DETAILED ACTION
This action is in reference to the communication filed on 5 APRIL 2026.
Applicant Elects Group III, claims 16-20, and withdraws the remaining claims.
Claims 16-20 are present and have been examined.
Claim Rejections - 35 USC § 101
Claims 16-20 are Rejected
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 16 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 16-20 the independent claim(s) [claim 16] recite(s) a non-transitory computer readable medium, which is a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) [16-20], the independent claim(s) (claims 16) is/are directed, in part, to:
establishing communication with multiple data sources associated with physical performance systems
receiving data from the multiple data sources in various structured formats;
normalizing the received data into a central structured format;
storing the normalized data
retrieving the normalized data
analyzing the retrieved data to generate health and performance insights; and
presenting the generated insights to an end user
These claim elements are considered to be abstract ideas because they are directed to a mental process, in that the claims ensconce concepts performed in the human mind including observation, evaluation, judgment, and opinion functions. Communicating, and receiving data in formats to be converted, storing/retrieving data, and analyzing/presenting a conclusion are all examples of these functions. If a claim limitation, under its broadest reasonable interpretation, covers a concept performed in the human mind, then it/they falls/ fall into the “mental processes” category.
Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional element – claim 1 recites a “non-transitory computer readable medium storing instructions executed by a processor, a communication with a “global communications network,” storing/retrieving data from a “central data store,” and using a “user interface” to present the insights. The non-transitory CRM/processor, as are the communication network, and “central data store,” are recited at a high level of generality and as such amount to no more than adding the words “apply it” to the judicial exception, or mere instructions to implement the abstract idea on a computer, or merely uses the computer as a tool to perform the abstract idea (see MPEP 2106.05f), or generally links the use of the judicial exception to a particular technological field of use/computing environment (see MPEP 2106.05h). Examiner notes that the use of a display to display information is generally found to be analogous to adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g).
Examiner finds no improvement to the functioning of the computer or any other technology or technical field in the above identified elements as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e).
Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as: claim 1 recites a “non-transitory computer readable medium storing instructions executed by a processor, a communication with a “global communications network,” storing/retrieving data from a “central data store,” and using a “user interface” to present the insights. When considered individually, the above identified 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. Examiner looks to Applicant’s specification:
[0019] The dynamic inclusive software system 100 may be designed for collecting and processing health and physical performance data. In some cases, the software system 100 may include a server 102 connected to a network 104. The network 104 may enable communication between various components of the system. A wireless transmitter 106 may facilitate wireless connectivity for devices such as a smartphone 108 and a wearable device 110.
[0022] In the context of the software system 100, exercise machines like the leg curl machine 112 and deadlift machine 114 may be connected to the network 104 through a network switch 118. This configuration may allow the machines to transmit data to the workout server 120. The workout server 120 may then process and store the exercise-related data, potentially aggregating information from multiple machines and users. The workout server may be one of many workout serves so that data from multiple machines, over multiple networks, at multiple locations can be aggregated. The transmission of data may occur in real-time during a workout, or it may be batched and sent at regular intervals or at the end of a session. The specific method may depend on factors such as network availability, data volume, and system design preferences.
[0024] In some implementations, the software system 100 may utilize cloud storage 134 for data storage and sharing capabilities across the network 104. The network 104 may serve as the central communication infrastructure, connecting all components and enabling data flow between the various devices, servers, and systems. The software system 100 may include artificial intelligence (AI) and machine learning capabilities. These capabilities may be used for analyzing human performance data, identifying correlations, predicting outcomes, and offering personalized recommendations. In some cases, the AI and machine learning components may process data collected from various sources such as the exercise equipment, medical devices, and personal monitoring devices. The AI analysis can be at the local machine lever, the workout server or data analysis can be of the aggregated data from multiple machines and workout services.
[0026] In some implementations, the software system 100 may include communications with a smartphone application that enables users to monitor their personal health and performance data, as well as compare their metrics with others. The smartphone 108 may serve as a central interface for users to access and interact with their collected data. The smartphone application may connect to the server 102 through the network 104, allowing it to retrieve data from the central data store. Users may be able to view their personal health and performance metrics through customizable dashboards within the smartphone application.
[0033] In some cases, the software system 200 may provide customized user interfaces, displays, and reports for specific industries such as healthcare, therapy, and sports performance. These customized interfaces may be tailored to present the normalized data in formats that are most relevant and useful for practitioners in each field. For example, a healthcare professional using the access device 216 may see a different interface and set of reports compared to a sports performance coach accessing the same underlying data.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. As disclosed, any device capable of executing the processing/sending/receiving/displaying of data would be suitable for use in the claimed invention. Examiner notes very little discussion if any of the computing elements of the claim itself. 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. 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.
As per dependent claims 17-50:
Dependent claims 17, 18, do not recite any additional abstract idea(s) than those identified above. However, the claims do recite non-abstract elements such as an “exercise machine” and “vital sign sensors.” Examiner finds no improvement to the functioning of the technology or technical field(s) in the exercise machine and/or sensors as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e) – i.e. exercise machines/monitoring. As such no practical application is found. Turning to significantly more, Examiner makes reference to:
[0030] In some implementations, the software system 100 may integrate data from a wide variety of gym exercise equipment. This equipment may include, but is not limited to: cardiovascular machines such as treadmills, elliptical trainers, stationary bikes, rowing machines, and stair climbers; strength training equipment like power racks, smith machines, and cable machines; resistance machines targeting specific muscle groups, such as leg press machines, chest press machines, lat pulldown machines, and shoulder press machines; specialized machines such as hack squat machines, leg extension machines, and calf raise machines. These machines can be equipped with sensors and connectivity features that allow them to integrate with the software system 100.
[0023] Medical monitoring devices such as a blood pressure monitor 130 and an oximeter 132 may be connected to the healthcare system 122, enabling the collection of vital health data.
These passages make it clear that the claims rely on an application of the existing technology as currently generally understood, rather than anything beyond a general link to said technology. No improvement to the exercise machines and/or sensors is found nor even implied in these passages. The claims are not found to recite significantly more than the abstract idea(s) identified above.
Dependent claims 19, 20 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as the analyzing and/or personalized recommendations and how they are determined by the invention. 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 heavier than the abstract concepts at the core of the claimed invention.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 16-20 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Bissonnette et al (US 20220047921 A1, hereinafter Bissonette).
In reference to claim 16:
Bissonette teaches: A non-transitory computer-readable medium storing instructions that, when executed by a processor cause the processor to perform operations for measuring and analyzing health and physical performance data (at least [011, 079,), the operations comprising:
establishing communication with multiple data sources associated with physical performance systems via a global communications network (at least [fig 1 and related text] “ ] FIG. 1 illustrates a high-level component diagram of an illustrative system architecture 10 according to certain embodiments of this disclosure. In some embodiments, the system architecture 10 may include a computing device 12 communicatively coupled to an exercise machine 100. The computing device 12 may also be communicatively coupled with a computing device 15 and a cloud-based computing system 16. As used herein, a cloud-based computing system refers, without limitation, to any remote or distal computing system accessed over a network link. Each of the computing device 12, computing device 15, and/or the exercise machine 100 may include one or more processing devices, memory devices, and network interface devices. In some embodiments, the computing device 12 may be included as part of the structure of the exercise machine 100.The cloud-based computing system may include a data source 67 that stores the training data for the training engine 50 and/or the artificial intelligence engine 65 to use to train the one or more machine learning models 60. The data source may include exercises, physical activity goals, levels of attainment, body portions targeted by exercises, weights and/or parameters used to configure a prioritization of certain levels of attainment throughout an exercise schedule, comorbidity information, health-related information, audio segments, video segments, motivational quotations, and so forth. The data source 67 may include various tags and/or keys (e.g., primary, foreign, etc.) to associate items of the data with each other in the data source 67. The data source 67 may be a relational database, a pivot table, or any suitable type of data structure configured to store data used for any of the operations described herein.”);
receiving data from the multiple data sources in various structured formats (at least [fig 1 and related text, including 0262] “In some embodiments, the format of the data obtained by the API may be in a different format than the format the application 17 uses. In such an instance, the application 17 may transform the data's format into an acceptable format (e.g., extensible markup language (XML)) for the application 17.”);
normalizing the received data into a central structured format (at least [fig 1 and related text, including 0262] “In some embodiments, the format of the data obtained by the API may be in a different format than the format the application 17 uses. In such an instance, the application 17 may transform the data's format into an acceptable format (e.g., extensible markup language (XML)) for the application 17.”);
storing the normalized data in a central data store (at least [fig 1 and related text] “The servers 28 may store profiles for each of the users that use the exercise machine 100. The profiles may include information about the users such as one or more disease protocols, one or more exercise plans, a historical performance (e.g., loads applied to the left load cell and right load cell, total weight in pounds, etc.) for each type of exercise that can be performed using the exercise machine 100, health, age, race, credentials for logging into the application 17, and so forth.”)
retrieving the normalized data from the central data store (at least [fig 1 and related text] data is retrieved either from data source 67 or server 28);
analyzing the retrieved data to generate health and performance insights (at least [fig 1 and related text including 0135-0136] “ The one or more of machine learning models 60 may refer to model artifacts created, using training data that includes training inputs and corresponding target outputs (correct answers for respective training inputs), by the artificial intelligence engine 65 and/or the training engine 50. The training engine 50 may find patterns in the training data that map the training input to the target output (the answer to be predicted), and provide the machine learning models 60 that capture these patterns…The virtual coach may be driven and controlled by artificial intelligence (e.g., via one or more machine learning models 60). For example, the machine learning model 60 may be trained to implement the virtual coach. Further, the training data may include inputs pertaining to user feedback and/or progress of the user and outputs pertaining to a persona for the virtual coach to implement. The training data may include inputs of the progress of the user (e.g., completion of an exercise) and output various incentives, rewards, and/or certificates. The training data may include inputs of the progress of the user and/or the exercise plan and may output notifications pertaining to the progress and/or the exercise plan.”); and
presenting the generated insights to an end user through a user interface (at least [fig 1 and related text] “. The user interface 18 may present various screens to a user that enable the user to login, enter personal information (e.g., health information; a disease protocol prescribed by a physician, trainer, or caretaker; age; gender; activity level; bone density; weight; height; patient measurements; etc.), view an exercise plan, initiate an exercise in the exercise plan, view visual representations of left load measurements and right load measurements that are received from left load cells and right load cells during the exercise, view a weight in pounds that are pushed, lifted, or pulled during the exercise, view an indication when the user has almost reached a target threshold, view an indication when the user has exceeded the target thresholds, view an indication when the user has set a new personal maximum for a load measurement and/or pounds pushed, lifted, or pulled, view an indication when a load measurement exceeds a safety limit, view an indication to instruct the user to begin another exercise, view an indication that congratulates the user for completing all exercises in the exercise plan, and so forth, as described in more detail below. The computing device 12 may also include instructions stored on the one or more memory devices that, when executed by the one or more processing devices of the computing device 12, perform operations to control the exercise machine 100…In some embodiments, the training data may include various inputs (e.g., a physical activity goal, range of motion of users, user-reported pain level of users, user-reported difficulty levels of exercises, exercise information, levels of attainment, characteristics of users (e.g., age, weight, height, gender, procedures performed, condition of user, goals for outcomes of exercising, etc.), performance measurements, and the like) and mapped outputs. The mapped outputs may include an exercise plan composed on various exercise sessions each including various exercises, schedule of the exercise sessions, etc. In some embodiments, the training data may include other inputs (e.g., state of the exercise session, exercise, exercise machine 100; progress of the user; events; characteristics of the user; measurements received from sensors, etc.) and other mapped outputs. The other mapped outputs may include comorbidity information pertaining to the user. The other mapped outputs may further include multimedia (e.g., video/audio) clips or segments for a virtual coach to speak, graphic images, video, and the like to be presented on the user interface 18 of the computing device 12 before, during, or after the user performs the exercises. The virtual coach may be implemented in computer instructions as part of application 17 executing on the computing device 12. The virtual coach may be driven and controlled by artificial intelligence (e.g., via one or more machine learning models 60).”).
In reference to claim 17:
Bissonnette further teaches: wherein the physical performance system is an exercise machine (at least [fig 1 and related text including 0130-0131] “he exercise machine 100 may be an osteogenic, muscular strengthening, isometric exercise and/or rehabilitation assembly. Solid state, static, or isometric exercise and rehabilitation equipment (e.g., exercise machine 100) can be used to facilitate osteogenic exercises that are isometric in nature and/or to facilitate muscular strengthening exercises. Such exercise and rehabilitation equipment can include equipment in which there are no moving parts while the user is exercising. The exercise machine 100 may include various load cells 110 disposed at various portions of the exercise machine 100. For example, one or more left load cells 110 may be located at one or more left feet plates or platforms, and one or more right load cells may be located at one or more right feet plates or platforms. Also, one or more left load cells may be located at one or more left handles, and one or more right load cells may be located at one or more right handles. Each exercise in the exercise system may be associated with both a left and a right portion (e.g., handle or foot plate) of the exercise machine 100. For example, a leg-press-style exercise is associated with a left foot plate and a right foot plate.”).
In reference to claim 18:
Bissonnette further teaches: including a healthcare provider system in communications with the central data store wherein the healthcare provider system includes vital sign sensors (at least [0287] “. The processing device may select the at least one set of exercises based on the first association, the second association, and the third association in order to provide an exercise plan that targets the body portions associated with the levels of attainment for the physical activity goal. In some embodiments, the data source 67 may include exercises that are curated by one or more health professionals, such as a trainer, a medical doctor, a physical therapist, a surgeon, or the like. Further, the associations between the levels of attainment, exercises, body portions, and the like may be curated, filtered, reviewed, revised, and the like by the health professionals.”
In reference to claim 19:
Bissonette further teaches: wherein analyzing the retrieved data comprises applying machine learning algorithms to identify patterns and trends in the health and physical performance data (at least [fig 1 and related text including 0135-0136] “ The one or more of machine learning models 60 may refer to model artifacts created, using training data that includes training inputs and corresponding target outputs (correct answers for respective training inputs), by the artificial intelligence engine 65 and/or the training engine 50. The training engine 50 may find patterns in the training data that map the training input to the target output (the answer to be predicted), and provide the machine learning models 60 that capture these patterns…The virtual coach may be driven and controlled by artificial intelligence (e.g., via one or more machine learning models 60). For example, the machine learning model 60 may be trained to implement the virtual coach. Further, the training data may include inputs pertaining to user feedback and/or progress of the user and outputs pertaining to a persona for the virtual coach to implement. The training data may include inputs of the progress of the user (e.g., completion of an exercise) and output various incentives, rewards, and/or certificates. The training data may include inputs of the progress of the user and/or the exercise plan and may output notifications pertaining to the progress and/or the exercise plan.”).
In reference to claim 20:
Bissonette further teaches: wherein the operations further comprise generating personalized recommendations based on the identified patterns and trends (at least [0136] “The training data may include inputs of the progress of the user (e.g., completion of an exercise) and output various incentives, rewards, and/or certificates. The training data may include inputs of the progress of the user and/or the exercise plan and may output notifications pertaining to the progress and/or the exercise plan.” At [0170] “he user interface 18 may present one or more visual representations 1020 for a weekly goal including how many sessions should be performed in the week and progress of the sessions as they are being performed. The user interface 18 may present a monthly goal including how many sessions should be performed in the month and progress of the sessions as they are being performed. Additional information and/or indications (e.g., incentivizing messages, recommendations, warnings, congratulatory messages, etc.) may be presented on the user interface 18, as discussed further below.” At [0282] “The machine learning models 60 may identify patterns between the data pertaining to the user and other data pertaining to other users, and may determine that the other users, by following the recommended exercise plan, achieved the same physical activity goals in the length of time. In some embodiments, the processing device may transmit the amount of time it will take the user to achieve the physical activity goal to the computing device 12 for presentation. “).
Relevant Prior Art
US 20200177386, to Mahmood discloses a centralized formatting of medical data to make recommendations to a patient.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached on Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at 571-270-7537 The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687
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