list, therefore the first content will be shown higher than the second content. A display of the content is also a display of visual indicators of the tags associated with the content).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 1) one or more processors communicatively coupled with the finger worn wearable ring device or the user device, 2) a machine learning model trained to identify content for regulating the physiological data, 3) a set of content for the user comprising first content and second content, 4) score each content of the set of content based at least in part on a respective effectiveness of each content for regulating a value of the physiological data, 5) assign a rank to each content of the set of content based at least in part on the pattern between the physiological data and the classifier data, wherein the rank assigned to each content is further based at least in part on a respective score that is indicative of the respective effectiveness of each content for regulating the physiological data as compared to other content, 6) order a list of content provided to the user via the graphical user interface of the user device based at least in part on assigning the rank to each content of the set of content, 7) determine that the first content in the list of content was previously selected, via the graphical user interface, by the user, 8) move the first content to a position on the graphical user interface higher in the list of content than the second content in the list of content based at least in part on the first content being previously selected by the user via the graphical user interface, and 10) to display, in a same viewing window, a visual indicator of the one or more tags, and the list of content comprising the first content higher in the list of content than the second content as taught by Aimone et al. in the system as taught by Southern et al. with the motivation(s) of improving the quality of the feedback and improving the user’s brain state (see: paragraphs [0281] and [0331] of Aimone et al.).
Altman et al. teaches:
9) --cause the graphical user interface of the user device to display the Sleep Score or the Readiness Score (see: paragraph [0058] and [0175] where this score information is being displayed in the GUI).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to 9) cause the graphical user interface of the user device to display the Sleep Score or the Readiness Score as taught by Altman et al. in the system as taught by Southern et al. and Aimone et al. in combination with the motivation(s) of improving the well-being of the individual (see: paragraph [0003] of Altman et al.).
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 Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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