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 .
Response to Amendments
The arguments have been sufficient to overcome to overcome the drawing objections. The amendments have been sufficient to overcome the drawing, claim objections and the 35 USC 112(b) rejections.
Claim objections
Claim 8 has been objected to as follows:
On line 9, “an AI unit” should be corrected to ---the AI unit---
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 1-2, 4, 6-9, and 12-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Eder (US 2015/0335950 A1).
Regarding claim 1, Eder discloses a force control method for fitness equipment (exercise apparatus shown in Fig. 1) comprising: an input step: manipulating the fitness equipment (Exercise apparatus shown in Fig. 1) with a control panel (Interfaces 140, Fig. 1; “the exercise apparatus 104 can also include or more communication interfaces 140 for communicating with fitness tracking computing system 102”, Paragraph 0035), the fitness equipment including an operation unit (Grips 162, Fig. 1; Applicant discloses the operation unit 20 as a device for the hands of a user for pulling in Fig. 4), a control unit (Processor 110, Fig. 1), a shaft (Shuttle 158), and a load unit (Resistance Assembly 152), the control panel electrically connected to the control unit (“The exercise apparatus 104 can also include one or more communications interfaces 140 for communicating with fitness tracking computing system 102”, Paragraph 0035 ln. 4-6; Processor 130 is connected to the Fitness Tracking Computing system as shown in Fig. 1 ), the operation unit connected to the load unit by a rope (Rope, Fig. 1) that is wound around the shaft (Rope is wound around Shuttle 158, Fig. 1), the control panel connected to an Al unit (Fitness Tracking Computing System 102, “The exercise apparatus 104 can also include one or more communications interfaces 140 for communicating with fitness tracking computing system 102”, Paragraph 0035 ln. 4-6; The fitness tracking computer system is capable of monitoring individuals’ interactions with the equipment and make recommendations based on these interactions making it capable of learning and improving the quality of the workout, these are characteristics of an AI unit, see Paragraph 0067) via a mobile device (“The fitness tracking computing system 102 can be provided using any suitable processor-based device or system, such as a personal computer, laptop, server, mainframe, mobile computer, other processor-based device, or a collection (e.g. network) of multiple computers, for example”, Paragraph 0029 ln. 4-9), the control panel and the mobile device being interconnected and interactively transmit information through the Al unit (“The exercise apparatus 104 can also include one or more communications interfaces 140 for communicating with fitness tracking computing system 102”, Paragraph 0035 ln. 4-6; The system is capable of being interfaced with the exercise apparatus having an interface in communication with a user while also the Fitness tracking computer system being connected to the exercise apparatus and connected to a computing device system manage by another user/administrator which can be a mobile device, See Paragraph 0057 and 0058, Fig. 9);
the AI unit links to a statistical module which collects and integrates operation information of the user in the past (“The fitness tracking computing system 702 can also track and store the exercise data received from each use of the exercise apparatus 704-704N. Such data can be aggregated, sorted, reported, or otherwise may be processed. In some embodiments, exercise data is tied to a particular type of exercise or therapy for tracking purposes.” Paragraph 0054 ln. 1-6; The self-learning module can monitor user’s exercises data such as resistance level, successfully completed number of sets, number of repetitions and configurations, based on this data the self-learning module can make recommendations, see Paragraph 0069; the aggregation, sorting, and processing data has been considered the statistical module, in the broadest reasonable interpretation, as statistics is merely “a branch of mathematics dealing with collecting, analysis, interpretation and presentation of masses of numerical data” according to Merriam-Webster. Therefore the collection via the sensors, the analysis is aggregation and sorting, and the interpretation and presentation is done by the fitness tracking computing system when submitting recommendations to the user; please see paragraphs 0067-0069 );
an operation step: the AI unit determining, reading, and accumulating a user operation data (“The exercise data can be collected, transmitted and stored in fitness tracking computing system”, Page 2 Paragraph 0018 ln. 33-34) and generating a recommendation information ( “The fitness tracking computing system is a system that can be comprise of a self-learning module capable of monitoring an individual interactions with one or more exercise apparatus and make suggestions to a user”, Paragraph 0067; Fitness tracking computing system can recommend level of resistance, based on user’s previous success metrics, that may be more appropriate for the user’s abilities, and can deliver this via the computing device, see Paragraph 0069) which is transmitted/linked to a processing unit (Processor 110), the processing unit calculating and generating a load data based on the recommendation information (“the self-learning module can monitor a user's exercises, resistance levels, and/or other parameters over time and based on the user's success metrics, automatically recommend various workouts, exercises and/or resistance amounts.” Paragraph 0069 ln. 1-6) and displaying the load data on the mobile device or on the control panel for user reference (“A self-learning module can provide the recommendations to users in any number of suitable formats or delivery techniques. For example, recommendations can be delivered to the user via the computing device 1010 (i.e., through a graphical user interface). Additionally, or alternatively, the recommendations can be delivered to a graphical user interface on the exercise apparatus.” Page 9 Paragraph 0069 ln. 3-11); a first load operation step: the user following or referring to the load data to adjust the load data from the mobile device and transmitting the adjusted load data to the control panel (“While the exercise apparatus 504 can receive the adjustment commands from the fitness tracking computing system 502, in some embodiments, local inputs received from a user can be used to initiate self-configuring. For example, a user may select a particular exercise or workout routine (i.e., set of exercises) from an interface associated with the exercise apparatus 504.” Paragraph 0051 ln. 1-7; “the visual display can be provided by another device viewable by a user, such as a smart phone, tablet computer, or a laptop, for example, that is in communication with the exercise apparatus 104 and/or the fitness tracking computing system 102.”, Paragraph 0038 ln. 5-8; an user connected to the computing device (which can be a mobile device) has access to a profile where a dashboard is provided within a user interface, where through interactions can receive or input data to the fitness tracking computing system, see Paragraph 0062 and Fig. 10A), or adjusting the load data from the control panel, the control panel setting a load weight of the load unit based on the adjusted load data from the mobile device (“While the exercise apparatus 504 can receive the adjustment commands from the fitness tracking computing system 502, in some embodiments, local inputs received from a user can be used to initiate self-configuring. For example, a user may select a particular exercise or workout routine (i.e., set of exercises) from an interface associated with the exercise apparatus 504.” Paragraph 0051 ln. 1-7; “the visual display can be provided by another device viewable by a user, such as a smart phone, tablet computer, or a laptop, for example, that is in communication with the exercise apparatus 104 and/or the fitness tracking computing system 102.”, Paragraph 0038 ln. 5-8), the user applying a force to the operation unit and operating the operation unit, a movement of the operation unit pulling the rope to pull the load weight of the load unit (“when the grips 162 are moved in the direction indicated by arrow 164, weights of the resistance assembly 152 are pulled in the directed indicated by arrow 166.”, Paragraph 0036 ln. 20-23, Fig. 1), and a second load operation step (S4): when the user releases the force from the operation unit, a sensing unit (Sensors 142, Fig. 1) connected to the control unit detects a magnitude of the force applied by the user to the operation unit and adjusts the load weight of the load unit accordingly (“the fitness tracking computing system 1002 can comprise a self-learning module 1014 to monitor individual user's interaction with one or more exercise apparatuses and, in an automated fashion, make suggestions to the user 1012 based on the individual's past interactions or otherwise automatically modify a workout routine or an exercise parameter.”, Paragraph 0067 ln. 14-18).
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Regarding claim 4, Eder discloses wherein the AI unit is connected to a database for storing operation information of the user in the past (“The fitness tracking computing system 102 can store and access data in a variety of databases 116.” Paragraph 0031 ln. 1-2).
Regarding claim 6, wherein the AI unit is connected to a judgment module which judges whether the load data entered by the user is feasible for operation based on an operating information of the user in the past (The fitness tracking computer system is capable of tracking successful metrics based on numbers of set completed and in the same manner it’s able to determine if the resistance level is not appropriated based on previous metrics and make recommendations based on resistance level or suggest exercises that will continue to challenge the user, Paragraph 0069).
Regarding claim 7, Eder wherein the AI unit is connected to an upload module (Private account 1170) which is wirelessly connected to a cloud device (Computing device 1200 can be cloud-based computing capability, Paragraph 0077 ln. 4-7), the mobile device (“The fitness tracking computing system 102 can be provided using any suitable processor-based device or system, such as a personal computer, laptop, server, mainframe, mobile computer, other processor-based device, or a collection (e.g. network) of multiple computers, for example”, Paragraph 0029 ln. 4-9), or the control panel, the AI unit sends the load data of the user in the past to the upload module for recording and storage on the mobile device, the cloud device, or the control panel (“The data presented in the graphical user interface 1030, 1040, 1050, 1060, 1070, along with other user data can be stored by fitness tracking computing systems.”, Paragraph 0070 ln. 1-3).
Regarding 8, Eder discloses a fitness equipment (Exercise apparatus shown in Fig. 1) comprising: a control panel (Interfaces 140, Fig. 1), an operation unit (Grips 162, Fig. 1; Applicant discloses the operation unit 20 as a device for the hands of a user for pulling in Fig. 4), a control unit (Processor 110, Fig. 1), a shaft (Shuttles 158), and a load unit (Resistance assembly 152, Fig. 1), the control panel electrically connected to the control unit (“The exercise apparatus 104 can also include one or more communications interfaces 140 for communicating with fitness tracking computing system 102”, Paragraph 0035 ln. 4-6; Processor 130 is connected to the Fitness Tracking Computing system as shown in Fig. 1 ), the load unit electrically connected to the control unit (Processor 110 is connected to the exercise apparatus through network 126, See fig. 1), the operation unit connected to the load unit via a rope (Rope, Fig. 1) that is wound around the shaft (Rope is wound around shuttle 158, Fig. 1), the operation unit pulling a load weight generated by the load unit by the rope (“when the grips 162 are moved in the direction indicated by arrow 164, weights of the resistance assembly 152 are pulled in the directed indicated by arrow 166.”, Paragraph 0036 ln. 20-23, Fig. 1);
the AI unit (Fitness tracking computing system 102) links to a statistical module which collects and integrates operation information of the user in the past (“The fitness tracking computing system 702 can also track and store the exercise data received from each use of the exercise apparatus 704-704N. Such data can be aggregated, sorted, reported, or otherwise may be processed. In some embodiments, exercise data is tied to a particular type of exercise or therapy for tracking purposes.” Paragraph 0054 ln. 1-6; The self-learning module can monitor user’s exercises data such as resistance level, successfully completed number of sets, number of repetitions and configurations, based on this data the self-learning module can make recommendations, see Paragraph 0069; the aggregation, sorting, and processing data has been considered the statistical module, in the broadest reasonable interpretation, as statistics is merely “a branch of mathematics dealing with collecting, analysis, interpretation and presentation of masses of numerical data” according to Merriam-Webster. Therefore the collection via the sensors, the analysis is aggregation and sorting, and the interpretation and presentation is done by the fitness tracking computing system when submitting recommendations to the user; please see paragraphs 0067-0069 );
an AI unit (Fitness tracking computing system 102, Fig. 1; The fitness tracking computer system is capable of monitoring individuals’ interactions with the equipment and make recommendations based on these interactions making it capable of learning and improving the quality of the workout, these are characteristics of an AI unit, see Paragraph 0067 ) and a mobile device (“The fitness tracking computing system 102 can be provided using any suitable processor-based device or system, such as a personal computer, laptop, server, mainframe, mobile computer, other processor-based device, or a collection (e.g. network) of multiple computers, for example”, Paragraph 0029 ln. 4-9) connected to the control panel (Fitness Tracking Computing System 102, “The exercise apparatus 104 can also include one or more communications interfaces 140 for communicating with fitness tracking computing system 102”, Paragraph 0035 ln. 4-6; The system is capable of being interfaced with the exercise apparatus having an interface in communication with a user while also the Fitness tracking computer system being connected to the exercise apparatus and connected to a computing device system manage by another user/administrator which can be a mobile device, See Paragraph 0057 and 0058, Fig. 9), the AI unit (Fitness tracking computing system 102) forming a connection and exchanges information between the control panel and the mobile device (“The exercise apparatus 104 can also include one or more communications interfaces 140 for communicating with fitness tracking computing system 102”, Paragraph 0035 ln. 4-6; The system is capable of being interfaced with the exercise apparatus having an interface in communication with a user while also the Fitness tracking computer system being connected to the exercise apparatus and connected to a computing device system manage by another user/administrator which can be a mobile device, See Paragraph 0057 and 0058, Fig. 9), the AI unit (Fitness tracking computing system 1002) connected to a database which records and stores user messages (“The fitness tracking computing system 102 can store and access data in a variety of databases 116.” Paragraph 0031 ln. 1-2), a sensing unit (Sensors 142) connected to the operation unit (Grips 162) and detecting changes of the load unit (“the fitness tracking computing system 1002 can comprise a self-learning module 1014 to monitor individual user's interaction with one or more exercise apparatuses and, in an automated fashion, make suggestions to the user 1012 based on the individual's past interactions or otherwise automatically modify a workout routine or an exercise parameter.”, Paragraph 0067 ln. 14-18), the AI unit (Fitness tracking computing system 102) integrating, calculating, and accumulating a usage information of the user to form a load data (“the self-learning module can monitor a user's exercises, resistance levels, and/or other parameters over time and based on the user's success metrics, automatically recommend various workouts, exercises and/or resistance amounts.” Paragraph 0069 ln. 1-6) and displaying the usage information on the mobile device or the control panel (“A self-learning module can provide the recommendations to users in any number of suitable formats or delivery techniques. For example, recommendations can be delivered to the user via the computing device 1010 (i.e., through a graphical user interface). Additionally, or alternatively, the recommendations can be delivered to a graphical user interface on the exercise apparatus.” Page 9 Paragraph 0069 ln. 3-11), the mobile device inputting the load data and simultaneously sending a setting information to the control panel (Interfaces 104), or the load data and the setting information being entered to the control panel (Interfaces 104), the control panel receiving the load data and sending the load data to the control unit (“While the exercise apparatus 504 can receive the adjustment commands from the fitness tracking computing system 502, in some embodiments, local inputs received from a user can be used to initiate self-configuring. For example, a user may select a particular exercise or workout routine (i.e., set of exercises) from an interface associated with the exercise apparatus 504.” Paragraph 0051 ln. 1-7; “the visual display can be provided by another device viewable by a user, such as a smart phone, tablet computer, or a laptop, for example, that is in communication with the exercise apparatus 104 and/or the fitness tracking computing system 102.”, Paragraph 0038 ln. 5-8), the control unit (Processor 110) generating a load weight for the load unit (Resistance assembly 152) according to the load data, and adjusting the load weight of the load unit by the sensing unit (“the fitness tracking computing system 1002 can comprise a self-learning module 1014 to monitor individual user's interaction with one or more exercise apparatuses and, in an automated fashion, make suggestions to the user 1012 based on the individual's past interactions or otherwise automatically modify a workout routine or an exercise parameter.”, Paragraph 0067 ln. 14-18), and wherein the AI unit (Fitness Tracking Computing system 102) retrieves internal data through the database to provide the user with the load data for use (“Based on the user's 1012 performance, or completion of certain milestones, the fitness tracking computing system 1002 can alter or recommend the regimen to better suit the user 1012 or otherwise adapt to the user's 1012 abilities or preferences.”, Paragraph 0067 ln. 8-12), wherein the user inputs the load data to the mobile device and sends the load data to the control panel (Interface 104), the control panel (Interfaces 104) adjusts the load weight of the load unit (“the fitness tracking computing system 1002 can comprise a self-learning module 1014 to monitor individual user's interaction with one or more exercise apparatuses and, in an automated fashion, make suggestions to the user 1012 based on the individual's past interactions or otherwise automatically modify a workout routine or an exercise parameter.”, Paragraph 0067 ln. 14-18), when the user applies a force to the operation unit (Grips 162), a movement of the operation unit pulls the rope to move the load weight of the load unit (“when the grips 162 are moved in the direction indicated by arrow 164, weights of the resistance assembly 152 are pulled in the directed indicated by arrow 166.”, Paragraph 0036 ln. 20-23, Fig. 1), when the user releases the operation unit (Grips 162), the sensing unit (Sensors 142) detects a magnitude of the force on the operation unit (Grips 162) so as to adjust the load weight of the load unit (“the fitness tracking computing system 1002 can comprise a self-learning module 1014 to monitor individual user's interaction with one or more exercise apparatuses and, in an automated fashion, make suggestions to the user 1012 based on the individual's past interactions or otherwise automatically modify a workout routine or an exercise parameter.”, Paragraph 0067 ln. 14-18).
Regarding claim 11, wherein the AI unit links to a statistical module which collects and integrates operation information of the user in the past (“The fitness tracking computing system 702 can also track and store the exercise data received from each use of the exercise apparatus 704-704N. Such data can be aggregated, sorted, reported, or otherwise may be processed. In some embodiments, exercise data is tied to a particular type of exercise or therapy for tracking purposes.” Paragraph 0054 ln. 1-6; The self-learning module can monitor user’s exercises data such as resistance level, successfully completed number of sets, number of repetitions and configurations, based on this data the self-learning module can make recommendations, see Paragraph 0069).
Regarding claim 12, wherein the AI unit is connected to a judgment module which judges whether the load data entered by the user is feasible for operation based on an operating information of the user in the past (The fitness tracking computer system is capable of tracking successful metrics based on numbers of set completed and in the same manner it’s able to determine if the resistance level is not appropriated based on previous metrics and make recommendations based on resistance level or suggest exercises that will continue to challenge the user, Paragraph 0069).
Regarding claim 13, Eder wherein the AI unit (fitness tracking computing system 1102) is connected to an upload module (Private account 1170) which is wirelessly connected to a cloud device (Computing device 1200 can be cloud-based computing capability, Paragraph 0077 ln. 4-7), the mobile device (“The fitness tracking computing system 102 can be provided using any suitable processor-based device or system, such as a personal computer, laptop, server, mainframe, mobile computer, other processor-based device, or a collection (e.g. network) of multiple computers, for example”, Paragraph 0029 ln. 4-9), or the control panel (Interfaces 140, Fig. 1), the AI unit (Fitness tracking computing system 1102) sends the load data of the user in the past to the upload module for recording and storage on the mobile device, the cloud device, or the control panel (“The data presented in the graphical user interface 1030, 1040, 1050, 1060, 1070, along with other user data can be stored by fitness tracking computing systems.”, Paragraph 0070 ln. 1-3).
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 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Eder (US 20150335950 A1) in view of Tam et al (US 20180353811 A1; hereinafter: Tam). Eder discloses the device as substantially claimed above.
Regarding claim 3, Eder fails to disclose wherein the operation unit includes a chip electrically connected to the AI unit, the chip is in contact with the user to detect physical conditions and transmit the physical conditions to the Al unit.
Tam, a switchable intelligent fitness handle, discloses that the device can be connected to a mobile terminal or a cloud server (Tam, Paragraph 0019 ln. 14-16). Tam discloses wherein the operation unit (Handle 7, Fig. 11) includes a chip (“a photoelectric sensor (for counting), a heart rate sensor, a distance measuring sensor, a pressure sensor and the like are further built in the handle 7, and such sensors are connected to the processor 11, respectively.”, Paragraph 0048; Applicant discloses the chip is integrated in the operation unit and is in contact with the user for measuring physical conditions, Page 9 ln-11-17) electrically connected to the AI unit (the device can be connected to a mobile terminal or a cloud server, Tam Paragraph 0019 ln. 14-16), the chip is in contact with the user to detect physical conditions and transmit the physical conditions to the Al unit (“a heart rate sensor is further built in the handle”, Paragraph 0011).
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It would have been obvious to a person of ordinary skills in the art before the effective filling date of the claimed invention to modify the device of Eder to include the operating unit (Handle 7) of Tam, so that the user and the system can track the heart rate and other physical conditions (Tam, “a photoelectric sensor (for counting), a heart rate sensor, a distance measuring sensor, a pressure sensor and the like are further built in the handle 7, and such sensors are connected to the processor 11, respectively.”, Paragraph 0048).
Regarding claim 10, Eder fails to disclose wherein the operation unit includes a chip electrically connected to the AI unit, the chip is in contact with the user to detect physical conditions and transmit the physical conditions to the Al unit.
Tam, a switchable intelligent fitness handle, the device can be connected to a mobile terminal or a cloud server (Tam, Paragraph 0019 ln. 14-16). Tam discloses wherein the operation unit (Handle 7, Fig. 11) includes a chip (“a photoelectric sensor (for counting), a heart rate sensor, a distance measuring sensor, a pressure sensor and the like are further built in the handle 7, and such sensors are connected to the processor 11, respectively.”, Paragraph 0048; Applicant discloses the chip is integrated in the operation unit and is in contact with the user for measuring physical conditions, Page 9 ln-11-17) electrically connected to the AI unit (the device can be connected to a mobile terminal or a cloud server, Tam Paragraph 0019 ln. 14-16), the chip is in contact with the user to detect physical conditions and transmit the physical conditions to the Al unit (“a heart rate sensor is further built in the handle”, Paragraph 0011).
It would have been obvious to a person of ordinary skills in the art before the effective filling date of the claimed invention to modify the device of Eder to include the operating unit (Handle 7) of Tam, so that the user and the system can track the heart rate and other physical conditions (Tam, “a photoelectric sensor (for counting), a heart rate sensor, a distance measuring sensor, a pressure sensor and the like are further built in the handle 7, and such sensors are connected to the processor 11, respectively.”, Paragraph 0048).
Claim 2 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Eder (US 20150335950 A1) in view of Burnfield et al (US 20110086742 A1; hereinafter: Burnfield). Eder discloses the device as substantially claimed above.
Regarding claim 2, Eder discloses wherein the load unit comprises a driving motor (Resistance assembly 152 can be provided by using one or more motors, See Paragraph 0035), one of two ends of the rope (Rope) is connected to the driving motor (Rope is connected to Resistance assembly 152, Fig. 1), another one of the two ends of the rope is wound around the shaft (Rope is wound around shuttle 158, Fig. 1) and connected to the operation unit (Grips 162 are connected to the Rope, Fig. 1), the control unit comprises a controller and a receiving and controlling unit (self-learning module 1014, Fig. 10E), the receiving and controlling unit receives the load data and transmits the load data to the microcontroller which calculates and converts the load data to set the driving motor to generate a corresponding resistance (“the fitness tracking computing system 1002 can comprise a self-learning module 1014 to monitor individual user's interaction with one or more exercise apparatuses and, in an automated fashion, make suggestions to the user 1012 based on the individual's past interactions or otherwise automatically modify a workout routine or an exercise parameter.”, Paragraph 0067 ln. 14-18).
Eder fails to specifically disclose the control unit comprises a microcontroller. Eder discloses the fitness tracking computing system can be provided using any suitable processor-based device (See Paragraph 0029).
Burnfield discloses an exercise machine with a computing device configured to receive data from sensors, and provide instructions for the motors and pulley assembly of the exercise machine. Burnfield further discloses a microcontroller (“The micro-control unit 119, also called a microcontroller, may also be configured to receive and process instructions from a computing device 120 based on user input and to transmit such instructions to the motor of the motor and pulley assembly 110 to control the speed of the motor of the motor and pulley assembly 110”, See Paragraph 0027).
It would have been obvious to a person of ordinary skills in the art before the effective filling date of the claimed invention to modify the device of Eder to include the micro-control unit of Burnfield, since microcontroller are known in the art for being processor-based devices with one or more processor core and memory unit (“The fitness tracking system computing system 102 can include one or more processor and one or more memory unit”, Eder Paragraph 0029; “micro-control unit is configured to receive and process data collected from different sensors”, see Paragraph 0027).
Regarding claim 9, Eder discloses wherein the load unit (Resistance assembly 152) comprises a driving motor (Resistance assembly 152 can be provided by using one or more motors, See Paragraph 0035), one of two ends of the rope (Rope) is connected to the driving motor (Rope is connected to Resistance assembly 152, Fig. 1), another one of the two ends of the rope is wound around the shaft ( Rope is wound around shuttle 158, Fig. 1) and connected to the operation unit (Grips 162 are connected to the Rope, Fig. 1), the control unit comprises a controller and a receiving and controlling unit (self-learning module 1014, Fig. 10E), the receiving and controlling unit receives the load data and transmits the load data to the microcontroller which calculates and converts the load data to set the driving motor to generate a corresponding resistance (“the fitness tracking computing system 1002 can comprise a self-learning module 1014 to monitor individual user's interaction with one or more exercise apparatuses and, in an automated fashion, make suggestions to the user 1012 based on the individual's past interactions or otherwise automatically modify a workout routine or an exercise parameter.”, Paragraph 0067 ln. 14-18).
Eder fails to specifically disclose the control unit comprises a microcontroller. Eder discloses the fitness tracking computing system can be provided using any suitable processor-based device (See Paragraph 0029).
Burnfield discloses an exercise machine with a computing device configured to receive data from sensors, and provide instructions for the motors and pulley assembly of the exercise machine. Burnfield further discloses a microcontroller (“The micro-control unit 119, also called a microcontroller, may also be configured to receive and process instructions from a computing device 120 based on user input and to transmit such instructions to the motor of the motor and pulley assembly 110 to control the speed of the motor of the motor and pulley assembly 110”, See Paragraph 0027).
It would have been obvious to a person of ordinary skills in the art before the effective filling date of the claimed invention to modify the device of Eder to include the micro-control unit of Burnfield, since microcontroller are known in the art for being processor-based devices with one or more processor core and memory unit (“The fitness tracking system computing system 102 can include one or more processor and one or more memory unit”, Eder Paragraph 0029; “micro-control unit is configured to receive and process data collected from different sensors”, see Paragraph 0027).
Response to Arguments
The arguments filed 03/03/2025 were considered, but not found persuasive.
With respect to the arguments discussing the differences between the instant application and the prior art of Eder (US 20150335950), the Applicant is arguing a more narrow scope of the claimed invention as the differences must be clearly stated in the claim language.
With respect to the arguments addressing the statistical module, the Examiner respectfully disagrees. The limitation regarding the statistical module is broad and merely states “a statistical module which collects and integrates operational information of the user in the past”. Eder teaches that the fitness tracking computing system aggregated, sorted, reported, or otherwise processes data of the user, both in the present and in the past (see paragraphs 0067-0068). The aggregation, sorting, and processing data has been considered the statistical module, in the broadest reasonable interpretation, as statistics is merely “a branch of mathematics dealing with collecting, analysis, interpretation and presentation of masses of numerical data” according to Merriam-Webster. Therefore the collection via the sensors, the analysis is aggregation and sorting, and the interpretation and presentation is done by the fitness tracking computing system when submitting recommendations to the user. While the Applicant argues that the purpose of the fitness tracking computing system is different than that of Eder, the differences have not yet been established in the claimed language. The Applicant is arguing a more narrow scope of the claimed invention. The Examiner further notes that the fact that Eder has a set up that uses data to suggest exercise does not mean that past data isn’t considered.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LOAN B JIMENEZ whose telephone number is (571)272-4966. The examiner can normally be reached Mon-Thu 6 am to 4 pm.
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LOAN B. JIMENEZ
Supervisory Patent Examiner
Art Unit 3784
/LOAN B JIMENEZ/Supervisory Patent Examiner, Art Unit 3784