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 Interpretation
During patent examination, pending claims must be “given their broadest reasonable interpretation consistent with the specification.” MPEP 2111; See also, MPEP 2173.02. Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969). See also, In re Zletz, 893 F.2d 319, 321-22, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) (“During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow”). The reason is simply that during patent prosecution when claims can be amended, ambiguities should be recognized, scope and breadth of language explored, and clarification imposed. An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.
The Examiner respectfully requests of the Applicant in preparing responses, to consider fully the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN.
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(s) 1-3, 5-7, and 9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 2019/011806 A1 to Cardona et al (hereinafter Cardona).
With regards to claim 1, Cardona discloses:
1. An automatic analysis system for fitness training, configured to assist in analyzing muscle groups of a user during fitness training, the automatic analysis system for fitness training (see, detailed description, including, A fitness training system discloses a process or method capable of providing interactive physical exercise using one or more smart fitness equipment (“SFE”). The process, in one aspect, is able to receive an authentication request from an SFE initiated by a user via an authenticator. After retrieving a profile representing a set of predefined information relating to the user from a user profile storage in accordance with the authentication request, an interactive fitness plan is generated based on the profile and a predefined set of datasets produced by one or more fitness machine learning modules using big data, para. 0005), comprising:
a database, configured to store a training course and a target muscle group data corresponding to the training course (see, detailed description, including, identifying the identity (“ID”) of the user, the profile and information relating to the user are uploaded to SFP or SFE if SFE is placed within SFP. Infrared camera 230 or 232, in one example, is used to detect thermal temperature across user's body to determine which part or muscle is being worked out more than other parts or muscle of user's body. For example, when a portion of muscle emits higher temperature, the muscle (with higher temperature) is being trained and exercised, para. 0033);
a thermal image capturing unit, configured to capture a first thermal image of the user after performing the training course (see, detailed description, including, user's posture is also being captured by equipment such as using cameras to capture the posture of the user while exercising. SFE also employs infrared cameras to capture user's thermal image identifying the body parts/muscle that is activated and/or exercised, para. 0060); and
an analysis device, connected to the database and the thermal image capturing unit, (see, detailed description, including, user's posture is also being captured by equipment such as using cameras to capture the posture of the user while exercising. SFE also employs infrared cameras to capture user's thermal image identifying the body parts/muscle that is activated and/or exercised, para. 0060) the analysis device further comprising:
a muscle identifying module, configured to identify the muscle groups of the user by analyzing the first thermal image with a muscle group model (see, detailed description, including, exercising. SFE also employs infrared cameras to capture user's thermal image identifying the body parts/muscle that is activated and/or exercised, para. 0060);
a muscle temperature calculating module, configured to calculate muscle temperatures of the muscle groups respectively, and obtain a main training muscle group while the user is performing the training course (see, as above, and detailed description, including, exercising. SFE also employs infrared cameras to capture user's thermal image identifying the body parts/muscle that is activated and/or exercised para. 0060); and
a comparing module, configured to compare the main training muscle group with the target muscle group data and generate a warning signal when the main training muscle group does not match the target muscle group data (see, detailed description, including, in one aspect, also provide a feedback and correcting mechanism. For example, SFE analyzes user's activity and prompts correctional changes in response to local processing capabilities as well as the cloud-based fitness networking as would be done by a physical trainer, para. 0062). (the Correcting Mechanism is interpreted to provide a warning measure, to correct a training activity that needs to be adjusted).
With regards to claim 2, Cardona discloses:
2. The automatic analysis system for fitness training of claim 1, wherein the muscle temperature calculating module analyzes the first thermal image and the muscle groups with an image recognition algorithm to generate a plurality of pixels corresponding to the muscle groups respectively, and further generates the muscle temperature by calculating color level values of the pixels (see, Fig. 6, and detailed description, including, mobile application (“Apps”) 612, nutritional services 610, virtual fitness 614, and remote training 616. In one aspect, IFC 602 includes a genetic analysis module 604, machine learning 608, and medical data 606, wherein machine learning 608 is used to analyze big data, para. 0065.
With regards to claim 3, Cardona discloses:
3. The automatic analysis system for fitness training of claim 1, wherein the muscle temperature calculating module pre-stores a temperature threshold value, and the muscle temperature calculating module determines the muscle group, which has the muscle group temperature greater than the threshold value, as the main training muscle group (see, Fig. 6, and detailed description, including, IFC 602 includes a genetic analysis module 604, machine learning 608, and medical data 606, wherein machine learning 608 is used to analyze big data, para. 0062).
With regards to claim 5, Cardona discloses:
5. The automatic analysis system for fitness training of claim 1, wherein the database comprises a plurality of human body thermal images and a plurality of muscle group labels, the muscle identifying module analyzes the human body thermal images and the muscle group labels with machine learning to establish the muscle group model (see, Fig. 6, and detailed description, including, IFC 602 includes a genetic analysis module 604, machine learning 608, and medical data 606, wherein machine learning 608 is used to analyze big data, para. 0062)..
With regard to claim 6, claim 6 (a method claim) recites substantially similar limitations to claim 1 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regards to claim 7, Cardona discloses:
7. The automatic analysis method for fitness training of claim 6, wherein in the obtaining a main training muscle group while the user is performing the training course of the step further comprises the following step of:
determining the muscle group, which has the muscle group temperature greater than the threshold value, as the main training muscle group (see, detailed description, including, an infrared camera 230 or 232, in one example, is used to detect thermal temperature across user's body to determine which part or muscle is being worked out more than other parts or muscle of user's body. For example, when a portion of muscle emits higher temperature, the muscle (with higher temperature) is being trained and exercised, para. 0033).
With regards to claim 9, Cardona discloses:
9. The automatic analysis method for fitness training of claim 6, further comprising the following step of:
analyzing human body thermal images and muscle group labels with machine learning to establish the muscle group model (see, Fig. 6, and detailed description, including, diagram 600 illustrating an integrated fitness cloud configured to facilitate interactive feedback for physical workout in accordance with one or more embodiments of the present invention. Diagram 600 includes an integrated fitness cloud (“IFC”) 602, mobile application (“Apps”) 612, nutritional services 610, virtual fitness 614, and remote training 616. In one aspect, IFC 602 includes a genetic analysis module 604, machine learning 608, and medical data 606, wherein machine learning 608 is used to analyze big data, para. 0065).
Allowable Subject Matter
Claim 4, and 8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
For convenience claims 4 and 8 are presented below.
4. The automatic analysis system for fitness training of claim 1, wherein the thermal image capturing unit captures a second thermal image of the user before performing the training course, the muscle identifying module analyzes the second thermal image with the muscle group model to identify the muscle groups of the user, respectively calculates an initial muscle group temperature corresponding to the muscle groups of the second thermal image, and determines the muscle group, which has the largest difference between its muscle group temperature and its initial muscle group temperature, as the main training muscle group.
8. The automatic analysis method for fitness training of claim 6, further comprising the following steps of:
capturing a second thermal image of the user before performing the training course;
identifying the muscle groups of the user by analyzing the second thermal image with the muscle group model;
calculating initial muscle group temperatures corresponding to the muscle groups of the second thermal image;
and, in the step of obtaining a main training muscle group while the user is performing the training course, further comprising the following step of:
determining the muscle group, which has the largest difference between its muscle group temperature and its initial muscle group temperature, as the main training muscle group.
A sampling of the prior art made of record and not relied upon and not relied upon and considered pertinent to Applicants’ disclosure includes: U.S> Patent Publication No. 2018/0050237 A1 to Dorombozi et al. that discusses: a method and apparatus for plyometric force application to muscle with a plyometric force application element for controlling the amount of plyometric force application. Plyometric force profile and temporal variability, plyometric force intensity, control, and force direction on a 360° plane, can be applied with 360 degrees of vector possibilities of 3-dimentional space, and control of other plyometric force factors for muscle activation of muscle or a group of muscles.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM D. TITCOMB whose telephone number is (571)270-5190. The examiner can normally be reached 9:30 AM - 6:30 PM (M-F).
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WILLIAM D. TITCOMB
Primary Examiner
Art Unit 2178
/WILLIAM D TITCOMB/ Primary Examiner, Art Unit 2178 2-12-26