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 Claims
This final rejection is in response to amendments/remarks filed on 12/29/2025. Claims 1-3, 8, 10, 15 and 19 have been amended. New claims 21-23 have been added. Claims 4, 12, and 17 have been cancelled. Therefore, claims 1-3, 5-11, 13-16, and 18-23 remain pending and are considered herein.
Priority
The present claims hold priority to US Provisional Application #63/327,972 filed on 04/06/2022.
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 1-3, 5-11, 13-16, and 18-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more
Step 1: The claims recite:
-A method in claims 1-3, 5-11, 13, and 21
- A system comprising: one or more processors; and one or more memory coupled with the one or more processors, the one or more memory storing executable instructions that when executed by the one or more processors cause the one or more processors to effectuate operations in claims 15, 16, and 18-20, and 22-23
Therefore all of the claims fall under potentially eligible subject matter categories including processes, machines, manufactures, or compositions of matter. Thus the claims are to be evaluated under step 2 of the 2 step analysis.
Step 2a Prong 1: The claims under the broadest reasonable interpretation in light of the specification are analyzed herein. Representative claims 1, 8, and 15 are marked up, isolating the abstract idea from additional elements, wherein the abstract idea is set in bold and the additional elements have been italicized as follows:
Claim 1- A method comprising:
receiving, by a device, wellness information comprising at least an indicator of a level of engagement and energy level represented by user interactions with content, by one or more user interfaces, associated with a collaborative platform associated with a user, wherein the user is associated with a user profile linked to a first group of a plurality of groups of the collaborative platform;
sending, by the device, an alert based on an indication of the wellness information associated with the user;
receiving, by the device, an indication of a selection, by a user interface, of an activity associated with the alert, wherein the activity comprises an energizer activity;
receiving, by the device, feedback information, presented by the user interface, associated with the activity, wherein the feedback information is linked to the user profile;
determining, via a trained machine learning model and based on the feedback information, whether the energizer activity satisfies a predetermined threshold in order to increase or decrease the energy level of the user, wherein the trained machine learning model utilize neural network operations to analyze temporal patterns in the received wellness information across a plurality of previous periods and generate in real-time predictive coefficients weighing one or more behavioral categories in order to apply the predictive coefficients to the subsequent wellness information; and
and transmitting, by the device, a second alert based on the determination and feedback information.
Claim 8- A method comprising:
receiving, by a device, wellness information associated with a user profile, the wellness information comprising at least an indicator of a level of engagement and energy level represented by user interactions with content, via one or more user interfaces, associated with a collaborative platform, wherein the user profile is linked to a group of the collaborative platform, wherein the collaborative platform is associated with one or more behavioral categories of a behavioral model;
training a machine learning model on the wellness information, at a plurality of previous periods, to do determine subsequent wellness information of a user associated with the user profile or the group during a subsequent period;
presenting, by a user interface, the subsequent wellness information to the user to facilitate interaction by the user with the subsequent wellness information.
determining, via the trained machine learning model and based on the subsequent wellness information, whether an activity satisfies a predetermined threshold in order to increase or decrease an energy level of the user; wherein the trained machine learning model utilize neural network operation to analyze temporal patterns in the received wellness information across a plurality of previous periods and generate in real-time predictive coefficients weighing one or more behavioral categories in order to apply the predictive coefficients to the subsequent wellness information; and
Sending an alert, based on the determination and the subsequent wellness information.
Claim 15- A system comprising:
one or more processors;
and one or more memories coupled with the one or more processors, the one or more memories storing executable instructions that when executed by the one or more processors cause the one or more processors to:
receive wellness information associated with a user profile, the wellness information comprising at least an indicator of a level of engagement and energy level represented by user interactions with content, by one or more user interfaces, associated with a collaborative platform, wherein the user profile is linked to a group of the collaborative platform, wherein the collaborative platform associated with one or more behavioral categories of a behavioral model;
train a machine learning model on the wellness information, at a plurality of previous periods, to determining subsequent wellness information of a user associated with the user profile or the group during a subsequent period;
utilize the wellness module to determine subsequent wellness information associated with the user.
present, by a user interface, the subsequent wellness information to the user to facilitate interaction by the user with the subsequent wellness information.
Determine, based on the subsequent wellness information and the trained machine learning model, whether an activity satisfies a predetermined threshold in order to increase or decrease an energy level of the user, wherein the trained machine learning model utilizes neural network operations to analyze temporal patterns in the received wellness information across the plurality of previous periods and in real-time generate predictive coefficients weighing the one or more behavioral categories in order to apply the predictive coefficients to the subsequent wellness information; and
Send an alert, based on the determination and the subsequent wellness information, to the user.
When evaluating the bolded limitations of the claims under the broadest reasonable interpretation in light of the specification, it is clear that representative claims 1, 8, and 15 recite an abstract idea category of certain methods of organizing human activity. This abstract idea grouping found in MPEP 2106.04(a)(2)(II) includes claims to “managing personal behavior or relationships or interactions between people.” The invention is directed to this subcategory which includes social activities, teaching, and following rules or instructions, which is supported by the background of the specification,
“[0003] Flexible work arrangements may lack chance beneficial occurrences between people or coworkers that may add some familiarity with the workplace and foster work relationships. The lack of these chance occurrences or serendipity may lead to feelings of isolation and loneliness of workers, which in turn may negatively impact knowledge workers’ sense of belonging, personal productivity, team success, and job satisfaction. Overall, such arrangements may affect a worker’s wellbeing or sense of wellness.”
The invention is no more than a mere tool to “facilitate collaboration between groups of users (e.g., employees) and may provide for a way to analyze and address issues associated with wellness” as stated in [0033]. The amended claim limitation merely further defines the abstract idea by adding steps of determining whether the activity satisfies a pre-determined threshold to determine whether to increase, or decrease, an energy level...or recommend a second activity. These steps merely define the steps used in order to determine the activity to recommend, which is more of the same abstract idea of managing personal behavior, which falls within “certain methods of organizing human activity.”
Even when considering the amendments which require the determining step to be based on a trained model, “wherein the trained machine learning model utilize neural network operation to analyze temporal patterns in the received wellness information across a plurality of previous periods and generate in real-time predictive coefficients weighing one or more behavioral categories in order to apply the predictive coefficients to the subsequent wellness information;” this is still part of the abstract idea because when focusing on the functions being performed, they are still carrying out the abstract of “managing personal behavior, interactions or relationships,” because it is merely reciting the analysis of patterns in wellness information, and generating coefficients weighing the behavioral categories. When considering that this data analysis merely results in the determining of the energy level of an activity, and sending an alert based on the determination and wellness information it is clear that it is still no more than instructions to an individual in order to manage their personal behavior. Furthermore, the data analysis steps are recited at such a high level of generality that they are not meaningfully limited to necessarily technological implementation. Using the trained model utilizing operations to analyze temporal patterns is merely claiming the idea of the outcome in a black box manner, wherein the machine learning and neural network steps are just recited as the avenue in achieving the outcome without specific steps or mechanisms. Therefore, when considering the data analysis steps themselves, they are recited at such a high level of generality that they encapsulates mere instructions to an individual to manage their personal behavior. See Step 2A Prong 2 for why the machine learning and neural networks are merely “apply it” level elements, because the data analysis being performed is still part of the abstract idea of “certain methods of organizing human activity.”
Therefore, the claims recite an abstract idea with the steps of “assessing the wellness of an individual, providing interventions to improve wellness, tracking their state over time, and adjusting based on successful interventions.”
Step 2A Prong 2: The claims include the following additional elements:
-a device in claims 1, 8 and 15
-a machine learning model in claims 1, 8 and 15
--neural network operations in claims 1, 8, and 15
-a wellness module in claims 8 and 15
-one or more processors; in claim 15
-one or more memory coupled with the one or more processors, executing instructions in claim 15
The additional elements listed above are no more than a recitation of the words “apply it” (or an equivalent) or mere instructions to implement an abstract idea or other exception on a computer on its ordinary capacity. In this case, the abstract idea of wellness coaching is merely being instructed to be performed on generic computing devices such as device, wellness module, one or more processors, and one or more memory. Please see MPEP 2106.05(f) for more information on Mere Instructions to Apply An Exception. In addition, the fields of use and technological environments of machine learning and neural networks are generally being linked the abstract idea in a manner that does not meaningfully limit the abstract idea. They are merely being used as a black box to carry out the data analysis (which is still part of the abstract idea), without meaningfully limiting how machine learning or neural network operations are supposed to arrive at the claimed limitation. In addition, because of the generality in which the claims utilize machine learning and neural networks, the claims fail to recite details of how a solution to a problem is accomplished, and the recitation of the claim limitations are merely an attempt to cover any solution to the identified problem using machine learning and neural networks, with no description of the mechanism for accomplishing the result. These additional elements are also part of the “apply it” umbrella because they are no more than mere instructions to implement the abstract idea on a computer, using generic computing components and generic machine learning/neural networks, as a tool to perform the abstract idea. Please refer to MPEP 2106.05(h) for technological environment and field of use. Therefore, whether viewed individually or as an ordered combination, the additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea without integration into a practical application.
Step 2B: The same additional elements set forth in the Prong 2 rejection are also analyzed for whether they recite an inventive concept, the additional elements being repeated as follows:
-a device in claims 1, 8 and 15
-a machine learning model in claims 1, 8 and 15
--neural network operations in claims 1, 8, and 15
-a wellness module in claims 8 and 15
-one or more processors; in claim 15
-one or more memory coupled with the one or more processors, executing instructions in claim 15
These additional elements are not found to include significantly more for the same reasons set forth in the Prong 2 rejection, specifically, that they are no more than a recitation of the words “apply it” (or an equivalent) or mere instructions to implement wellness coaching on the generic computing devices, supported by MPEP 2106.05(f). In addition the claims generally link the abstract idea of “assessing the wellness of an individual, providing interventions to improve wellness, tracking their state over time, and adjusting based on successful interventions” to machine learning and neural networks. MPEP 2106.05(h) states a claim directed to a judicial exception cannot be made eligible “simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.” These claim limitations which generally link the use of the judicial exception to a particular technological environment or field of use do not meaningfully limit the claim because it does purport a significant improvement to any of the technological fields that are included. The additional element of machine learning generally links machine learning without providing a particular algorithm that improves the field of machine learning instead. Finally, no improvements to computing devices have been purported to the claims since they simply are instructions to perform simple steps on generic computing devices, and not new computing devices themselves. The claims and disclosure do not make it apparent to one of ordinary skill in the art that the scope of the claims reflects an improvement to computer functionality, a technological environment, or a technical field. Please review MPEP 2106.05(a) for more information regarding improvements to computing devices, or technological fields. Even when viewed as a whole, nothing in the claims provide significantly more to the abstract idea in order for it to be a inventive concept, because even as a whole, the claims are nothing more than using computers as a tool to carry out the abstract idea using generic machine learning and neural networks. Therefore, the claims are directed to an abstract idea without significantly more.
The dependent claims 2-3, 5-7, 9-11, 13-14, 16, and 18-23 are also given the full two-part analysis, individually and in combination with the claims they depend on, in the following analysis:
-Claims 2, and 10 recite additional steps corresponding to recommending a second energizer activity, and the wellness information further includes an indicator of a level of mood represented by user interactions with the content on the one or more user interfaces. Recommending a second activity to an individual, and recording a level of mood represented by user interactions with content still falls within “certain methods of organizing human activity,” because it still falls within mere instructions to an individual to manage their personal behavior and analyzing user interactions to produce an output to an individual. The fact that the content is found on the one or more user interfaces is an “apply it” level element because it is merely using a device in its ordinary capacity to carry out economic or other tasks. Even when considered individually or as an ordered combination, the additional elements do not integrate the abstract idea into a practical application. Even when viewed as a whole, nothing in the claims meaningfully limits the abstract idea such that it is significantly more. Therefore claims 2 and 10 are not found to integrate the abstract idea into a practical application and are not found to be significantly more.
-Claim 3 further defines the abstract idea by adding the steps of the level of engagement comprising a predetermined threshold associated with user interactions. These steps are more of the same abstract idea recited above since they merely define the data processing steps towards measuring a level of engagement, which is part of “certain methods of organizing human activity.” There are no further additional elements to consider. Therefore, whether analyzed individually or as a combination, the additional elements still do not integrate the abstract idea into a practical application or provide significantly more. Therefore, the claims are directed to an abstract idea without an inventive concept.
-Claim 5, 13, and 19 further limits the type of data included in “wellness information” to include preferred energizers(activities) associated with a user or a calculable value illustrated by a vector. Therefore the abstract idea is still “assessing the wellness of an individual, providing interventions to improve wellness, tracking their state over time, and adjusting based on successful interventions based on feedback,” because even when substituting these elements into the independent claims, the claims are still directed to “certain methods of organizing human activity.” There are no further additional elements therefore claim 5, 13, and 19 are not found to integrate the abstract idea into a practical application and are not found to be significantly more.
-Claim 6 adds the additional step of comprises feedback information from a trusted member, wherein the feedback information from the trusted member is weighted differently compared to other feedback information, wherein the trusted member is determined based on user-indicated selection of a member, likes, views, posts, or comments associated with the first group of the collaborative platform. This is an additional step that is still directed to the abstract idea category of managing personal behavior or interactions between people because it assesses the interactions of different people and determines a member whose data is weighed more heavily than others. Therefore the combined abstract idea is now, “assessing the wellness of an individual, providing interventions to improve wellness, tracking their state over time, adjusting based on successful interventions based on feedback, and wherein the feedback of trusted members is weighed more heavily.” The additional element of collaborative platform is still not found to be integrated into practical application or found to recite significantly more than the abstract idea because it is still the field of collaborative platforms being generally linked to wellness coaching. Please refer to MPEP 2106.05(h) for more information on technological environments and fields of use.
-Claim 7 and 20 merely further limits the abstract idea by limiting the behavioral categories to comprise of “roles, rules, respect, recognition, or routines.” This is more of the same abstract idea, since even when substituting these categories into the claims they depend on, they still recite certain methods of organizing human activity. These categories merely describe management of personal behavior or interactions, or relationships between people, for example, “roles, rules, respect, and routines” describe the relationships and interactions of people. Furthermore, there are no further additional elements to consider, therefore claim 7 is not integrated into a practical application or provided significantly more. This applies to claim 20 as well, which merely recite elements from claim 5 and 7, which are both more of the same abstract idea without additional elements. Therefore, claims 7 and 20 are not patent eligible for being directed to an abstract idea without an inventive concept.
-Claims 9 and 16 adds the additional step of “wherein the alert comprises an activity for the user to complete.” The bolded claims still fall under the abstract idea of “assessing the wellness of an individual, providing interventions to improve wellness, tracking their state over time, and adjusting based on successful interventions based on feedback.” Specifically, claim 9 still falls under “providing interventions to improve wellness.” The additional element, “alert,” has already been rejected in the amended claim 1 rejection for reciting transmitting alerts but does not introduce a significant improvement to how alerts are transmitted. Please see MPEP 2106.05(h) and MPEP 2106.05(a) for more information. Therefore claim 9 is still not found to integrate the abstract idea into a practical application and are not found to be significantly more.
-Claims 11, 14, and 18 recite additional steps which further analyze the mood or engagement of multiple users within a group with each other. More specifically these claims state, “wherein the wellness information comprises a mood associated with the group or level of engagement associated with the group, wherein the group comprises a plurality of different user profiles” and “wherein the machine learning model is further trained using likes, views, posts, or comments associated with the group.” Analyzed individually, this still an abstract idea directed to “certain methods of organizing human activity” because it recites steps that further analyze the interactions between users and an overall “mood” associated with a group as a whole. This is an example of managing personal relationships and interactions between people as described in MPEP 2106.04(a)(2)(II). Furthermore, the additional element machine learning module is repeated but it still not found to be integrated into a practical application or found to be significantly more for the same reasons set forth in the independent claims. Specifically that the machine learning module is merely an example of instructions to perform the abstract idea on a computing device in its ordinary capacity as described in MPEP 2106.05(f). And also generally linking wellness coaching to the field of machine learning as described in MPEP 2106.05(h). Utilizing inputs such as likes, comments or post does not provide improvements to the field of use or to the computing devices, therefore the additional elements do not meaningfully limit the claims in a manner that would provide significantly more to consider it an inventive concept.
-Claims 21, 22, and 23 further limit the abstract idea by limiting the indicator of the mood to be a two-scale vector, however, since the inputs of the vectors are merely personal behaviors/interactions, this is still part of the abstract idea of “certain methods of organizing human activity.” The fact that the indicators are structured as vectors is still part of the abstract idea because it merely describes the format or type of data, and even when considering the two-part vector, it still does not enough specificity or particularity to be anything more than instructions to an individual to manage their personal behavior. Furthermore, since there are no further additional elements to consider, and even when considering these further limitations with previous additional elements, the claims are no more than a generic use of computers to perform an existing abstract idea process. Therefore, even when viewing the claim as a whole, nothing meaningfully limits the claims to be significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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, 7-11, 14-16, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Anders et al.(US 20200090067 A1) hereinafter Anders in view of Tiwari (US 20170193420 A1) hereinafter Tiwari, further in view of Condie et al. (US 20200090812 A1) hereinafter Condie.
Regarding Claim 1:
Anders discloses embodiments related to solving a problem of occupational behaviors in a workplace being affected by individual’s mental wellbeing, by creating a method of assessing one’s emotional state over time and providing recommendations to improve one’s mood, utilizing a feedback loop to continuously make adjustments, which teaches:
- A method comprising: receiving, by a device, wellness information, associated with a user,(Anders[0004] The method includes, for instance: obtaining, by one or more processor, inputs of emotion time series data of a user and environmental factor data from one or more data collection device for a user assistance service. ) Anders’ emotion time series data and emotional factor data teach the wellness information of the present disclosure because “wellness” broadly refers to any sort of health data whether that is emotional, physical, or anything to do with wellness.
-wherein the user is associated with a user profile linked to a first group of a plurality of groups; (Anders[0048] In certain embodiments of the present invention, the emotion service engine 130 is configured with user profiles, or filters, respective to the user 105 for screening the information coming from the external data sources 180, such as names of organizations, family members, and friends, names of interested cities and neighborhoods, and keywords identifying areas of interest for the user 105...[0081] Certain embodiments of the present invention include the emotion knowledgebase that stores information on activities to recommend to the subject user or group, in order to attain the desired state of emotion. Certain embodiments of the present invention, receives feedback and recommendation and trains the emotion time series models respective to environmental factors, for individual users or for a group, by machine learning.) In Anders the user is associated with a user profile, which is linked to information from external data sources such as organizations, family, and friends which are the plurality of groups. The subject group in [0081] is the first group that the user profile is linked to.
- sending, by the device, an alert based on an indication of the wellness information associated with the user; (Anders[0039] The emotion service engine 130 delivers the recommendation to the user 105 by use of preconfigured methods for the serviced environment 101, the subject population 103, or for individual users. For example, the emotion service engine 130 sends a chat message “How about taking a walk for 5 minutes? The weather is beautiful today!” The emotion service engine 130 also can send an email with multimedia content/link “This little kitten video went viral. Want to check out? Click here.” The emotion service engine 130 can also modify the calendar of the user 105 by adding additional alerts for an existing schedule or generating a new event on the calendar, with a notice “Don't forget the office outing is this Friday! Two more days to go!” or “The quarterly report is due in two weeks. Please have it ready by next Wednesday for a review before the presentation!”)
-receiving, by the device, an indication of a selection, by a user interface of an activity associated with the alert, wherein the activity comprises an energizer activity; (Anders[0037] In certain embodiments of the present invention, the emotion service engine 130 configures remedial actions to attain the target emotion-time 150 respective to each basic emotion, and respective to each factor. [0038] A few examples of the remediation steps further include, but are not limited to, taking a walk/water break, or doing yoga, or any other personally preferred activities to attain a refreshed state of emotion, to promote a more efficient work performance; scheduling a much anticipated event/meeting, or performing tasks relevant to such occasion; viewing cute, funny, or any other amusing content, such as cartoon, pet video, or quote of the day, for a short period of time to promote general sense of happiness, which corresponds to a high emotion score. [0110] the display 24, which can be configured to provide user interface functionality...can be configured as a touch screen render and can be configured to provide user interface functionality) Ander’s remedial steps anticipate the energizer activities of the present disclosure, because both activities are defined as activities that can promote a better mood. The emotion service engine configuring remedial actions is an indication of a selection of an activity.
-receiving, by the device, feedback information, presented by the user interface associated with the activity, wherein the feedback information is linked to the user profile; (Anders[0023] Each emotion time series model 160 represents the pattern of change in the state of emotion for the user 105, or in the state of collective emotion of the subject population 103, per environmental factor. The content of the Emotional Knowledge Base (EKB) 140 include, but not limited to, a variety of knowledge on how to interpret the inputs in the context of the state of emotion, a list of activities that are established to have a certain effect on the state of emotion for an individual user or for general public for recommendation, past recommendations and feedbacks, as well as data from external data sources 180 that are likely to affect the emotion time series model 160. [0024] The user 105 receives the recommendation 191 and acts on the received recommendation 191. The user 105 sends a feedback 199 assessing effectiveness of the recommendation 191 in timely attaining the target state of emotion for the user 105, as specified in the target emotion-time value pair 150. The emotion service engine 130 subsequently trains the emotion time series model 160 with the recommendation 191 and the feedback 199 by machine learning. [0110] the display 24, which can be configured to provide user interface functionality...can be configured as a touch screen render and can be configured to provide user interface functionality) Since the emotion time series models each represents a pattern for an individual user, the data corresponding to that user can be referred to as the “user profile.” Therefore, by providing feedback based on the effectiveness of the recommendation, Anders’ system anticipates the reception of feedback information associated with an activity, linked to a user profile.
-determining, via a trained machine learning model and based on the feedback information, whether the energizer activity satisfies a predetermined threshold in order to increase or decrease a level of the user. (Anders [0040] In block 250, the emotion service engine 130 obtains a feedback 199 on effectiveness of the recommendation 191 from the user 105. The emotion service engine 130 also obtains updates on the emotion time series data 113 from the data collection devices 107. The emotion service engine 130 trains the emotion time series model 160, by machine learning with the updated inputs and/or the feedback 199 from the user 105, in order to improve accuracy of the emotion time series model 160... The emotion service engine 130 repeats blocks 210 through 250 when preconfigured conditions for re-processing are met, including, but not limited to, cumulating a predefined amount of input data, obtaining a new instance of the target emotion-time value pair 150, etc. [0034] In block 240, the emotion service engine 130 determines which environmental factor would cause the baseline emotion time graph to move close to the point of the target emotion-time 150. If the emotion service engine 130 ascertains one or more known environmental factor that will move the baseline emotion time graph toward the point of the target emotion-time 150 within a certain threshold distance of the target emotion-time point, then the emotion service engine 130 generates the recommendation 191 including activities respective to the ascertained environmental factor for the user 105. The emotion service engine 130 subsequently delivers the generated recommendation 191 to the user 105 via a preconfigured channel. Then, the emotion service engine 130 proceeds with 250.) The examiner notes, that while this limitation was originally indicated as free of prior art in the prior rejection, in the present rejection, upon further consideration of the BRI of the claims in view of the specification, the claims only require either one of “increase or decrease an energy level of the user” or “recommend a second energizer activity.” Upon further review of Anders, the examiner notes that Anders teaches “recommending a second energizer activity” therefore, satisfying the limitation. Following along the process of Anders, Anders teaches the limitation because, Ander obtains feedback on the effectiveness of the recommendation (which includes energizer activities), then repeats the process in blocks 210-250. In block 240, the engine determines whether an environmental factor (which includes recommended energizer activities) will move the baseline emotion time graph (energy level), within a certain threshold distance of the target emotion-time point. This satisfies “determining whether the energizer activity satisfied a predetermined threshold” because it tests whether a recommended activity reached a threshold distance on the target emotion graph. The certain threshold distance in Anders is the “predetermined threshold.” After this, the system generates another recommendation. Since this is the second time blocks 210-250 were repeated, this would be a “second energizer activity.” Since the claims only require one of “increase or decrease the energy level of the user or to recommend a second energizer activity,” the limitation has been satisfied because the latter has been met.
-and transmitting, by the device, a second alert based on the determination and the feedback information. (Anders[0040] In block 250, the emotion service engine 130 obtains a feedback 199 on effectiveness of the recommendation 191 from the user 105. The emotion service engine 130 also obtains updates on the emotion time series data 113 from the data collection devices 107. The emotion service engine 130 trains the emotion time series model 160, by machine learning with the updated inputs and/or the feedback 199 from the user 105, in order to improve accuracy of the emotion time series model 160 with the time progression of the baseline as well as the predictions. Then, the emotion service engine 130 terminates processing the input data. The emotion service engine 130 repeats blocks 210 through 250 when preconfigured conditions for re-processing are met, including, but not limited to, cumulating a predefined amount of input data, obtaining a new instance of the target emotion-time value pair 150, etc.) Since blocks 210 through 250 are repeated and in block 240 is when the recommendation alert occurs, therefore during the second iteration of block 240 it can be said that Anders teaches the second alert based on the feedback information.
However, Anders fails to teach:
-the wellness information comprising at least an indicator of a level of engagement and an energy level represented by user interactions with content, by one or more user interfaces, associated with a collaborative platform
-wherein the user is associated with a user profile linked to a first group of a plurality of groups of a collaborative platform;
-the “level” being decreased in the “determining...” step is specifically an energy level.
-wherein the trained machine learning model utilizes neural network operations to analyze temporal patterns in the received wellness information across a plurality of previous periods and generate in real-time predictive coefficients weighing one or more behavioral categories in order to apply the predictive coefficients to the subsequent wellness information;
Alternatively, Tiwari is directed towards a platform consisting of modules that promote engagement through the gamification of employee performance which teaches:
- the wellness information comprising at least an indicator of a level of engagement represented by user interactions with content by one or more user interfaces, associated with a collaborative platform(Tiwari [0015] Yet another objective of the embodiments herein is to provide a system and method to enable an approach to define and award two types of points to an employee; one for the activities performed (“activity points”) and the other for outputs achieved (“impact points”), thereby enabling mechanisms for motivating employees and stimulating behaviors around not just the results, but also around the input factors or activities. [0027] The points module is configured to convert input activities to “activity points”, and output achieved to “impact points”. Points are assigned to users based on the user's activity determined by the action tracking module. The points module uses a customizable logic for conversion of activity to activity points, and output achieved by the employee to impact points. The points are awarded to an employee in near real-time basis. [0028] The rank and badge module is configured to award the plurality of ranks (across a number of variables i.e., various activity types and output types), badges (awards), levels performance level of an employee based on quantitative, configurable metrics) and leaderboard mentions (social metrics i.e., “likes”, “comments” or “follow”) to the employees. The rank and badge module uses the points provided by the points module for determining ranks and badges. The rank and badge module is communicably coupled to the points module.) The broadest reasonable interpretation of “level of engagement” is any measure of engagement based on user interactions with content, which is satisfied by Tiwari’s points based on social metrics such as “likes,” “comments,” or “follows.”
-wherein the user is associated with a user profile linked to a first group of a plurality of groups of a collaborative platform; (Tiwari [0121] As another use case, an employee may want to engage another peer who is performing better than the employee into a challenge. [0134] The first page of the application depicts a dashboard/“quick view” screen. The quick view screen includes the various details of an employee including the ranks, points and levels achieved by the employee during a particular cycle. The quick view screen further depicts the progress of the employee against the targets for different KPIs as well as provides the details on activities performed by the employee at KPI level. Furthermore, the quick view screen includes SWOT analysis of the employee. The SWOT analysis is a useful technique for understanding the strengths and weaknesses, and for identifying both, the opportunities open to the employee and the threat areas for the employee.[0135] FIG. 4B illustrates a screenshot of the second page of the application depicting leaderboard for competition, according to one embodiment herein. The second page of the application depicts the leaderboard based on the points earned by the employee. The leaderboard is a scoreboard showing the names and overall points of all the peers of the employee in the decreasing order of their overall points. Leaderboard inspires the employees by showcasing the performance of the top performers.) Tiwari’s application is a collaborative platform, since as seen in [0121] employees are able to engage with other employees. The profiles are seen in [0134-0135] which includes badges and achievements associated with each employee, as well as potential recommendations for the employee to improve their scores. The employees within a single workplace being in a single leaderboard seen in [0135], teaches the first group of a plurality of groups.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to combine the emotion analysis system of Anders with the employer gamification system of Tiwari because both are directed to improving the mental state and productivity of employees within a workplace by providing recommendations. Performing the modification would yield the predictable outcome of using Tiwari’s gamification metrics along with Ander’s time series data to generate the recommendations to achieve the target state of emotion. One would be motivated to make this modification because the combined system provides the benefit of further increasing wellbeing, thereby increasing workplace motivation through social metrics and collaboration.(Tiwari [0017])
However, neither Anders nor Tiwari teach or suggest: -the wellness information also comprises “an energy level.”
-the “level” being decreased in the “determining...” step is specifically an energy level.
-wherein the trained machine learning model utilizes neural network operations to analyze temporal patterns in the received wellness information across a plurality of previous periods and generate in real-time predictive coefficients weighing one or more behavioral categories in order to apply the predictive coefficients to the subsequent wellness information;
However, Condie discloses a system for monitoring and recording the effectiveness of “boosters” on the health information of a user using neural networks. Condie remedies these deficiencies by teaching:
-the wellness information also comprises “an energy level.”(Condie [0025] Embodiments of the disclosure provide a quick and simple means to enter metrics for categories that are known to directly impact the user's mental health. The categories may include, for example, the user's stress levels, self-esteem, confidence, energy, exercise, diet, sleep, and others. The categories may be tailored for each user depending on which categories have a greater impact on that user's wellbeing. The user may interact with an application or other user interface to enter a score for each of the categories. In an embodiment, the score is a rating from zero to ten indicating the user's current feeling for that category.)
-determining whether the energizer activity satisfies a predetermined threshold in order to increase or decrease an energy level of the user. (Condie [0062] In an embodiment, the neural network is trained on the user history 112 over time. The neural network may assess trends across the user's historical ratings 106, booster 108, journal entries 110, health data 114, and so forth to identify what events, time periods, or triggers are mostly likely to cause the user to have an increase or decrease in mental health. [0064] The trigger event may be identified at 216 in response to the user's ratings 106, boosters 108, journal entries 110, and/or health data 114 for that day or time period. The system may recognize, based on the user-entered data 208, that the user is experiencing some kind of difficulty. The system may then automatically notify a contact at 218 that the user is experiencing a difficulty and could benefit from extra support. [0067] The ratings 106 are customized to each user based on which categories appear to have the greatest impact on the user's wellbeing. Example categories include stress, self-esteem, confidence, energy, exercise, diet, and sleep as shown in FIG. 3. Additional example categories include mindfulness, charity work, anger, mood, traveling, interactions with family, interactions with friends, hobbies specific to the user, and so forth. In an embodiment, the user can specify certain ratings 106 categories and/or the system may provide categories based on which categories appear to have the greatest impact on the user's wellbeing. [0065] In an example, the trigger event is the user's failure to exercise for a certain time duration. The user may specify this trigger event and indicate that the system should contact the user's workout partner when the user fails to exercise for a certain time period. In an example, the trigger event is the user entering high stress ratings 106 for a period of time. The user may specify this trigger event and indicate that the system should contact the user's friend and suggest a night out or other activity to help relieve the user's stress levels.) Various examples of trigger events through [0062-0067] satisfies the limitation. An example of a predetermined threshold in order to increase or decrease the activity level of a user is the “trigger event is the user’s failure to exercise for a certain time duration,” wherein the recommended booster suggests an activity to lower stress levels (lower energy levels). Furthermore, since the user health data indicates the energy level of a user, and the booster activities fall within the scope of “energizer activities,” based on the citations above, Condie satisfies the limitation.
-wherein the trained machine learning model utilizes neural network operations to analyze temporal patterns in the received wellness information across a plurality of previous periods and (Condie [0062] In an embodiment, the neural network is trained on the user history 112 over time. The neural network may assess trends across the user's historical ratings 106, booster 108, journal entries 110, health data 114, and so forth to identify what events, time periods, or triggers are mostly likely to cause the user to have an increase or decrease in mental health. The neural network may be trained on a large training set of journal entry writings so the neural network can scan the user's journal entries 110 and identify if the user is experiencing a good or poor mental health. [0063] In an example, the neural network is trained on a large dataset of journal writings and other writings, so the neural network is trained to scan the user's journal entries 110 and identify the meaning of those journal entries 110. The example neural network may read a user's journal entry 110 indicating that the user's family member passed away at a certain time of year, and that this event is difficult for the user at that time of year. The neural network may then indicate to the system that the user will likely experience mental health struggles at that time of year, every year.) Both Condie [0062] and [0063] satisfy the use of a neural network trained machine learning model that analyzes temporal patterns (trends across...time periods/family member passed away at a certain time of the year and is difficult for the user that time of year). Since this is based on user history/journals over a plurality of previous periods, the limitation is satisfied.
-generate in real-time predictive coefficients weighing one or more behavioral categories in order to apply the predictive coefficients to the subsequent wellness information; (Condie [0025] The categories may be tailored for each user depending on which categories have a greater impact on that user's wellbeing. [0067] the system may provide categories based on which categories appear to have the greatest impact on the user's wellbeing. [0077] The data may be adjusted by backpropagation, weighting, and biases based on correlation with existing user-specific data from daily ratings, environmental data, critical events, and so forth. [0091] The neural network 1002 may function by forward propagation and backward propagation of training data in input data. The neural network 102 may be configured with parameters such as weights and biases guiding the analysis of input data. The neural network 1002 may take a set of training data that may include one or more of journal entries 1004, sensor metrics 1006, user-input metrics 1008, environmental data 1010, and critical events. The training data may be processed by the neural network 1002 to generate a mental health prediction 1014. These mental health predictions 1014 may be obtained with values of expected labels to calculate loss. [0082] Example datasets include journal entries 1004, sensor metrics 1006, user-input metrics 1008, environmental data 1010, and critical events 1012. The neural network 1002 may be trained to identify correlations or patterns within the input datasets to generate a mental health prediction 1014. [0079] In further embodiments, the VAE 901 may include an encoder-decoder, may receive training data for, and may output reconstructed data for multiple different datasets. Other datasets include, for example, daily ratings, journal entries, support contact notification and/or response, heart rate, blood pressure, sleep patterns, temperature, precipitation, season of the year, hours of light per day, correlation to critical events, and so forth. A dataset pertaining to daily ratings may be combine with other datasets to correlate highs and lows in daily ratings with other events, such as a decreased number of hours of daylight, an anniversary of a critical event in the user's life, decreased sleep, and so forth.) Based on Condie [0025], and [0067] it is clear that the system determines the impact of each behavioral category on the user’s wellbeing, and based on [0077], and [0091], this impact is based on weights and biases (which satisfy predictive coefficients). Various examples through [0082], and [0079] show that these weights and biases are applied to the wellness information to generate mental health predictions. Therefore, Condie satisfies this limitation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify Anders and Tiwari by adding the teachings of Condie particularly, the inclusion of “energy level” of the user, and considering whether the energizer activities satisfies a threshold to increase or decrease the energy level. Furthermore, it would have been obvious to add Condie’s neural network operations which provide weights and biases to certain behavioral categories to generate predictive coefficients to the subsequent wellness information. By implementing these features from Condie into the combination, one would have arrived at the predictable outcome of integrating the energy levels of a user, because energy level would simply be substituted into Ander’s emotion data. Furthermore, one would arrive at using Condie’s neural network based techniques in Ander’s system as it would provide the benefit of improving the accuracy and predictiveness to not only remediate the current mood of the user in Anders, but anticipate certain moods based on historic temporal data as taught by Condie. (Condie [0083] For example, the journal entries 1004 may be assessed to determine that an anniversary of a critical life event for the user is upcoming. The date of that critical life event may be logged for future use in generating future mental health predictions 1014. [0088] The neural network 1002 outputs the mental health prediction 1014 based on one or more of the datasets. The mental health prediction 1014 may include an indication that the user is likely to experience a drop in mental health and may benefit from additional support from contact people, healthcare providers, and so forth. The mental health prediction 1014 may be provided to the user by way of a notification to remind the user to prepare for an upcoming struggle or other issue.)
Regarding Claim 2:
The combination of Anders, Tiwari, and Condie teaches or suggests the method of claim 1
Furthermore, Anders teaches:
-the determining step further includes recommending a second energizer activity, and(Anders [0034] In block 240, the emotion service engine 130 determines which environmental factor would cause the baseline emotion time graph to move close to the point of the target emotion-time 150. If the emotion service engine 130 ascertains one or more known environmental factor that will move the baseline emotion time graph toward the point of the target emotion-time 150 within a certain threshold distance of the target emotion-time point, then the emotion service engine 130 generates the recommendation 191 including activities respective to the ascertained environmental factor for the user 105. The emotion service engine 130 subsequently delivers the generated recommendation 191 to the user 105 via a preconfigured channel. Then, the emotion service engine 130 proceeds with 250. [0024] The emotion service engine 130 generates a recommendation 191 for the user 105 and sends the recommendation 191 to the user 105. The recommendation 191 includes one or more activity for the user 105 to perform. In this specification, terms “remediation step(s)”, “remedial action(s)”, or “recommendation” are used interchangeably to indicate the activities prescribed in the recommendation 191 for the purpose of timely attaining a target state of emotion for the user 105. The user 105 receives the recommendation 191 and acts on the received recommendation 191. The user 105 sends a feedback 199 assessing effectiveness of the recommendation 191 in timely attaining the target state of emotion for the user 105, as specified in the target emotion-time value pair 150. The emotion service engine 130 subsequently trains the emotion time series model 160 with the recommendation 191 and the feedback 199 by machine learning. Detailed operations of the emotion service engine 130 are presented in FIG. 2 and corresponding description.) Ander’s sending of a recommendation after utilizing feedback teaches the “second energizer activity.”
-the wellness information further includes an indicator of a level of mood represented by user interactions with the content on the one or more user interfaces. (Anders [0023] The user assistance service system 120 includes an emotion service engine 130, an emotion knowledge base (EKB) 140, and a target emotion-time value pair 150... Each emotion time series model 160 represents the pattern of change in the state of emotion for the user 105, or in the state of collective emotion of the subject population 103, per environmental factor. The content of the EKB 140 include, but not limited to, a variety of knowledge on how to interpret the inputs in the context of the state of emotion, a list of activities that are established to have a certain effect on the state of emotion for an individual user or for general public for recommendation, past recommendations and feedbacks, as well as data from external data sources 180 that are likely to affect the emotion time series model 160. Examples of the external data sources 180 include, but are not limited to, a press release regarding a fiscal year revenue report of an organization represented by the serviced environment 101, news regarding the structure of the organization such as a merger and acquisition, a company picnic, an office party, announcement of new hires or retirements, a news report involving people close to the user 105, local news on neighborhood of the user 105, or the similar that can affect the morale/collective mood in the serviced environment 101 or the individual state of emotions for the user 105. [0110] In addition to or in place of having external devices 14 and the display 24, which can be configured to provide user interface functionality, computing node 10 in one embodiment can include another display 25 connected to bus 18. In one embodiment, the display 25 can be configured as a touch screen render and can be configured to provide user interface functionality, e.g. can facilitate virtual keyboard functionality and input of total data. [0020] In this specification, terms “emotion” is used to indicate general feeling of an individual. An individual user is in a state of emotion consisting of a preselected basic emotions. Embodiments of the present invention acknowledge that, in psychological nomenclature, the term “sentiment” indicates a mental attitude, opinion, judgment or evaluation on a certain topic, whilst the term “emotion” refers to general feelings, and more specifically, a natural instinctive state of mind deriving from one's circumstances, mood, or relationships with others.) Ander’s time series model includes an indicator of “mood” represented by user interactions with content on user interfaces (such as the news on display).
Regarding Claim 3:
The combination of Anders, Tiwari and Condie teaches or suggest the method of claim 1,
Furthermore, Anders teaches:
-And the level (emotion inputs) comprises a predetermined threshold quantity (Anders [0040] The emotion service engine 130 trains the emotion time series model 160, by machine learning with the updated inputs and/or the feedback 199 from the user 105, in order to improve accuracy of the emotion time series model 160 with the time progression of the baseline as well as the predictions. Then, the emotion service engine 130 terminates processing the input data. The emotion service engine 130 repeats blocks 210 through 250 when preconfigured conditions for re-processing are met, including, but not limited to, cumulating a predefined amount of input data, obtaining a new instance of the target emotion-time value pair 150, etc.) Checking when pre-configured conditions are met, such as cumulating a predefined amount of input data is an example of a predetermined threshold quantity. The only difference is that the inputs in Ander’s are not specifically user interactions or level of engagements, but the emotion series inputs.
However, Anders fails to teach:
-the level of engagement is represented by the user interactions, engagement associated with the user interactions.
Alternatively, Tiwari teaches:
-the level of engagement is represented by the user interactions, engagement associated with the user interactions. (Tiwari [0015] (“activity points”) and the other for outputs achieved (“impact points”), thereby enabling mechanisms for motivating employees and stimulating behaviors around not just the results, but also around the input factors or activities. [0027] Points are assigned to users based on the user's activity determined by the action tracking module. The points module uses a customizable logic for conversion of activity to activity points, and output achieved by the employee to impact points. The points are awarded to an employee in near real-time basis. [0028] The rank and badge module is configured to award the plurality of ranks (across a number of variables i.e., various activity types and output types), badges (awards), levels performance level of an employee based on quantitative, configurable metrics) and leaderboard mentions (social metrics i.e., “likes”, “comments” or “follow”) to the employees. The rank and badge module uses the points provided by the points module for determining ranks and badges. The rank and badge module is communicably coupled to the points module.) The broadest reasonable interpretation of “level of engagement” is any measure of engagement based on user interactions with content, which is satisfied by Tiwari’s points based on social metrics such as “likes,” “comments,” or “follows.”
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to combine the emotion analysis system of Anders with the employer gamification system of Tiwari because both are directed to improving the mental state and productivity of employees within a workplace by providing recommendations. By using user interactions as taught by Tiwari as a measure of engagement, inputting into Ander’s thresholds, one would arrive at the predictable outcome the level of engagement comprising a predetermined threshold quantity of engagements associated with user interactions. One would be motivated to make this modification because the combined system provides the benefit of further increasing wellbeing, thereby increasing workplace motivation through social metrics and collaboration.(Tiwari [0017])
Regarding Claim 7:
The combination of Anders, Tiwari, and Condie teach or suggest the method of claim 1:
Furthermore, Anders teaches:
-wherein the collaborative platform is associated with one or more behavioral categories of a behavioral model,(Anders [0049] In certain embodiment of the present invention, the emotion service engine 130 quantifies the input data to respective emotion scores. Where the emotion service engine 130 utilizes a framework with five (5) basic emotions of Joy, Sadness, Fear, Disgust, and Anger, for the user assistance service system 120, the emotion service engine 130 scales Joy as positive and the rest as negative. The emotion service engine 130 scales the emotion scores for respective basic emotions such that the emotion score (ES) greater than one (1) indicates a positive state of emotions, that ES equal to zero (0) indicates a neutral state of emotions, and that ES less than minus one (−1) indicates a negative state of emotions.) The 5 emotions of Joy, Sadness, Fear, Disgust, and Anger are interpreted to teach the 5 behavioral model.
However, Anders fails to teach:
-wherein the behavioral categories comprise one or more of roles, rules, respect, recognition or routines of the first group.
Alternatively, Tiwari teaches:
-wherein the behavioral categories comprise one or more of recognition of the first group. (Tiwari [0117] The communication module 114 provides an intra-company social communication platform for the senior and mid-level managers, line managers and peers to broadcast recognition, publish suggestions/appreciations (a virtual “pat on the back”) and public praise for any employee. The social metrics in the application includes actions such as ‘like’ and ‘follow’. Managers and the peers choose to ‘like’ or ‘follow’ the employee on the basis of the employee's performance. The social metrics serve to motivate the employee through social recognition and public praise thereby driving engagement.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to further modify Anders by adding the use of social recognition as a social metric in Tiwari, as a category to Ander’s behavioral model. Adding this metric to Ander’s would yield the predictable outcome of the collaborative platform being associated with behavioral categories including social recognition. One of ordinary skill would have been motivated by the benefit of driving social engagement and boosting productivity levels of employees. (Tiwari [0117] The social metrics serve to motivate the employee through social recognition and public praise thereby driving engagement. The social metrics are publicized using the communication module 114, thereby boosting the productivity levels of the employee.)
Regarding Claims 8, & 15:
Anders teaches:
-A method(claim 8) (Anders[0004] The method includes, for instance: obtaining, by one or more processor, inputs of emotion time series data of a user and environmental factor data from one or more data collection device for a user assistance service)
- A system comprising: one or more processors; and one or more memories coupled with the one or more processors, the one or more memories storing executable instructions that when executed by the one or more processors cause the one or more processors to:(claim 15) (Anders[0123] These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose 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.)
The body of claim 8 also representative of claim 15:
-receiving wellness information associated with a user profile(Anders[0023] The user assistance service system 120 includes an emotion service engine 130, an emotion knowledge base (EKB) 140, and a target emotion-time value pair 150. The emotion service engine 130 processes the input data 113, 117 by use of external machine learning or natural language understanding (ML/NLU) tools 170 coupled to the user assistance service system 120. The emotion service engine 130 builds one or more emotion time series model 160 based on the input data 113, 117, and content of the EKB 140. Each emotion time series model 160 represents the pattern of change in the state of emotion for the user 105, or in the state of collective emotion of the subject population 103, per environmental factor. The content of the EKB 140 include, but not limited to, a variety of knowledge on how to interpret the inputs in the context of the state of emotion, a list of activities that are established to have a certain effect on the state of emotion for an individual user or for general public for recommendation, past recommendations and feedbacks, as well as data from external data sources 180 that are likely to affect the emotion time series model 160. Examples of the external data sources 180 include, but are not limited to, a press release regarding a fiscal year revenue report of an organization represented by the serviced environment 101, news regarding the structure of the organization such as a merger and acquisition, a company picnic, an office party, announcement of new hires or retirements, a news report involving people close to the user 105, local news on neighborhood of the user 105, or the similar that can affect the morale/collective mood in the serviced environment 101 or the individual state of emotions for the user 105.)
-wherein the collaborative platform is associated with one or more behavioral categories of a behavioral model(Anders [0049] In certain embodiment of the present invention, the emotion service engine 130 quantifies the input data to respective emotion scores. Where the emotion service engine 130 utilizes a framework with five (5) basic emotions of Joy, Sadness, Fear, Disgust, and Anger, for the user assistance service system 120, the emotion service engine 130 scales Joy as positive and the rest as negative. The emotion service engine 130 scales the emotion scores for respective basic emotions such that the emotion score (ES) greater than one (1) indicates a positive state of emotions, that ES equal to zero (0) indicates a neutral state of emotions, and that ES less than minus one (−1) indicates a negative state of emotions.) The 5 emotions of Joy, Sadness, Fear, Disgust, and Anger are interpreted to teach the 5 behavioral model.
- train a machine learning model on the wellness information, at a plurality of previous periods, (Anders[0081] Certain embodiments of the present invention may offer various technical computing advantages, including the use of natural language understanding tool and/or topic modeling in quantifying various individual outputs as emotion score. By performing regression analysis, one or more environmental factor affecting changes in states of emotion on a person, or cumulatively a group of people, is identified. The emotion time series model is built per environmental factor for prediction of future changes in the state of emotion of the subject person or group and for recommending certain activities to attain a desired state of emotion in the future. Mean average time lapse of changes in the states of emotion as represented in respective emotion scores at different times is statistically simulated. Mean interval of occurrences of a certain environmental factor, or mean duration of a certain environmental factor is also statistically simulated. Certain embodiments of the present invention include the emotion knowledgebase that stores information on activities to recommend to the subject user or group, in order to attain the desired state of emotion. Certain embodiments of the present invention, receives feedback and recommendation and trains the emotion time series models respective to environmental factors, for individual users or for a group, by machine learning.)
-to determine subsequent wellness information of a user associated with the user profile or the group during a subsequent period; and(Anders[0078] The application-emotion time series graph 700 in a first area 701 is for a chat application, which represents a decreasing emotion score over time for the duration between “0:00:00” and “0:18:00”, which indicates that using the chat application program has a negative effect on the emotion score. A first point 715 on the application-emotion time series graph 700 from the first area 701 represents an emotion score calculated from measured values of inputs. A second point 720 represents a current time, and any depiction on the right side of the second point 720, including a third point 725, are forecasted points based on one or more emotion time series model built from regression analysis on respective environmental factors. [0076] In the same embodiment as where the emotion service engine 130 modeled the Basic Emotion Time Series: Joy 510, the emotion service engine 130 collects input data 113, 117, and models an application-emotion time series graph 700 that represents a pattern of varying state of joy on the user 105, as respective emotion scores, over a certain period of time while using a chat application program. The emotion service engine 130 can perform regression analysis on all the environmental factor data 117 in order to identify all environmental factors affecting the state of emotions of the user 105, or the subject population 103. Examples of the environmental factors that are likely to affect the state of emotion include, but are not limited to, respective application programs which the user 105 is presently using, a location of the user 105, as represented in a GPS coordinate, in and around the serviced environment 101, time of the day, day of the week, season in a year, weather, and a noise level in the serviced environment 101.) The determination of an emotion such as Joy is an example of determining subsequent wellness information associated with the user during a subsequent time period.
-and present, by a user interface, the subsequent wellness information to the user to facilitate interaction by the user with the subsequent wellness information.(Anders [0040] In block 250, the emotion service engine 130 obtains a feedback 199 on effectiveness of the recommendation 191 from the user 105. [0078] The application-emotion time series graph 700 in a first area 701 is for a chat application, which represents a decreasing emotion score over time for the duration between “0:00:00” and “0:18:00”, which indicates that using the chat application program has a negative effect on the emotion score. A first point 715 on the application-emotion time series graph 700 from the first area 701 represents an emotion score calculated from measured values of inputs. [0081] The emotion time series model is built per environmental factor for prediction of future changes in the state of emotion of the subject person or group and for recommending certain activities to attain a desired state of emotion in the future.) Ander’s system displays the wellness information in the form of graphs in Fig. 5 and Fig. 7, provides the user with recommendations to improve their wellness in [0081], and facilitates interaction by receiving feedback in [0040].
-determine(ing), via the trained machine learning model and based on the feedback information, whether the energizer activity satisfied a predetermined threshold in order to increase or decrease an level of the user(Anders [0040] In block 250, the emotion service engine 130 obtains a feedback 199 on effectiveness of the recommendation 191 from the user 105. The emotion service engine 130 also obtains updates on the emotion time series data 113 from the data collection devices 107. The emotion service engine 130 trains the emotion time series model 160, by machine learning with the updated inputs and/or the feedback 199 from the user 105, in order to improve accuracy of the emotion time series model 160... The emotion service engine 130 repeats blocks 210 through 250 when preconfigured conditions for re-processing are met, including, but not limited to, cumulating a predefined amount of input data, obtaining a new instance of the target emotion-time value pair 150, etc. [0034] In block 240, the emotion service engine 130 determines which environmental factor would cause the baseline emotion time graph to move close to the point of the target emotion-time 150. If the emotion service engine 130 ascertains one or more known environmental factor that will move the baseline emotion time graph toward the point of the target emotion-time 150 within a certain threshold distance of the target emotion-time point, then the emotion service engine 130 generates the recommendation 191 including activities respective to the ascertained environmental factor for the user 105. The emotion service engine 130 subsequently delivers the generated recommendation 191 to the user 105 via a preconfigured channel. Then, the emotion service engine 130 proceeds with 250.) Following along the process of Anders, Anders teaches the limitation because, Ander obtains feedback on the effectiveness of the recommendation (which includes energizer activities), then repeats the process in blocks 210-250. In block 240, the engine determines whether an environmental factor (which includes recommended energizer activities) will move the baseline emotion time graph (energy level), within a certain threshold distance of the target emotion-time point. This satisfies “determining whether the energizer activity satisfied a predetermined threshold” because it tests whether a recommended activity reached a threshold distance on the target emotion graph. The certain threshold distance in Anders is the “predetermined threshold.” After this, the system generates another recommendation. Since this is the second time blocks 210-250 were repeated, this would be a “second energizer activity.” Since the claims only require one of “increase or decrease the energy level of the user or to recommend a second energizer activity,” the limitation has been satisfied because the latter has been met.
-send(ing) an alert, based on the determination and the subsequent wellness information. (Anders[0040] In block 250, the emotion service engine 130 obtains a feedback 199 on effectiveness of the recommendation 191 from the user 105. The emotion service engine 130 also obtains updates on the emotion time series data 113 from the data collection devices 107. The emotion service engine 130 trains the emotion time series model 160, by machine learning with the updated inputs and/or the feedback 199 from the user 105, in order to improve accuracy of the emotion time series model 160 with the time progression of the baseline as well as the predictions. Then, the emotion service engine 130 terminates processing the input data. The emotion service engine 130 repeats blocks 210 through 250 when preconfigured conditions for re-processing are met, including, but not limited to, cumulating a predefined amount of input data, obtaining a new instance of the target emotion-time value pair 150, etc.) Since blocks 210 through 250 are repeated and in block 240 is when the recommendation alert occurs, therefore during the second iteration of block 240 it can be said that Anders teaches the second alert based on the feedback information.
However, Anders fails to teach:
-the wellness information comprising at least an indicator of a level of engagement and energy level represented by user interactions with content, by one or more user interfaces, associated with a collaborative platform
-wherein the user profile is linked to a group of a collaborative platform,
-the “level” being decreased in the “determining...” step is specifically an energy level.
-wherein the trained machine learning model utilizes neural network operations to analyze temporal patterns in the received wellness information across a plurality of previous periods and generate in real-time predictive coefficients weighing one or more behavioral categories in order to apply the predictive coefficients to the subsequent wellness information;
Alternatively, Tiwari teaches:
- the wellness information comprising at least an indicator of a level of engagement represented by user interactions with content by one or more user interfaces, associated with a collaborative platform(Tiwari [0015] Yet another objective of the embodiments herein is to provide a system and method to enable an approach to define and award two types of points to an employee; one for the activities performed (“activity points”) and the other for outputs achieved (“impact points”), thereby enabling mechanisms for motivating employees and stimulating behaviors around not just the results, but also around the input factors or activities. [0027] The points module is configured to convert input activities to “activity points”, and output achieved to “impact points”. Points are assigned to users based on the user's activity determined by the action tracking module. The points module uses a customizable logic for conversion of activity to activity points, and output achieved by the employee to impact points. The points are awarded to an employee in near real-time basis. [0028] The rank and badge module is configured to award the plurality of ranks (across a number of variables i.e., various activity types and output types), badges (awards), levels performance level of an employee based on quantitative, configurable metrics) and leaderboard mentions (social metrics i.e., “likes”, “comments” or “follow”) to the employees. The rank and badge module uses the points provided by the points module for determining ranks and badges. The rank and badge module is communicably coupled to the points module.) The broadest reasonable interpretation of “level of engagement” is any measure of engagement based on user interactions with content, which is satisfied by Tiwari’s points based on social metrics such as “likes,” “comments,” or “follows.”
-wherein the user profile is linked to a group of a collaborative platform, (Tiwari [0121] As another use case, an employee may want to engage another peer who is performing better than the employee into a challenge. [0134] The first page of the application depicts a dashboard/“quick view” screen. The quick view screen includes the various details of an employee including the ranks, points and levels achieved by the employee during a particular cycle. The quick view screen further depicts the progress of the employee against the targets for different KPIs as well as provides the details on activities performed by the employee at KPI level. Furthermore, the quick view screen includes SWOT analysis of the employee. The SWOT analysis is a useful technique for understanding the strengths and weaknesses, and for identifying both, the opportunities open to the employee and the threat areas for the employee.[0135] FIG. 4B illustrates a screenshot of the second page of the application depicting leaderboard for competition, according to one embodiment herein. The second page of the application depicts the leaderboard based on the points earned by the employee. The leaderboard is a scoreboard showing the names and overall points of all the peers of the employee in the decreasing order of their overall points. Leaderboard inspires the employees by showcasing the performance of the top performers.) Tiwari’s application is a collaborative platform, since as seen in [0121] employees are able to engage with other employees. The profiles are seen in [0134-0135] which includes badges and achievements associated with each employee, as well as potential recommendations for the employee to improve their scores. The employees within a single workplace being in a single leaderboard seen in [0135], teaches the first group of a plurality of groups.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to combine the emotion analysis system of Anders with the employer gamification system of Tiwari because both are directed to improving the mental state and productivity of employees within a workplace by providing recommendations. Performing the modification would yield the predictable outcome of using Tiwari’s gamification metrics along with Ander’s time series data to generate the recommendations to achieve the target state of emotion. One would be motivated to make this modification because the combined system provides the benefit of further increasing wellbeing, thereby increasing workplace motivation through social metrics and collaboration.(Tiwari [0017])
However, neither Anders nor Tiwari teach or suggest: -the wellness information also comprises “an energy level.”
--the “level” being decreased in the “determining...” step is specifically an energy level.
-wherein the trained machine learning model utilizes neural network operations to analyze temporal patterns in the received wellness information across a plurality of previous periods and generate in real-time predictive coefficients weighing one or more behavioral categories in order to apply the predictive coefficients to the subsequent wellness information;
Condie remedies these deficiencies by teaching:
-the wellness information also comprises “an energy level.”(Condie [0025] Embodiments of the disclosure provide a quick and simple means to enter metrics for categories that are known to directly impact the user's mental health. The categories may include, for example, the user's stress levels, self-esteem, confidence, energy, exercise, diet, sleep, and others. The categories may be tailored for each user depending on which categories have a greater impact on that user's wellbeing. The user may interact with an application or other user interface to enter a score for each of the categories. In an embodiment, the score is a rating from zero to ten indicating the user's current feeling for that category.)
-determining whether the energizer activity satisfies a predetermined threshold in order to increase or decrease an energy level of the user. (Condie [0062] In an embodiment, the neural network is trained on the user history 112 over time. The neural network may assess trends across the user's historical ratings 106, booster 108, journal entries 110, health data 114, and so forth to identify what events, time periods, or triggers are mostly likely to cause the user to have an increase or decrease in mental health. [0064] The trigger event may be identified at 216 in response to the user's ratings 106, boosters 108, journal entries 110, and/or health data 114 for that day or time period. The system may recognize, based on the user-entered data 208, that the user is experiencing some kind of difficulty. The system may then automatically notify a contact at 218 that the user is experiencing a difficulty and could benefit from extra support. [0067] The ratings 106 are customized to each user based on which categories appear to have the greatest impact on the user's wellbeing. Example categories include stress, self-esteem, confidence, energy, exercise, diet, and sleep as shown in FIG. 3. Additional example categories include mindfulness, charity work, anger, mood, traveling, interactions with family, interactions with friends, hobbies specific to the user, and so forth. In an embodiment, the user can specify certain ratings 106 categories and/or the system may provide categories based on which categories appear to have the greatest impact on the user's wellbeing. [0065] In an example, the trigger event is the user's failure to exercise for a certain time duration. The user may specify this trigger event and indicate that the system should contact the user's workout partner when the user fails to exercise for a certain time period. In an example, the trigger event is the user entering high stress ratings 106 for a period of time. The user may specify this trigger event and indicate that the system should contact the user's friend and suggest a night out or other activity to help relieve the user's stress levels.) Various examples of trigger events through [0062-0067] satisfies the limitation. An example of a predetermined threshold in order to increase or decrease the activity level of a user is the “trigger event is the user’s failure to exercise for a certain time duration,” wherein the recommended booster suggests an activity to lower stress levels (lower energy levels). Furthermore, since the user health data indicates the energy level of a user, and the booster activities fall within the scope of “energizer activities,” based on the citations above, Condie satisfies the limitation.
-wherein the trained machine learning model utilizes neural network operations to analyze temporal patterns in the received wellness information across a plurality of previous periods and (Condie [0062] In an embodiment, the neural network is trained on the user history 112 over time. The neural network may assess trends across the user's historical ratings 106, booster 108, journal entries 110, health data 114, and so forth to identify what events, time periods, or triggers are mostly likely to cause the user to have an increase or decrease in mental health. The neural network may be trained on a large training set of journal entry writings so the neural network can scan the user's journal entries 110 and identify if the user is experiencing a good or poor mental health. [0063] In an example, the neural network is trained on a large dataset of journal writings and other writings, so the neural network is trained to scan the user's journal entries 110 and identify the meaning of those journal entries 110. The example neural network may read a user's journal entry 110 indicating that the user's family member passed away at a certain time of year, and that this event is difficult for the user at that time of year. The neural network may then indicate to the system that the user will likely experience mental health struggles at that time of year, every year.) Both Condie [0062] and [0063] satisfy the use of a neural network trained machine learning model that analyzes temporal patterns (trends across...time periods/family member passed away at a certain time of the year and is difficult for the user that time of year). Since this is based on user history/journals over a plurality of previous periods, the limitation is satisfied.
-generate in real-time predictive coefficients weighing one or more behavioral categories in order to apply the predictive coefficients to the subsequent wellness information; (Condie [0025] The categories may be tailored for each user depending on which categories have a greater impact on that user's wellbeing. [0067] the system may provide categories based on which categories appear to have the greatest impact on the user's wellbeing. [0077] The data may be adjusted by backpropagation, weighting, and biases based on correlation with existing user-specific data from daily ratings, environmental data, critical events, and so forth. [0091] The neural network 1002 may function by forward propagation and backward propagation of training data in input data. The neural network 102 may be configured with parameters such as weights and biases guiding the analysis of input data. The neural network 1002 may take a set of training data that may include one or more of journal entries 1004, sensor metrics 1006, user-input metrics 1008, environmental data 1010, and critical events. The training data may be processed by the neural network 1002 to generate a mental health prediction 1014. These mental health predictions 1014 may be obtained with values of expected labels to calculate loss. [0082] Example datasets include journal entries 1004, sensor metrics 1006, user-input metrics 1008, environmental data 1010, and critical events 1012. The neural network 1002 may be trained to identify correlations or patterns within the input datasets to generate a mental health prediction 1014. [0079] In further embodiments, the VAE 901 may include an encoder-decoder, may receive training data for, and may output reconstructed data for multiple different datasets. Other datasets include, for example, daily ratings, journal entries, support contact notification and/or response, heart rate, blood pressure, sleep patterns, temperature, precipitation, season of the year, hours of light per day, correlation to critical events, and so forth. A dataset pertaining to daily ratings may be combine with other datasets to correlate highs and lows in daily ratings with other events, such as a decreased number of hours of daylight, an anniversary of a critical event in the user's life, decreased sleep, and so forth.) Based on Condie [0025], and [0067] it is clear that the system determines the impact of each behavioral category on the user’s wellbeing, and based on [0077], and [0091], this impact is based on weights and biases (which satisfy predictive coefficients). Various examples through [0082], and [0079] show that these weights and biases are applied to the wellness information to generate mental health predictions. Therefore, Condie satisfies this limitation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify Anders and Tiwari by adding the teachings of Condie particularly, the inclusion of “energy level” of the user, and considering whether the energizer activities satisfies a threshold to increase or decrease the energy level. Furthermore, it would have been obvious to add Condie’s neural network operations which provide weights and biases to certain behavioral categories to generate predictive coefficients to the subsequent wellness information. By implementing these features from Condie into the combination, one would have arrived at the predictable outcome of integrating the energy levels of a user, because energy level would simply be substituted into Ander’s emotion data. Furthermore, one would arrive at using Condie’s neural network based techniques in Ander’s system as it would provide the benefit of improving the accuracy and predictiveness to not only remediate the current mood of the user in Anders, but anticipate certain moods based on historic temporal data as taught by Condie. (Condie [0083] For example, the journal entries 1004 may be assessed to determine that an anniversary of a critical life event for the user is upcoming. The date of that critical life event may be logged for future use in generating future mental health predictions 1014. [0088] The neural network 1002 outputs the mental health prediction 1014 based on one or more of the datasets. The mental health prediction 1014 may include an indication that the user is likely to experience a drop in mental health and may benefit from additional support from contact people, healthcare providers, and so forth. The mental health prediction 1014 may be provided to the user by way of a notification to remind the user to prepare for an upcoming struggle or other issue.)
Regarding Claims 9, & 16:
The combination of Anders, Tiwari, and Condie teaches or suggests the method of claim 8 and the system of claim 15
Furthermore, Anders teaches:
-wherein the alert comprises a modification of the activity for the user to complete. (Anders[0039] The emotion service engine 130 delivers the recommendation to the user 105 by use of preconfigured methods for the serviced environment 101, the subject population 103, or for individual users. For example, the emotion service engine 130 sends a chat message “How about taking a walk for 5 minutes? The weather is beautiful today!” The emotion service engine 130 also can send an email with multimedia content/link “This little kitten video went viral. Want to check out? Click here.” The emotion service engine 130 can also modify the calendar of the user 105 by adding additional alerts for an existing schedule or generating a new event on the calendar, with a notice “Don’t forget the office outing is this Friday! Two more days to go!” or “The quarterly report is due in two weeks. Please have it ready by next Wednesday for a review before the presentation!” [0040] The emotion service engine 130 repeats blocks 210 through 250 when preconfigured conditions for re-processing are met, including, but not limited to, cumulating a predefined amount of input data, obtaining a new instance of the target emotion-time value pair 150, etc.) Since blocks 210 and 250 are repeated based on new data, then Anders teaches an alert comprising a “modification of the activity” for the user to complete, because Anders teaches “additional alerts for an existing schedule.”
Regarding Claim 10:
The combination of Anders, Tiwari, and Condie teaches the method of claim 8
Furthermore, Anders teaches:
-the wellness information further includes an indicator of a level of mood represented by user interactions with the content on the one or more user interfaces.(Anders [0023] The user assistance service system 120 includes an emotion service engine 130, an emotion knowledge base (EKB) 140, and a target emotion-time value pair 150... Each emotion time series model 160 represents the pattern of change in the state of emotion for the user 105, or in the state of collective emotion of the subject population 103, per environmental factor. The content of the EKB 140 include, but not limited to, a variety of knowledge on how to interpret the inputs in the context of the state of emotion, a list of activities that are established to have a certain effect on the state of emotion for an individual user or for general public for recommendation, past recommendations and feedbacks, as well as data from external data sources 180 that are likely to affect the emotion time series model 160. Examples of the external data sources 180 include, but are not limited to, a press release regarding a fiscal year revenue report of an organization represented by the serviced environment 101, news regarding the structure of the organization such as a merger and acquisition, a company picnic, an office party, announcement of new hires or retirements, a news report involving people close to the user 105, local news on neighborhood of the user 105, or the similar that can affect the morale/collective mood in the serviced environment 101 or the individual state of emotions for the user 105. [0110] In addition to or in place of having external devices 14 and the display 24, which can be configured to provide user interface functionality, computing node 10 in one embodiment can include another display 25 connected to bus 18. In one embodiment, the display 25 can be configured as a touch screen render and can be configured to provide user interface functionality, e.g. can facilitate virtual keyboard functionality and input of total data. [0020] In this specification, terms “emotion” is used to indicate general feeling of an individual. An individual user is in a state of emotion consisting of a preselected basic emotions. Embodiments of the present invention acknowledge that, in psychological nomenclature, the term “sentiment” indicates a mental attitude, opinion, judgment or evaluation on a certain topic, whilst the term “emotion” refers to general feelings, and more specifically, a natural instinctive state of mind deriving from one's circumstances, mood, or relationships with others.) Ander’s time series model includes an indicator of “mood” represented by user interactions with content on user interfaces (such as the news on display).
Regarding Claim 11, & 18:
The combination of Anders, Tiwari, and Condie teaches the method of claim 8 and the system of claim 15
Anders fails to teach:
-wherein the wellness information comprises a level of engagement associated with the group, wherein the group comprises a plurality of different user profiles.
Furthermore, Tiwari teaches:
-wherein the wellness information comprises a level of engagement associated with the group, wherein the group comprises a plurality of different user profiles. (Tiwari[0117] According to one embodiment herein, the communication module 114 drives employee engagement in the enhanced gamified PMS, system. The communication module 114 provides an intra-company social communication platform for the senior and mid-level managers, line managers and peers to broadcast recognition, publish suggestions/appreciations (a virtual “pat on the back”) and public praise for any employee. The social metrics in the application includes actions such as ‘like’ and ‘follow’. Managers and the peers choose to ‘like’ or ‘follow’ the employee on the basis of the employee’s performance. The social metrics serve to motivate the employee through social recognition and public praise thereby driving engagement. The social metrics are publicized using the communication module 114, thereby boosting the productivity levels of the employee. Additionally, the communication module is used by Managers to provide virtual one-on-one feedback on performance of their reportees and have a virtual group conversation with a wider set of reportees/peers.) In Tiwari, wellness information is mapped to the scores generated on the leaderboards, which can be improved through engagement on the platform with others on the group. We know that these others in the group correspond with profiles because it teaches a communication module wherein employees can have a virtual group conversation with other employees. An example of a profile can be seen in Fig. 4D.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to further modify Anders with Tiwari’s features of adding engagement metrics within a platform that consists of a plurality of profiles of members with the group as it creates a gamified system where others can utilize natural competitive nature to improve these metrics compared to their peers. This would provide the benefit of enhancing the experience of a user by further motivating them to use the recommendation module of Anders. One would be motivated to make such a combination since it provides the benefit of increasing employee motivation because of the platforms enjoyable nature.(Tiwari[0129])
Regarding Claim 14:
The combination of Anders, Tiwari, and Condie teaches the method of claim 8
Furthermore, Anders teaches:
-the machine learning model(Anders[0024] The emotion service engine 130 subsequently trains the emotion time series model 160 with the recommendation 191 and the feedback 199 by machine learning. Detailed operations of the emotion service engine 130 are presented in FIG. 2 and corresponding description.)
However, Anders fails to teach:
-is further trained using likes, posts, or comments associated with the group.
Alternatively Tiwari teaches:
-using the following as a metric: likes, posts, or comments associated with the group.
(Tiwari [0136] Firstly, an overall points leaderboard indicates top performers scoring the highest total composite points along with the details of each performer. The details include the name of the employee, the points earned, the number of likes etc. Secondly, ail impact points leaderboard indicates top performers scoring the maximum impact points along with the details of each performer. The impact points are awarded based on the outputs achieved by the employee. Thirdly, an activity points leaderboard indicate top performers scoring the maximum activity points along with the points details of each performer. The activity points are awarded to the employees when the employee performs an action. [0138] The internal communication includes likes, comments, and appreciation received by the employee thereby serving to motivate employees socially.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to further modify Anders, by adding Tiwari’s features to further add engagement metrics such as likes and comments within a platform that consists of a plurality of profiles of members with the group as it creates a gamified system where others can utilize individual’s innate competitive nature to improve their metrics compared to their peers. The combined system would use these metrics as inputs of Anders’ machine learning module in a number of different ways, such as determining the trusted member, or determining the mood of a user, or any other use since the present disclosure does not specifically limit how the machine learning module uses these metrics. (Tiwari[0129])
Regarding Claim 19:
The combination of Anders, Tiwari, and Condie teaches the system of claim 15
Furthermore, Anders fail to teach:
-wherein the level of engagement is associated with the user or a preferred energizer associated with the user.
Alternatively, Tiwari teaches:
-wherein the level of engagement is associated with the user (Tiwari[0117] According to one embodiment herein, the communication module 114 drives employee engagement in the enhanced gamified PMS, system. The communication module 114 provides an intra-company social communication platform for the senior and mid-level managers, line managers and peers to broadcast recognition, publish suggestions/appreciations (a virtual “pat on the back”) and public praise for any employee. The social metrics in the application includes actions such as ‘like’ and ‘follow’. Managers and the peers choose to ‘like’ or ‘follow’ the employee on the basis of the employee’s performance. The social metrics serve to motivate the employee through social recognition and public praise thereby driving engagement. The social metrics are publicized using the communication module 114, thereby boosting the productivity levels of the employee. Additionally, the communication module is used by Managers to provide virtual one-on-one feedback on performance of their reportees and have a virtual group conversation with a wider set of reportees/peers.)
Claims 5, 13 and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Anders(US 20200090067 A1) in view of Tiwari(US 20170193420 A1), further in view of Condie (US 20200090812 A1), further in view of Dhillon et al. (US 20230317246 A1) hereinafter Dhillon.
Regarding Claim 5:
The combination of Anders, Tiwari, and Condie teach or suggest the method of claim 1
Furthermore, Anders teaches:
- and a preferred energizer associated with the user.(Anders [0038] A few examples of the remediation steps further include, but are not limited to, taking a walk/water break, or doing yoga, or any other personally preferred activities to attain a refreshed state of emotion, to promote a more efficient work performance;)
However, neither Anders, Tiwari, nor Condie teach:
- wherein the wellness information comprises a calculable value illustrated by a vector.
Alternatively, Dhillon is directed to a system for facilitating mental health assessment through facial recognition and then delivers recommendations based on trained artificial intelligence algorithms to enhance or maintain their self-assessed emotional states which teaches:
- wherein the wellness information comprises a calculable value illustrated by a vector. (Dhillon [0010] In certain embodiments, the system and methods may incorporate the use of algorithms that track various activities performed and/or participated in by an individual that helps improve or manage mood, health, and relationships. In certain embodiments, signals including data associated with such activities may be digital and anatomical, and may be used to score the user and, in turn, the score may be utilized to recommend activities that may assist the individual in overcoming and/or improving a mental and/or emotional health issue. The system and methods may analyze sensor data, such as images of facial features taken at a certain point in time to serve as mood indicators and may predict emotional and/or mental states of individuals based on the sensor data. In certain embodiments, artificial intelligence models and/or machine learning models may be trained to correlate features and/or vectors extracted from sensor data to emotions, moods, and the like. [0063] In certain embodiments, such vectors could vary over time for the same user. In certain embodiments, the artificial intelligence/machine learning models supporting the system 100 may be built to predict the current state of emotion based on vectors collected over a time period until the current moment) Dhillon’s vectors extracted from sensor data are mapped to the calculable values illustrated by a vector because Dhillon uses sensor data which are calculable values, which predict emotional or mental states, which is mapped to the wellness information.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify the combination of Anders and Tiwari by adding Dhillon’s calculable vectors, as the use of vectors gives a larger possibility of different models(such as neural networks) that can be used to more accurately generate the recommendations. One would be motivated by the fact that vectors are generally used as inputs in neural networks, and support vector machines, which are more advanced learning methods for creating a predictive algorithm. (Dhillon [0063])
Regarding Claim 13:
The combination of Anders, Tiwari, and Condie teach or suggest the method of claim 8,
Furthermore, Anders teaches:
- and a preferred energizer associated with the user.(Anders [0038] A few examples of the remediation steps further include, but are not limited to, taking a walk/water break, or doing yoga, or any other personally preferred activities to attain a refreshed state of emotion, to promote a more efficient work performance;)
However, neither Anders, Tiwari, nor Condie teach:
- wherein the wellness information comprises a calculable value illustrated by a vector.
Alternatively, Dhillon teaches:
- wherein the wellness information comprises a calculable value illustrated by a vector. (Dhillon [0010] In certain embodiments, the system and methods may incorporate the use of algorithms that track various activities performed and/or participated in by an individual that helps improve or manage mood, health, and relationships. In certain embodiments, signals including data associated with such activities may be digital and anatomical, and may be used to score the user and, in turn, the score may be utilized to recommend activities that may assist the individual in overcoming and/or improving a mental and/or emotional health issue. The system and methods may analyze sensor data, such as images of facial features taken at a certain point in time to serve as mood indicators and may predict emotional and/or mental states of individuals based on the sensor data. In certain embodiments, artificial intelligence models and/or machine learning models may be trained to correlate features and/or vectors extracted from sensor data to emotions, moods, and the like. [0063] In certain embodiments, such vectors could vary over time for the same user. In certain embodiments, the artificial intelligence/machine learning models supporting the system 100 may be built to predict the current state of emotion based on vectors collected over a time period until the current moment) Dhillon’s vectors extracted from sensor data are mapped to the calculable values illustrated by a vector because Dhillon uses sensor data which are calculable values, which predict emotional or mental states, which is mapped to the wellness information.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify the combination of Anders, and Tiwari by adding Dhillon’s calculable vectors, as the use of vectors enables different models(such as neural networks) to be implemented to more accurately generate the recommendations. One would be motivated by the fact that vectors are generally used as inputs in neural networks, and support vector machines, which are advanced techniques that can provide a more accurate prediction. (Dhillon [0063])
Regarding Claim 20:
The combination of Anders, Tiwari, and Condie teach or suggest The system of claim 15,
However, Anders fails to teach:
- wherein the wellness information comprises a calculable value illustrated by a vector.
-the behavioral categories, of the behavioral model, comprises one or more of roles, rules, respect, recognition or routines of the group.
Alternatively, Tiwari teaches:
(Tiwari [0117] The communication module 114 provides an intra-company social communication platform for the senior and mid-level managers, line managers and peers to broadcast recognition, publish suggestions/appreciations (a virtual “pat on the back”) and public praise for any employee. The social metrics in the application includes actions such as ‘like’ and ‘follow’. Managers and the peers choose to ‘like’ or ‘follow’ the employee on the basis of the employee's performance. The social metrics serve to motivate the employee through social recognition and public praise thereby driving engagement.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to further modify Anders by adding the use of social recognition as a social metric in Tiwari, as a category to Ander’s behavioral model. Adding this metric to Ander’s would yield the predictable outcome of the collaborative platform being associated with behavioral categories including social recognition. One of ordinary skill would have been motivated by the benefit of driving social engagement and boosting productivity levels of employees. (Tiwari [0117] The social metrics serve to motivate the employee through social recognition and public praise thereby driving engagement. The social metrics are publicized using the communication module 114, thereby boosting the productivity levels of the employee.)
However, neither Anders, Tiwari, nor Condie teach:
- wherein the wellness information comprises a calculable value illustrated by a vector.
Alternatively, Dhillon teaches:
- wherein the wellness information comprises a calculable value illustrated by a vector. (Dhillon [0010] In certain embodiments, the system and methods may incorporate the use of algorithms that track various activities performed and/or participated in by an individual that helps improve or manage mood, health, and relationships. In certain embodiments, signals including data associated with such activities may be digital and anatomical, and may be used to score the user and, in turn, the score may be utilized to recommend activities that may assist the individual in overcoming and/or improving a mental and/or emotional health issue. The system and methods may analyze sensor data, such as images of facial features taken at a certain point in time to serve as mood indicators and may predict emotional and/or mental states of individuals based on the sensor data. In certain embodiments, artificial intelligence models and/or machine learning models may be trained to correlate features and/or vectors extracted from sensor data to emotions, moods, and the like. [0063] In certain embodiments, such vectors could vary over time for the same user. In certain embodiments, the artificial intelligence/machine learning models supporting the system 100 may be built to predict the current state of emotion based on vectors collected over a time period until the current moment) Dhillon’s vectors extracted from sensor data are mapped to the calculable values illustrated by a vector because Dhillon uses sensor data which are calculable values, which predict emotional or mental states, which is mapped to the wellness information.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify the combination of Anders, and Tiwari by adding Dhillon’s calculable vectors, as the use of vectors enables different models(such as neural networks) to be implemented to more accurately generate the recommendations. One would be motivated by the fact that vectors are generally used as inputs in neural networks, and support vector machines, which are advanced techniques that can provide a more accurate prediction. (Dhillon [0063])
Regarding Claims 21 and 22:
The combination of Anders, Tiwari, and Condie teach or suggest: The method of claim 2, and the method of claim 10
However, neither Anders, Tiwari, nor Condie teach or suggest:
- wherein the indicator of the level of mood is based on a two-scale vector
-wherein an input of a first vector is based on a positive or negative feeling
-wherein an input of a second vector is based on an internal or external level of connectedness.
Alternatively, Dhillon teaches:
-wherein the indicator of the level of mood is based on a two-scale vector(Dhillon [0035] In operation, the system and methods may include capturing signals, content and/or data associated with an individual's mood and/or mental state from devices, applications, and/or systems that are utilized to interact with individuals. [0010] In certain embodiments, artificial intelligence models and/or machine learning models may be trained to correlate features and/or vectors extracted from sensor data to emotions, moods, and the like. [0063] In certain embodiments, pictures, videos, and/or other content and/or sensor data all may be used as features and/or vectors to facilitate predictions and confirmations of self-assessments by the system 100. In certain embodiments, such vectors could vary over time for the same user. In certain embodiments, the artificial intelligence/machine learning models supporting the system 100 may be built to predict the current state of emotion based on vectors collected over a time period until the current moment.) The broadest reasonable interpretation of “two-scale vector” in view of the spec is a two-degree/two input vector.
-wherein an input of a first vector is based on a positive or negative feeling(Dhillon [0071] For example, the self-assessed emotional states may indicate that the user is happy, sad, angry, frustrated, anxious, nervous, irritated, depressed, furious, hurt, rejected, insecure, bored,) Since all pictures, videos, content, or sensor data can be inputs to the vectors, the input of a first vector can be based on positive or negative feelings as taught by Dillion.
-wherein an input of a second vector is based on an internal or external level of connectedness. (Dhillon [0062] Notably, in certain embodiments, the functionality provided by the system 100 may be amplified by factoring in various types of markers (which may be included in the user profile) when predicting and/or identifying the user's emotional state, mental health state, or a combination thereof. For example, in certain embodiments, the markers such as,... new experiences (particularly related to social interaction), intimacy, romantic love, social interactions,... social/family conditions and may include, but are not limited to, loss/separation/grief, financial pressures, academic performance, school attendance, teacher interactions, family living conditions, romantic relationships, peer pressure, uncertainty regarding the future, conflict within school/leisure environment, and emerging adult responsibility) All of the examples provided enough fall within the BRI of “input based on an internal or external level of connectedness. Since any of these markers can be inputted into the vectors, then the limitation is satisfied.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify the combination of Anders, and Tiwari by adding Dhillon’s calculable vectors, as the use of vectors enables different models(such as neural networks) to be implemented to more accurately generate the recommendations. By performing this combination, it would have been obvious to try a “two-scale” vector, though Dhillon does not specify the amount of vectors, the vectors can have any range of inputs, including two. One would be motivated to use the self-assess emotional state and Dhillon’s markers as inputs into the vectors as it would accurately predict the mood through use of neural networks, and support vector machines, which are advanced techniques that can provide a more accurate prediction. (Dhillon [0063] and [0032] In certain embodiments, the system 100 and methods are configured to receive additional information associated with the user to facilitate the determinations of the system 100. For example, the additional information associated with the user may include a plurality of markers associated with the user, including, but not limited to, location information (e.g., user's location and/or device's location), demographic information, psychographic information, life event information, emotional action information, movement information (e.g., the user's movements), health information, audio information, virtual reality information, augmented reality information, time-related information, physical activity information, mental activity information, diet information, experience information, sociocultural information, political information, relationship information, or a combination thereof.)
Regarding Claim 23:
The combination of Anders, Tiwari, and Condie teach or suggest: The system of claim 15
Furthermore, Anders teaches:
-wherein the wellness information further includes an indicator of a level of mood represented by user interactions with the content on the one or more user interfaces.(Anders [0023] The user assistance service system 120 includes an emotion service engine 130, an emotion knowledge base (EKB) 140, and a target emotion-time value pair 150... Each emotion time series model 160 represents the pattern of change in the state of emotion for the user 105, or in the state of collective emotion of the subject population 103, per environmental factor. The content of the EKB 140 include, but not limited to, a variety of knowledge on how to interpret the inputs in the context of the state of emotion, a list of activities that are established to have a certain effect on the state of emotion for an individual user or for general public for recommendation, past recommendations and feedbacks, as well as data from external data sources 180 that are likely to affect the emotion time series model 160. Examples of the external data sources 180 include, but are not limited to, a press release regarding a fiscal year revenue report of an organization represented by the serviced environment 101, news regarding the structure of the organization such as a merger and acquisition, a company picnic, an office party, announcement of new hires or retirements, a news report involving people close to the user 105, local news on neighborhood of the user 105, or the similar that can affect the morale/collective mood in the serviced environment 101 or the individual state of emotions for the user 105. [0110] In addition to or in place of having external devices 14 and the display 24, which can be configured to provide user interface functionality, computing node 10 in one embodiment can include another display 25 connected to bus 18. In one embodiment, the display 25 can be configured as a touch screen render and can be configured to provide user interface functionality, e.g. can facilitate virtual keyboard functionality and input of total data. [0020] In this specification, terms “emotion” is used to indicate general feeling of an individual. An individual user is in a state of emotion consisting of a preselected basic emotions. Embodiments of the present invention acknowledge that, in psychological nomenclature, the term “sentiment” indicates a mental attitude, opinion, judgment or evaluation on a certain topic, whilst the term “emotion” refers to general feelings, and more specifically, a natural instinctive state of mind deriving from one's circumstances, mood, or relationships with others.) Ander’s time series model includes an indicator of “mood” represented by user interactions with content on user interfaces (such as the news on display).
However, neither Anders, Tiwari, nor Condie teach or suggest:
- wherein the indicator of the level of mood is based on a two-scale vector
-wherein an input of a first vector is based on a positive or negative feeling
-wherein an input of a second vector is based on an internal or external level of connectedness.
Alternatively, Dhillon teaches:
-wherein the indicator of the level of mood is based on a two-scale vector(Dhillon [0035] In operation, the system and methods may include capturing signals, content and/or data associated with an individual's mood and/or mental state from devices, applications, and/or systems that are utilized to interact with individuals. [0010] In certain embodiments, artificial intelligence models and/or machine learning models may be trained to correlate features and/or vectors extracted from sensor data to emotions, moods, and the like. [0063] In certain embodiments, pictures, videos, and/or other content and/or sensor data all may be used as features and/or vectors to facilitate predictions and confirmations of self-assessments by the system 100. In certain embodiments, such vectors could vary over time for the same user. In certain embodiments, the artificial intelligence/machine learning models supporting the system 100 may be built to predict the current state of emotion based on vectors collected over a time period until the current moment.) The broadest reasonable interpretation of “two-scale vector” in view of the spec is a two-degree/two input vector.
-wherein an input of a first vector is based on a positive or negative feeling(Dhillon [0071] For example, the self-assessed emotional states may indicate that the user is happy, sad, angry, frustrated, anxious, nervous, irritated, depressed, furious, hurt, rejected, insecure, bored,) Since all pictures, videos, content, or sensor data can be inputs to the vectors, the input of a first vector can be based on positive or negative feelings as taught by Dillion.
-wherein an input of a second vector is based on an internal or external level of connectedness. (Dhillon [0062] Notably, in certain embodiments, the functionality provided by the system 100 may be amplified by factoring in various types of markers (which may be included in the user profile) when predicting and/or identifying the user's emotional state, mental health state, or a combination thereof. For example, in certain embodiments, the markers such as,... new experiences (particularly related to social interaction), intimacy, romantic love, social interactions,... social/family conditions and may include, but are not limited to, loss/separation/grief, financial pressures, academic performance, school attendance, teacher interactions, family living conditions, romantic relationships, peer pressure, uncertainty regarding the future, conflict within school/leisure environment, and emerging adult responsibility) All of the examples provided enough fall within the BRI of “input based on an internal or external level of connectedness. Since any of these markers can be inputted into the vectors, then the limitation is satisfied.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify the combination of Anders, and Tiwari by adding Dhillon’s calculable vectors, as the use of vectors enables different models(such as neural networks) to be implemented to more accurately generate the recommendations. By performing this combination, it would have been obvious to try a “two-scale” vector, though Dhillon does not specify the amount of vectors, the vectors can have any range of inputs, including two. One would be motivated to use the self-assess emotional state and Dhillon’s markers as inputs into the vectors as it would accurately predict the mood through use of neural networks, and support vector machines, which are advanced techniques that can provide a more accurate prediction. (Dhillon [0063] and [0032] In certain embodiments, the system 100 and methods are configured to receive additional information associated with the user to facilitate the determinations of the system 100. For example, the additional information associated with the user may include a plurality of markers associated with the user, including, but not limited to, location information (e.g., user's location and/or device's location), demographic information, psychographic information, life event information, emotional action information, movement information (e.g., the user's movements), health information, audio information, virtual reality information, augmented reality information, time-related information, physical activity information, mental activity information, diet information, experience information, sociocultural information, political information, relationship information, or a combination thereof.)
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Anders(US 20200090067 A1) in view of Tiwari(US 20170193420 A1), further in view of Condie (US 20200090812 A1), further in view of Galuten; Albhy(US 20160189084 A1) hereinafter Galuten
The combination of Anders, Tiwari, and Condie teach or suggest the method of claim 1
Furthermore Anders teaches wherein:
-the feedback information comprises feedback information from a member. (Anders [0040] In block 250, the emotion service engine 130 obtains a feedback 199 on effectiveness of the recommendation 191 from the user 105. [0081] The emotion time series model is built per environmental factor for prediction of future changes in the state of emotion of the subject person or group and for recommending certain activities to attain a desired state of emotion in the future. Mean average time lapse of changes in the states of emotion as represented in respective emotion scores at different times is statistically simulated. Mean interval of occurrences of a certain environmental factor, or mean duration of a certain environmental factor is also statistically simulated. Certain embodiments of the present invention include the emotion knowledgebase that stores information on activities to recommend to the subject user or group, in order to attain the desired state of emotion. Certain embodiments of the present invention, receives feedback and recommendation and trains the emotion time series models respective to environmental factors, for individual users or for a group, by machine learning.)
However, neither Anders, Tiwari, nor Condie teach:
-a trusted member,
-wherein the feedback information from the trusted member is weighted differently compared to other feedback information,
-wherein the trusted member is determined based on a user-indicated selection of a member, likes, views, posts, or comments associated with the first group of the collaborative platform.
Alternatively, Galuten is directed to a digital architecture that allows users to gauge the reputation of other users and weighing their recommendations more, if they have a greater reputation score which teaches:
- wherein the feedback information from the trusted member is weighted differently compared to other feedback information, (Galuten [0071] One additional factor to be included in the creation of the reputation indices is the weighting of the value of each recommendation (804, 805, 806). For example, if a reviewer, such as, a director, has a historical box office of multiple successful movies, their recommendation on the commercial viability of a writer would be weighted more heavily than an unknown director. The reviewers are able to not only be rated on publicly available data like box office success but also on historical accuracy. For example, if a person who has reviewed hundreds of actors gives 10 new actors a high rating, and those actors go on to be successful, that person’s reviewer rating, with regard to selection of actors, will be high.) Galuten’s reviewers with high reputation indices, such as a renowned director, is mapped to trusted members. The feedback and recommendations of those members with high reputation scores are weighted more heavily than others with lower reputation scores.
-wherein the trusted member is determined based on likes, posts, or comments associated with the first group of the collaborative platform. (Galuten [0002] The system and methods pertain generally to the reputations of entities or individuals. People perform many tasks and others have opinions about how well they perform those tasks. For some tasks, the success of the person performing that task can be measured by success in the marketplace. This system and methods pertain to the field of establishing reputation based on a number of these features. [0003] Today, people review the work of others in a few areas. Angie’s list applies to workers in the home improvement trade. Trip Advisor applies to the quality of lodging and other locations and services tourists typically use. Facebook uses a “thumbs-up” and “thumbs-down” approach to liking things or not. None of these systems integrate a holistic approach to the multiple axes that can combine to create a more robust form of reputation grading.
[0172] In addition to these commercial aggregators of data there is data from Anonymous Contributors (2506). This data is gathered by an Anonymous Contributor Crawler (2507) which crawls the web including Facebook, Twitter and the Blogosphere, collecting posts, tweets, likes and comments from the web about various media properties and the participants in the creation of those properties. Intelligent text parsing algorithms are able to take this data and use it to develop reputation reflecting public sentiment regarding all the participants.) Since Galuten determines the reputation of a member(whether they are a trusted member) based on data from posts, likes, comments etc., Galuten teaches the entire limitation above.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combination of Anders, Tiwari, and Condie to further include the reputation system of Galuten to create a collaborative platform that analyzes the emotional state of the members of the platform and provides recommendations, further valuing recommendations sourced from certain members who have generated better results(which is determined based on the likes, comments or other metrics of their posts). One would be motivated to make the combination as it would provide the benefit of creating more accurate recommendations using more data from people who have established a stronger emotional state compared to their peers. This would be beneficially over equally weighed recommendations from all peers, because the data from those who have not received ideal emotional scores might not be as helpful in providing good recommendations. (Galuten[0155-0159])
Response to Arguments
Applicant's arguments filed 12/29/2025 have been fully considered but they are not persuasive.
Regarding applicant’s arguments over rejections under 35 U.S.C. 101, the applicant argues that amended claim 1 is not directed to an abstract idea. However, the examiner respectfully disagrees. The applicant concedes that the claims at least recite an abstract idea because it involves “activities to recommend to users.” However, the applicant alleges that the “technical aspects,” of a machine learning model applying particular received feedback information to generate real-time predictive coefficients weighing behavioral categories... This argument is not persuasive because as explained by the examiner, the claims only refer to machine learning and neural networks at a level of generality such that they are no more than “apply it” level elements. The claims merely recite the idea of the outcome because they merely provide the inputs and desired outputs of the models without teaching a particular set of steps to approach the solution. Since no improvements to either machine learning or neural networks have been purported, nor do the claims improve computer functionality, the claims also fail to provide a technical improvement under MPEP 2106.05(a). Furthermore, the applicant’s arguments that the technical field of “adaptive wellness systems” is improved by the alleged invention are not persuasive because adaptive wellness systems fall under “certain methods of organizing human activity,” particularly when it is merely a collection of data, a general processing of the data, and output of data to an individual to manage their behavior. Furthermore, the applicant’s assertion that the claim “merely involves” a judicial exception but does not rise to the level of reciting a judicial exception is not persuasive because the majority of the claim limitations fall within the scope of “managing personal behavior, interactions, or relationships between people,” except for the additional elements which fail to provide integration into a practical application or significantly more.
Furthermore, in response to applicant’s arguments under Step 2a Prong Two, the applicant asserts that the claim as a whole improves the technology or technical field of adaptive wellness systems. However, this is not persuasive because the improvements are at the level of an abstract idea, more accurate recommendations to a user is merely an improvement to how “managing personal behavior, interactions, or relationships” are carried out. MPEP 2106.05(a) states “Notably, the court did not distinguish between the types of technology when determining the invention improved technology. However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” Even when considering that the claim features “deliver an appropriate alert...employed as feedback to help further train and improve the machine learning model’s recommendation” this is merely an improvement to the abstract idea lent by a generic implementation of machine learning to train the data. In order for a machine learning related improvement to result in a practical application or significantly more, the field of machine learning itself must be improved upon through an improvement machine learning technique. Therefore, the applicant’s arguments that the claims integrate the abstract idea into a practical application because it is distinguishable from a general purpose computer than performs an existing process is not persuasive because a general purpose computer carrying a generic machine learning model would inherently result in the improvement to the abstract idea.
Furthermore, in response to the applicant’s arguments over Step 2B, the applicant asserts that the features as a whole are not routine, well-known, or conventional activities previously known in the industry. However, this argument is not persuasive because the rejection does not rely on an assertion that the additional elements are well-understood, routine, or conventional. The features purported to be more than routine, well-known or conventional by the applicant are still part of the abstract idea. The applicant is reminded that the well-understood, routine, or conventional consideration applies to the integration of the additional elements, and not to the abstract idea. MPEP 2106.05(d) states, “If the additional element (or combination of elements) is a specific limitation other than what is well-understood, routine and conventional in the field, for instance because it is an unconventional step that confines the claim to a particular useful application of the judicial exception, then this consideration favors eligibility. If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.” Since the rejection does not rely on this consideration to show that the claims are ineligible, and even assuming arguendo that the examiner had the burden of showing well-understood, routine, or conventional, the applicant’s arguments are not persuasive because the consideration is based on the additional elements and it overlaps with the improvement consideration (MPEP 2106.05(a), mere instructions to apply an exception (MPEP 2106.05(f), and insignificant extra-solution activity (MPEP 2106.05(g)).
Since the applicant has not provided a persuasive argument for either the independent and dependent claims showing that the claims are more likely than not, to be patent eligible, the claims remain rejected under 35 U.S.C. 101 for being directed to an abstract idea without significantly more.
Regarding applicant’s arguments over rejections under 35 U.S.C. 103, the applicant’s remarks have been fully considered, however, the arguments are moot in view of the updated combinations which now include Condie. While the examiner agrees that neither Anders nor Tiwari teach that the determining step includes “satisfies a predetermined threshold in order to increase or decrease the energy level of the user,” the combination including Anders, Tiwari, and Condie now satisfy this limitation. Therefore, the applicant’s arguments are moot, and claims 1, 8, and 15 are still rejected as being obvious over the prior art. Regarding the argument that neither Anders nor Tiwari teach “the wellness information includes an indicator of level of mood represented by user interactions with the content on the one or more user interfaces,” this argument is not persuasive because as seen in the rejection above, when giving the claims their broadest reasonable interpretation (BRI), the combination still satisfies the claim. No specific arguments regarding the remaining dependent claims have been provided by the applicant, therefore, all of the pending claims remain rejected under 35 U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
-Liu et al. (US 11141088 B2) discloses the use of deep neural networks to store a set of EEG signals to determine emotions, mood, or affective states and provides feedback and advice in various forms such as recommendations of activities or practices to improve wellbeing, productivity, increase concentration or engagement.
-Wexler et al. (US 20210383925 A1) discloses obtaining user history items, estimating a state of a user using neural networks, identifying and executing an action for affecting a response of the user in assisting the user to adjust the behavior, and updating the model based on the response of the user.
-Arnold et al. (US 20150018991 A1) discloses a fitness monitoring device that determines the level of engagement of a user with the fitness device, and determining an engagement metric threshold to encourage user engagement with the device.
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 NICO LAUREN PADUA whose telephone number is (703)756-1978. The examiner can normally be reached Mon to Fri: 8:30 to 5:00pm.
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/NICO L PADUA/ Junior Patent Examiner, Art Unit 3626
/SANGEETA BAHL/ Primary Examiner, Art Unit 3626