DETAILED ACTION
This is a Non-Final Office Action in response to the application filed 06/27/2024.
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
Claims 1-20 are currently pending in the application and have been examined.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
With respect to claims 1-20, the independent claims (claims 1 and 12) are directed, in part, to a method and a system for wellness predictions. Step 1 – First pursuant to step 1 in the January 2019 Guidance, claims 1-11 are directed to a method comprising a series of steps which falls under the statutory category of a process and claims 12-20 are directed to a system which falls under the statutory category of a machine. However, these claim elements are considered to be abstract ideas because they are directed to a mental process which includes observations or evaluations.
As per Step 2A - Prong 1 of the subject matter eligibility analysis, the claims are directed, in part, to predicting employee wellness; collecting survey data from one or more individuals; passively collecting electronic data associated with the one or more individuals; extracting a subset of extracted information from the electronic data to create extracted alues; determining a relationship between the collected survey data and the extracted values and storing a model of the relationship in a database; passively collecting further electronic data associated with one or more employees in a work environment; extracting a subset of extracted information from the further electronic data to create further extracted values; based on the model of the determined relationship between the collected survey data and the extracted values, predicting responses to a hypothetical further survey directed to the one or more employees using the further extracted values to create predicted survey data; and predicting employee wellness of the one or more employees by applying predicted survey data to a framework for predicting employee wellness based on actual survey data. If a claim limitation, under its broadest reasonable interpretation covers an observation or evaluation, then it falls under the “mental process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
As per Step 2A - Prong 2 of the subject matter eligibility analysis, this judicial exception is not integrated into a practical application. In particular, independent claim 1 is directed to a method and recite additional elements: electronic data, survey data, database, model. Independent claim 12 is directed to a system and in addition to a system includes additional elements processor, devices, database, electronic data, survey data, model. The dependent claims recite the use of Machine Learning (i.e. claims 4/14) and Natural Language Processing (i.e. claims 10/19). These additional element in both steps are recited at a high-level of generality (i.e., as a generic device performing a generic computer function of receiving and storing data) such that these elements amount no more than mere instructions to apply the exception using a generic computer component. Examiner looks to Applicant’s specification in at least figures 1 and 4A and related text and [0007-0008]; [0041]; [0117] to understand that the invention may be implemented in a generic environment that “the system comprises a processor configured to collect information from one or more devices associated with one or more employees in a work environment; a database operatively connected to the processor,”; “In various embodiments, there may be included any one or more of the following features of the system: a display operative connected to the processor”; “in some embodiments, Natural Language Processing tools may be used, such as: IBM WatsonTM Tone Analyzer or AmazonTM Comprehend, which analyze the tone of messages with four scores, such as Positive, Negative, Mixed, and Neutral. In other embodiments, other scores for emotions could be detected, including: Anger, Fear, Joy, Sadness, Analytical, Confident, Tentative. Other attributes of the messages are extracted such as words count, characters count, question marks count.”; “Embodiment of the system and method use custom built processing routines for O365TM, G-SuiteTM, and SlackTM that extract key features from the email, chat, and calendar data that are required for the AI/ML routines. These routines are written in Python and are hosted in serverless AmazonTM Web Services (AWS) Lambda functions. The data connectors call the API endpoint for the Lambda functions and pass the communication data (email, chat, calendar). This processing step utilizes AWS Comprehend to perform Natural Language Processing (NLP) on the content of communication data, allowing the system to extract useful information without storing anything private. The Lambda function then returns the resulting feature set and values in a single JSON object which is stored on AWS Relational Database Service along with the censored source communication data.”
As per Step 2B of the subject matter eligibility analysis, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are mere instructions to apply the abstract idea on a computer. When considered individually, these claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements and the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that amount to significantly more than the abstract idea itself. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility. Regarding the use of Natural Language Processing, the Examiner notes that this activity is recognized as well-understood, routine, and conventional in the art, which does not amount to significantly more than the abstract idea itself. See, e.g., Morsa, US 2006/0085408 (paragraph 0144: well -known-to-the-arts natural language processing (NLP) (computational linguistics) or some other method as is well known to the arts may be used). See also, Szabo, US Pat. No. 5,966,126 (col. 6, lines 57-62 and col. 28, lines 16-19: e.g., definitions may be produced in known manner, such as by explicit definition, or through use of assistive technologies, such as natural language translators; user defines a search using prior known techniques, such as natural language searching). Next, when the “machine learning” is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning does not add significantly more to the claims.
The dependent claims further refine the abstract idea. These claims do not provide a meaningful linking to the judicial exception. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as by describing the nature and content of the data that is received/sent. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not significantly more than the abstract concepts at the core of the claimed invention.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub. No. 2020/0200416 (hereinafter; Granger) in view of US Pub. No. 2017/0091633 (hereinafter; Vemula).
Regarding claims 1/12, Granger discloses:
A method; a system for predicting employee wellness, the method/system comprising a processor for: collecting survey data from one or more individuals; (Granger [0053] discloses Well-being often is measured through surveys and a person's well-being may change over time. See also [0157].) passively collecting electronic data associated with the one or more individuals; Granger [0029] discloses the use of wearable sensors; See also [0068].) extracting a subset of extracted information from the electronic data to create extracted values; (Granger [0146] discloses The modifiable factor or problem categories may be further subdivided into lower level factors or problems, one or more of which may have a one-to-many relationship with causes of the factors or problems, and a known, expected, or predicted outcome value in one or more of the four major impact metrics or outcomes (e.g., burden addressed, burden avoided, amenity satisfaction, physical comfort).) determining a relationship between the collected survey data and the extracted values and storing a model of the relationship in a database; (Granger [0143] discloses an example data infrastructure for a system for assessing optional interventions within a high-level data-flow framework, demonstrating the relationship between major data sources, data repositories, data models and data outputs; Each data repository then can contribute its processed, structured or unstructured, anonymized data and meta-data into a master data-model environment.) passively collecting further electronic data associated with one or more employees in a work environment; (Granger [0029] discloses the use of wearable sensors; See also [0068]; [0106] discloses The survey is distributed to diverse populations of workers.) extracting a subset of extracted information from the further electronic data to create further extracted values; (Granger [0108] discloses The purpose of Workplace Amenity Satisfaction Weighting is to identify the amenities that contribute most to workplace satisfaction, under different budget scenarios, and derive the cost-effectiveness of each amenity's contribution to overall workplace satisfaction. By one approach, Workplace Amenity Satisfaction Weights are used to derive the output metric Amenity Satisfaction (%).) and predicting employee wellness of the one or more employees by applying predicted survey data to a framework for predicting employee wellness based on actual survey data. (Granger [0096-0098] disclose estimates derived from data models to identify new factors that impact health and well-being, establish protocols to measure health and well-being outcomes, and establish a systematic approach to evaluating efforts to improve those outcomes.)
Although Granger discloses systems and methods for predicting employee wellness using survey data, Granger does not specifically disclose predicting responses to a hypothetical survey. However, Vemula discloses the following limitations:
based on the model of the determined relationship between the collected survey data and the extracted values, predicting responses to a hypothetical further survey directed to the one or more employees using the further extracted values to create predicted survey data; (Vemula [0060] discloses the system learns from each survey conducted, creating better predictive fit models with each survey and better preparing the system to ultimately predicting new or hypothetical user responses based on the user characteristics of each new or hypothetical user.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for assessing and improving well-being of Granger with the decision engine of Vemula in order to perform automatic predictive decision making using predictive fit models derived from previous user responses and the user characteristics of each responding user (Vemula abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claims 2/13, Granger discloses:
The method of claim 1 further comprising; the system of claim 12 further comprising a display operative connected to the processor and configured to displaying the predicted employee wellness to a user on a dashboard. (Granger [0203] discloses a user interface with a display.)
Regarding claim 3, Granger discloses:
The method of claim 1 further comprising implementing a change to a workplace environment based on the predicted employee wellness of the one or more employees. (Granger [0033] discloses the intervention assessment engine can instruct the built environmental control system to adjust the lighting, temperature, or air quality based on the preferred intervention identified. As described below, the health outcomes database or the scientific literature database may include, for example, information on optimal performance ranges for occupants, circadian rhythm-based operational parameters, and health outcomes, among many other data sets.)
Regarding claims 4/14, Although Granger discloses systems and methods for predicting employee wellness using survey data, Granger does not specifically disclose predicting responses to a hypothetical survey. However, Vemula discloses the following limitations:
The method of claim 1; the system of claim 12 in which determining a relationship between the collected survey data and the extracted values further comprises using artificial intelligence and machine learning to predict employee responses to survey data. (Vemula [0005] discloses using a self-learning predictive fit model system.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for assessing and improving well-being of Granger with the decision engine of Vemula in order to perform automatic predictive decision making using predictive fit models derived from previous user responses and the user characteristics of each responding user (Vemula abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claims 5/15, Granger discloses:
The method of claim 1; the system of claim 12 in which the further electronic data associated with the one or more employees further comprises one or more of: a number of employees in an organization, the relationship between the employee and other employees in the organization, internal communications between employees at the organization, external communication between employees and third parties, calendar event data, and time zone information. (Granger [0203] discloses the user interface 510 include one or more output display devices, such as lights, visual indicators, display screens, etc. to convey or request information to or from a user, such as but not limited to communication information […])
Regarding claims 6/16, Although Granger discloses systems and methods for predicting employee wellness using survey data, Granger does not specifically disclose individual level data and organization level data. However, Vemula discloses the following limitations:
The method of claim 5; the system of claim 15 in which the further electronic data associated with the one or more employees comprises individual level data and organization level data. (See Vemula [0006].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for assessing and improving well-being of Granger with the decision engine of Vemula in order to perform automatic predictive decision making using predictive fit models derived from previous user responses and the user characteristics of each responding user (Vemula abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claims 7/17, Granger discloses:
The method of claim 5; the system of claim 15 in which the further electronic data further comprises communication data. (Granger [0203] discloses the user interface 510 include one or more output display devices, such as lights, visual indicators, display screens, etc. to convey or request information to or from a user, such as but not limited to communication information […])
Regarding claim 8, Granger discloses:
The method of claim 1 further comprising: collecting survey data from the one or more employees, and predicting employee wellness of the one or more employees based on collected survey data in addition to the predicted survey data. (Granger [0096] discloses an approach to improving human health and well-being through the built environment or separately from the built environment using data in combination with scientifically-based evidence to tailor intervention and other recommendations to diverse locations and populations worldwide. The output estimates derived from data models can be continuously refined and updated, as data capture and data quality grow over time.)
Regarding claim 9, Granger discloses:
The method of claim 1 further comprising providing one or more recommended action items based on predicted employee wellness of the one or more employees. (Granger [0033] discloses the intervention assessment engine can instruct the built environmental control system to adjust the lighting, temperature, or air quality based on the preferred intervention identified. As described below, the health outcomes database or the scientific literature database may include, for example, information on optimal performance ranges for occupants, circadian rhythm-based operational parameters, and health outcomes, among many other data sets.)
Regarding claims 10/19, Although Granger discloses systems and methods for predicting employee wellness using survey data, Granger does not specifically disclose predicting responses to a hypothetical survey. However, Vemula discloses the following limitations:
The method of claim 7; the system of claim 12 in which the passive collection of further electronic data further comprises performing natural language processing on the content of the collected information. (Vemula [0005] discloses using a self-learning predictive fit model system.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for assessing and improving well-being of Granger with the decision engine of Vemula in order to perform automatic predictive decision making using predictive fit models derived from previous user responses and the user characteristics of each responding user (Vemula abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claims 11/20, Although Granger discloses systems and methods for predicting employee wellness using survey data, Granger does not specifically disclose predicting responses to a hypothetical survey. However, Vemula discloses the following limitations:
The method of claim 8; the system of claim 8 further comprising verifying the predicted survey data in part by comparing the predicted survey data with the collected survey data. (Vemula [0064] discloses comparing predictive models.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for assessing and improving well-being of Granger with the decision engine of Vemula in order to perform automatic predictive decision making using predictive fit models derived from previous user responses and the user characteristics of each responding user (Vemula abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 18, Granger discloses:
The system of claim 12 in which the processor is further configured to: collect survey data from the one or more employees, and predict employee wellness of the one or more employees based on collected survey data in addition to the predicted survey data. (Granger [0061] discloses conducting a poll or survey related to at least one problem or potential problem (e.g., does a particular problem occur, how serious is a particular problem, what is the short-term or long-term impact of a particular problem), conducting a literature review associated with at least one problem or potential problem (e.g., the likelihood, impact or severity of a particular problem such as occurrence of a specific VOC or other material occurring in a particular type, design or location of a habitable environment).)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCIS Z SANTIAGO-MERCED whose telephone number is (571)270-5562. The examiner can normally be reached M-F 7am-4:30pm EST.
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/FRANCIS Z. SANTIAGO MERCED/Primary Examiner, Art Unit 3625