DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 11, 2026, has been entered.
Response to Amendment
Claims 1, 2, 4-7, 20, and 21 have been amended. Claim 19 has been canceled. Claims 1-18, 20, and 21 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments with respect to claims 1-18, 20, and 21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A response is provided below in bold where appropriate.
Applicant argues 35 USC §101 Rejection, starting pg. 13 of Remarks:
Rejection Of Claims Under 35 U.S.C. § 101
Claims 1-18, 20 and 21 stand rejected under 35 U.S.C.§ 101 for being directed to non-statutory subject matter. These rejections are respectfully traversed for the following reasons.
Regarding Step 1 of the § 101 analysis, amended claim 1 is directed to a recommendation method (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
Regarding Prong 1 of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. See MPEP 2106(A)(ll)(1) and MPEP 2106.04(a)-(c).
Additionally. MPEP 2106.04(11) states:
Step 2A asks: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? In the context of the
flowchart in MPEP § 2106, subsection III, Step 2A determines whether:
* The claim as a whole is not directed to a judicial exception (Step 2A: NO) and thus is eligible at Pathway B, thereby concluding the eligibility analysis; or
* The claim as a whole is directed to a judicial exception (Step 2A: YES) and thus requires further analysis at Step 2B to determine if the claim as a whole amounts to significantly more than the exception itself
Regarding independent claims 1, 20 and 21, the Office contends that the claims are directed to an abstract idea under Prong 1 of Step 2A, because "Using a human body model to provide a fitness regimen is managing personal behavior. This is also teaching a user", then the Office maintains it falls within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas.
However, by virtue of the above amendments, independent Claim 1 clearly defines the following plurality of steps executed by a computer: (i) receiving, by a computer, physiological feature information input by a user; (ii) constructing, by a computer, a user human body model (digital human body model or 3D human body model) of the user based on the physiological feature information input by the user; (iii) determining a matching human body model from a plurality of comparable human body models in a fitness database; (iv) displaying, by a computer, a human body model corresponding to fitness effect information of a comparable user associated with the matching human body model to the user; and (v) recommending, by a computer, a corresponding fitness regimen to the user based on the user's selection of the human body model corresponding to the fitness effects.
Steps (i) to (iii) involve internal data processing by a computer based on user input, comprising: receiving user input, constructing a human body model based on the user input, and performing model matching in the computer's internal database; while steps (iv) and (v) involve processes of displaying a human body model corresponding to the fitness effects to the user, and pushing a fitness regimen by human-computer interaction (such as the user's selection of the human body model).
In other words, the above steps defined in the subject matter of amended Claim 1 constitute a complete process of data processing and human-computer interaction, to the extent that such a process can only be realized by means of a computer.
Respectfully, using a computer is not enough to make abstract claims statutory. Also, a person can construct with pen and paper a human body model, determine matching of models, display with pen and paper a human body model corresponding to fitness effects, determine a fitness regimen information for a user, based on a selection of a human body model.
Even if constructing a model based on user input could be done by personal behavior, as alleged in the Office Action, a person cannot perform mode matching in a database, display a human body model corresponding to the fitness effect to the user who input the physiological feature information, and push a fitness regimen to the user based on the user's intuitive selection of the displayed model, all without a computer.
Using a computer is not enough to make abstract claims statutory as claimed.
Therefore, Applicants respectfully submit that the amended Claim 1 does not recite abstract ideas.
The Examiner respectfully maintains the claims recite abstract elements.
If claims 1-18, 20, and 21 are still considered to stand rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter and in particular to an abstract idea without significantly more, then Applicants respectfully submit the claims as amended are not directed to an abstract idea under Step 2A, Prong 2 for reasons including that the claims contain additional elements that clearly integrate any alleged abstract idea into practical application.
In amended Claim 1, the alleged abstract idea is integrated into practical application, where "a human body model corresponding to the fitness effect information can be displayed to the user to be recommended, allowing the user to more intuitively select the desired fitness effect, thereby improving the accuracy of the recommendation (par. [0111]) ".
That is to say, the process in amended Claim 1 of displaying a human body model corresponding to the fitness effect to a user by human-computer interaction, and pushing a fitness regimen based on the user's selection of the displayed human body model corresponding to the fitness effects, achieves, as a whole, an application of a computer information pushing a fitness regimen within the field of fitness data, thereby realizing the technical purpose of "improving the accuracy of computer-pushed fitness regimen information".
Accuracy of a fitness regimen is not claimed. Even if accuracy was claimed, there is no way to assure accuracy is actually achieved.
Thus, the technical features in amended independent claim 1 reflects a non- abstract improvement in computer technology itself, within the field of fitness data. This non- abstract and technical improvement integrates the claimed subject matter into a specific improvement over prior art systems as it improves the accuracy of computer-pushed fitness regimen information by human-computer interaction.
With all due respect, computer technology itself is not improved. Using a computer to improve the accuracy of a fitness regimen is not improving computer technology itself.
Accordingly, the additional elements in amended independent claim 1, when considered as a whole, integrate the judicial exception into a practical application of improving the accuracy of computer-pushed fitness regimen information.
Additionally, if the claims are found to not integrate a judicial exception into a practical application, Applicants respectfully submit that the claims as amended are not directed to an abstract idea under Step 2B for reasons including that the claims amount to significantly more than the judicial exception itself by reciting improvements of computer information pushing a fitness regimen within the field of fitness data.
As described above, by human-computer interaction, a computer can display a human body model corresponding to the fitness effect to a user and push a fitness regimen to the user based on the user's selection of the displayed human body model, thereby improving the accuracy of computer-pushed fitness regimen information. Therefore, the claimed subject matter is directed to an improvement of computer technology itself within the field of fitness data.
A computer pushing a fitness regimen information, while not claimed, is not enough to make abstract claims statutory.
Thus, the subject matter of amended independent claim 1 (and the dependent claims) qualify as eligible subject matter under 35 U.S.C § 101. Independent claims 20 and 21 have also been amended to include this non-abstract feature and are allowable for similar reasons.
Accordingly, it is respectfully requested that the rejections under 35 U.S.C. § 101 should be withdrawn.
The rejection is respectfully maintained but modified for the claim amendments.
Applicant argues 35 USC §103 Rejection, starting pg. 16 of Remarks:
Rejection Of Claims Under 35 U.S.C. § 103
Claims 1-17, 20,21 under 35 U.S.C. § 103 are rejected as being anticipated by King-US2018036591A1, Kaleal-US20170027528A1 and Boker-US20090259648A1. Office Action, at 17.
These rejections are respectfully traversed for the following reasons.
Compared with King, amended claim 1 has at least the following distinguishing technical features:
determining a matching human body model matching with the user human body model from a plurality of comparable human body models in a fitness database, the plurality of comparable human body models being generated based on physiological feature information of a plurality of comparable users;
displaying a human body model corresponding to fitness effect information of a comparable user associated with the matching human body model to the user to be recommended; and
determining the fitness regimen information for the user to be recommended based on a selection of the human body model corresponding to the fitness effect information by the user to be recommended.
For the above distinguishing technical feature:
In the embodiments of the amended claim 1, "a human body model corresponding to the fitness effect information can be displayed to the user to be recommended, allowing the user to more intuitively select the desired fitness effect (Par. [0111])"; 'fitness effects, training periods, and training intensities associated with a matching human body model may be shown first, and then determine the recommended fitness regimen information based on the selection of the user to be recommended (par. [0115])".
That is, in amended Claim 1, after determining the matching human body model, fitness effects of the comparable user associated with the matching human body model are displayed to the user. The fitness regimen is recommended to the user based on the user's selection of the human body model corresponding to the fitness effects.
In other words, amended Claim 1 recommends a fitness regimen based on the user's selection of the fitness effects by displaying fitness effects to the user.
Respectfully, the above is not claimed.
From Claim 1…
“determining the fitness regimen information for the user to be recommended based on a selection of the human body model corresponding to the fitness effect information by the user to be recommended.”
“To be recommended” never happens since it is intended use language. The claim determines fitness regimen based on selection of a model corresponding to fitness effect.
However, King only discloses that "a workout video block may include a combination of instructions provided (described or demonstrated) by a human and a machine generated character (Par. [0062]) "; "a machine-learning model may be trained to select workouts based on a user's goal.------some embodiments may receive a request for workout from a given user, determine which cluster most closely matches that given user, and then select a workout for that user that includes the futures detected among the users in that cluster within their workouts (Par. [0123]) ".
Exercise with fitness goal and workout routine specified by real-time user feedback…
“A separate data structure may provide relatively rich labeling of attributes of the videos, and that labeling may be used to select among the videos when dynamically sequencing blocks in a workout. In some embodiments, the exercise included in an individual workout video block may target one or more of a specific body-region (e.g., lower body, upper body, core, arms, etc.), may target a specific fitness goal, a specific physiological function (e.g., breathing, etc.), or other targets. A data structure, described below, may associate each video block with these parameters, and that data structure may be interrogated to dynamically select and sequence videos in accordance with the general parameters of a workout routine specified by another data structure and real-time user feedback, in some cases.” [0052]
Even if King does not teach this, the secondary art does.
Kaleal, III et al. teaches:
Pick and choose different fitness programs to see how user would appear (fitness effect) in the future…
“This feature of avatar visualization platform 1302 allows a user to dynamically pick and choose different health and fitness programs and/or change different variables of a health and fitness program and see how the user would appear in the future based on the selected health and fitness program and/or the different variables. Accordingly, a user can select a health and fitness program that will cause the user to achieve an optimally desired appearance. For example, as the user selects different health and fitness programs and/or can changes variables of a selected health and fitness program, avatar visualization platform 1302 can dynamically adapt the appearance of an avatar presented to the user that corresponds to a predicted visualization of how the user will appear based on completion and adherence to the different health and fitness or the health and fitness program with the respectively chosen variables. As a result, the user can select a health and fitness program based on how it will affect the user's appearance.” [0200]
That is, in King, a workout video is recommended to the user based on the data provided by the user indicating the health status and fitness goals of the user (e.g., the user profile, Par. [0063]) by a machine learning model.
In other words, in the workout video recommended in King, what is displayed is fitness guidance information, i.e., directing users on how to exercise, rather than showing the fitness effect after the user has exercised.
Showing the fitness effect after the user has exercised is not claimed.
From King et al…
What the user wants to achieve in the future and fitness goals…
“User profiles may include the features of user profiles described above. In some cases, a user's profile is a set of data collected from various sources that define exactly who the user is currently, what their health and fitness history has been in the past, and what they want to achieve in the future. The engine, in some embodiments, takes some or all of these factors as inputs when determining based on these inputs what is most appropriate to prescribe at any given moment responsive to an event. Example profile elements include the following: Age; Gender; Height; Weight; Body Composition; Blood Panel; Hormone Panel; DNA (or genes therein); Profession; Current Fitness Level; Current Fitness Habits; Current Injuries; Injury History; Fitness Goals; Emotional/Stress Goals; Access to Resources (like Equipment, Space, Devices, Attire, or People); Fitness Preferences; Personality Type; IQ; Learning style; Home Address; Work Address; Workout History; Activity History; Current Fitness Knowledge; Schedule; Devices (like Phone, Tablet; Computer; Smart Speaker, Smart TV, Smart Watch, VR System, AR system or accounts thereon); and User Role (like Client, Trainer, Nutritionist, Athlete).” [0142]
Even if the user avatar is considered equivalent to a digital model as alleged by the examiner, King neither discloses nor suggests any manner for displaying the post-workout fitness effects by displaying such an avatar.
Respectfully, displaying the post-workout fitness effects is not claimed.
From Applicant’s claim 1….
“…displaying a human body model corresponding to fitness effect information of a comparable user associated with the matching human body model to the user to be recommended;…”
Therefore, displaying a model corresponding to fitness effect information could be at any time there is fitness activity.
Therefore, King does not disclose the method in Claim 1 of recommending the fitness regimen by displaying to the user a human body model corresponding to the fitness effect based on the user's intuitive selection of the human body model corresponding to the fitness effect.
User’s intuitive selection is not claimed
From Applicant’s claim 1…
“… determining the fitness regimen information for the user to be recommended based on a selection of the human body model corresponding to the fitness effect information by the user to be recommended.”
The above only teaches a selection, which does not even require a user’s intuitive selection.
That is, the manners of recommending the fitness regimen in King and amended Claim 1 are not the same.
Consequently, King fails to disclose the above distinguishing technical features.
Applicant has amended their claim but above is arguing things not in their claims.
In addition, Kaleal only discloses: "the user can employ the avatar guidance system 200 to set up a personal training avatar to monitor an exercise and fitness program for the user as well as a personal assistant avatar configured to monitor the user's adherence to a personal weekly schedule designed by the user (Par. [0076])".
In other words, in Kaleal, a virtual avatar is used to provide fitness guidance information to the user, that is, to guide the user on how to exercise.
It can be seen that Kaleal also fails to disclose any manner for displaying the post- workout fitness effects by displaying such a virtual avatar.
The above post-workout is not claimed.
Therefore, Kaleal does not disclose the method in Claim 1 of recommending the fitness regimen by displaying to the user a human body model corresponding to the fitness effect based on the user's intuitive selection of the human body model corresponding to the fitness effect.
“Recommending “is not claimed.
Intuitive selection is not claimed.
Respectfully, Applicant’s arguments are not commensurate with the scope of their claims.
That is, the manners of recommending the fitness regimen in Kaleal and amended Claim 1 are also not the same.
Consequently, Kaleal fails to disclose the above distinguishing technical features.
Kaleal, III et al. teaches:
Pick and choose different fitness programs to see how user would appear (fitness effect) in the future…
“This feature of avatar visualization platform 1302 allows a user to dynamically pick and choose different health and fitness programs and/or change different variables of a health and fitness program and see how the user would appear in the future based on the selected health and fitness program and/or the different variables. Accordingly, a user can select a health and fitness program that will cause the user to achieve an optimally desired appearance. For example, as the user selects different health and fitness programs and/or can changes variables of a selected health and fitness program, avatar visualization platform 1302 can dynamically adapt the appearance of an avatar presented to the user that corresponds to a predicted visualization of how the user will appear based on completion and adherence to the different health and fitness or the health and fitness program with the respectively chosen variables. As a result, the user can select a health and fitness program based on how it will affect the user's appearance.” [0200]
Therefore, Kaleal teaches a user can select fitness program to see how they would appear in the future.
Further, Boker cited by the examiner to compare with the above distinguishing technical features only discloses: "a method for automated avatar creation and interaction in a virtual world may include detecting if a user's avatar has entered a predefined proximity area in the virtual world (Par. [0004])"; "the automated avatar may be presented to the user to autonomously interact with the user's avatar. The automated avatar may be presented to the user at anytime the user is detect to be within the predefined proximity area, the predefined proximity area being previously discussed with respect to block 102 (Par. [0024]) ".
That is, Boker only discloses that users interact in a virtual world by matched avatars, but does not disclose any manner of recommending fitness regimen information to users, still less the manner of recommending a fitness regimen by displaying to the user a human body model corresponding to the fitness effect based on the user's intuitive selection of the human body model corresponding to the fitness effect.
Moreover, in Boker, matching avatars is for enabling users to conduct social activities in the virtual world, rather than to recommend fitness regimen information to users as in amended Claim 1.
That is, the technical purposes of the matching operation in Boker and amended Claim 1 are different.
Applicant is reminded of piecemeal analysis…
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Boker was only used to teach matching models.
Therefore, both King, Kaleal and Boker fail to provide a teaching for the above distinguishing technical features, and even combining King, Kaleal and Boker, those skilled in the art cannot arrive the technical solution defined in the amended claim 1.
In summary, the amended Claim 1 is inventive.
For similar reasons, amended claims 20 and 21 are also inventive. The claims depending from claims 1, 20 and 21 are also inventive at least for the inventiveness of claims 1, 20 and 21.
Accordingly, withdrawal of the 35 U.S.C. § 103 rejection of claims is respectfully requested.
The rejection is respectfully maintained but modified for the claim amendments.
Applicant argues 35 USC §112 Rejection, starting pg. 19 of Remarks:
Rejection Of Claims Under 35 U.S.C.§112
Claims 1, 4-7, 13-18, 20 and 21 stand rejected under 35 U.S.C.112(b) for indefiniteness as failing to particularly point out and distinctly claim the subject matter which Applicants regard as the invention.
(1) Claims 1, 20 and 21 are rejected under 35 U.S.C.112(b) or 35 U.S.C.112(pre- AIA ). Office Action, at 14. These rejections are respectfully traversed for the following reasons:
Although ignoring the description of "to be recommended" does not affect the novelty and the inventiveness of Claim 1, it needs to be clarified that "to be recommended modifies "the user", that is, "the user to be recommended" refers to the user who is to be recommended the fitness plan information.
If the user is never recommended the fitness plan, it is intended use language. Based on further consideration, the rejection will be withdrawn but use of “to be recommended” is not given patentable weight for prior art purposes.
For the record and clarity purposes, the Examiner will provide a claim interpretation as to how the claims are being interpreted.
Claims 4-7, and 13-18 stand rejected under 35 U.S.C.112(b) or 35 U.S.C.112(pre- AIA ). Office Action, at 15. These rejections are respectfully traversed for the following reasons:
Claim 4 defines that the degree of similarity between models is determined based on the size of an overlap area between the models or the cosine value of an angle between feature vectors corresponding to the models. A person skilled in the art could understand that the larger the cosine value of the angle between two feature vectors, the higher the degree of similarity between the two feature vectors.
The degree of similarity needs to be specified, otherwise it could be anything. Limitations from the specification are not read into the claim.
Therefore, regardless of the specific numerical value of the cosine value, the degree of similarity (e.g., higher or lower) can be determined based on the magnitude of the cosine value.
That is, the matching result based on cosine similarity is determinate.
Accordingly, Claim 4 is clear, and claims 5-7 and 13-17 are also clear for similar reasons.
This rejection is respectfully maintained as cosine similarity could be any value.
Furthermore, in claim 18, "degrees of match" refers to the degree of matching between pieces of information.
Claim 18 recites…
“determining a match matrix based on degrees of match between fitness effect information and different fitness regimen information”
The matrix itself is not determined by the match degrees but contains the match degrees. The degrees could be anything (see Table 2 of the instant disclosure).
Determining matching based on cosine similarity requires some type of threshold or limit in order to determine when matching occurs. For example, what is the maximum angle or degree that allows matching to happen?
According to the present disclosure (see Pars. [00120]-[00121]), different fitness regimens may achieve different fitness effects, and a match matrix represents the result of information matching, which can reflect the matching between different fitness plans and different fitness effects.
A person skilled in the art could understand that such information matching may be implemented in various manners, for example, by means of a machine learning model, or by the cosine value of an angle between feature vectors corresponding to two pieces of information, etc., without limitation by the present disclosure.
Therefore, claim 18 is clear. Accordingly, it is respectfully requested that the rejections under 35 U.S.C. § 112(b) should be withdrawn.
Applicant has amended their claims regarding matching
From Claim 4…
“…determining the matching human body model based on a degree of similarity between the user human body model and the plurality of comparable human body models, wherein the degree of similarity is determined based on a size of an overlap area between the user human body model and the plurality of comparable human body models, or the degree of similarity is determined based on a cosine value of an angle between a user feature vector corresponding to the user human body model and comparable feature vectors corresponding to the plurality of comparable human body models.
The above should provide the size of the overlap area or value of the cosine angle. This rejection is maintained but modified for the claim amendments.
Claim Interpretation
Claim 1 recites limitations of “to be recommended” which are interpreted as an intended result as recommended never happens. The term is not given patentable weight for examination purposes.
For example, claim 1 has the following limitations where “to be recommended” has been struck through to show it is not considered:
A recommendation method for fitness regimen information, executed by a processor, comprising:
receiving physiological feature information input by a user
constructing a user human body model of the user
determining a matching human body model matching with the user human body model from a plurality of comparable human body models, in a fitness database, the plurality of comparable human body models being generated based on physiological feature information of a plurality of comparable users; and
displaying a human body model corresponding to fitness effect information of a comparable user associated with the matching human body model to the user
determining fitness regimen information for the user
wherein the determining fitness regimen information for the user
determining the fitness regimen information for the user
The term “to be recommended” is not given patentable weight for 35 USC 103 prior art analysis as it is interpreted to be intended use language. Claims 2-4, 7-16, 18, 20, and 21 have similar issues.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18, 20, and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-18, 20, and 21 are directed to a method, system or product, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified system Claim 1 as the claim that represents the claimed invention for analysis and is similar to system claim 20 and product claim 21.
Claim 1 recites the limitations of:
A recommendation method for fitness regimen information, executed by a processor, comprising:
receiving physiological feature information input by a user to be recommended;
constructing a user human body model of the user to be recommended based on physiological feature information input by the user to be recommended, wherein the user body model is a digital human body model or a 3D human body model;
determining a matching human body model matching with the user human body model from a plurality of comparable human body models, in a fitness database, the plurality of comparable human body models being generated based on physiological feature information of a plurality of comparable users;
displaying a human body model corresponding to fitness effect information of a comparable user associated with the matching human body model to the user to be recommended; and
determining fitness regimen information for the user to be recommended based on fitness regimen information of the comparable user associated with the matching human body model,
wherein the determining fitness regimen information for the user to be recommended based on the fitness regimen information of the comparable user associated with the matching human body model comprises:
determining the fitness regimen information for the user to be recommended based on a selection of the human body model corresponding to the fitness effect information by the user to be recommended.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim recites elements, highlighted in bold above, which covers performance of the limitation as managing personal behavior. Receiving physiological input by a user (following rules/instructions) to be recommended, constructing a user human body model of a user to be recommended (teaching) based on the feature information, based on physiological feature information input by the user (following rules or instruction) and determining a fitness regimen information for the user to be recommended based on fitness regimen of a comparable user associated with the matching human body model (teaching) is managing personal behavior. Determining the fitness regimen information for the user to be recommended is teaching. Determining a matching human body model matching with the user from a plurality of comparable human body models and displaying a human body model corresponding to fitness effect of a comparable user is managing relationships between people as a user’s body model is matched to other body models. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as managing personal behavior or managing relationships between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 20 and 21 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
In as much as the claims are determining a matching human body model for physiological features and determining fitness regimen information, the claims are also abstract under Mental Processes grouping of abstract ideas. A person in their mind and with pen and paper can construct a 3D human body model, determine in their mind a matching with the human body model, and determine a fitness regimen information for the user to be recommended based on a comparable user associated with the matching human body model. The digital human body model could be constructed with a generic computer. Mental processes have been shown to encompass using a generic computer.
This judicial exception is not integrated into a practical application. In particular, the claims only recite: processor (Claim 1); memory, processor (Claim 20); non-transitory computer-readable storage medium, processor (Claim 21). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 20, and 21 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1, 20, and 21 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-18 further define the abstract idea that is present in their independent claim 1 and thus correspond to Certain Methods of Organizing Human Activity and Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. The dependent claims themselves are abstract or just further limiting abstract ideas. Claims 4-7 and 13-16 16 are also rejected as mathematical concepts as they recite or depend from claims that recite cosine similarity which is a formula for determining matching. Therefore, the claims 2-18 are directed to an abstract idea. Thus, the claims 1-18, 20, and 21 are not patent-eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-7 and 13-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites “…determining the matching human body model based on a degree of similarity between the user human body model and the plurality of comparable human body models, wherein the degree of similarity is determined based on a size of an overlap area between the user human body model and the plurality of comparable human body models, or the degree of similarity is determined based on a cosine value of an angle between a user feature vector corresponding to the user human body model and comparable feature vectors corresponding to the plurality of comparable human body models.” It is indefinite as to determining matching based on the size of an overlap area or cosine value as the size of the overlap area and cosine value could be anything. Claims 5-7, 13, and 16 have a similar problem.
Claims 5-7 and 14-17 are further rejected as they depend from their respective claims 4 and 13.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 8-12, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2018/0036591 to King et al. in view of Pub. No. US 2017/0027528 to Kaleal, III et al. in view of Pub. No. US 2009/0259648 to Boker et al.
Regarding claims 1, 20, and 21
(claim 1) A recommendation method for fitness regimen information, executed by a processor, comprising:
receiving physiological feature information input by a user to be recommended;
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
King teaches:
Information such as heart rate, blood pressure sensor, weight scale (physiological data) and user input, user selection…
“Sensors 104 may be configured to generate output signals conveying information related to the user. In some embodiments, sensors 104 may include audiovisual sensors, activity sensors, physiological sensors, biometric sensors, or other sensors. Examples of such sensors may include a heart rate sensor, a blood pressure sensor/monitor, a weight scale, motion sensors, an optical sensor, biometric sensors, a video sensor, an audio sensor, a color sensor, a blood glucose monitor, a blood oxygen saturation monitor (e.g., a pulse oximeter), a hydration monitor, a skin/body temperature thermometer, a respiration monitor, electroencephalogram (EEG) electrodes, accelerometers, activity sensors/trackers, a GPS sensor, or other sensors. These examples should not be considered limiting. Sensors 104 are configured to generate various output signals conveying information related to the user that allows computing environment 100 to function as described herein. In some embodiments, sensors 104 may be disposed in a plurality of locations within or outside of computing environment 100. For example, sensors 104 may be on the user (e.g., wearable device), coupled with the client computing platforms 102, located in a medical device used by the user, positioned to point at the user (e.g., a video camera), or in other locations within or outside of computing environment 100. In some embodiments, information related to the user may be obtained through a combination of user input, user selection, sensor outputs, or other methods.” [0066]
constructing a user human body model of the user to be recommended based on physiological feature information input by the user to be recommended, wherein the user body model is a digital human body model or a 3D human body model;
[No Patentable Weight is given to alternative claim language where only one of the alternatives is required. In this case, digital human body model is provided.]
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
{
From Applicant’s specification on physiological features…
“For example, the system obtains physiological feature information input by comparable users such as fitness models, fitness coaches, and other users willing to share data. The physiological feature information may comprise height, weight, body fat, etc. The system can also obtain image information of the comparable users, such as full-body 2D photos or partial body photos.” [0062]
Therefore, an image of a user, as well as height, weight, etc. are examples of physiological information.
}
King et al. teaches:
Computer systems…
“The present disclosure relates generally to computer systems and, more specifically, to systems and methods for prescribing fitness-related activities responsive to events.” [0002]
User may upload (input) an image of themselves (physiological feature information) to create (construct) an avatar (digital human body model)…
“In some embodiments, a workout video block may include instructions provided by a human instructor (e.g., a coach, a trainer, a physical therapist, a chiropractor, a healthcare provider, or other person giving the workout instructions). In some cases, the instructions may be described or demonstrated by the person giving the instructions in the video, described by a person different than the persons demonstrating the workout instructions, or any combination thereof. In some embodiments, a workout video block may include instructions provided by a machine generated character configured to describe or demonstrate workout instructions (e.g., an avatar). In some embodiments, the user may choose an avatar from a list of avatars (e.g., avatar that looks like the user or an avatar that looks like a celebrity, etc.). In some embodiments, the user may upload an image (of themselves of someone else to be used in the creation of the avatar). In some cases, a workout video block may include a combination of instructions provided (described or demonstrated) by a human and a machine generated character. For example, a video may show a real person describing the workout and an avatar performing the workout. In some embodiments, workout video blocks in the same group (e.g., family, block type, set, stream, or other groups) may be provided by the same instructor (human, or machine generated). For example, a user may choose any particular instructor for any workout video block (every instructor provides instructions for all the workout sessions available to the user). In some cases, specific instructors (human, or machine generated) may provide specific workout instructions. For example, some instructors may only give instructions for specific body-regions (e.g., legs, arms, etc.), for specific level of difficulty, for specific workout type (e.g., warm-up, cool down, cardio, etc.), or other specific workout instructions.” [0062]
determining a matching human body model matching with the user human body model from a plurality of comparable human body models, in a fitness database, the plurality of comparable human body models being generated based on physiological feature information of a plurality of comparable users; and
User may choose an avatar (human body model) from a list (database) of avatars (human body models) that look like (match) the user…
“In some embodiments, a workout video block may include instructions provided by a human instructor (e.g., a coach, a trainer, a physical therapist, a chiropractor, a healthcare provider, or other person giving the workout instructions). In some cases, the instructions may be described or demonstrated by the person giving the instructions in the video, described by a person different than the persons demonstrating the workout instructions, or any combination thereof. In some embodiments, a workout video block may include instructions provided by a machine generated character configured to describe or demonstrate workout instructions (e.g., an avatar). In some embodiments, the user may choose an avatar from a list of avatars (e.g., avatar that looks like the user or an avatar that looks like a celebrity, etc.). In some embodiments, the user may upload an image (of themselves of someone else to be used in the creation of the avatar). In some cases, a workout video block may include a combination of instructions provided (described or demonstrated) by a human and a machine generated character. For example, a video may show a real person describing the workout and an avatar performing the workout. In some embodiments, workout video blocks in the same group (e.g., family, block type, set, stream, or other groups) may be provided by the same instructor (human, or machine generated). For example, a user may choose any particular instructor for any workout video block (every instructor provides instructions for all the workout sessions available to the user). In some cases, specific instructors (human, or machine generated) may provide specific workout instructions. For example, some instructors may only give instructions for specific body-regions (e.g., legs, arms, etc.), for specific level of difficulty, for specific workout type (e.g., warm-up, cool down, cardio, etc.), or other specific workout instructions.” [0062]
Example of avatar and video where avatar looks like (matching human body model) the user, and video with the same group (comparable human body models)….
“In some embodiments, a workout video block may include instructions provided by a human instructor (e.g., a coach, a trainer, a physical therapist, a chiropractor, a healthcare provider, or other person giving the workout instructions). In some cases, the instructions may be described or demonstrated by the person giving the instructions in the video, described by a person different than the persons demonstrating the workout instructions, or any combination thereof. In some embodiments, a workout video block may include instructions provided by a machine generated character configured to describe or demonstrate workout instructions (e.g., an avatar). In some embodiments, the user may choose an avatar from a list of avatars (e.g., avatar that looks like the user or an avatar that looks like a celebrity, etc.). In some embodiments, the user may upload an image (of themselves of someone else to be used in the creation of the avatar). In some cases, a workout video block may include a combination of instructions provided (described or demonstrated) by a human and a machine generated character. For example, a video may show a real person describing the workout and an avatar performing the workout. In some embodiments, workout video blocks in the same group (e.g., family, block type, set, stream, or other groups) may be provided by the same instructor (human, or machine generated). For example, a user may choose any particular instructor for any workout video block (every instructor provides instructions for all the workout sessions available to the user). In some cases, specific instructors (human, or machine generated) may provide specific workout instructions. For example, some instructors may only give instructions for specific body-regions (e.g., legs, arms, etc.), for specific level of difficulty, for specific workout type (e.g., warm-up, cool down, cardio, etc.), or other specific workout instructions.” [0062]
Workout video with avatar (human body model) that looks like the user…
“…In some embodiments, a workout video block may include instructions provided by a machine generated character configured to describe or demonstrate workout instructions (e.g., an avatar). In some embodiments, the user may choose an avatar from a list of avatars (e.g., avatar that looks like the user or an avatar that looks like a celebrity, etc.). In some embodiments, the user may upload an image (of themselves of someone else to be used in the creation of the avatar). In some cases, a workout video block may include a combination of instructions provided (described or demonstrated) by a human and a machine generated character. For example, a video may show a real person describing the workout and an avatar performing the workout…” [0062]
Video based on user profile information…
“Selection component 130 may be configured to select a first workout video block from the collection of video blocks. In some cases, selection of the first video block may be based on one or more of the user profile information, the user preference, information obtained from sensors 104, the groupings of the workout video blocks (e.g., body-region), workout intensity level, information obtained from other components of computing environment 100, or any combination thereof. For example, a first workout video may be selected based on one or more of the fitness goal of the user, a fitness level of the user, exercise constraints, health information, medical information, or other user profile attributes (described above)…” [0069]
User profiles including physiological information with fitness information and storage (database), therefore fitness database…
“In some embodiments, user profile component 124 may be configured to obtain one or more user profiles or user other information associated with users of computing environment 100. The one or more user profiles or user information may include information located in one or more of the client computing platforms 102, external resources 106, sensors 104, storage 115, or other locations within or outside computing environment 100 (e.g., websites, web platforms, servers, storage mediums, cloud storage, etc.). The user profiles may include one or more of user profile attributes, for example, information identifying users (e.g., a username, a number, an identifier, or other identifying information), security login information (e.g., a login code or password), account information, subscription information, health information, medical information (e.g., medical history, medications, etc.), physiological information (e.g., height, weight, age, gender, etc.), fitness information (e.g., fitness level, fitness achievements, etc.), restrictions (e.g., exercise constraint), fitness goals, physiological information, relationship information (e.g., information related to relationships between users in the computing environment 100), usage information, demographic information, history, client computing platforms associated with a user, or other information related to users. In some embodiments, user profile component 124 may be configured to access websites, web platforms, servers, storage mediums, or other locations from where user profiles may be accessed, obtained, retrieved, or requested in response to requests from users, components within or outside computing environment 100, or other requests. For instance, third-party API's for fitness trackers, networked scales, or networked fitness equipment may be accessed to retrieve metrics by which the profiles are enhanced. Profiles may include user feedback on particular exercises and instructors, as well as user constraints, like indications that certain body areas are susceptible or subject to injury. These values may be referenced when composing automatically workouts, e.g., embodiments may match an injured body area to a body area of workout videos and in response select corresponding, e.g., lower, level of intensity of video block.” [0063]
See Human Body Model below.
See Match below.
displaying a human body model corresponding to fitness effect information of a comparable user associated with the matching human body model to the user to be recommended; and
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
[No Patentable Weight is given to non-functional descriptive claim language of “displaying a human body model…” as the model is just displayed with no functional interaction.]
See Display Fitness Effect below.
determining fitness regimen information for the user to be recommended based on fitness regimen information of the comparable user associated with the matching human body model,
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
Detected features to recommend workouts (determining fitness regimen) using features cluster (comparable user)…
“…Some embodiments may then use these detected features and clusters to recommend workouts for other users. For example, some embodiments may receive a request for workout from a given user, determine which cluster most closely matches that given user, and then select a workout for that user that includes the futures detected among the users in that cluster within their workouts.” [0123]
“Some embodiments may suggest trainers or friends for a user to work out with. Some embodiments may cluster users and trainers according to various criteria, for example, attributes of user profiles, like goals or workout patterns. For instance, some embodiments may model users as feature vectors, with user profile attributes like workout goals, performance, timing, and feedback being mapped to scalars of the vectors. Some embodiments may cluster the vectors with a DBSCAN algorithm and suggesting pairings. Or some embodiments may rank pairings based on Euclidian distance in the vector space, e.g., suggesting to a user the five closest other users or trainers.” [0138]
wherein the determining fitness regimen information for the user to be recommended based on the fitness regimen information of the comparable user associated with the matching human body model comprises:
determining the fitness regimen information for the user to be recommended based on a selection of the human body model corresponding to the fitness effect information by the user to be recommended.
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
See Determine Fitness Regimen below.
Human Body Model
King et al. teaches avatar and video as well as profile. The do not specifically teach human body model.
Kaleal, III et al. also in the business of avatar and profile teaches:
Example of avatar and
“This avatar visualization system allows a user to dynamically pick and choose different health and fitness programs and/or change different variables of a health and fitness program and see how the user would appear in the future based on the selected health and fitness program and/or the different variables. Accordingly, a user can select a health and fitness program that will cause the user to achieve an optimally desired appearance. For example, as the user selects different health and fitness programs and/or can changes variables of a selected health and fitness program, the avatar visualization system can dynamically adapt the appearance of an avatar presented to the user that corresponds to a predicted visualization of how the user will appear based on completion and adherence to the different health and fitness or the health and fitness program with the respectively chosen variables. As a result, the user can select a specific health and fitness program based on how it will affect the user's appearance.” [0039]
Video…
“In yet another aspect, physical and physiological activity data about a user corresponding to movement/motion and appearance of the user 102 can be captured by client device 106. For example, client device 106 can include a visual capture device 110 such as a still image camera, a video camera, or a three dimensional camera/scanner configured to capture image and/or video data of the user 102. According to this example, client device 106 can collect video and still images of the user 102 as the user performs an activity, task, or routine (e.g., a workout routine). The image data can be analyzed using pattern recognition to determine whether the user's movement corresponds to model movement metrics for the activity, task or routine. For instance, while performing a fitness routine such as a yoga or dance routine, image data captured by visual capture device 110 can be processed and analyzed (e.g., in real-time) to determine whether the user is executing the correct movements/poses and using proper form.” [0044]
Example of 504 and human body…
“In an aspect, a user can establish a profile with avatar guidance system 200 that includes a variety of personal information related to the user and the user's usage of avatar guidance system 200. For example, when employing avatar guidance system 200 for fitness and health related purposes, a user can be present with interface 500 to facilitate establishing a user profile and entering various personal information related to the user's health and fitness profile. For instance, interface 500 include can information section 502 that facilitates receiving user input regarding the user's physical profile, the user's physical limitations, and the user's dietary restrictions. Interface 500 can also include section 504 that presents an interactive pictorial representation of a human with the various muscle groups displayed. In an aspect, using this section the user can select body parts and/or muscle groups to indicate where the user has injuries and/or physical limitations.” [0148]
My profile with physical profiles and human body model…
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It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of King et al. the ability to relate a human body model with a profile as taught by Kaleal, III et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by King et al. who also teaches profile related to avatar and video as models.
Match
The combined references teach avatar. They also teach fitness database. They do not teach match.
Boker et al. also in the business of avatar teaches:
Match user’s avatar (user’s human body model) to avatar in inventory (database)….
“The attributes of the user's avatar received in response to the query may be matched to attributes of automated or un-manned avatars in the inventory or any other similar database. In determining whether a match exists between the attributes of the user's avatar and any of the automated avatars, several factors and/or pre-defined matching criteria may be considered. Examples of factors or criteria may include: which attributes of the user's avatar may be used in the matching process; a predetermined number of attributes may need to be matched or substantially matched to select the automated avatar; what constitutes a match; certain attributes of the user's avatar may be assigned a higher priority or weight that other attributes for matching purposes; and any other criteria to facilitate selecting an automated avatar to effectively interact with the user's avatar and enhance the user's experience. If a match is determined to exist based on the matching criteria, then one or more matched automated avatars from the inventory of automated avatars may be selected.” [0022]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to match avatars as taught by Boker et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Boker et al. who also teaches the benefits of matching avatars to enhance user’s experience.
Display Fitness Effect
The combined references teach model. They do not teach display fitness effect.
Kalcal, III et al. also in the business of model teaches:
Fitness program and picture (displaying) an athlete that cause the user to look like the athlete (corresponding fitness effect)…
“In another aspect, in association with generation of a custom health and fitness program for a user, the user can provide program builder 904 with an image or visual representation of physical features the user desires. For example, the user could provide program builder component 904 with a picture of an athlete and request to have a program designed for the of the user that will cause the user to look like the athlete. In an another example, the user could provide program builder component 904 with an image of a supermodel and indicate that she would like to transform her body to look like the supermodel. In another example, the user could provide program builder component 904 with a picture of a specific body part and request a program that would help the user transform his or her corresponding body part to appear like that in the picture. For example, the user can provide program builder component 904 with an image of a person's shoulders and indicate he would like his shoulder to look like the picture.” [0181]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to display a fitness model as taught by Kaleal, III et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Kaleal, III et al. who also teaches the benefits of providing to a user what they will look at by performing a fitness program.
Determine Fitness Regimen
The combined references teach fitness. They do not teach determine fitness regimen.
Kaleal, III et al. also in the business of fitness teaches:
User to pick and choose (user select) different fitness programs to see how user would appear (fitness effect) in the future…
“This feature of avatar visualization platform 1302 allows a user to dynamically pick and choose different health and fitness programs and/or change different variables of a health and fitness program and see how the user would appear in the future based on the selected health and fitness program and/or the different variables. Accordingly, a user can select a health and fitness program that will cause the user to achieve an optimally desired appearance. For example, as the user selects different health and fitness programs and/or can changes variables of a selected health and fitness program, avatar visualization platform 1302 can dynamically adapt the appearance of an avatar presented to the user that corresponds to a predicted visualization of how the user will appear based on completion and adherence to the different health and fitness or the health and fitness program with the respectively chosen variables. As a result, the user can select a health and fitness program based on how it will affect the user's appearance.” [0200]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to determine a fitness regimen as taught by Kaleal, III et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Kaleal, III et al. who also teaches the benefits of a user selecting a fitness regimen and they can select a program that will provide them what they want to look like in the future.
Regarding claim 2
The recommendation method according to claim 1, wherein
the displaying the human body model corresponding to the fitness effect information of the comparable user associated with the matching human body model to the user to be recommended comprises:
displaying corresponding fitness regimen information of the fitness effect information to the user to be recommended; and
[No Patentable Weight is given to non-functional descriptive claim language of “ displaying fitness effect information of the comparable user associated with the matching human body model and corresponding fitness regimen information of the fitness effect information to the user to be recommended” as this is just displaying information.]
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
King et al. teaches:
Display workout video (fitness effect information)…
“…For example, in some embodiments, communication component 140 causes a user interface to display the selected workout video block. In some cases, communications component 140 may be configured to communicate other information related to the selected workout video block (e.g., description, reviews, ratings, etc.). In some cases, communications component 140 may be configured to send other information to the user. For example, the user may receive promotions, health tips, workout tips, reminders (e.g., it has been “a number of days” since your last workout), and/or other information.” [0084]
See Display Fitness Effect below.
the determining the fitness regimen information for the user to be recommended based on the selection of the human body model corresponding to the fitness effect information by the user to be recommended comprises:
determining the fitness regimen information of the user to be recommended based on a selection of the corresponding fitness regimen information by the user to be recommended.
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
Instructions (determining) physical activities (fitness regimen information) for (based on) strengthening muscles, weight loss, etc. (effect information)…
“Access component 120 may be configured to access a collection of workout video blocks. In some embodiments, a workout video block is a video containing one or more of a portion of a workout, an exercise, a yoga pose, training, a warm-up, aerobics, a dance, a routine, a drill, other types of physical activities, or any combination thereof. In some cases, a workout video block may include instructions of physical activities for the cardiovascular system, strengthening muscles, weight loss, to help enhance or maintain physical fitness or overall health and wellness. A workout video block may have a duration of about three minutes, or in some cases, an integral multiple of some quanta, like 1 minute, to facilitate dynamic composition. In some cases, a workout video block may have a duration of less than three minutes. In some cases, a workout video block may have a duration of more than three minutes. In some embodiments, a given individual workout video block may be the smallest video segment that can be provided to a user. For example, an individual workout video block may include one exercise.” [0049]
Display Fitness Effect
The combined references teach model. They do not teach display fitness effect.
Kalcal, III et al. also in the business of model teaches:
Fitness program and picture (displaying) an athlete that cause the user to look like the athlete (corresponding fitness effect)…
“In another aspect, in association with generation of a custom health and fitness program for a user, the user can provide program builder 904 with an image or visual representation of physical features the user desires. For example, the user could provide program builder component 904 with a picture of an athlete and request to have a program designed for the of the user that will cause the user to look like the athlete. In an another example, the user could provide program builder component 904 with an image of a supermodel and indicate that she would like to transform her body to look like the supermodel. In another example, the user could provide program builder component 904 with a picture of a specific body part and request a program that would help the user transform his or her corresponding body part to appear like that in the picture. For example, the user can provide program builder component 904 with an image of a person's shoulders and indicate he would like his shoulder to look like the picture.” [0181]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to display a fitness model as taught by Kaleal, III et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Kaleal, III et al. who also teaches the benefits of providing to a user what they will look at by performing a fitness program.
Determine Fitness Regimen
The combined references teach fitness. They do not teach determine fitness regimen.
Kaleal, III et al. also in the business of fitness teaches:
User to pick and choose (user select) different fitness programs to see how user would appear (fitness effect) in the future…
“This feature of avatar visualization platform 1302 allows a user to dynamically pick and choose different health and fitness programs and/or change different variables of a health and fitness program and see how the user would appear in the future based on the selected health and fitness program and/or the different variables. Accordingly, a user can select a health and fitness program that will cause the user to achieve an optimally desired appearance. For example, as the user selects different health and fitness programs and/or can changes variables of a selected health and fitness program, avatar visualization platform 1302 can dynamically adapt the appearance of an avatar presented to the user that corresponds to a predicted visualization of how the user will appear based on completion and adherence to the different health and fitness or the health and fitness program with the respectively chosen variables. As a result, the user can select a health and fitness program based on how it will affect the user's appearance.” [0200]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to determine a fitness regimen as taught by Kaleal, III et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Kaleal, III et al. who also teaches the benefits of a user selecting a fitness regimen and they can select a program that will provide them what they want to look like in the future.
Regarding claim 3
The recommendation method according to claim 1, wherein the determining fitness regimen information for the user to be recommended based on the fitness regimen information of the comparable user associated with the matching human body model comprises:
displaying a plurality of fitness effect information of a target part associated with the matching human body model to the user to be recommended, training periods and training intensities corresponding to the plurality of fitness effect information, according to the target part selected by the user to be recommended; and
[No Patentable Weight is given to non-functional descriptive claim language of “displaying a plurality of fitness effect information of a target part associated with the matching human body model to the user to be recommended, training periods and training intensities corresponding to the plurality of fitness effect information, according to the target part selected by the user to be recommended;” as this is just displaying information.]
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
King et al. teaches:
Display workout video (fitness effect information)…
“…For example, in some embodiments, communication component 140 causes a user interface to display the selected workout video block. In some cases, communications component 140 may be configured to communicate other information related to the selected workout video block (e.g., description, reviews, ratings, etc.). In some cases, communications component 140 may be configured to send other information to the user. For example, the user may receive promotions, health tips, workout tips, reminders (e.g., it has been “a number of days” since your last workout), and/or other information.” [0084]
Video (displaying) with legs, chest, back (target part)…
“In some embodiments, the selection of video blocks in a sequence is based on both a workout stream, the location of the user in the workout stream, and real-time feedback. For instance, a workout stream may specify a low-intensity warmup, a high-intensity warmup, legs, chest, back, arms, cardio, legs, chest, back, arms, cardio, legs, chest, back, arms, cardio, and a low-intensity cool down. Within each of these stages, each of which may correspond to a selection for a video block, some embodiments may select a video block consistent with the stage based on real-time feedback. For instance, after arms, the user may indicate they are overly tired via a native application, or a heart-rate monitor may indicate a heart rate above a threshold. In response, for the next stage, cardio, some embodiments may select a lower level of intensity than would have otherwise been choses, dynamically selecting a video block while staying within the confines of the stream to have a consistent workout sequence that is tailored to their experience. Variants are described below in which a gradient descent is used to train a model for selecting subsequent video blocks, workouts, or workout plans based on things like user preferences, injuries, and patterns in previous user feedback, and the like. Further, some embodiments may achieve this with a server architecture designed to serve a relatively large number of users bandwidth intensive video feeds.” [0032]
determining, based on target fitness effect information selected from the plurality of fitness effect information by the user to be recommended, a training period and a training intensity corresponding to the target fitness effect information as the fitness regimen information.
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
Example of low and high intensity (training intensity)…
“In some embodiments, the selection of video blocks in a sequence is based on both a workout stream, the location of the user in the workout stream, and real-time feedback. For instance, a workout stream may specify a low-intensity warmup, a high-intensity warmup, legs, chest, back, arms, cardio, legs, chest, back, arms, cardio, legs, chest, back, arms, cardio, and a low-intensity cool down. Within each of these stages, each of which may correspond to a selection for a video block, some embodiments may select a video block consistent with the stage based on real-time feedback. For instance, after arms, the user may indicate they are overly tired via a native application, or a heart-rate monitor may indicate a heart rate above a threshold. In response, for the next stage, cardio, some embodiments may select a lower level of intensity than would have otherwise been choses, dynamically selecting a video block while staying within the confines of the stream to have a consistent workout sequence that is tailored to their experience. Variants are described below in which a gradient descent is used to train a model for selecting subsequent video blocks, workouts, or workout plans based on things like user preferences, injuries, and patterns in previous user feedback, and the like. Further, some embodiments may achieve this with a server architecture designed to serve a relatively large number of users bandwidth intensive video feeds.” [0032]
Instructions for physical activities (recommended target fitness) Workout period of time (training period)…
“Access component 120 may be configured to access a collection of workout video blocks. In some embodiments, a workout video block is a video containing one or more of a portion of a workout, an exercise, a yoga pose, training, a warm-up, aerobics, a dance, a routine, a drill, other types of physical activities, or any combination thereof. In some cases, a workout video block may include instructions of physical activities for the cardiovascular system, strengthening muscles, weight loss, to help enhance or maintain physical fitness or overall health and wellness. A workout video block may have a duration of about three minutes, or in some cases, an integral multiple of some quanta, like 1 minute, to facilitate dynamic composition. In some cases, a workout video block may have a duration of less than three minutes. In some cases, a workout video block may have a duration of more than three minutes. In some embodiments, a given individual workout video block may be the smallest video segment that can be provided to a user. For example, an individual workout video block may include one exercise.” [0049]
Regarding claim 8
The recommendation method according to claim 1, wherein the determining, based on the physiological feature information of the user to be recommended, the matching human body model for the physiological feature information from the plurality of comparable human body models comprises:
determining the matching human body model from the plurality of comparable human body models based on the physiological feature information and living habit information of the user to be recommended.
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
King et al. teaches:
Instructions (determining) physical activities (fitness regimen information) for (based on) strengthening muscles, weight loss, etc. (effect information)…
“Access component 120 may be configured to access a collection of workout video blocks. In some embodiments, a workout video block is a video containing one or more of a portion of a workout, an exercise, a yoga pose, training, a warm-up, aerobics, a dance, a routine, a drill, other types of physical activities, or any combination thereof. In some cases, a workout video block may include instructions of physical activities for the cardiovascular system, strengthening muscles, weight loss, to help enhance or maintain physical fitness or overall health and wellness. A workout video block may have a duration of about three minutes, or in some cases, an integral multiple of some quanta, like 1 minute, to facilitate dynamic composition. In some cases, a workout video block may have a duration of less than three minutes. In some cases, a workout video block may have a duration of more than three minutes. In some embodiments, a given individual workout video block may be the smallest video segment that can be provided to a user. For example, an individual workout video block may include one exercise.” [0049]
Habits such as drink water…
“Some embodiments may also include video blocks that pertain to things other than exercises. For example, some embodiments may use the techniques described above to infer that particular habits would be helpful for the user and select videos advocating for and educating it about those habits. For example, some embodiments may follow a workout with the video block instructing the user to drink 64 ounces of water, to stretch, to engage in particular nutritional patent practices.” [0104]
Sleeping behavior (living habit information)…
“The third-party application program interface servers 928 are, in some cases, any of the various types of servers described above by which events and data relating to those events may be obtained from third-party applications, such as airline or hotel reservation systems, fitness-facility member management systems, third-party application program interface servers that expose data gathered by Internet of things appliances 930 or by wearable computing devices 926, and the like. In some embodiments, the Internet of things appliances 930 are home-based Internet of things appliances, like the examples described above, such as smart lights, network-connected scales, smart thermostats, smart refrigerators, security systems configured to indicate whether the user is present, smart beds or pillows configured to indicate a user sleeping behavior, and the like. In some embodiments, the Internet of things appliances 930 are appliances in a fitness facility, such as networked gym equipment having actuators by which intensity or difficulty of the workout equipment may be modulated and sensors by which a user's use of the equipment may be monitored, including biometric sensors and sensors indicative of movement of the equipment, like tachometers, accelerometers, strain gages, and the like. In some embodiments, these Internet of things appliances 930 may also expose application program interfaces, either directly to the prescription engine 922 or via one of the third-party API servers 928, by which actuators of the Internet of things appliances may be adjusted, for instance, responsive to a command changing a set point, content may be sent for presentation on the appliance, and by which data gathered by sensors of the Internet of things appliances 930 may be interrogated.” [0176]
Regarding claim 9
The recommendation method according to claim 8, wherein the determining the matching human body model from the plurality of comparable human body models based on the physiological feature information and the living habit information of the user to be recommended comprises:
determining a plurality of candidate human body models from the plurality of comparable human body models based on the physiological feature information of the user to be recommended; and
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
King et al. teaches:
Suggest trainers or friends (recommend) by cluster users (plurality of candidate) based on user profiles (human body model)…
“Some embodiments may suggest trainers or friends for a user to work out with. Some embodiments may cluster users and trainers according to various criteria, for example, attributes of user profiles, like goals or workout patterns. For instance, some embodiments may model users as feature vectors, with user profile attributes like workout goals, performance, timing, and feedback being mapped to scalars of the vectors. Some embodiments may cluster the vectors with a DBSCAN algorithm and suggesting pairings. Or some embodiments may rank pairings based on Euclidian distance in the vector space, e.g., suggesting to a user the five closest other users or trainers.” [0138]
determining the matching human body model from the plurality of candidate human body models based on the living habit information of the user to be recommended;
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
Profile (human body model) with behavior (habit)…
“In some embodiments, some of the user profile information may be obtained through accessing the user profile on one or more social media sites, or the user's online presence (instead of asking the user questions, user profile component 124 may be able to extract information about the user's behavior, past activities, preferences, user's ratings of other workouts, likes and dislikes, etc.). This operation may be performed by user profile component 124, or other components within or outside of computing environment 100. User profile component 124 may be configured to collect and store data about the user obtained from one or more social media sites, or from the user's online presence (e.g., products viewed, or bought online, viewing times, rating, behavior, etc.)” [0068]
or
determining a plurality of candidate human body models from the plurality of comparable human body models based on the living habit information of the user to be recommended; and
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
[No Patentable Weight is given to alternative claim language where only one limitation is required.]
determining the matching human body model from the plurality of candidate human body models based on the physiological feature information of the user to be recommended.
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
[No Patentable Weight is given to alternative claim language where only one limitation is required.]
Regarding claim 10
The recommendation method according to claim 8, wherein the determining the matching human body model from the plurality of comparable human body models based on the physiological feature information and the living habit information of the user to be recommended comprises:
determining the matching human body model from the plurality of comparable human body models, based on health condition information of the user to be recommended.
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
King et al. teaches:
User profile with health information…
“In some embodiments, user profile component 124 may be configured to obtain one or more user profiles or user other information associated with users of computing environment 100. The one or more user profiles or user information may include information located in one or more of the client computing platforms 102, external resources 106, sensors 104, storage 115, or other locations within or outside computing environment 100 (e.g., websites, web platforms, servers, storage mediums, cloud storage, etc.). The user profiles may include one or more of user profile attributes, for example, information identifying users (e.g., a username, a number, an identifier, or other identifying information), security login information (e.g., a login code or password), account information, subscription information, health information, medical information (e.g., medical history, medications, etc.), physiological information (e.g., height, weight, age, gender, etc.), fitness information (e.g., fitness level, fitness achievements, etc.), restrictions (e.g., exercise constraint), fitness goals, physiological information, relationship information (e.g., information related to relationships between users in the computing environment 100), usage information, demographic information, history, client computing platforms associated with a user, or other information related to users. In some embodiments, user profile component 124 may be configured to access websites, web platforms, servers, storage mediums, or other locations from where user profiles may be accessed, obtained, retrieved, or requested in response to requests from users, components within or outside computing environment 100, or other requests.” [0063]
Profiles to identify groups of users similar (comparable) to one another…
“In some embodiments, a machine-learning model may be trained to select workouts based on a user's goal. For example, in some cases, a training set may include a goal set by previous users, profiles of those users, workouts by those users, and an indication of whether the users achieve their goals. Some embodiments may filter the training set according to whether users satisfy their stated goals. Some embodiments may cluster users (e.g., with a density based clustering algorithm, like DB-SCAN) according to profiles to identify groups of users who are similar to one another, for example, of similar profiles and have sent similar goals. In some cases, some embodiments may then detect features of workouts within each of the clusters, for example, patterns in chosen workouts associated with meeting the goal for those in the cluster. For instance, for each cluster, embodiments may train a decision tree to classify users as likely to meet their goal based on workout history. Some embodiments may then use these detected features and clusters to recommend workouts for other users. For example, some embodiments may receive a request for workout from a given user, determine which cluster most closely matches that given user, and then select a workout for that user that includes the futures detected among the users in that cluster within their workouts.” [0123]
Regarding claim 11
The recommendation method according to claim 10, wherein the determining the matching human body model from the plurality of comparable human body models based on the health condition information of the user to be recommended comprises:
determining a plurality of candidate human body models from the plurality of comparable human body models based on the physiological feature information of the user to be recommended; and
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
King et al. teaches:
Suggest trainers or friends (recommend) by cluster users (plurality of candidate) based on user profiles (human body model)…
“Some embodiments may suggest trainers or friends for a user to work out with. Some embodiments may cluster users and trainers according to various criteria, for example, attributes of user profiles, like goals or workout patterns. For instance, some embodiments may model users as feature vectors, with user profile attributes like workout goals, performance, timing, and feedback being mapped to scalars of the vectors. Some embodiments may cluster the vectors with a DBSCAN algorithm and suggesting pairings. Or some embodiments may rank pairings based on Euclidian distance in the vector space, e.g., suggesting to a user the five closest other users or trainers.” [0138]
determining the matching human body model from the plurality of candidate human body models based on the living habit information and the health condition information of the user to be recommended;
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
“In some embodiments, user profile component 124 may be configured to obtain one or more user profiles or user other information associated with users of computing environment 100. The one or more user profiles or user information may include information located in one or more of the client computing platforms 102, external resources 106, sensors 104, storage 115, or other locations within or outside computing environment 100 (e.g., websites, web platforms, servers, storage mediums, cloud storage, etc.). The user profiles may include one or more of user profile attributes, for example, information identifying users (e.g., a username, a number, an identifier, or other identifying information), security login information (e.g., a login code or password), account information, subscription information, health information, medical information (e.g., medical history, medications, etc.), physiological information (e.g., height, weight, age, gender, etc.), fitness information (e.g., fitness level, fitness achievements, etc.), restrictions (e.g., exercise constraint), fitness goals, physiological information, relationship information (e.g., information related to relationships between users in the computing environment 100), usage information, demographic information, history, client computing platforms associated with a user, or other information related to users. In some embodiments, user profile component 124 may be configured to access websites, web platforms, servers, storage mediums, or other locations from where user profiles may be accessed, obtained, retrieved, or requested in response to requests from users, components within or outside computing environment 100, or other requests.” [0063]
or
determining a plurality of candidate human body models from the plurality of comparable human body models based on the living habit information and the health condition information of the user to be recommended; and
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
[No Patentable Weight is given to alternative claim language where only one limitation is required.]
determining the matching human body model from the plurality of candidate human body models based on the physiological feature information of the user to be recommended.
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
[No Patentable Weight is given to alternative claim language where only one limitation is required.]
Regarding claim 12
The recommendation method according to claim 1, further comprising:
obtaining physiological feature information after training of the user to be recommended after training for a preset period of time based on the fitness regimen information;
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
King et al. teaches:
Feedback of action on prescription acts (recommendations of fitness acts).…
“Further, in some embodiments, the prescription engine implements a feedback loop, where the action a user takes on any given prescription acts as input data to further influence future decision making by the prescription engine. By taking a machine learning approach, some embodiments improve the accuracy of prescriptions over time.” [0030]
Instructions for physical activities (recommended target fitness) Workout period of time (training period)…
“Access component 120 may be configured to access a collection of workout video blocks. In some embodiments, a workout video block is a video containing one or more of a portion of a workout, an exercise, a yoga pose, training, a warm-up, aerobics, a dance, a routine, a drill, other types of physical activities, or any combination thereof. In some cases, a workout video block may include instructions of physical activities for the cardiovascular system, strengthening muscles, weight loss, to help enhance or maintain physical fitness or overall health and wellness. A workout video block may have a duration of about three minutes, or in some cases, an integral multiple of some quanta, like 1 minute, to facilitate dynamic composition. In some cases, a workout video block may have a duration of less than three minutes. In some cases, a workout video block may have a duration of more than three minutes. In some embodiments, a given individual workout video block may be the smallest video segment that can be provided to a user. For example, an individual workout video block may include one exercise.” [0049]
generating a human body model after training based on the physiological feature information after training;
User has advanced to next stage and select (generating) second video block (human body model) based on feedback and after first video block….
“At operation 912 a second workout video block may be selected from the collection. In some cases, a session record may be updated to indicate the user has advanced to a next stage of a workout stream, e.g., from legs to back. The second video block may be selected based on the feedback, the intensity of the second workout video block, a current state of the user in a workout stream, and a body-region grouping of the second video block. In some embodiments, operation 912 may be performed by a selection component the same as or similar to selection component 130 (shown in FIG. 1 and described herein).” [0115]
determining a matching human body model for the human body model after training from the plurality of comparable human body models based on the human body model after training; and
Workout video with avatar (human body model) that looks like the user
“…In some embodiments, a workout video block may include instructions provided by a machine generated character configured to describe or demonstrate workout instructions (e.g., an avatar). In some embodiments, the user may choose an avatar from a list of avatars (e.g., avatar that looks like the user or an avatar that looks like a celebrity, etc.). In some embodiments, the user may upload an image (of themselves of someone else to be used in the creation of the avatar). In some cases, a workout video block may include a combination of instructions provided (described or demonstrated) by a human and a machine generated character. For example, a video may show a real person describing the workout and an avatar performing the workout…” [0062]
Video based on user profile information…
“Selection component 130 may be configured to select a first workout video block from the collection of video blocks. In some cases, selection of the first video block may be based on one or more of the user profile information, the user preference, information obtained from sensors 104, the groupings of the workout video blocks (e.g., body-region), workout intensity level, information obtained from other components of computing environment 100, or any combination thereof. For example, a first workout video may be selected based on one or more of the fitness goal of the user, a fitness level of the user, exercise constraints, health information, medical information, or other user profile attributes (described above)…” [0069]
determining new fitness regimen information for the user to be recommended based on fitness regimen information associated with the matching human body model for the human body model after training.
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
Second video block with workout (new fitness regimen information)…
“At operation 912 a second workout video block may be selected from the collection. In some cases, a session record may be updated to indicate the user has advanced to a next stage of a workout stream, e.g., from legs to back. The second video block may be selected based on the feedback, the intensity of the second workout video block, a current state of the user in a workout stream, and a body-region grouping of the second video block. In some embodiments, operation 912 may be performed by a selection component the same as or similar to selection component 130 (shown in FIG. 1 and described herein).” [0115]
Claims 4, 6, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (9) above in further view of Pub. No. US 2021/0406324 to Cuan et al.
Regarding claim 4
The recommendation method according to claim 1, wherein the determining, based on the physiological feature information of the user to be recommended, the matching human body model for the physiological feature information from the plurality of comparable human body models comprises:
generating a user human body model of the user to be recommended based on the physiological feature information of the user to be recommended; and
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
King et al. teaches:
Example of avatar and video (matching human body model) and video with the same group (comparable human body models)….
“In some embodiments, a workout video block may include instructions provided by a human instructor (e.g., a coach, a trainer, a physical therapist, a chiropractor, a healthcare provider, or other person giving the workout instructions). In some cases, the instructions may be described or demonstrated by the person giving the instructions in the video, described by a person different than the persons demonstrating the workout instructions, or any combination thereof. In some embodiments, a workout video block may include instructions provided by a machine generated character configured to describe or demonstrate workout instructions (e.g., an avatar). In some embodiments, the user may choose an avatar from a list of avatars (e.g., avatar that looks like the user or an avatar that looks like a celebrity, etc.). In some embodiments, the user may upload an image (of themselves of someone else to be used in the creation of the avatar). In some cases, a workout video block may include a combination of instructions provided (described or demonstrated) by a human and a machine generated character. For example, a video may show a real person describing the workout and an avatar performing the workout. In some embodiments, workout video blocks in the same group (e.g., family, block type, set, stream, or other groups) may be provided by the same instructor (human, or machine generated). For example, a user may choose any particular instructor for any workout video block (every instructor provides instructions for all the workout sessions available to the user). In some cases, specific instructors (human, or machine generated) may provide specific workout instructions. For example, some instructors may only give instructions for specific body-regions (e.g., legs, arms, etc.), for specific level of difficulty, for specific workout type (e.g., warm-up, cool down, cardio, etc.), or other specific workout instructions.” [0062]
determining the matching human body model based on a degree of similarity between the user human body model and the plurality of comparable human body models,
Density based clustering (degree of similarity) with similar profiles (body models)…
“…Some embodiments may cluster users (e.g., with a density based clustering algorithm, like DB-SCAN) according to profiles to identify groups of users who are similar to one another, for example, of similar profiles and have sent similar goals. In some cases, some embodiments may then detect features of workouts within each of the clusters, for example, patterns in chosen workouts associated with meeting the goal for those in the cluster. For instance, for each cluster, embodiments may train a decision tree to classify users as likely to meet their goal based on workout history. Some embodiments may then use these detected features and clusters to recommend workouts for other users. For example, some embodiments may receive a request for workout from a given user, determine which cluster most closely matches that given user, and then select a workout for that user that includes the futures detected among the users in that cluster within their workouts.” [0123]
Profile with physiological information…
“…The user profiles may include one or more of user profile attributes, for example, information identifying users (e.g., a username, a number, an identifier, or other identifying information), security login information (e.g., a login code or password), account information, subscription information, health information, medical information (e.g., medical history, medications, etc.), physiological information (e.g., height, weight, age, gender, etc.), fitness information (e.g., fitness level, fitness achievements, etc.), restrictions (e.g., exercise constraint), fitness goals, physiological information, relationship information (e.g., information related to relationships between users in the computing environment 100), usage information, demographic information, history, client computing platforms associated with a user, or other information related to users. In some embodiments, user profile component 124 may be configured to access websites, web platforms, servers, storage mediums, or other locations from where user profiles may be accessed, obtained, retrieved, or requested in response to requests from users, components within or outside computing environment 100, or other requests…” [0063]
See Degree of Similarity below.
wherein the degree of similarity is determined based on a size of an overlap area between the user human body model and the plurality of comparable human body models, or the degree of similarity is determined based on a cosine value of an angle between a user feature vector corresponding to the user human body model and comparable feature vectors corresponding to the plurality of comparable human body models.
Model users as feature vectors with user profile attributes…
“Some embodiments may suggest trainers or friends for a user to work out with. Some embodiments may cluster users and trainers according to various criteria, for example, attributes of user profiles, like goals or workout patterns. For instance, some embodiments may model users as feature vectors, with user profile attributes like workout goals, performance, timing, and feedback being mapped to scalars of the vectors. Some embodiments may cluster the vectors with a DBSCAN algorithm and suggesting pairings. Or some embodiments may rank pairings based on Euclidian distance in the vector space, e.g., suggesting to a user the five closest other users or trainers.” [0138]
Degree of Similarity
The combined reverences teach vector and similarity. They do not literally teach degree of similarity.
Cuan et al. also in the business of vector and similarity teaches:
Cosine of an angle between two feature vectors equal to 1 then similar…
“After features are extracted and a feature vector is generated representing the extracted features, a similarity score may be computed indicating how similar a given image's feature vector is to a feature vector representing features extracted from an image used to train the object recognition model. In some embodiments, a similarity between two images may be determined by computing a distance in an n-dimensional feature space between the feature vector representing an image captured by client device 104 and a feature vector of a corresponding image from the training data set. For example, the distance computed may be a cosine distance, a Minkowski distance, a Euclidean distance, or other metric by which similarity may be computed. In some embodiments, the distance between the two feature vectors may be compared to a threshold distance. If the distance is less than or equal to the threshold distance, then the two images may be classified as being similar, classified as depicting a same or similar object, or both. For example, if a cosine of an angle between the two feature vectors produces a value that is approximately equal to 1 (e.g., Cos(θ)≥0.75, Cos(θ)≥0.8, Cos(θ)≥0.85, Cos(θ)≥0.9, Cos(θ)≥0.95, Cos(θ)≥0.99, etc.), then the two feature vectors may describe similar visual features, and therefore the objects depicted within the images with which the features were extracted from may be classified as being similar.” [0035]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use degree of similarity as taught by Caun et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Caun et al. who teaches the benefits of using angles for cosine similarity.
Regarding claim 6
The recommendation method according to claim 4, wherein the determining the matching human body model based on the degree of similarity between the user human body model and the plurality of comparable human body models comprises:
proportionally scaling the user human body model to generate a scaled user human body model which matches the size of the plurality of comparable human body models; and
King et al. teaches:
Example of avatar and a video (scaled human body model)…
“…In some embodiments, the user may choose an avatar from a list of avatars (e.g., avatar that looks like the user or an avatar that looks like a celebrity, etc.). In some embodiments, the user may upload an image (of themselves of someone else to be used in the creation of the avatar). In some cases, a workout video block may include a combination of instructions provided (described or demonstrated) by a human and a machine generated character. For example, a video may show a real person describing the workout and an avatar performing the workout. In some embodiments, workout video blocks in the same group (e.g., family, block type, set, stream, or other groups) may be provided by the same instructor (human, or machine generated)…” [0062]
determining a matching human body model based on the degree of similarity between the scaled user human body model and the comparable human body models.
Avatar looks like (degree of similarity) the user…
“…In some embodiments, the user may choose an avatar from a list of avatars (e.g., avatar that looks like the user or an avatar that looks like a celebrity, etc.). In some embodiments, the user may upload an image (of themselves of someone else to be used in the creation of the avatar). In some cases, a workout video block may include a combination of instructions provided (described or demonstrated) by a human and a machine generated character. For example, a video may show a real person describing the workout and an avatar performing the workout. In some embodiments, workout video blocks in the same group (e.g., family, block type, set, stream, or other groups) may be provided by the same instructor (human, or machine generated)…” [0062] Inherent with avatar and video is scaled.
Degree of Similarity
The combined reverences teach vector and similarity. They do not literally teach degree of similarity.
Cuan et al. also in the business of vector and similarity teaches:
Cosine of an angle between two feature vectors equal to 1 then similar…
“After features are extracted and a feature vector is generated representing the extracted features, a similarity score may be computed indicating how similar a given image's feature vector is to a feature vector representing features extracted from an image used to train the object recognition model. In some embodiments, a similarity between two images may be determined by computing a distance in an n-dimensional feature space between the feature vector representing an image captured by client device 104 and a feature vector of a corresponding image from the training data set. For example, the distance computed may be a cosine distance, a Minkowski distance, a Euclidean distance, or other metric by which similarity may be computed. In some embodiments, the distance between the two feature vectors may be compared to a threshold distance. If the distance is less than or equal to the threshold distance, then the two images may be classified as being similar, classified as depicting a same or similar object, or both. For example, if a cosine of an angle between the two feature vectors produces a value that is approximately equal to 1 (e.g., Cos(θ)≥0.75, Cos(θ)≥0.8, Cos(θ)≥0.85, Cos(θ)≥0.9, Cos(θ)≥0.95, Cos(θ)≥0.99, etc.), then the two feature vectors may describe similar visual features, and therefore the objects depicted within the images with which the features were extracted from may be classified as being similar.” [0035]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use degree of similarity as taught by Caun et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Caun et al. who teaches the benefits of using angles for cosine similarity.
Regarding claim 13
The recommendation method according to claim 1, wherein the determining, based on the physiological feature information of the user to be recommended, the matching human body model for the physiological feature information from the plurality of comparable human body models comprises:
generating a user feature vector based on the physiological feature information of the user to be recommended;
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
King et al. teaches:
Training set with profiles of users (physiological feature information)…
“In some embodiments, a machine-learning model may be trained to select workouts based on a user's goal. For example, in some cases, a training set may include a goal set by previous users, profiles of those users, workouts by those users, and an indication of whether the users achieve their goals. Some embodiments may filter the training set according to whether users satisfy their stated goals. Some embodiments may cluster users (e.g., with a density based clustering algorithm, like DB-SCAN) according to profiles to identify groups of users who are similar to one another, for example, of similar profiles and have sent similar goals. In some cases, some embodiments may then detect features of workouts within each of the clusters, for example, patterns in chosen workouts associated with meeting the goal for those in the cluster. For instance, for each cluster, embodiments may train a decision tree to classify users as likely to meet their goal based on workout history. Some embodiments may then use these detected features and clusters to recommend workouts for other users. For example, some embodiments may receive a request for workout from a given user, determine which cluster most closely matches that given user, and then select a workout for that user that includes the futures detected among the users in that cluster within their workouts.” [0123]
Example of predictive model a using vector…
“… In some cases, a predictive model (e.g., a vector of weights) may be calculated as a batch process run periodically. Some embodiments may construct the model by, for example, assigning randomly selected weights; calculating an error amount with which the model describes the historical data and a rates of change in that error as a function of the weights in the model in the vicinity of the current weight (e.g., a derivative, or local slope); and incrementing the weights in a downward (or error reducing) direction. In some cases, these steps may be iteratively repeated until a change in error between iterations is less than a threshold amount, indicating at least a local minimum, if not a global minimum. To mitigate the risk of local minima, some embodiments may repeat the gradient descent optimization with multiple initial random values to confirm that iterations converge on a likely global minimum error. Other embodiments may iteratively adjust other machine learning models to reduce the error function, e.g., with a greedy algorithm that optimizes for the current iteration. The resulting, trained model, e.g., a vector of weights or thresholds, may be stored in memory and later retrieved for application to new calculations on newly calculated aggregate estimates.” [0124]
determining a matching user from the plurality of comparable users based on a cosine similarity between the user feature vector and comparable feature vectors of the plurality of comparable users, the comparable feature vectors being generated based on physiological feature information of the comparable users; and
Cluster users based on user profiles and feature vectors…
“Some embodiments may suggest trainers or friends for a user to work out with. Some embodiments may cluster users and trainers according to various criteria, for example, attributes of user profiles, like goals or workout patterns. For instance, some embodiments may model users as feature vectors, with user profile attributes like workout goals, performance, timing, and feedback being mapped to scalars of the vectors. Some embodiments may cluster the vectors with a DBSCAN algorithm and suggesting pairings. Or some embodiments may rank pairings based on Euclidian distance in the vector space, e.g., suggesting to a user the five closest other users or trainers.” [0138]
See Cosine Similarity below.
determining a comparable human body model of the matching user as the matching human body model.
Rank pairing of closes other users…
“Some embodiments may suggest trainers or friends for a user to work out with. Some embodiments may cluster users and trainers according to various criteria, for example, attributes of user profiles, like goals or workout patterns. For instance, some embodiments may model users as feature vectors, with user profile attributes like workout goals, performance, timing, and feedback being mapped to scalars of the vectors. Some embodiments may cluster the vectors with a DBSCAN algorithm and suggesting pairings. Or some embodiments may rank pairings based on Euclidian distance in the vector space, e.g., suggesting to a user the five closest other users or trainers.” [0138]
Cosine Similarity
The combined reverences teach vector and similarity. They do not literally teach cosine similarity.
Cuan et al. also in the business of vector and similarity teaches:
Cosine of an angle between two feature vectors equal to 1 then similar…
“After features are extracted and a feature vector is generated representing the extracted features, a similarity score may be computed indicating how similar a given image's feature vector is to a feature vector representing features extracted from an image used to train the object recognition model. In some embodiments, a similarity between two images may be determined by computing a distance in an n-dimensional feature space between the feature vector representing an image captured by client device 104 and a feature vector of a corresponding image from the training data set. For example, the distance computed may be a cosine distance, a Minkowski distance, a Euclidean distance, or other metric by which similarity may be computed. In some embodiments, the distance between the two feature vectors may be compared to a threshold distance. If the distance is less than or equal to the threshold distance, then the two images may be classified as being similar, classified as depicting a same or similar object, or both. For example, if a cosine of an angle between the two feature vectors produces a value that is approximately equal to 1 (e.g., Cos(θ)≥0.75, Cos(θ)≥0.8, Cos(θ)≥0.85, Cos(θ)≥0.9, Cos(θ)≥0.95, Cos(θ)≥0.99, etc.), then the two feature vectors may describe similar visual features, and therefore the objects depicted within the images with which the features were extracted from may be classified as being similar.” [0035]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use degree of similarity as taught by Caun et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Caun et al. who teaches the benefits of using angles for cosine similarity.
Regarding claim 14
The recommendation method according to claim 13, wherein the generating the user feature vector based on the physiological feature information of the user to be recommended comprises:
generating the user feature vector based on at least one of living habit information of the user to be recommended and a weight of the living habit information, or health condition information of the user to be recommended and a weight of the health condition information, and
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
[No Patentable Weight is given to alternative claim language where only one limitation is required.]
King et al. teaches:
Vectors with user profiles…
“Some embodiments may suggest trainers or friends for a user to work out with. Some embodiments may cluster users and trainers according to various criteria, for example, attributes of user profiles, like goals or workout patterns. For instance, some embodiments may model users as feature vectors, with user profile attributes like workout goals, performance, timing, and feedback being mapped to scalars of the vectors. Some embodiments may cluster the vectors with a DBSCAN algorithm and suggesting pairings. Or some embodiments may rank pairings based on Euclidian distance in the vector space, e.g., suggesting to a user the five closest other users or trainers.” [0138]
Profiles may include health information…
“In some embodiments, user profile component 124 may be configured to obtain one or more user profiles or user other information associated with users of computing environment 100. The one or more user profiles or user information may include information located in one or more of the client computing platforms 102, external resources 106, sensors 104, storage 115, or other locations within or outside computing environment 100 (e.g., websites, web platforms, servers, storage mediums, cloud storage, etc.). The user profiles may include one or more of user profile attributes, for example, information identifying users (e.g., a username, a number, an identifier, or other identifying information), security login information (e.g., a login code or password), account information, subscription information, health information, medical information (e.g., medical history, medications, etc.), physiological information (e.g., height, weight, age, gender, etc.), fitness information (e.g., fitness level, fitness achievements, etc.), restrictions (e.g., exercise constraint), fitness goals, physiological information, relationship information (e.g., information related to relationships between users in the computing environment 100), usage information, demographic information, history, client computing platforms associated with a user, or other information related to users. In some embodiments, user profile component 124 may be configured to access websites, web platforms, servers, storage mediums, or other locations from where user profiles may be accessed, obtained, retrieved, or requested in response to requests from users, components within or outside computing environment 100, or other requests.” [0063]
Example of predictive model a using vector…
“… In some cases, a predictive model (e.g., a vector of weights) may be calculated as a batch process run periodically. Some embodiments may construct the model by, for example, assigning randomly selected weights; calculating an error amount with which the model describes the historical data and a rates of change in that error as a function of the weights in the model in the vicinity of the current weight (e.g., a derivative, or local slope); and incrementing the weights in a downward (or error reducing) direction. In some cases, these steps may be iteratively repeated until a change in error between iterations is less than a threshold amount, indicating at least a local minimum, if not a global minimum. To mitigate the risk of local minima, some embodiments may repeat the gradient descent optimization with multiple initial random values to confirm that iterations converge on a likely global minimum error. Other embodiments may iteratively adjust other machine learning models to reduce the error function, e.g., with a greedy algorithm that optimizes for the current iteration. The resulting, trained model, e.g., a vector of weights or thresholds, may be stored in memory and later retrieved for application to new calculations on newly calculated aggregate estimates.” [0124]
the physiological feature information of the user to be recommended and a weight of the physiological feature information, the comparable feature vector of the comparable user being generated based on at least one of living habit information of the comparable user and a weight of the living habit information, or health condition information of the comparable user and a weight of the health condition information, and physiological feature information of the comparable user and a weight of the physiological feature information.
Regarding claim 15
The recommendation method according to claim 14, wherein
the weight of the health condition information of the user to be recommended is greater than the weight of the living habit information of the user to be recommended, and the weight of the living habit information of the user to be recommended is greater than the weight of the physiological feature information of the user to be recommended; and
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
See Assign Weight below.
the weight of the health condition information of the comparable user is greater than the weight of the living habit information of the comparable user, and the weight of living habit information of the comparable user is greater than the weight of the physiological feature information of the comparable user.
See Assign Weight below.
Assign Weight
The combined references teach vector and weight. They do not explicitly teach weight for health condition greater than living habit information and greater than physiological feature information for a user and comparable user. However, one of ordinary skill in the art would recognize that weights can be assigned based on various criteria to achieve a desired outcome or priority.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that weights can be assigned based on desired outcome or priorities. This would have been known work in the field of endeavor prompting variations of it in the same field based on use of weights and would provide predictable results.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (10) above in further view of Pub. No. US 2018/0353836 to Li et al.
Regarding claim 5
The recommendation method according to claim 4, wherein the determining the matching human body model based on the degree of similarity between the user human body model and the plurality of comparable human body models comprises:
determining a comparable human body model with an overlap area greater than a threshold value with the user human body model as the matching human body model.
The combined references teach images. They do not teach overlap.
Li et al. also in the business of images teaches:
Overlay representing a upper and lower ranges of individuals and spanning the distance between (greater than) the upper and lower ranges…
“The key frame detector 230 may work in conjunction with the output generator 240 to provide the user with an opportunity to adjust the key frame for increased accuracy. The key frame including a representation of a reference position zone (e.g., an overlay representing a positional range of a human body at the key stage, etc.) corresponding to the key stage of the activity may be generated for output on a display device of a mobile device of the participant. In an example, the reference position zone may key be generated using a model for the stage of the activity. For example, the model may include upper and lower ranges for images of individuals engaged in the activity labeled as corresponding with the key stage and the reference position zone may be created by generating an overlay spanning the distance between the upper and the lower ranges of the images of the individuals engaged in the activity. The generated key frame including the representation of the reference zone position and a set of frames having timestamps between a start timestamp of the activity and an end timestamp for the activity may be transmitted (e.g., by the transceiver 215) for display on the display device. An input may be received indicating that a new frame has been selected for the key stage, and the new frame may be selected as the key frame. For example, the baseball player may be presented a display of the selected key frame with an overlay of a position of a model player at the key stage. The user may be able to scroll forward and backward through frames of the video stream and may select a frame the player feels is a better match to the position overlay. The frame selected by the player may then be used as the key frame for the key stage.” [0031]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to determine an overlap area as taught by Li et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Li et al. who teaches the advantages of ensuring an overlay covers upper and lower images of an individual.
Degree of Similarity
The combined reverences teach vector and similarity. They do not literally teach degree of similarity.
Cuan et al. also in the business of vector and similarity teaches:
Cosine of an angle between two feature vectors equal to 1 then similar…
“After features are extracted and a feature vector is generated representing the extracted features, a similarity score may be computed indicating how similar a given image's feature vector is to a feature vector representing features extracted from an image used to train the object recognition model. In some embodiments, a similarity between two images may be determined by computing a distance in an n-dimensional feature space between the feature vector representing an image captured by client device 104 and a feature vector of a corresponding image from the training data set. For example, the distance computed may be a cosine distance, a Minkowski distance, a Euclidean distance, or other metric by which similarity may be computed. In some embodiments, the distance between the two feature vectors may be compared to a threshold distance. If the distance is less than or equal to the threshold distance, then the two images may be classified as being similar, classified as depicting a same or similar object, or both. For example, if a cosine of an angle between the two feature vectors produces a value that is approximately equal to 1 (e.g., Cos(θ)≥0.75, Cos(θ)≥0.8, Cos(θ)≥0.85, Cos(θ)≥0.9, Cos(θ)≥0.95, Cos(θ)≥0.99, etc.), then the two feature vectors may describe similar visual features, and therefore the objects depicted within the images with which the features were extracted from may be classified as being similar.” [0035]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use degree of similarity as taught by Caun et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Caun et al. who teaches the benefits of using angles for cosine similarity.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (10) above in further view of Patent No. US 12004871 to Fazeli et al.
Regarding claim 7
The generation method according to claim 4, wherein the generating the user human body model of the user to be recommended based on the physiological feature information of the user to be recommended comprises:
generating a user current human body model of the user to be recommended based on the physiological feature information of the user to be recommended; and
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
King et al. teaches:
User may choose (generating) an avatar that looks like the user (current human body model)…
“…In some embodiments, the user may choose an avatar from a list of avatars (e.g., avatar that looks like the user or an avatar that looks like a celebrity, etc.). In some embodiments, the user may upload an image (of themselves of someone else to be used in the creation of the avatar). In some cases, a workout video block may include a combination of instructions provided (described or demonstrated) by a human and a machine generated character. For example, a video may show a real person describing the workout and an avatar performing the workout. In some embodiments, workout video blocks in the same group (e.g., family, block type, set, stream, or other groups) may be provided by the same instructor (human, or machine generated)…” [0062] Inherent with avatar and video is scaled.
generating a user target human body model according to an adjustment of the user to be recommended for the user current human body model based on a fitness target;
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
Target profile (human body model) and prescriptions (recommendations)…
“Some embodiments mitigate some (and in some cases all) of the above-described issues with a system referred to as a “prescription engine” that, in some embodiments, guides a user through their fitness journey by leveraging resources, both physical and digital, for prescription at any given moment to advance them towards their health goals. It does this by responding to specific events (such as triggers) to activate prescriptions based on a user's profile and needs. Some implementations tell a user exactly what to do, how to do it, where to do it, and why they should be doing it at any second throughout the day when the prescription engine or determines such guidance relevant. As explained below, events activate prescriptions based on the target user's profile and needs.” [0029]
Where prescribe is fitness activities…
“Some aspects include a process to prescribe fitness-related activities responsive to events, the process including: receiving, with one or more processors, with a fitness prescription engine, an event indicative of a current or future state of a user, wherein: the user is associated with a mobile computing device upon which a native application executes; the native application is configured to send events to the fitness prescription engine via a network; and the fitness prescription engine is configured to prescribe fitness-related activities responsive to events for a user-base with a plurality of users including the user; determining, with one or more processors, with the fitness prescription engine, a fitness need of the user based on a user profile of the user, wherein: the fitness need is determined based on a fitness goal of the user in the user profile; and the fitness need is determined based on a previous fitness-related prescription in the user profile; determining, with one or more processors, with the fitness prescription engine, a current fitness-related prescription in response to both the fitness need and the event, wherein: determining the current fitness-related prescription comprises selecting a fitness resource from among a plurality of candidate fitness resources; and the selected fitness resource is designated in the current prescription as a fitness resource to be accessed by the user in accordance with the current prescription; and causing, with one or more processors, with the fitness prescription engine, the current fitness-related prescription to be presented to the user.” [0012]
See Target Body below.
the determining the matching human body model based on the degree of similarity between the user human body model and the plurality of comparable human body models comprises;
Avatar looks like (degree of similarity) the user…
“…In some embodiments, the user may choose an avatar from a list of avatars (e.g., avatar that looks like the user or an avatar that looks like a celebrity, etc.). In some embodiments, the user may upload an image (of themselves of someone else to be used in the creation of the avatar). In some cases, a workout video block may include a combination of instructions provided (described or demonstrated) by a human and a machine generated character. For example, a video may show a real person describing the workout and an avatar performing the workout. In some embodiments, workout video blocks in the same group (e.g., family, block type, set, stream, or other groups) may be provided by the same instructor (human, or machine generated)…” [0062] Inherent with avatar and video is scaled.
determining the matching human body model based on the degree of similarity between the user target human body model and the plurality of comparable human body models.
See Target Body below.
Target Body
The combined references teach body model. They do not teach target body model and similarity.
Fazeli et al. also in the business of body model teaches:
Target body model and measurements similar to another person…
“In other examples, a user may select a 2D image of a body of another person, such as a celebrity, and the disclosed implementations will utilize the image of that other body to determine a target body composition and to generate a predicted personalized 3D body model of the body of the user with body measurements similar to those of the body of the other person. For example, the selected or provided 2D image of the body of another person, such as a celebrity, may be processed to determine body composition of the body represented in the image and/or to identify the person in the image so that body measurements and body parameters of the body of that other person can be obtained from other sources. For example, if the selected image is an image of a body of a celebrity, the celebrity may be identified and body measurements and body parameters of the body of the celebrity obtained from one or more public sources. Based on the body measurements and body parameters of the body of the other person, the personalized 3D body model of the body of the user may be altered to generate a predicted personalized 3D body model of that user with body measurements that correspond to those of the body represented in the selected or provided image (e.g., the image of a celebrity).” (col. 4, lines 12-34)
“Still further, the disclosed implementations, using the current body measurements of the user and user selected predicted body measurements, referred to herein as target body measurements, may generate a body change journey that the user can follow that guides the user through nutrition, sleep, exercise, etc., so that the user can change their current body composition to the target body composition. Periodic check-ins with visual updates of the predicted and actual changes in body measurements may be provided to encourage the user, adjust the body change journey if necessary, and to keep the user motivated toward a selected goal/target.” (col. 4, lines 35-46)
Using plurality of bodies…
“Each silhouette 1154 representative of the body may then be processed to determine body traits or features of the human body. For example, different CNNs may be trained using silhouettes of bodies, such as human bodies, from different orientations with known features. In some implementations, different CNNs may be trained for different orientations. For example, a first CNN 1156A-1 may be trained to determine front view features from front view silhouettes 1154-1. A second CNN 1156A-2 may be trained to determine right side features from right side silhouettes. A third CNN 1156A-3 may be trained to determine back view features from back view silhouettes. A fourth CNN 1156A-4 may be trained to determine left side features from left side silhouettes. Different CNNs 1156A-1 through 1156A-N may be trained for each of the different orientations of silhouettes 1154-1 through 1154-N. Alternatively, one CNN may be trained to determine features from any orientation silhouette.” (col. 32, lines 40-57)
“Utilizing the personalized 3D body parameters, a personalized 3D body model of the body is generated. For example, the personalized 3D body parameters may be provided to a body model, such as the Shape Completion and Animation of People (“SCAPE”) body model, a Skinned Multi-Person Linear (“SMPL”) body model, etc., and the body model may generate the personalized 3D body model of the body of the user based on those predicted body parameters.” (col. 26, lines 23-30)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to determine similarity with a target body as taught by Fazeli et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Fazeli et al. who teaches the advantages of knowing the difference between a target body model and current body.
Degree of Similarity
The combined reverences teach vector and similarity. They do not literally teach degree of similarity.
Cuan et al. also in the business of vector and similarity teaches:
Cosine of an angle between two feature vectors equal to 1 then similar…
“After features are extracted and a feature vector is generated representing the extracted features, a similarity score may be computed indicating how similar a given image's feature vector is to a feature vector representing features extracted from an image used to train the object recognition model. In some embodiments, a similarity between two images may be determined by computing a distance in an n-dimensional feature space between the feature vector representing an image captured by client device 104 and a feature vector of a corresponding image from the training data set. For example, the distance computed may be a cosine distance, a Minkowski distance, a Euclidean distance, or other metric by which similarity may be computed. In some embodiments, the distance between the two feature vectors may be compared to a threshold distance. If the distance is less than or equal to the threshold distance, then the two images may be classified as being similar, classified as depicting a same or similar object, or both. For example, if a cosine of an angle between the two feature vectors produces a value that is approximately equal to 1 (e.g., Cos(θ)≥0.75, Cos(θ)≥0.8, Cos(θ)≥0.85, Cos(θ)≥0.9, Cos(θ)≥0.95, Cos(θ)≥0.99, etc.), then the two feature vectors may describe similar visual features, and therefore the objects depicted within the images with which the features were extracted from may be classified as being similar.” [0035]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use degree of similarity as taught by Caun et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Caun et al. who teaches the benefits of using angles for cosine similarity.
Claims 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (10) above in further view of Pub. No. US 2016/0203263 to Maier et al.
Regarding claim 16
The recommendation method according to claim 13, wherein the determining the matching user from the plurality of comparable users based on the cosine similarity between the user feature vector and comparable feature vectors of the plurality of comparable users comprises:
determining, using a machine learning model, a user training target vector of the user to be recommended based on the user feature vector, the user training target vector comprising training target information corresponding to a body part which the user to be recommended wants to achieve;
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
King et al. teaches:
Feature vectors with user profiles…
“Some embodiments may suggest trainers or friends for a user to work out with. Some embodiments may cluster users and trainers according to various criteria, for example, attributes of user profiles, like goals or workout patterns. For instance, some embodiments may model users as feature vectors, with user profile attributes like workout goals, performance, timing, and feedback being mapped to scalars of the vectors. Some embodiments may cluster the vectors with a DBSCAN algorithm and suggesting pairings. Or some embodiments may rank pairings based on Euclidian distance in the vector space, e.g., suggesting to a user the five closest other users or trainers.” [0138]
Training set with profiles of users (physiological feature information) and select workout (training target) for a user …
“In some embodiments, a machine-learning model may be trained to select workouts based on a user's goal. For example, in some cases, a training set may include a goal set by previous users, profiles of those users, workouts by those users, and an indication of whether the users achieve their goals. Some embodiments may filter the training set according to whether users satisfy their stated goals. Some embodiments may cluster users (e.g., with a density based clustering algorithm, like DB-SCAN) according to profiles to identify groups of users who are similar to one another, for example, of similar profiles and have sent similar goals. In some cases, some embodiments may then detect features of workouts within each of the clusters, for example, patterns in chosen workouts associated with meeting the goal for those in the cluster. For instance, for each cluster, embodiments may train a decision tree to classify users as likely to meet their goal based on workout history. Some embodiments may then use these detected features and clusters to recommend workouts for other users. For example, some embodiments may receive a request for workout from a given user, determine which cluster most closely matches that given user, and then select a workout for that user that includes the futures detected among the users in that cluster within their workouts.” [0123]
Machine generated workout instructions for specific body-regions…
“… For example, a user may choose any particular instructor for any workout video block (every instructor provides instructions for all the workout sessions available to the user). In some cases, specific instructors (human, or machine generated) may provide specific workout instructions. For example, some instructors may only give instructions for specific body-regions (e.g., legs, arms, etc.), for specific level of difficulty, for specific workout type (e.g., warm-up, cool down, cardio, etc.), or other specific workout instructions.” [0062]
determining a user feature matrix of the user to be recommended based on the user feature vector and the user training target vector; and
[No Patentable Weight is given to intended use language “to be recommended” as recommending never happens.]
“In some embodiments, the block type groups may be grouped into sets. For example, as shown in FIG. 2, a set 240 may include one or more block type groups 230 each block type group 230 representing a different body region (e.g., a “Warm Up” set may include a “lower”, “upper”, “back” block type groups).” [0055]
Example of matrix with attributes (feature matrix)…
“Three blocks, three families, and three types are shown, but commercially relevant embodiments are expected to include substantially more of each, with substantially more families in each type, and substantially more blocks in each family. In some cases, the blocks may be arranged in a multi-dimensional matrix, with a pointer to a given video block file at each value of the matrix. Dimensions of the matrix may correspond to attributes of the video blocks, e.g., a muscle, a muscle group, an intensity, a range of movement, and the like. A data structure need not be referred to as a matrix in program code to serve as a matrix, provided that the data structure associates each video block with a plurality of attributes that can correspond to dimensions of a matrix. In some cases, the dimensions may include those listed in FIG. 3.” [0058]
determining a matching user from the plurality of comparable users based on a degree of similarity between the user feature matrix and comparable feature matrices of the plurality of comparable users, the comparable feature matrices being generated based on comparable feature vectors and comparable training target vectors of the plurality of comparable users.
Matrix with user feature….
“Three blocks, three families, and three types are shown, but commercially relevant embodiments are expected to include substantially more of each, with substantially more families in each type, and substantially more blocks in each family. In some cases, the blocks may be arranged in a multi-dimensional matrix, with a pointer to a given video block file at each value of the matrix. Dimensions of the matrix may correspond to attributes of the video blocks, e.g., a muscle, a muscle group, an intensity, a range of movement, and the like. A data structure need not be referred to as a matrix in program code to serve as a matrix, provided that the data structure associates each video block with a plurality of attributes that can correspond to dimensions of a matrix. In some cases, the dimensions may include those listed in FIG. 3.” [0058]
The combined references teach matrix. They do not teach similarity.
Maier et al. also in the business of matrix teaches:
Similarity matrix…
“In other embodiments, the systems and methods of the present disclosure may perform an on-the-fly comparison of a patient's image(s) to comparison image(s) of the same or similar tissue regions from one or more other individuals for whom the corresponding health status and/or outcomes are known. The on-the-fly comparison may comprise calculating metrics related to the degree of similarity between the patient's medical image(s) and the comparison image(s) (a similarity matrix, for example), and identifying a corresponding cohort of the one or more individuals represented by the comparison image(s) whose image(s) are the most similar to the patient's image(s) based on these similarity metrics…” [0022]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to determine similarity matrix as taught by Maier et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Maier et al. who teaches the advantages of determining similarity metrics.
Degree of Similarity
The combined reverences teach vector and similarity. They do not literally teach degree of similarity.
Cuan et al. also in the business of vector and similarity teaches:
Cosine of an angle between two feature vectors equal to 1 then similar…
“After features are extracted and a feature vector is generated representing the extracted features, a similarity score may be computed indicating how similar a given image's feature vector is to a feature vector representing features extracted from an image used to train the object recognition model. In some embodiments, a similarity between two images may be determined by computing a distance in an n-dimensional feature space between the feature vector representing an image captured by client device 104 and a feature vector of a corresponding image from the training data set. For example, the distance computed may be a cosine distance, a Minkowski distance, a Euclidean distance, or other metric by which similarity may be computed. In some embodiments, the distance between the two feature vectors may be compared to a threshold distance. If the distance is less than or equal to the threshold distance, then the two images may be classified as being similar, classified as depicting a same or similar object, or both. For example, if a cosine of an angle between the two feature vectors produces a value that is approximately equal to 1 (e.g., Cos(θ)≥0.75, Cos(θ)≥0.8, Cos(θ)≥0.85, Cos(θ)≥0.9, Cos(θ)≥0.95, Cos(θ)≥0.99, etc.), then the two feature vectors may describe similar visual features, and therefore the objects depicted within the images with which the features were extracted from may be classified as being similar.” [0035]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use degree of similarity as taught by Caun et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Caun et al. who teaches the benefits of using angles for cosine similarity.
Regarding claim 17
The recommendation method according to claim 16, wherein the machine learning model is trained using the comparable feature vectors as inputs and the comparable training target vectors as outputs.
King et al. teaches:
Feature vectors with user profiles…
“Some embodiments may suggest trainers or friends for a user to work out with. Some embodiments may cluster users and trainers according to various criteria, for example, attributes of user profiles, like goals or workout patterns. For instance, some embodiments may model users as feature vectors, with user profile attributes like workout goals, performance, timing, and feedback being mapped to scalars of the vectors. Some embodiments may cluster the vectors with a DBSCAN algorithm and suggesting pairings. Or some embodiments may rank pairings based on Euclidian distance in the vector space, e.g., suggesting to a user the five closest other users or trainers.” [0138]
Training set with profiles of users (physiological feature information) and select workout (training target) for a user …
“In some embodiments, a machine-learning model may be trained to select workouts based on a user's goal. For example, in some cases, a training set may include a goal set by previous users, profiles of those users, workouts by those users, and an indication of whether the users achieve their goals. Some embodiments may filter the training set according to whether users satisfy their stated goals. Some embodiments may cluster users (e.g., with a density based clustering algorithm, like DB-SCAN) according to profiles to identify groups of users who are similar to one another, for example, of similar profiles and have sent similar goals. In some cases, some embodiments may then detect features of workouts within each of the clusters, for example, patterns in chosen workouts associated with meeting the goal for those in the cluster. For instance, for each cluster, embodiments may train a decision tree to classify users as likely to meet their goal based on workout history. Some embodiments may then use these detected features and clusters to recommend workouts for other users. For example, some embodiments may receive a request for workout from a given user, determine which cluster most closely matches that given user, and then select a workout for that user that includes the futures detected among the users in that cluster within their workouts.” [0123]
Machine generated workout instructions for specific body-regions…
“… For example, a user may choose any particular instructor for any workout video block (every instructor provides instructions for all the workout sessions available to the user). In some cases, specific instructors (human, or machine generated) may provide specific workout instructions. For example, some instructors may only give instructions for specific body-regions (e.g., legs, arms, etc.), for specific level of difficulty, for specific workout type (e.g., warm-up, cool down, cardio, etc.), or other specific workout instructions.” [0062]
Claim Analysis - 35 USC § 103
Based on search for Claim 18, no prior art rejection is made at this time.
Conclusion
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684
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