DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 07/15/2025.
Claims 7-9 have been cancelled.
Claims 1-6 are currently pending and have been examined.
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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Beltran (US 2023/0099519 A1) in view of Bulut (US 2020/0394334 A1).
Claim 1:
Beltran discloses A chatbot for promoting exercise habit formation of a patient with chronic diseases (See P0343 recommending exercise, P0088-P0090 a wearable device (e.g., Fitbit®, Jawbone®), detecting heart rate variability (HRV), diabetes, depression, congestive heart failure and P0330 medical conditions as chronic diseases.), comprising:
a processor (See Fig. 1, P0032, P0082 Processor 202. Also, see Fig. 32, P0368.); and
a memory having program instructions stored thereon (See memory in P0031-P0032, P0368.)
wherein when the processor executes the program instructions stored on the memory, the processor is configured to performing following operations by using following modules (See P0368 computer readable medium is operable to provide volatile or non-volatile storage for one or more sets of instructions data structures, program modules and applications.):
a user information and health record module configured to store a patient's personal information, a health status and medical history by using a database and connected to a personalized modeling and recommendation system module (See user profile, stored historical data 254, resources recommended and accessed by a user account in [P0083-P0084] display graphs showing heart rate, blood pressure, perspiration, sleep data, step count, anxiety, mood, and/or any other measurable biometric or physical attribute over time, and P0110 access to health records as a database, user's log in credentials. Also, see AI/ML via analytics engine used when providing mindfulness exercises to assist in stress reduction in P0130, conversation models in P0135-P0155 prompt chatbot, recommendations and group support based on change dynamic feedback of the user.);
the personalized modeling and recommendation system module configured to establish a personalized exercise plan recommendation system (Exemplary wearable devices such as Fitbit® and Jawbone® in P0088-P0090 would allow the user to establish recommended, personalized exercise plans.) by machine learning algorithms based on user information and a health record and connected to the user information and health record module and a neuro linguistic programming (NLP) module (See [P0343] the platform provides a recommendation to the user such as, but not limited to, an exercise and/or a resource aimed to lower the stress associated with the user account before that timeslot to aid in managing the stress experienced at during the specific timeslot. See user profile, stored historical data 254, resources recommended and accessed by a user account mentioned in [P0083-P0084] and in [P0129-P0131] conversations are provided to the system as audio data and/or text data, further operable to be referred to as chat data, that are operable to be processed and analyzed by natural language processing (NLP).);
the NLP module configured to process a natural language input of a user and provide a dialogue and connected to the personalized modeling and recommendation system module, the user information and health record module, and a real-time feedback system module (Taught as conversations with chatbot avatar with offered resolutions shown in Fig. 10-11, Fig. 29, P0126, P0350-P0351.), wherein the NLP module is implemented by using speech recognition technology, text analysis and understanding, emotional analysis, entity recognition, dialogue management, intention recognition and multilingual support (Besides detecting languages including negative connotations in P0101, P0104, authenticating facial, voice and ear recognition in P0111, conversational AI, sentiment analysis in P0331-P0332, see [P0126] conversation models include, but are not limited to, speeches that are advantageous to improving the situation and/or dialogues suitable for aiding the user to settle conflict regarding stressful situations.);
the real-time feedback system module configured to provide a real-time exercise feedback, comprising exercise data and suggestions and connected to the personalized modeling and recommendation system module, an exercise data acquisition and analysis module, and the NLP module (See P0338-P0339 where the user’s stress, noise stress event and exercising are detected based on biometric data. Also, see wearable device (e.g., Fitbit®, Jawbone®) in P0089-P0090 as an exercise data acquisition, exemplary real-time predicting and developing recommendations for the user in stressful situations as personalized modeling in [P0077] to receive phrases and questions such as “How do I handle an abusive boss?”, “My significant other is cheating on me”, etc. and based on ML, natural language process (NLP), and/or sentiment analysis, determine how the phrases and questions align with resources operable to be presented by the platform.);
the exercise data acquisition and analysis module configured to obtain the patient's exercise data from an exercise tracking device or application for analysis and interpretation and connected to the real-time feedback system module and the personalized modeling and recommendation system module (See Fig. 1 collected from data sources 300 include the wearable device (e.g., Fitbit®, Jawbone®) (P0089-P0090) as the exercise data acquisition and exercise tracking device, detecting heart rate variability (HRV), diabetes, depression, congestive heart failure and P0330 medical conditions as chronic diseases (P0088-P0090, P0099) are analyzed (P0079-P00800) with AI/ML via analytics engine used when providing mindfulness exercises to assist in stress reduction in P0130, conversation models in P0135-P0155 prompt chatbot, recommendations and group support based on change dynamic feedback of the user.);
a multi-channel integration module configured to enable the chatbot to run on different platforms and connected to the personalized modeling and recommendation system module, the user information and health record module, and the NLP module (Besides server, cloud-based, video chat and voice chat platforms mentioned in in P0076, see multiple channels provide high resolution in P0091 and in [P0129-P0131] conversations are provided to the system as audio data and/or text data, further operable to be referred to as chat data, that are operable to be processed and analyzed by natural language processing (NLP).);
wherein the multi-channel integration module is configured to:
design an application programming interface (API), so that a function of the chatbot is invoked by means of the interface (See audio data/text data that is interpreted by the chatbot in P0117-P0118 from accessed data collected by the wearable device through an API.);
use a responsive design principle, so that an interface of the chatbot adapts to screen sizes and resolution of different terminals (Taught as Hybrid App in [P0105] using a web view control to present the HTML and JAVASCRIPT files in a full-screen format, rendering engine, displayed in a full-screen web view control. Also, see the multiple channels provide high resolution in P0091.);
develop a mobile application platform and use hardware functions of a mobile device (See P0076 application on a mobile device.);
create a Web-based user interface, so that the user accesses the chatbot via a browser (See online services such as external applications, web sites and databases in P0099, a user account through a chatbot in P0117, P0037.);
use an authentication and authorization mechanism of the API to ensure secure and reliable data transmission on different platforms (See authentication with credentials in P0110 and exchanging security information in P0114.);
achieve adaptive design, so that the chatbot automatically adjusts a layout and functions on different platforms based on a user device and environment (See adaptive platform serves as automatically adjusted layout and functions on different platforms in [P0137] conversation models are operable to be presented to a user account through a chatbot. Advantageously, this allows for the conversation model to be dynamic and change based on input from the user. As a non-limiting example, the platform receives input from a user that is counterproductive to conflict resolution. The platform is operable to adapt its conversation model based on the input received.); and
ensure that the multi-channel integration module is connected to the user information and health record module and the personalized modeling and recommendation system module (See [P0347] See multiple channels include electroencephalogram (EEG) and brain state channels in P0091 and P0110 access to health records. Also, see exemplary real-time predicting and developing recommendations for the user in stressful situations as personalized modeling in P0077.);
a social interaction module configured to provide a social interaction function and connected to the personalized modeling and recommendation system module and the NLP module (See linked social media accounts and communities in P0120-P0121 such as FACEBOOK, TWITTER, and INSTAGRAM, etc, third-party sources and accounts operable linked to the dashboard and stress management platforms in P0117.);
wherein the social interaction module is configured to:
provide a user registration function to allow the patient to create a personal account and fill in personal data (Besides the user account in P0074 and user profile in P0083, see registration in P0107.);
achieve a user search and matching function, so that the patient finds and connects to other users with similar interest or the same health goal (See members of FACEBOOK, TWITTER, INSTAGRAM and NEXTDOOR in P0120-P0121 to share health goals with.);
provide a real-time chat function, so that patients interact directly (See SNAPCHAT in P0120-P0121 for real-time chatting.);
allow the patient to share exercise achievements and progress (See P0135, P0138-P0139 working in a team with conversations and receiving coaching.);
allow the user to create an exercise group for a wider range of social interaction (See P0156-P0170 hearing team members prescriptive, continuing with and wrapping up conversations.);
allow the user to know group activities and new chat information in time by using a push notification function (See [P0120-P0121] Using the internal social media platform, posts from the community are operable to viewed by other members of the community.);
provide a channel for the user to provide a feedback and suggestions to continuously optimize functions of the social interaction module (See coaching in sports and ways to optimize functions of social interactions [P0356] The present invention is operable to implement conversation models and provide resources recited herein such as blogs, Q&A, simulations, podcasts, etc. in coaching embodiments. The present invention is also operable to use the same data sources used for counseling embodiments for coaching embodiments, including body sensors, calendar applications, and location sensors. A connection with a coach is operable to be provided directly through the platform (i.e. through video chat, voice chat, etc.) and/or by providing contact information for a recommended coach.); and
ensure, by means of the API, that the social interaction module is connected to the personalized modeling and recommendation system module (Taught as using the artificial intelligence engine or the machine learning engine in P0134-P0135 as conversational models and recommending counseling.);
a security and privacy module configured to ensure that the patient's health data is securely stored and processed, and connected to the user information and health record module (Taught as passwords, biometric recognition when authenticating the user in P0111.);
wherein the security and privacy module is configured (See second authentication for security and privacy in P0110.) to:
encrypt the patient's health data to ensure security during storage and transmission (See encrypted token, session ID for authenticating in P0110-P0111.);
implement an access control mechanism to ensure that only authorized personnel access the patient's health data (See [P0110] two-factor authentication (2FA) functionality is enabled via receipt of input through the GUI. This includes applications that provide access to sensitive information (e.g., credit card numbers, health records).);
implement user permission management in the chatbot to ensure that each user only accesses data and functions required by the user's job responsibilities (Besides different credentialling platforms of single sign-on (SSO) functionality such as ticket-granting (P0114, P0118), see exemplary conversation model with chatbot in P0174-P0186 Paraphrase—check your perception. Your job is to help the individual discover what this conversation wants and needs to be about.).
Although Beltran discloses a chatbot for promoting exercise habit formation of a patient with chronic diseases as mentioned above, Hoar does not exility teach security features such as anonymizing the health data and implementing a security audit and monitoring mechanism and vulnerability management mechanism. Bulut teaches:
anonymize the health data to reduce an identification risk of the individual patient (See P0009, P0020 anonymizing to protect patient privacy.);
implement a security audit and monitoring mechanism to track a security event and an access record in the chatbot (See where audits are formal, systematic or independent examination and P0061-P0062, when conveying data with speech-to-speech interface, text-based interface, and/or chatbot.);
regularly update security of the chatbot, fix vulnerabilities in time, implement a vulnerability management mechanism, and regularly perform security assessment and penetration testing (See P0007, identifying when data may be vulnerable to breaches and hacking.); and
ensure, by means of the API, that the security and privacy module is connected to the user information and health record module (See privacy sharing system in P0020-P0021, in healthcare record sharing (P0005).).
Therefore, it would have been obvious to one of ordinary skill in the art of medical record sharing before the effective filing date of the claimed invention to modify the system of Beltran to include security features such as anonymizing the health data and implementing a security audit and monitoring mechanism and vulnerability management mechanism as taught by Bulut when there is a risk of compromising patient identity by taking actions on the part of the requester or recipient of the request as mentioned in Bulut’s paragraph 11.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Beltran (US 2023/0099519 A1) in view of Bulut (US 2020/0394334 A1) further in view of Hoar (US 2023/0008055 A1).
Regarding claim 2, although Beltran and Bulut teach the chatbot for promoting exercise habit formation of a patient with chronic diseases according to claim 1 mentioned above, Beltran and Bulut do not explicitly teach encrypting patient’s personal information, a user authentication mechanism to ensure that only legally authorized personnel access and modify the patient's information and viewing updated database when the patient updates the personal information or has a new medical record. Hoar teaches wherein the user information and health record module is configured to:
use the database to store the patient's personal information and health record, comprising name, age, gender, physical condition, past medical history, and medication (See P0108, where profile creation construes comprising name, age, gender, the patient's type of diabetes, treatment type and exercise profile in P0122 construe physical condition, past medical history, and medication.);
implement data encryption for storage of personal health information; introduce a user authentication mechanism to ensure that only legally authorized personnel access and modify the patient's information (See P0104, transmitting encrypted data regarding diabetes and disease management to a cloud server and P0112, where user’s physician construe legally authorized personnel.);
provide the (API) so that other modules access the patient's information (See [P0109] communication protocols application programming interface (API) to enable transmission of data between users and systems. Some examples are a proprietary cloud or “closed API” that allows users to create accounts and gain direct access to data and functionality.);
update the database when the patient updates the personal information or has a new medical record (See P0069 database stores integrated diabetes management data and upload health data.); and
provide a user interface to enable the patient to view and edit the personal information (See Fig. 7, P0115] the predictive analysis and machine learning modules 136 and 138 and user data such as, but not limited to, any of the following: dates, times and amounts or levels of BG readings and delivered medication, information related to user's level of activity, and Fig. 8, where the patient can conveniently rate their day.).
Therefore, it would have been obvious to one of ordinary skill in the art of disease management before the effective filing date of the claimed invention to modify the system of Beltran to include encrypting patient’s personal information, a user authentication mechanism to ensure that only legally authorized personnel access and modify the patient's information and viewing updated database when the patient updates the personal information or has a new medical record as taught by Hoar to securely transmit patient’s information among the patient's physician or other healthcare provider (HCP), family member or other caregiver, pharmacist, disease management company, medical supplier or payor as mentioned in Hoar’s paragraph 104.
Claims 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Beltran (US 2023/0099519 A1) in view of Bulut (US 2020/0394334 A1) further in view of Hoar (US 2023/0008055 A1) and Jayalath (US 2020/0151595 A1).
Regarding claim 3, although Beltran, Bulut and Hoar teach the chatbot for promoting exercise habit formation of a patient with chronic diseases according to claim 2 mentioned above, Beltran and Bulut do not explicitly teach using a data analysis technology based on the patient's personal information and health record data, to identify the patient's exercise preference, physical condition and response mode, developing a rule engine to make an exercise plan for the patient based on medical knowledge and health guidelines, ensuring that the personalized modeling and recommendation system module accesses and updates data of the user information and health record module and enabling the personalized exercise plan recommendation system to understand a natural language input of the patient, and adjust an output of the personalized model based on the patient's questions and needs. Hoar teaches wherein the personalized modeling and recommendation system module is configured to:
establish a personalized model by using a data analysis technology based on the patient's personal information and health record data, to identify the patient's exercise preference, physical condition and response mode (With the personalized modeling and recommendation system module as platforms for establishing personalized exercise plan with recommendations, see recommended walking in Fig. 7, exercise time ranges for personalized selection in Fig. 8 and exemplary customized experience for a diabetes patient in P0126 and chatbot inquiries/responses regarding blood sugar readings and exercising, various chatbot screens Fig. 4B, Fig. 14-Fig. 18, chatbot inquiries/responses as it relates to eating (P0115, P0121). See P0114, user profile serves as establishing the patient's personal information and Fig. 1, P0115-P0116, predictive analytic module 136 and machine learning module 138 construe using a data analysis technology based on the patient's personal information.);
develop a rule engine to make an exercise plan for the patient based on medical knowledge and health guidelines (Besides the interactive engine (Abstract, P0079), see P0126, where walking is suggested in a tailored disease management conversation or chatbot string based on employing artificial intelligence and machine learning and predictive analysis techniques.);
ensure, by means of the API, that the personalized modeling and recommendation system module accesses and updates data of the user information and health record module (See [P0109] communication protocols application programming interface (API) to enable transmission of data between users and systems. Some examples are a proprietary cloud or “closed API” that allows users to create accounts and gain direct access to data and functionality.);
enable, by the NLP module, the personalized exercise plan recommendation system to understand a natural language input of the patient (See exemplary neuro linguistic programming (NLP) module in Hoar’s [P0080-P0083] The natural language processor 132 can include a voice recognition module that can recognize the spoken question and parse the question such that it can be understood by the interactive engine 130.) and sharing patient EMRs in P0112 and exemplary neuro linguistic programming (NLP) module in P0080-P0083.), and adjust an output of the personalized model based on the patient's questions and needs (See [P0064] The website can include online content 22 related to diabetes, food choices, exercise, or other topics. As will be described below, the IDM system 100 can link users to the web server 20 to access the online content 22 in response to user questions.).
Therefore, it would have been obvious to one of ordinary skill in the art of disease management before the effective filing date of the claimed invention to modify the system of Beltran and Bulut to include using a data analysis technology based on the patient's personal information and health record data, to identify the patient's exercise preference, physical condition and response mode, developing a rule engine to make an exercise plan for the patient based on medical knowledge and health guidelines, ensuring that the personalized modeling and recommendation system module accesses and updates data of the user information and health record module and enabling the personalized exercise plan recommendation system to understand a natural language input of the patient, and adjust an output of the personalized model based on the patient's questions and needs as taught by Hoar for patients to take initiative in better managing diseases such as diabetes as mentioned in Hoar’s paragraphs 4-5.
Although Beltran, Bulut and Hoar teach an exercise plan as mentioned above, Beltran, Bulut and Hoar do not explicitly teach adjusting the exercise plan when the patient's health status changes or a new medical record is added and inducing a feedback loop to continuously adjust the model by monitoring the patient's exercise feedback and progress. Jayalath teaches:
adjust the exercise plan when the patient's health status changes or a new medical record is added (See athletic garment detecting biometric signals as status changes mentioned in P0020-P0022, where the couching entity serve as workout suggestions shown in Fig. 5A, Fig. 5B.);
introduce a feedback loop to continuously adjust the model by monitoring the patient's exercise feedback and progress (Besides adjusting equipment and device settings based on biofeedback (P0057, P0070), see exemplary feedback loop in [P0006] acquisition of sensor and other data, analysis of sensor and other data in order to understand user-specific deficiencies (e.g., in relation to performance goals and other goals), generation of relevant system outputs and interventions for improving user training and performance, and providing outputs to users and associated entities for improving user engagement during training. Such a feedback loop is configured to drive users to efficiently achieve goals.).
Therefore, it would have been obvious to one of ordinary skill in the art of physical training before the effective filing date of the claimed invention to modify the system of Beltran, Bulut and Hoar to include adjusting the exercise plan when the patient's health status changes or a new medical record is added and inducing a feedback loop to continuously adjust the model by monitoring the patient's exercise feedback and progress as taught by Jayalath to automatically drive users to efficiently achieve goals as mentioned in paragraph 6.
Regarding claim 4, although Beltran, Bulut, Hoar and Jayalath teach the chatbot for promoting exercise habit formation of a patient with chronic diseases according to claim 3 mentioned above, Beltran, Bulut and Jayalath do not explicitly teach converting oral input into text, using natural language processing technology for analyzing the text input to extract a user intention and key information, considering emotional analysis of the user's emotional state, using an entity recognition technology to extract a key entity introduce dialogue management for coherency and fluency, determining the user's specific intention through intention recognition and connecting to the personalized modeling and recommendation system module, the user information and health record module, and the real-time feedback system module. Hoar further teaches wherein the NLP module is configured to:
convert the user's oral input into text (See [P0045] The internet-enabled user device 10 can include a display. The display can include a screen and/or speaker. The display can display (e.g., visually or audibly) information received from the IDM system 100 to the user, such as answers to the user's questions and/or or prompts.);
use a natural language processing technology to analyze and understand the user's text input to extract a user intention and key information (See [P0081-P0082] The natural language processor 132 can include a voice recognition module that can recognize the spoken question and parse the question such that it can be understood by the interactive engine 130.);
consider integrating emotional analysis to understand the user's emotional state (See emotions of the patient in Fig. 8, as moods of user include sad, ok and happy.);
extract a key entity in the user input by using an entity recognition technology (Taught as parsing a spoken question in P0081-P0082.);
introduce dialogue management to keep a context of the dialogue and ensure coherence and fluency (See Fig. 15, Fig. 16, engaging conversation shown in screens.);
determine the user's specific intention through intention recognition (See P0083-P0084, where responses to questions, requested additional information and predicted analysis is used for predicting content that a user will enjoy or that will be beneficial to the user.); and
ensure, by means of the API, that the NLP module is connected to the personalized modeling and recommendation system module, the user information and health record module, and the real-time feedback system module (See exemplary neuro linguistic programming (NLP) module in [P0080-P0083] The natural language processor 132 can include a voice recognition module that can recognize the spoken question and parse the question such that it can be understood by the interactive engine 130.) and sharing patient EMRs in P0112 and exemplary neuro linguistic programming (NLP) module in P0080-P0083.).
Therefore, it would have been obvious to one of ordinary skill in the art of disease management before the effective filing date of the claimed invention to modify the system of Beltran, Bulut and Jayalath to include converting oral input into text, using natural language processing technology for analyzing the text input to extract a user intention and key information, considering emotional analysis of the user's emotional state, using an entity recognition technology to extract a key entity introduce dialogue management for coherency and fluency, determining the user's specific intention through intention recognition and connecting to the personalized modeling and recommendation system module, the user information and health record module, and the real-time feedback system module as taught by Hoar to provide information regarding healthy diabetic lifestyle via interactive prompts and/or responding to questions provided by users as mentioned in Hoar’s paragraph 7.
Regarding claim 5, although Beltran, Bulut, Hoar and Jayalath teach the chatbot for promoting exercise habit formation of a patient with chronic diseases according to claim 4 mentioned above, Beltran, Bulut and Jayalath do not explicitly teach connecting the exercise data acquisition and analysis module to receive the real-time exercise data for performing real-time processing and analysis, generating a personalized exercise feedback based on an analysis result, connecting to the NLP module, receiving the user's natural language input, and adjusting feedback content in time. Hoar further teaches wherein the real-time feedback system module is configured to:
connect to the exercise data acquisition and analysis module to receive the real-time exercise data (See P0060, P0012, where Fitbit, fitness trackers and devices operate in real time. See recommended walking in Fig. 7, exercise time ranges for personalized selection in Fig. 8 and exemplary customized experience for a diabetes patient in [P0126] a chatbot to tell a user “If you are planning on eating pizza tonight, but you indicated you were not feeling well today and yesterday (FIG. 11) and your BG levels have been high this week, then you may want to exercise or reduce your portion and have a lower carb item from the menu.”.);
perform real-time processing and analysis on the received exercise data (See P0060, P0012, where Fitbit, fitness trackers and devices operate in real time); and
use an algorithm comprising a real-time data stream processing technology, (See artificial intelligence (P0104) for processing of a large data volume as technology processing a large data volume. See conversations engaging the patient with the application and data tracking (P0122) serve as caching.);
generate a personalized exercise feedback based on an analysis result (See P0123, where self-reported tracking data serves as analysis reporting.);
connect to the NLP module, receive the user's natural language input, and adjust feedback content in time (See P0093-P0094, P0099 interactive questions using natural language based on previously stores user information.); and
ensure, by means of the API, that the real-time feedback system module is connected to the personalized modeling and recommendation system module (See [P0109] communication protocols application programming interface (API) to enable transmission of data between users and systems. Some examples are a proprietary cloud or “closed API” that allows users to create accounts and gain direct access to data and functionality.).
Therefore, it would have been obvious to one of ordinary skill in the art of disease management before the effective filing date of the claimed invention to modify the system of Beltran, Bulut and Jayalath to include connecting the exercise data acquisition and analysis module to receive the real-time exercise data for performing real-time processing and analysis, generating a personalized exercise feedback based on an analysis result, connecting to the NLP module, receiving the user's natural language input, and adjusting feedback content in time as taught by Hoar to provide information regarding healthy diabetic lifestyle via interactive prompts and/or responding to questions provided by users as mentioned in Hoar’s paragraph 7.
Although Hoar discloses a chatbot for promoting exercise habit formation of a patient with chronic diseases as mentioned above, Beltran, Bulut and Hoar do not exility teach ensuring that the real-time performance of feedback information, enabling the patient to know the patient's own exercise status and obtain suggestions in time, and achieve personalized adjustment of a real-time feedback with the personalized modeling and recommendation system module. Jayalath teaches:
ensure real-time performance of feedback information, to enable the patient to know the patient's own exercise status and obtain suggestions in time, and achieve personalized adjustment of a real-time feedback with the personalized modeling and recommendation system module (See training plan with exercise status and obtained suggestions in exemplary, “Personalized plan to target glutes, which are lacking” all shown in Fig. 5B, P0090, where specialized feedback serve as real-time feedback with the personalized modeling and recommendation system module.).
Therefore, it would have been obvious to one of ordinary skill in the art of physical training before the effective filing date of the claimed invention to modify the system of Hoar and Bulut to include ensuring that the real-time performance of feedback information, enabling the patient to know the patient's own exercise status and obtain suggestions in time, and achieve personalized adjustment of a real-time feedback with the personalized modeling and recommendation system module as taught by Jayalath to be able to increase the intensity of a workout to better challenge the user as mentioned in paragraph 68.
Regarding claim 6, although Beltran, Bulut, Hoar and Jayalath teach the chatbot for promoting exercise habit formation of a patient with chronic diseases according to claim 5 mentioned above, Beltran discloses wherein the exercise data acquisition and analysis module is configured to: access the exercise tracking device or application; communicate with the exercise tracking device or application by means of an API to obtain the real-time exercise data mentioned above, Beltran, Bulut and Jayalath do not teach cleaning and preprocessing collected original data to deal with an abnormal value and a missing value, using an efficient data acquisition algorithm to ensure real-time performance of the data, using a data analysis algorithm to analyze the exercise data, using a caching and streaming technology to achieve real-time processing of a large data volume, using a machine learning technology to analyze the exercise data, comprising exercise pattern recognition, exercise habit analysis, and exercise effect evaluation, to obtain deeper exercise information, generating a readable analysis report based on an analysis result, comprising the patient's exercise trend and health status evaluation and ensuring by means of the-an API, that the exercise data acquisition and analysis module is connected to the real-time feedback system module and the personalized modeling and recommendation system module. Hoar further teaches clean and preprocess collected original data to deal with an abnormal value and a missing value (See Fig. 7, where chatbot asks the user if they need help by replying “Not now” construes collecting original data to deal with an abnormal value and a missing value.);
use an efficient data acquisition algorithm to ensure real-time performance of the data (Besides seeing Fig. 19, P0126, where artificial intelligence and machine learning and predictive analysis techniques are used for chatbot interactions, see P0070, where the machine learning serve as algorithm to ensure real-time performance of the data.);
use data analysis algorithm to analyze of the exercise data (The machine learning (P0070) ensures a level of accuracy. Also, see P0063.);
use a caching and streaming technology to achieve real-time processing of a large data volume (See artificial intelligence (P0104) for processing of a large data volume as technology processing a large data volume. See conversations engaging the patient with the application and data tracking (P0122) serve as caching.;
use a machine learning technology to analyze the exercise data, comprising exercise pattern recognition, exercise habit analysis, and exercise effect evaluation, to obtain deeper exercise information (See P0060, P0012, where Fitbit, fitness trackers and devices operate to recognize exercise patterns.);
generate a readable analysis report based on an analysis result, comprising the patient's exercise trend and health status evaluation (See P0123, where self-reported tracking data serves as analysis reporting.); and
ensure, by means of the API, that the exercise data acquisition and analysis module is connected to the real-time feedback system module and the personalized modeling and recommendation system module (See [P0109] communication protocols application programming interface (API) to enable transmission of data between users and systems. Some examples are a proprietary cloud or “closed API” that allows users to create accounts and gain direct access to data and functionality.).
Therefore, it would have been obvious to one of ordinary skill in the art of disease management before the effective filing date of the claimed invention to modify the system of Beltran, Bulut and Jayalath to include cleaning and preprocessing collected original data to deal with an abnormal value and a missing value, using an efficient data acquisition algorithm to ensure real-time performance of the data, using a data analysis algorithm to analyze the exercise data, using a caching and streaming technology to achieve real-time processing of a large data volume, using a machine learning technology to analyze the exercise data, comprising exercise pattern recognition, exercise habit analysis, and exercise effect evaluation, to obtain deeper exercise information, generating a readable analysis report based on an analysis result, comprising the patient's exercise trend and health status evaluation and ensuring by means of the-an API, that the exercise data acquisition and analysis module is connected to the real-time feedback system module and the personalized modeling and recommendation system module as taught by Hoar to provide information regarding healthy diabetic lifestyle via interactive prompts and/or responding to questions provided by users as mentioned in Hoar’s paragraph 7.
Response to Arguments
Applicant argues on the basis that the Beltran, Hoar, Jayalath and Bulut references do not teach “at least provisioning of channel for the user to provide feedback and suggestions to continuously optimize functions of the social interaction module”. With a channel as being a platform a chatbot to run on such as a mobile application and a web page, as described in paragraph 82 of Applicant’s specification, this does not preclude Beltran’s server, cloud-based, video chat and voice chat platforms mentioned in in P0076, include multiple channels provide high resolution (P0091) and see [P0129-P0131] conversations are provided to the system as audio data and/or text data, further operable to be referred to as chat data, that are operable to be processed and analyzed by natural language processing (NLP). Also, see online app and web sites in P0099.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TERESA S WILLIAMS whose telephone number is (571)270-5509. The examiner can normally be reached Mon-Fri, 8:30 am -6:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.S.W./Examiner, Art Unit 3687 10/17/2025
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687