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 . This non-final office action on merits is in response to the Patent Application filed on 06/17/2025.
Status of claims
Claims 1-14 are pending and considered below. This application is a Continuation Application of PCT/JP2023/004844 filed on 02/13/2023.
Information Disclosure Statement
The information disclosure statements (IDSs) filed on 06/17/2025, 10/09/2025, and 02/03/2026 have been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 1-3 are drawn to an a happiness model generation apparatus, claims 4-9 are drawn to a happiness calculation apparatus, claim 10 is drawn to a happiness calculation system, claim 11 is drawn to a happiness model generation method, claim 12 is drawn to a happiness calculation method, and claims 13-14 are drawn to a non-transitory computer readable medium. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Step 2A Prong One
Claim 1 recites the limitations of construct as a questionnaire for model construction, a questionnaire for constructing a happiness model that is a model that deduces based on a result, happiness of the target person, and that is a model that describes the happiness based on three factors, a factor related to work of the target person, a first private life factor that is a factor related to a private life of the target person, and a second private life factor that is a factor among factors related to the private life of the target person that is not included in the first private life factor; and
construct the happiness model based on a questionnaire item to understand happiness extracted based on a result questionnaire for model construction among questionnaire items of the questionnaire for model construction, and based on a questionnaire item that correlates with the happiness extracted based on the result of administering the questionnaire for model construction among the questionnaire items of the questionnaire for model construction, wherein the happiness survey questionnaire is a questionnaire. These limitations, as drafted, are processes that, under their broadest reasonable interpretations, cover performance of the limitations in the mind or by using a pen and paper. Even when considering the “processing circuitry to” language, the claim encompasses a user constructing questionnaire categories, evaluating questionnaire responses, identifying correlations between questionnaire items and happiness, and constructing a conceptual happiness model in their mind or by using a pen and paper. The mere nominal recitation of a processing circuitry does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process which is an abstract idea.
Claim 4 recites the limitations of calculate, using a happiness model that is a model that deduces based on a result, happiness of the target person, and that is a model that describes the happiness based on three factors, a factor related to work of the target person, a first private life factor that is a factor related to a private life of the target person, and a second private life factor that is a factor among factors related to the private life of the target person that is not included in the first private life factor, and the result, the happiness of the target person, wherein the happiness survey questionnaire is a questionnaire for obtaining information to be inputted to the happiness model. These limitations, as drafted, are processes that, under their broadest reasonable interpretations, cover performance of the limitations in the mind or by using a pen and paper. Even when considering the “processing circuitry to” language, the claim encompasses a user evaluating questionnaire responses, applying the happiness model to the responses, categorizing the responses into the recited factors, and calculating a happiness level of the target person in their mind or by using a pen and paper. The mere nominal recitation of a processing circuitry does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process which is an abstract idea.
Independent claim 11 and 13 recite identical or nearly identical steps with respect to claim 1, and independent claims 12 and 14 recite identical or nearly identical steps with respect to claim 4 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Under Step 2A Prong Two
The claimed limitations, as per claim 1, include:
processing circuitry to:
construct as a questionnaire for model construction, a questionnaire for constructing a happiness model that is a model that deduces based on a result of administering a happiness survey questionnaire to a target person, happiness of the target person, and that is a model that describes the happiness based on three factors, a factor related to work of the target person, a first private life factor that is a factor related to a private life of the target person, and a second private life factor that is a factor among factors related to the private life of the target person that is not included in the first private life factor; and
construct the happiness model based on a questionnaire item to understand happiness extracted based on a result of administering the questionnaire for model construction among questionnaire items of the questionnaire for model construction, and based on a questionnaire item that correlates with the happiness extracted based on the result of administering the questionnaire for model construction among the questionnaire items of the questionnaire for model construction, wherein the happiness survey questionnaire is a questionnaire for obtaining information to be inputted to the happiness model.
The claimed limitations, as per claim 4, include:
processing circuitry to:
calculate, using a happiness model that is a model that deduces based on a result of administering a happiness survey questionnaire to a target person, happiness of the target person, and that is a model that describes the happiness based on three factors, a factor related to work of the target person, a first private life factor that is a factor related to a private life of the target person, and a second private life factor that is a factor among factors related to the private life of the target person that is not included in the first private life factor, and the result of administering the happiness survey questionnaire to the target person, the happiness of the target person, wherein the happiness survey questionnaire is a questionnaire for obtaining information to be inputted to the happiness model.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claims 1 and 4 are not integrated into a practical application. In particular, the claims recite an additional element , using a processing circuitry to construct a questionnaire and construct a happiness model (claim 1) and using a processing circuitry to calculate happiness using a happiness model and questionnaire results (claim 4). The processing circuitry in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of collecting, analyzing, and processing information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The judicial exception expressed in claims 1 and 4 are not integrated into a practical application. The claims recite the additional elements of administering a happiness survey questionnaire to a target person (claim 1 and 4), of administering the questionnaire (claim 1), for obtaining information to be inputted to the happiness model (claim 1), and of administering the happiness survey questionnaire to the target person (claim 4). These limitations are recited at a high level of generality (i.e., as a general means of collecting and obtaining information from a user), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claims 1 and 4 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a processing circuitry to construct a questionnaire and construct a happiness model (claim 1) to calculate happiness using a happiness model and questionnaire results (claim 4) amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
For claims 1 and 4, under step 2B, the additional elements of administering a happiness survey questionnaire to a target person (claim 1 and 4), of administering the questionnaire (claim 1), for obtaining information to be inputted to the happiness model (claim 1), and of administering the happiness survey questionnaire to the target person (claim 4) have been evaluated. The apparatuses comprising a processing circuitry to perform a general function of receiving questionnaire responses and user input information for analysis and calculation of happiness, which represents a well-understood, routine, and conventional activity in the field of data processing and computerized survey analysis. The specification discloses that the processing circuitry is used in its ordinary capacity as a data input device and does not describe any improvement to the computer itself or to the functioning of the overall computer system (see [0048]-[0051]). Also noted in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016), merely collecting information for analysis without a technological improvement does not add significantly more to an abstract idea. The use of the apparatuses are no more than collecting information before analyzing questionnaire responses and determining happiness using the happiness model and do not integrate the abstract idea into a practical application. Therefore, the claims do not recite an inventive concept and are not patent eligible.
Claims 2 and 10 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claims 3, and 5-9 recite the additional elements of the processing circuitry (claims 3, and 5-9). However, this additional element amounts to implementing an abstract idea on a generic computing device. As such, this additional element, when considered individually or in combination with the prior devices, does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claim is rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (U.S. Publication 2021/0064984 A1), referred to hereinafter as Wang, in view of Ro (KR Publication KR20140123684 A), referred to hereinafter as Ro, and Granger et al. (International Publication No. WO 2019046580A1), referred to hereinafter as Granger.
Regarding claim 1, Wang teaches a happiness model generation apparatus comprising (Wang [0042] “In some examples, the static data includes, without limitation, O-Data provided from one or more of the applications agents of the enterprise use (e.g., ERP, CRM, HCM), and X-Data from an experience management (XM) service (e.g., Qualtrics owned by SAP SE of Walldorf, Germany). For example, X-Data can include employee surveys of recent weeks, months, quarters of years, and can include data tables of survey questionnaires and scores in various metrics. Example metrics can include, without limitation, compensation, personal growth, wellness, happiness, goal alignment.”, Wang [0017] “In view of the above context, implementations of the present disclosure provide a platform for real-time prediction of employee engagement, which enables enterprises to take preemptive action to mitigate engagement issues and/or avoid an engagement issue altogether. More particularly, implementations of the present disclosure provide an engagement prediction platform that uses one or more ML models that are trained using both static data and dynamic data in a multi-stage training process and are deployed to provide real-time engagement prediction.”, and Wang [0018] “In general, engagement can be described as a measure of a relationship between entities. In the context of the present disclosure, engagement is representative of a relationship between agents (e.g., employees) of an enterprise and the enterprise. For example, agents having a relatively higher engagement can be described as being absorbed by and enthusiastic about their work (e.g., having a positive attitude about the enterprise and their work). Such agents have a higher likelihood of taking positive actions to further the efforts of the enterprise and remain with the enterprise. On the other hand, agents having a relatively low engagement can be described as being disengaged, which can include, for example, doing minimum work, and/or actively damaging the efforts of the enterprise.”):
processing circuitry to (Wang [0006] “The present disclosure further provides a system for implementing the methods provided herein. The system includes one or more processors, and a computer-readable storage medium coupled to the one or more processors having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein.”):
for constructing a happiness model that is a model, and happiness of the target person, and that is a model that describes the happiness (Wang [0042] “In some examples, the static data includes, without limitation, O-Data provided from one or more of the applications agents of the enterprise use (e.g., ERP, CRM, HCM), and X-Data from an experience management (XM) service (e.g., Qualtrics owned by SAP SE of Walldorf, Germany). For example, X-Data can include employee surveys of recent weeks, months, quarters of years, and can include data tables of survey questionnaires and scores in various metrics. Example metrics can include, without limitation, compensation, personal growth, wellness, happiness, goal alignment.”, Wang [0017] “In view of the above context, implementations of the present disclosure provide a platform for real-time prediction of employee engagement, which enables enterprises to take preemptive action to mitigate engagement issues and/or avoid an engagement issue altogether. More particularly, implementations of the present disclosure provide an engagement prediction platform that uses one or more ML models that are trained using both static data and dynamic data in a multi-stage training process and are deployed to provide real-time engagement prediction.”, and Wang [0018] “In general, engagement can be described as a measure of a relationship between entities. In the context of the present disclosure, engagement is representative of a relationship between agents (e.g., employees) of an enterprise and the enterprise. For example, agents having a relatively higher engagement can be described as being absorbed by and enthusiastic about their work (e.g., having a positive attitude about the enterprise and their work). Such agents have a higher likelihood of taking positive actions to further the efforts of the enterprise and remain with the enterprise. On the other hand, agents having a relatively low engagement can be described as being disengaged, which can include, for example, doing minimum work, and/or actively damaging the efforts of the enterprise.”);
construct the happiness model based on a questionnaire item to understand happiness extracted; for model construction, and based on a questionnaire item that correlates with the happiness extracted; and for obtaining information to be inputted to the happiness model ((Wang [0042] “In some examples, the static data includes, without limitation, O-Data provided from one or more of the applications agents of the enterprise use (e.g., ERP, CRM, HCM), and X-Data from an experience management (XM) service (e.g., Qualtrics owned by SAP SE of Walldorf, Germany). For example, X-Data can include employee surveys of recent weeks, months, quarters of years, and can include data tables of survey questionnaires and scores in various metrics. Example metrics can include, without limitation, compensation, personal growth, wellness, happiness, goal alignment.”, Wang [0017] “In view of the above context, implementations of the present disclosure provide a platform for real-time prediction of employee engagement, which enables enterprises to take preemptive action to mitigate engagement issues and/or avoid an engagement issue altogether. More particularly, implementations of the present disclosure provide an engagement prediction platform that uses one or more ML models that are trained using both static data and dynamic data in a multi-stage training process and are deployed to provide real-time engagement prediction.”, and Wang [0018] “In general, engagement can be described as a measure of a relationship between entities. In the context of the present disclosure, engagement is representative of a relationship between agents (e.g., employees) of an enterprise and the enterprise. For example, agents having a relatively higher engagement can be described as being absorbed by and enthusiastic about their work (e.g., having a positive attitude about the enterprise and their work). Such agents have a higher likelihood of taking positive actions to further the efforts of the enterprise and remain with the enterprise. On the other hand, agents having a relatively low engagement can be described as being disengaged, which can include, for example, doing minimum work, and/or actively damaging the efforts of the enterprise.”).
Wang fails to explicitly teach construct as a questionnaire for model construction, a questionnaire and based on a result of administering a happiness survey questionnaire to a target person; based on three factors, a factor related to work of the target person, a first private life factor that is a factor related to a private life of the target person, and a second private life factor that is a factor among factors related to the private life of the target person that is not included in the first private life factor; and based on a result of administering the questionnaire; among questionnaire items of the questionnaire; and wherein the happiness survey questionnaire is a questionnaire.
Ro teaches construct as a questionnaire for model construction, a questionnaire and based on a result of administering a happiness survey questionnaire to a target person (Ro [0023] “The wellness authentication control server 100 is a wellness information communication module 110 for transmitting and receiving information from the cooperation group server 200 and the personal terminal 300 through a communication network, and the wellness information communication module 110 And a controller 120 controlling the wellness authentication control server 100, a data classification / standardization module 130 for classifying / standardizing information obtained from the wellness information communication module 110, and the classification / standardized data. Is stored in the individual standard information database 141, the survey content database in which prepared field-specific questionnaire content is stored 142, the standardized data stored in the individual standard information database 141, and the contents of the questionnaire content database 142. A questionnaire module 150 for conducting a questionnaire through the wellness information communication module 110 and the user's personal terminal 300 based on the data, and the wellness table for each field previously prepared. A wellness data analysis module for comparing / analyzing the wellness standard information database 143 storing information data and the wellness data of the questionnaire results of the questionnaire module 150 or the wellness data of the individual standard information with the wellness standard information of the wellness standard information database 143. And the wellness solution database 144 in which the solution for the analysis result of the wellness data analysis module 160 is stored, and the wellness solution database 144 according to the result of the wellness data analysis module 160. Custom information generation module 170 for selecting and providing the constructed solution data, an authentication determination module 180 for determining whether to be authenticated according to the result of the wellness data analysis module 160, and the authentication determination module 180 And an authentication procedure progress module 181 for processing and transmitting the result of the wellness information communication module 110.”); and
based on a result of administering the questionnaire; among questionnaire items of the questionnaire; and wherein the happiness survey questionnaire is a questionnaire (Ro [0023] “The wellness authentication control server 100 is a wellness information communication module 110 for transmitting and receiving information from the cooperation group server 200 and the personal terminal 300 through a communication network, and the wellness information communication module 110 And a controller 120 controlling the wellness authentication control server 100, a data classification / standardization module 130 for classifying / standardizing information obtained from the wellness information communication module 110, and the classification / standardized data. Is stored in the individual standard information database 141, the survey content database in which prepared field-specific questionnaire content is stored 142, the standardized data stored in the individual standard information database 141, and the contents of the questionnaire content database 142. A questionnaire module 150 for conducting a questionnaire through the wellness information communication module 110 and the user's personal terminal 300 based on the data, and the wellness table for each field previously prepared. A wellness data analysis module for comparing / analyzing the wellness standard information database 143 storing information data and the wellness data of the questionnaire results of the questionnaire module 150 or the wellness data of the individual standard information with the wellness standard information of the wellness standard information database 143. And the wellness solution database 144 in which the solution for the analysis result of the wellness data analysis module 160 is stored, and the wellness solution database 144 according to the result of the wellness data analysis module 160. Custom information generation module 170 for selecting and providing the constructed solution data, an authentication determination module 180 for determining whether to be authenticated according to the result of the wellness data analysis module 160, and the authentication determination module 180 And an authentication procedure progress module 181 for processing and transmitting the result of the wellness information communication module 110.”, Ro [0087] “A questionnaire content obtaining step (s220) of selecting and obtaining questionnaire content from the questionnaire content database 142 according to the field and characteristic of the authentication target stored in the individual standard information database 141 in the questionnaire module 150.”).
Granger teaches based on three factors, a factor related to work of the target person, a first private life factor that is a factor related to a private life of the target person, and a second private life factor that is a factor among factors related to the private life of the target person that is not included in the first private life factor (Granger [0001] “This invention relates generally to assessing, monitoring, improving and/or modifying health and well-being for one or more people, and/or one or more factors that may improve and/or modify health and well-being for one or more people, wherein the one or more people may or may not be associated with a habitable or other built environment and/or spaces therein.”, Granger [0096] “Job satisfaction can be evaluated through self-report, observational, implicit attitude, and physiological measures. Self-report measures, such as the Job Descriptive Index (JDI), Minnesota Satisfaction Questionnaire (MSQ), and Index of Organizational Reactions (IOR), often are used because of their acceptance as valid psychometric tools and administration ease (e.g., convenience, cost). Observational, implicit attitude, and psychological measures can be used in combination with self-report measures because they may provide further insight into one or more peoples' experiences that may not be revealed through self-reported job satisfaction.”, Granger [0040] “In some embodiments, a method for evaluating an intervention or determining whether to incorporate or employ an intervention may include determining or identifying at least one problem associated with a person or a group of people, a neighborhood or other community, determining or identifying at least one indicator associated with the identified problem, and determining or identifying at least one potential intervention based on the at least one indicator, wherein the at least one potential intervention can reduce the prevalence of the at least one problem for the person or a group of people. Such a group of people may include, for example, a neighborhood or other community. In some embodiments, the method also may include one or more of the following: ranking one or more indicators, ranking one or more interventions, implementing one or more interventions, identifying or otherwise determining one or more built environments associated with the person, a group of people, a neighborhood or other community, identifying or otherwise determining a characteristic of the person or a group of people that includes the person, or other steps.”, Granger [00111] “In addition to noise, light can profoundly influence health and well-being or the occupant of a built environment. The way the indoor lighting environments are designed not only impacts a person's ability to perform visual tasks, but it also affects comfort, mood, and a wide range of physiological and psychological functions that influence our cognition and sleep quality. Visual comfort is a subjective condition caused by an individual's experience with the visual environment and may impacted by the physiology of the eye, the amount of light, its distribution in space, and its spectral power distribution.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the wellness questionnaire and analysis system of Ro with the survey predictive modeling techniques of Wang and the multi factor wellbeing assessment concepts of Granger in order to construct a questionnaire happiness model that predicts or deduces a target person’s happiness from questionnaire responses. Ro teaches a wellness management platform including a questionnaire content database, a questionnaire module for administering questionnaires to users, and a wellness data analysis module that analyzes questionnaire results and wellness information. Ro further teaches selecting questionnaire content according to characteristics of a target user, which demonstrates the use of extracted or selected questionnaire items for wellness analysis and solution generation. Wang teaches using survey questionnaire data and associated metrics, including compensation, personal growth, wellness, happiness, and goal alignment, as inputs to machine learning models trained to predict employee engagement and related behavioral or emotional states. Wang further explains that engagement reflects a user’s relationship to work and positive or negative attitudes toward work-related experiences. Therefore, Wang teaches constructing predictive models using questionnaire metrics associated with happiness, wellness, and work conditions.
It would have further been obvious to incorporate the teachings of Granger into the combined system because Granger teaches assessing and improving health and wellbeing using multiple dimensions of a person’s experiences, including work and job satisfaction, personal physiological and psychological wellbeing, and broader environmental or community factors. Granger specifically teaches psychometric self report tools for evaluating job satisfaction, as well as evaluating factors associated with neighborhoods, communities, environmental comfort, mood, cognition, and sleep quality. A person of ordinary skill in the art would have recognized that organizing questionnaire happiness or wellness information into multiple life factors, such as work-related factors, personal and private-life factors, and broader community and environmental factors, would enable more targeted recommendations, interventions, or wellness solutions.
Additionally, one of ordinary skill in the art would have been motivated to use questionnaire items associated with or correlated to happiness and wellness outcomes in constructing the predictive model because Wang’s machine learning prediction platform implicitly relies on identifying questionnaire metrics that are predictive of engagement, wellness, or happiness outcomes. Ro similarly teaches selecting questionnaire content from a questionnaire database based on user characteristics and analyzing questionnaire results to generate wellness solutions. Accordingly, the combined teachings of Ro and Wang suggest extracting or selecting questionnaire items from administered questionnaires and using questionnaire items associated with happiness or wellness outcomes as model inputs for constructing the predictive happiness model.
The combination of Ro, Wang, and Granger would have yielded predictable results because each reference is directed to computerized wellness, engagement, or wellbeing assessment using questionnaire information and analysis. Combining Wang’s predictive modeling techniques and happiness and wellness metrics with Ro’s questionnaire generation and wellness analysis framework, while further incorporating Granger’s multi-dimensional work, personal, and community and environmental wellness factors, would involve the predictable use of known survey analysis and predictive modeling techniques to improve wellness and happiness evaluation systems.
Regarding claim 2, Wang, Ro, and Granger teach the invention in claim 1, as discussed above, and further teach wherein wording of the questionnaire item of the questionnaire for model construction is such wording that a respondent of the questionnaire for model construction can answer imagining a deviation from a best state for the respondent (Wang [0042] “In some examples, the static data includes, without limitation, O-Data provided from one or more of the applications agents of the enterprise use (e.g., ERP, CRM, HCM), and X-Data from an experience management (XM) service (e.g., Qualtrics owned by SAP SE of Walldorf, Germany). For example, X-Data can include employee surveys of recent weeks, months, quarters of years, and can include data tables of survey questionnaires and scores in various metrics. Example metrics can include, without limitation, compensation, personal growth, wellness, happiness, goal alignment). Each metric can be associated with several questions that had been answered by agents (e.g., answered as a score between 0-10, or 0-5). In some examples, the X-Data can also include open questions, which can be answered in short text. In some examples, a metrics equation can be defined and customized for different enterprises to adjust the weight of every metric. The scores are labelled with different metrics. In some examples, answers to the open question can be analyzed with a text analytics engine to provide quantitative values. In some implementations, historical engagement scores are provided based on the metrics equation, labelled scores and quantitative values of text analytics, attrition rate, retention rate, and turnover rate (e.g., during a defined period (months, quarters, years). The historical employee engagement scores are provided as static data.”, and
Granger [0096] “Job satisfaction can be evaluated through self-report, observational, implicit attitude, and physiological measures. Self-report measures, such as the Job Descriptive Index (JDI), Minnesota Satisfaction Questionnaire (MSQ), and Index of Organizational Reactions (IOR), often are used because of their acceptance as valid psychometric tools and administration ease (e.g., convenience, cost). Observational, implicit attitude, and psychological measures can be used in combination with self-report measures because they may provide further insight into one or more peoples' experiences that may not be revealed through self-reported job satisfaction.”, and
Ro [0027] “If the analyzed wellness information is more than the wellness standard information, it is preferable to generate a guideline for ideal management of the wellness standard information or more from the wellness solution database 144 as the wellness fitting information. In other words, the balance management guidelines are suggested to manage the shortcomings, and the superiority is to improve the quality for certification by presenting guidelines for ideal management for further development.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to configure the wording of questionnaire items in a wellness or happiness model questionnaire such that respondents evaluate themselves relative to an ideal or best state because Wang teaches questionnaire engagement and wellness metrics in which multiple scored survey questions are associated with wellness, happiness, personal growth, and other psychometric metrics used in predictive evaluation models, while Granger teaches the use of self report psychometric tools for evaluating subjective job satisfaction and personal experiences. Ro further teaches evaluating wellness information relative to wellness standard information and generating ideal management guidance directed toward achieving improved or ideal wellness conditions. A person of ordinary skill in the art would have recognized that phrasing questionnaire items in a manner that prompts respondents to compare their current condition against an ideal or preferred state would improve the consistency and predictive usefulness of the questionnaire wellness or happiness metrics, which improves the effectiveness of the resulting wellness or happiness model.
Regarding claim 3, Wang, Ro, and Granger teach the invention in claim 1, as discussed above, and further teach wherein the processing circuitry constructs the happiness model using a covariance structure analysis (Wang [0042] “In some examples, the static data includes, without limitation, O-Data provided from one or more of the applications agents of the enterprise use (e.g., ERP, CRM, HCM), and X-Data from an experience management (XM) service (e.g., Qualtrics owned by SAP SE of Walldorf, Germany). For example, X-Data can include employee surveys of recent weeks, months, quarters of years, and can include data tables of survey questionnaires and scores in various metrics. Example metrics can include, without limitation, compensation, personal growth, wellness, happiness, goal alignment.”, Wang [0017] “In view of the above context, implementations of the present disclosure provide a platform for real-time prediction of employee engagement, which enables enterprises to take preemptive action to mitigate engagement issues and/or avoid an engagement issue altogether. More particularly, implementations of the present disclosure provide an engagement prediction platform that uses one or more ML models that are trained using both static data and dynamic data in a multi-stage training process and are deployed to provide real-time engagement prediction.”, and Wang [0018] “In general, engagement can be described as a measure of a relationship between entities. In the context of the present disclosure, engagement is representative of a relationship between agents (e.g., employees) of an enterprise and the enterprise. For example, agents having a relatively higher engagement can be described as being absorbed by and enthusiastic about their work (e.g., having a positive attitude about the enterprise and their work). Such agents have a higher likelihood of taking positive actions to further the efforts of the enterprise and remain with the enterprise. On the other hand, agents having a relatively low engagement can be described as being disengaged, which can include, for example, doing minimum work, and/or actively damaging the efforts of the enterprise.”, and
Granger [00247] “In some embodiments, a more objective and statistically astute approach may be used. For example, as a preliminary step for the computation, data diagnostics can be done to understand the functional form of each relevancy indicator for an intervention or intervention set and the associations between different relevancy indicators for an intervention or intervention set. Alternatively, correlation coefficients between proposed independent relevancy indicators for an intervention or intervention set can provide insight into the associations between the relevancy indicators. For example, if the correlation between any two relevancy indicators for an intervention or an intervention set was found to be too high (defined based on a predetermined threshold), those indicators may be combined to form one relevancy indicator. If the relevancy indicators for an intervention or an intervention set are recorded as categorical/ordinal variables, they might be transformed using the Alternating Least Squares Optimal Scaling (ALSOS) methodology. Subsequently, weights may be statistically determined via methods such as Principal Component Analysis (PCA) to determine the linear combination of criteria that captures most of the variation of the underlying data. As a result, an indicator recommendation index as below may be used: IRI=6iCost + 62Effectiveness + 63Feasibility”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to construct the questionnaire happiness model of Wang using covariance structure analysis or a similar multivariate statistical relationship modeling technique because Wang teaches generating predictive engagement and wellness models using questionnaire metrics including wellness, happiness, compensation, personal growth, and goal alignment collected from employee surveys, while Granger teaches statistically analyzing relationships among correlated indicators using correlation coefficients, weighted statistical relationships, and Principal Component Analysis (PCA) to determine weighted combinations of variables that capture variation within underlying data. A person of ordinary skill in the art would have recognized that covariance structure analysis, PCA, factor analysis, and other multivariate statistical modeling techniques were known alternative methods for analyzing correlated questionnaire variables and deriving weighted latent factor relationships in predictive wellness or behavioral models. Accordingly, it would have been obvious to apply covariance structure analysis as a known statistical modeling technique within Wang’s questionnaire happiness and engagement prediction framework in order to improve identification of relationships among questionnaire factors and generate a more statistically robust predictive happiness model.
Regarding claim 4, Wang teaches a happiness calculation apparatus comprising (Wang [0042] “In some examples, the static data includes, without limitation, O-Data provided from one or more of the applications agents of the enterprise use (e.g., ERP, CRM, HCM), and X-Data from an experience management (XM) service (e.g., Qualtrics owned by SAP SE of Walldorf, Germany). For example, X-Data can include employee surveys of recent weeks, months, quarters of years, and can include data tables of survey questionnaires and scores in various metrics. Example metrics can include, without limitation, compensation, personal growth, wellness, happiness, goal alignment.”, Wang [0017] “In view of the above context, implementations of the present disclosure provide a platform for real-time prediction of employee engagement, which enables enterprises to take preemptive action to mitigate engagement issues and/or avoid an engagement issue altogether. More particularly, implementations of the present disclosure provide an engagement prediction platform that uses one or more ML models that are trained using both static data and dynamic data in a multi-stage training process and are deployed to provide real-time engagement prediction.”, and Wang [0018] “In general, engagement can be described as a measure of a relationship between entities. In the context of the present disclosure, engagement is representative of a relationship between agents (e.g., employees) of an enterprise and the enterprise. For example, agents having a relatively higher engagement can be described as being absorbed by and enthusiastic about their work (e.g., having a positive attitude about the enterprise and their work). Such agents have a higher likelihood of taking positive actions to further the efforts of the enterprise and remain with the enterprise. On the other hand, agents having a relatively low engagement can be described as being disengaged, which can include, for example, doing minimum work, and/or actively damaging the efforts of the enterprise.”):
processing circuitry to (Wang [0006] “The present disclosure further provides a system for implementing the methods provided herein. The system includes one or more processors, and a computer-readable storage medium coupled to the one or more processors having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein.”):
calculate, using a happiness model that is a model that deduces; happiness of the target person, and that is a model that describes the happiness; and for obtaining information to be inputted to the happiness model (Wang [0042] “In some examples, the static data includes, without limitation, O-Data provided from one or more of the applications agents of the enterprise use (e.g., ERP, CRM, HCM), and X-Data from an experience management (XM) service (e.g., Qualtrics owned by SAP SE of Walldorf, Germany). For example, X-Data can include employee surveys of recent weeks, months, quarters of years, and can include data tables of survey questionnaires and scores in various metrics. Example metrics can include, without limitation, compensation, personal growth, wellness, happiness, goal alignment.”, Wang [0017] “In view of the above context, implementations of the present disclosure provide a platform for real-time prediction of employee engagement, which enables enterprises to take preemptive action to mitigate engagement issues and/or avoid an engagement issue altogether. More particularly, implementations of the present disclosure provide an engagement prediction platform that uses one or more ML models that are trained using both static data and dynamic data in a multi-stage training process and are deployed to provide real-time engagement prediction.”, and Wang [0018] “In general, engagement can be described as a measure of a relationship between entities. In the context of the present disclosure, engagement is representative of a relationship between agents (e.g., employees) of an enterprise and the enterprise. For example, agents having a relatively higher engagement can be described as being absorbed by and enthusiastic about their work (e.g., having a positive attitude about the enterprise and their work). Such agents have a higher likelihood of taking positive actions to further the efforts of the enterprise and remain with the enterprise. On the other hand, agents having a relatively low engagement can be described as being disengaged, which can include, for example, doing minimum work, and/or actively damaging the efforts of the enterprise.”).
Wang fails to explicitly teach deduces based on a result of administering a happiness survey questionnaire to a target person; and based on three factors, a factor related to work of the target person, a first private life factor that is a factor related to a private life of the target person, and a second private life factor that is a factor among factors related to the private life of the target person that is not included in the first private life factor, and the result of administering the happiness survey questionnaire to the target person, the happiness of the target person, wherein the happiness survey questionnaire is a questionnaire.
Ro teaches based on a result of administering a happiness survey questionnaire to a target person; and the result of administering the happiness survey questionnaire to the target person, the happiness of the target person, wherein the happiness survey questionnaire is a questionnaire (Ro [0023] “The wellness authentication control server 100 is a wellness information communication module 110 for transmitting and receiving information from the cooperation group server 200 and the personal terminal 300 through a communication network, and the wellness information communication module 110 And a controller 120 controlling the wellness authentication control server 100, a data classification / standardization module 130 for classifying / standardizing information obtained from the wellness information communication module 110, and the classification / standardized data. Is stored in the individual standard information database 141, the survey content database in which prepared field-specific questionnaire content is stored 142, the standardized data stored in the individual standard information database 141, and the contents of the questionnaire content database 142. A questionnaire module 150 for conducting a questionnaire through the wellness information communication module 110 and the user's personal terminal 300 based on the data, and the wellness table for each field previously prepared. A wellness data analysis module for comparing / analyzing the wellness standard information database 143 storing information data and the wellness data of the questionnaire results of the questionnaire module 150 or the wellness data of the individual standard information with the wellness standard information of the wellness standard information database 143. And the wellness solution database 144 in which the solution for the analysis result of the wellness data analysis module 160 is stored, and the wellness solution database 144 according to the result of the wellness data analysis module 160. Custom information generation module 170 for selecting and providing the constructed solution data, an authentication determination module 180 for determining whether to be authenticated according to the result of the wellness data analysis module 160, and the authentication determination module 180 And an authentication procedure progress module 181 for processing and transmitting the result of the wellness information communication module 110.”, Ro [0087] “A questionnaire content obtaining step (s220) of selecting and obtaining questionnaire content from the questionnaire content database 142 according to the field and characteristic of the authentication target stored in the individual standard information database 141 in the questionnaire module 150.”).
Granger teaches based on three factors, a factor related to work of the target person, a first private life factor that is a factor related to a private life of the target person, and a second private life factor that is a factor among factors related to the private life of the target person that is not included in the first private life factor (Granger [0001] “This invention relates generally to assessing, monitoring, improving and/or modifying health and well-being for one or more people, and/or one or more factors that may improve and/or modify health and well-being for one or more people, wherein the one or more people may or may not be associated with a habitable or other built environment and/or spaces therein.”, Granger [0096] “Job satisfaction can be evaluated through self-report, observational, implicit attitude, and physiological measures. Self-report measures, such as the Job Descriptive Index (JDI), Minnesota Satisfaction Questionnaire (MSQ), and Index of Organizational Reactions (IOR), often are used because of their acceptance as valid psychometric tools and administration ease (e.g., convenience, cost). Observational, implicit attitude, and psychological measures can be used in combination with self-report measures because they may provide further insight into one or more peoples' experiences that may not be revealed through self-reported job satisfaction.”, Granger [0040] “In some embodiments, a method for evaluating an intervention or determining whether to incorporate or employ an intervention may include determining or identifying at least one problem associated with a person or a group of people, a neighborhood or other community, determining or identifying at least one indicator associated with the identified problem, and determining or identifying at least one potential intervention based on the at least one indicator, wherein the at least one potential intervention can reduce the prevalence of the at least one problem for the person or a group of people. Such a group of people may include, for example, a neighborhood or other community. In some embodiments, the method also may include one or more of the following: ranking one or more indicators, ranking one or more interventions, implementing one or more interventions, identifying or otherwise determining one or more built environments associated with the person, a group of people, a neighborhood or other community, identifying or otherwise determining a characteristic of the person or a group of people that includes the person, or other steps.”, Granger [00111] “In addition to noise, light can profoundly influence health and well-being or the occupant of a built environment. The way the indoor lighting environments are designed not only impacts a person's ability to perform visual tasks, but it also affects comfort, mood, and a wide range of physiological and psychological functions that influence our cognition and sleep quality. Visual comfort is a subjective condition caused by an individual's experience with the visual environment and may impacted by the physiology of the eye, the amount of light, its distribution in space, and its spectral power distribution.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the engagement prediction platform of Wang with the questionnaire administration and wellness analysis techniques of Ro and the multi-dimensional wellbeing assessment teachings of Granger in order to calculate a target person’s happiness or wellness state using questionnaire-derived information processed through a predictive model. Wang teaches a processor prediction system employing machine learning models trained using survey static and dynamic data, including employee surveys and questionnaire metrics associated with compensation, personal growth, wellness, happiness, and goal alignment, to predict employee engagement and related behavioral or emotional states. Wang further teaches that employee engagement reflects a user’s relationship to work and positive or negative work attitudes, which teach the use of work related factors in predictive wellness or engagement analysis.
Ro teaches a wellness questionnaire framework including a questionnaire module for administering questionnaires, a questionnaire content database storing field questionnaire content, and a wellness data analysis module for analyzing questionnaire results and generating customized wellness solutions based on the analyzed questionnaire data. Ro further teaches selecting questionnaire content according to characteristics of a target user, thereby demonstrating the use of questionnaire information as inputs to a wellness analysis and prediction framework. A person of ordinary skill in the art would have recognized that the survey predictive models of Wang would predictably benefit from incorporation into Ro’s wellness questionnaire and analysis system to improve automated assessment and prediction of user wellness or happiness states from questionnaire responses.
Granger further teaches assessing and improving health and wellbeing using multiple categories of factors associated with a person’s experiences and environment, including job satisfaction, psychological wellbeing, environmental comfort, mood, cognition, sleep quality, neighborhoods, communities, and built environments. Granger therefore teaches or at least suggests organizing happiness or wellness analysis using multiple life-domain factors including work-related factors, personal and private life wellbeing factors, and broader community or environmental factors. It would have been obvious to incorporate such multi-dimensional wellness factors into the combined Wang and Ro predictive wellness framework because doing so would improve interpretability of the calculated happiness and wellness result and enable more targeted interventions, recommendations, or environmental adjustments based on the particular category of factors affecting the user’s wellbeing.
The combination of Wang, Ro, and Granger would have yielded predictable results because each reference is directed to computerized assessment and prediction of human wellness, engagement, satisfaction, or wellbeing using questionnaire information and analytics. Combining Wang’s predictive machine learning techniques with Ro’s questionnaire acquisition and wellness analysis architecture, while incorporating Granger’s teachings regarding work, personal, and community and environmental well-being factors, would involve the predictable use of known survey analysis and predictive modeling techniques to improve computerized happiness and wellness calculation systems.
Regarding claim 5, Wang, Ro, and Granger teach the invention in claim 4, as discussed above, and further teach wherein in the happiness model, a plurality of items are stratified, and a contribution rate corresponding to each item of the plurality of items is set, wherein the processing circuitry extracts at least one of an item of which a contribution rate to happiness that is calculated among items that the happiness model indicates is equal to or greater than a high contribution standard value, and an item of which the contribution rate to the happiness that is calculated among the items that the happiness model indicates is equal to or less than a low contribution standard value (Granger [00234] “In some embodiments, interventions within an intervention set may partially or fully dependency on each other. To calculate a ranking score for these intervention sets, relative partial dependency weights may be assigned to each intervention included in the intervention set. When aggregating the individual intervention II or other scores, the substitution rates among the interventions within the bundle may be equal to the weights of the interventions up to the multiplicative coefficient. However, given the complexity of computing partial dependency between each pair of interventions, these weights may symbolize the partial dependency between each intervention and the score for the intervention set. When II or other scores are aggregated using these weights, a Til (Total Intervention Index) score for the intervention set can be used reflect the synergistic effect of the interventions within the intervention set, such that dropping one or more interventions from the intervention set would have a multiplier effect on the computed Til score. To account for this multiplicative effect, the intervention set score may be calculated as: Tl Ik = Wa*lla+Wb*llb+Wc*llc....+Wn*lln where Tl I k refers to the total intervention set index of the kth intervention set; and I In refers to II for the nth intervention, and Wa, Wb,... Wn are weightings.”, and Granger [00236] “In some embodiments, only the adaptability and compatibility scores are weighted to compute the All. The exposure indicator may be weighted at a later stage. The intervention set adaptability and compatibility scores may be determined through a combination of objective evaluation of the context and subjective expert judgement. These may be rated on a 1-5 point ordinal scale as outlined below….One potential way for computing All is: Allk = WFTIIk+ WCComp + WAAdap where Allk refers to the adjusted intervention set Index for the kth intervention set; WF is the weight assigned to the total intervention set score for the kth intervention set; WC refers to the weight assigned to the compatibility score of the kth intervention set; and WA refers to the weight assigned to adaptability score of the kth intervention set.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the happiness calculation apparatus of Wang to include weighted and stratified contribution analysis as taught by Granger in order to identify items having relatively higher or lower influence on an overall calculated score. Granger teaches assigning relative weights to individual interventions and aggregating those weighted values into overall scores, where the weights represent differing levels of contribution and influence within a model. Granger further teaches calculating weighted index values using multiple weighted factors and evaluating resulting scores using ranked or scaled values. One of ordinary skill in the art would have recognized that applying Granger’s weighted contribution and score aggregation techniques to Wang’s happiness and engagement prediction framework would have predictably enabled identification of questionnaire items or factors having comparatively high or low contribution values within the happiness model. The combination applies known weighted scoring and contribution analysis techniques to the known happiness and engagement modeling system of Wang to improve interpretability of calculated happiness results and facilitate identification of influential contributing factors, yielding predictable results.
Regarding claim 6, Wang, Ro, and Granger teach the invention in claim 4, as discussed above, and further teach wherein when a plurality of happiness models, each of which is the happiness model, are prepared, and each happiness model of the plurality of happiness models is a model prepared according to an individual attribute, the processing circuitry singles out a happiness model from the plurality of happiness models according to an individual attribute of the target person, and calculates happiness of the target person using the happiness model singled out (Wang [0045] “In some examples, in the identify features phase, the DMP detects and identifies features in the preprocessed dataset. In some examples, a list of features is provided (e.g., in a plain text file in JSON format). A feature can be described as an input variable that is used in making predictions from an ML model. In the context of the present disclosure, features can include, without limitation, the presence or absence of application names and user identifiers, the time a user spent on an application, a frequency of specific terms (e.g., ticket, calendar), the structure and sequence of usage logging records, logged actions (e.g., updated setting, new entries input). In some examples, the selection of features varies between different enterprises and different departments. Consequently, feature selection can be optimized for different lines of operations and/or different use cases to achieve higher predictive accuracy from respective ML models. Using engagement of a customer support team as a non-limiting example, features related to engagement can include, without limitation, customer rating for call tickets, number of dropped calls in a specific time period, average length of retention period for specific groups of employees, and average time to complete specific regular tasks (e.g., resolving call tickets).”, Wang [0017] “In view of the above context, implementations of the present disclosure provide a platform for real-time prediction of employee engagement, which enables enterprises to take preemptive action to mitigate engagement issues and/or avoid an engagement issue altogether. More particularly, implementations of the present disclosure provide an engagement prediction platform that uses one or more ML models that are trained using both static data and dynamic data in a multi-stage training process and are deployed to provide real-time engagement prediction. As described in further detail herein, implementations of the present disclosure use enterprise master data to train a ML model that is used to predict engagement, the master data including static data and dynamic data. The ML model is statically trained in a first stage, and dynamically trained in a second stage, and is deployed to provide real-time engagement prediction.”, Wang [0024] “As introduced above, the present disclosure provides an engagement prediction platform that uses one or more ML models that are trained using both static data and dynamic data in a multi-stage training process and are deployed to provide real-time engagement prediction. In further detail, and as described herein, the engagement prediction platform of the present disclosure uses one or more ML models that are trained using enterprise master data (EMD) to provide engagement predictions. In some implementations, EMD data integrates data sources across different sources in an enterprise landscape. In some examples, the EMD includes operational data (O-Data) and experience data (X-Data). O-Data include data generated from enterprise operations and can be generated and managed by enterprise software systems such as ERP, CRM, HCM, and the like. X-Data can be considered as qualitative data that is contextualised with human factors and includes satisfaction levels and various aspects of human experience.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to prepare and use multiple happiness or engagement prediction models for different user attributes or groups and to select an appropriate model for a target person because Wang teaches an engagement prediction platform using one or more machine learning models trained using operational and experience data associated with human factors, including satisfaction and engagement information. Wang further teaches that feature selection and model optimization may vary between different enterprises, departments, lines of operation, and use cases in order to improve predictive accuracy of the respective ML models. A person of ordinary skill in the art would have recognized that different categories of users or individuals may exhibit different behavioral or engagement characteristics and that tailoring or selecting predictive models according to user attributes or group characteristics would improve prediction accuracy and relevance of the resulting happiness or engagement calculations. Accordingly, it would have been obvious to prepare multiple happiness models corresponding to different individual attributes and to select an appropriate model for a target person based on the person’s attributes before calculating the person’s happiness or engagement level.
Regarding claim 7, Wang, Ro, and Granger teach the invention in claim 4, as discussed above, and further teach wherein the processing circuitry deduces responses to at least some of questionnaire items among questionnaire items of the happiness survey questionnaire based on objective data that is data that has objectivity (Wang [0042] “In some examples, the static data includes, without limitation, O-Data provided from one or more of the applications agents of the enterprise use (e.g., ERP, CRM, HCM), and X-Data from an experience management (XM) service (e.g., Qualtrics owned by SAP SE of Walldorf, Germany). For example, X-Data can include employee surveys of recent weeks, months, quarters of years, and can include data tables of survey questionnaires and scores in various metrics. Example metrics can include, without limitation, compensation, personal growth, wellness, happiness, goal alignment). Each metric can be associated with several questions that had been answered by agents (e.g., answered as a score between 0-10, or 0-5). In some examples, the X-Data can also include open questions, which can be answered in short text. In some examples, a metrics equation can be defined and customized for different enterprises to adjust the weight of every metric. The scores are labelled with different metrics. In some examples, answers to the open question can be analyzed with a text analytics engine to provide quantitative values. In some implementations, historical engagement scores are provided based on the metrics equation, labelled scores and quantitative values of text analytics, attrition rate, retention rate, and turnover rate (e.g., during a defined period (months, quarters, years). The historical employee engagement scores are provided as static data.”, and
Granger [00134] “As illustrated, one or more occupants 126 may have one or more of a variety of wearable devices associated therewith having wearable sensors 108. As used herein, a sensor may be wearable both in the temporary sense and the permanent sense. That is, for example, the wearable sensors 108 may include, for example, fitness trackers, hear rate monitors, breathing rate monitors, sweat and body temperature monitors, smart glasses, and smart watches, among other devices, and it also may include more permanently wearable sensors, such as, for example, a pacemaker or continuous glucose monitor or blood sugar level or blood oxygen saturation level monitor. Further, the wearable sensor 108 may be both external or internal to the occupant 126. In addition, the wearable sensor 108 may include multiple wearable sensors for a single occupant 126. By one approach, the wearable sensor 108 associated with an occupant of the built structure 150 is configured to detect, for example, biometric information of the associated occupant, ambient lighting levels proximate the associated occupant, ambient temperature levels proximate the associated occupant, and/or ambient air quality levels proximate the associated occupant. By measuring the proximate lighting, temperature, and air quality levels of or nearby the associated occupant, the wearable sensor 108 is typically measuring these metrics or parameters within several feet of the occupant and within the same room, space, or zone as the occupant.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to deduce responses to at least some questionnaire items based on objective data because Wang teaches a questionnaire wellness and happiness evaluation framework using employee survey metrics associated with wellness, happiness, personal growth, and engagement, and further teaches generating quantitative values and engagement scores from analyzed data. Granger teaches collecting objective biometric and environmental data using wearable sensors, including heart rate, breathing rate, body temperature, ambient lighting, ambient temperature, and air quality measurements associated with an occupant of a built environment. A person of ordinary skill in the art would have recognized that objectively collected biometric and environmental measurements could be used to infer or supplement subjective questionnaire responses relating to wellness, comfort, satisfaction, or happiness in order to reduce questionnaire burden, improve data accuracy, and enable more continuous or automated wellness assessment. Accordingly, it would have been obvious to combine Wang’s questionnaire happiness analysis system with Granger’s wearable and environmental sensing techniques so that responses to at least some questionnaire items are deduced from objective sensor data.
Regarding claim 8, Wang, Ro, and Granger teach the invention in claim 4, as discussed above, and further teach wherein the processing circuitry stores time series data consisting of data that indicates happiness calculated, and verifies a change in the happiness based on time series data stored in the happiness database (Wang [0052] “During real-time prediction, the ML model is loaded by the inference engine 240 from the active model cache 248. The ML model is used to predict the most probable engagement scores in the future. In some examples, the prediction result is a time-series of engagement scores, which are saved in the prediction score store 230 in the multi-channel digital workplace 202. In some examples, the time-series of engagement scores includes historical engagement scores from analytics of historical data from EMD (e.g., historical engagement scores calculated from static X-Data), and current and future scores provided from the ML model. In some examples, an interactive chart is rendered and bound with engagement scores by a metadata-driven UI, which renders the interactive chart across multiple channels.”, Wang [0053] “FIGS. 3A and 3B depict example UIs 300, 302, respectively, in accordance with implementations of the present disclosure. The example UI 300 includes a custom metrics UI that can be used to set values for respective metrics and respective groups of agents. In the depicted example, the metrics are set for a customer support team. The example UI 302 includes an interactive chart UI, which displays time-series data of engagement scores. In the depicted examples, the UI 302 includes time-series data of engagement scores for multiple groups including sales, customer support, and R&D. In accordance with implementations of the present disclosure, a first portion of each of the time-series data reflects historical engagement scores (e.g., calculated from X-Data) and a second portion of each of the time-series data reflects current and/or predicted engagement scores provided from the ML model. For example, the ML model can provide a current engagement score for a current date and provided future engagement scores for one or more future dates.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to store time-series data representing calculated happiness or engagement values and verify changes in those values over time because Wang teaches generating, storing, and displaying time-series engagement scores including historical, current, and future predicted engagement values derived from questionnaire and wellness data. Specifically, Wang teaches storing time-series engagement scores in a prediction score store and generating interactive charts reflecting historical engagement scores and current and future predicted engagement scores over time. A person of ordinary skill in the art would have recognized that storing such time series engagement data inherently enables analysis and verification of changes in a user’s engagement, wellness, or happiness condition over time and would have found it obvious to apply the same known time series storage and trend-analysis techniques to a happiness calculation system in order to monitor changes in calculated happiness values, identify trends or deterioration and improvement in user wellbeing, and improve predictive wellness analysis.
Regarding claim 9, Wang, Ro, and Granger teach the invention in claim 4, as discussed above, and further teach wherein the processing circuitry determines an environmental goal value of a target structure based on happiness calculated, and sets an equipment parameter that controls equipment of the target structure based on the environmental goal value determined (Granger [00177] “In this example, the user receives both a signal indicative of the indicator and a signal regarding potential interventions. In some embodiments, the user also may receive from the system a recommendation for which of the potential interventions to pursue. More particularly, the system may provide a ranking of the plurality of potential interventions. The ranking may be based, at least in part, on one or more of disability adjusted life years, years lived with disability, days of comfort lost, amenity satisfaction, and/or workplace amenity satisfaction for a person associated with the built environment, among other factors. In some configurations, the ranking is based on at least two of these aspects. Further, these aspects, such as, for example, disability adjusted life years, years lived with disability, days of comfort lost, and amenity satisfaction may be given based on their association with the potential interventions. In this manner, the system may rank potential interventions, in part, upon a relevance to a person associated with the built environment, effectiveness of at least two of the plurality of potential interventions, cost of materials associated with the at least two of the plurality of interventions, design changes needed to a built environment, efficiency of at least two of the plurality of potential interventions, cost of at least two of the plurality of potential interventions, feasibility of at least two of the plurality of potential interventions, implementability of at least two of the plurality of potential interventions within a given time period, physical comfort of at least one person associated with the built environment, work satisfaction of at least one person associated with the built environment, at least one environmental condition in the built environment, and/or number of people that would be impacted by at least two of the plurality of potential interventions.”, Granger [0042] “In some embodiments, a method of operation of an intervention assessment system (which includes at least one processor, at least one non-transitory processor-readable medium communicatively coupled to the at least one processor and which stores at least one of instructions or data executable by the at least one processor) may include determining or identifying at least one problem associated with a built environment, determining or identifying at least one indicator associated with the at least one problem, and determining or identifying at least one potential intervention based on the at least one indicator, wherein the at least one potential intervention can reduce the prevalence of the at least one problem in the built environment.”, Granger [0066] “In some embodiments, a potential intervention for a built environment may include or be related to one or more of the following: changing indoor environmental quality (IEQ) in at least part of the built environment; changing a comfort feature in at least part of the built environment; raising, reducing, or changing the air temperature in at least part of the built environment; changing air quality in at least part of the built environment; reducing humidity in at least part of the built environment; increasing availability of biophilia in at least part of the built environment; increasing quality of biophilia in at least part of the built environment; changing air purification capability in at least part of the built environment; improving water quality in at least part of the built environment; changing water filter capability in at least part of the built environment; changing water quality control capability in at least part of the built environment; changing air quality control capability in at least part of the built environment; changing air temperature control capability in at least part of the built environment; changing air filter capability in at least part of the built environment; changing food availability in at least part of the built environment.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to determine an environmental goal value for a target structure based on calculated happiness, wellness, or satisfaction information and to set equipment control parameters accordingly because Granger teaches evaluating occupant wellness, comfort, and workplace satisfaction within a built environment and determining potential environmental interventions based on those evaluated indicators. Specifically, Granger teaches ranking and selecting interventions based on factors including physical comfort, work satisfaction, and environmental conditions in the built environment, and further teaches modifying environmental control characteristics such as air temperature, humidity, air quality, air purification capability, and other indoor environmental quality parameters within the built environment. A person of ordinary skill in the art would have recognized that environmental-control equipment parameters, such as HVAC and environmental quality settings, could be adjusted in response to calculated occupant wellness or satisfaction metrics in order to improve comfort, wellbeing, and workplace satisfaction of occupants. Accordingly, it would have been obvious to apply Granger’s known environmental intervention and control techniques to a happiness calculation system so that calculated happiness or wellness values are used to determine desired environmental conditions and automatically adjust equipment parameters to achieve those conditions.
Regarding claim 10, Wang, Ro, and Granger teach the invention in claim 9, as discussed above, and further teach a target equipment that is equipment of the target structure and is equipment that is controlled according to an equipment parameter set (Granger [0066] “In some embodiments, a potential intervention for a built environment may include or be related to one or more of the following: changing indoor environmental quality (IEQ) in at least part of the built environment; changing a comfort feature in at least part of the built environment; raising, reducing, or changing the air temperature in at least part of the built environment; changing air quality in at least part of the built environment; reducing humidity in at least part of the built environment; increasing availability of biophilia in at least part of the built environment; increasing quality of biophilia in at least part of the built environment; changing air purification capability in at least part of the built environment; improving water quality in at least part of the built environment; changing water filter capability in at least part of the built environment; changing water quality control capability in at least part of the built environment; changing air quality control capability in at least part of the built environment; changing air temperature control capability in at least part of the built environment; changing air filter capability in at least part of the built environment; changing food availability in at least part of the built environment.”, (Granger [0042] “In some embodiments, a method of operation of an intervention assessment system (which includes at least one processor, at least one non-transitory processor-readable medium communicatively coupled to the at least one processor and which stores at least one of instructions or data executable by the at least one processor) may include determining or identifying at least one problem associated with a built environment, determining or identifying at least one indicator associated with the at least one problem, and determining or identifying at least one potential intervention based on the at least one indicator, wherein the at least one potential intervention can reduce the prevalence of the at least one problem in the built environment.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to provide a system including both a happiness or wellness calculation apparatus and target environmental control equipment that is controlled according to determined environmental parameters because Granger teaches a processor based intervention assessment system that identifies problems associated with a built environment, determines indicators associated with the problems, and determines environmental interventions to reduce those problems. Granger further teaches that such interventions include changing indoor environmental quality, air temperature, humidity, air quality, air purification capability, air temperature control capability, and air quality control capability within the built environment. A person of ordinary skill in the art would have understood that implementing such environmental interventions requires controllable environmental equipment, such as HVAC, ventilation, filtration, or environmental control systems, operated according to determined control parameters. Accordingly, it would have been obvious to integrate environmental control equipment with a happiness or wellness calculation system so that calculated occupant satisfaction, comfort, or wellness values are used to control target equipment within a structure in order to improve occupant wellbeing and environmental conditions.
Claims 11 and 13 are analogous to claim 1, thus claims 11 and 13 are similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Claims 12 and 14 are analogous to claim 4, thus claims 12 and 14 are similarly analyzed and rejected in a manner consistent with the rejection of claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. (CN Publication CN110110288 A) teaches a system and method for evaluating enterprise employee happiness by combining subjective employee assessments with objective metrics using a hierarchical evaluation model to qualitatively calculate employee happiness indexes and support enterprise policy optimization.
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/K.R.L./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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