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 .
Formal Matters
Applicant's response, filed 19 February 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1-20 are currently pending and have been examined.
Claims 1, 2, 18, and 19 have been amended.
Claims 1-20 have been rejected.
Priority
The instant application claims the benefit of priority under 35 U.S.C 119(e) or under 35 U.S.C. § 120, 121, or 365(c). Accordingly, the effective filing date for the instant application is 01 June 2023 claiming benefit to Provisional Application 63/505,517.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
scoring module (claims 1, 5, 6)
output module (claims 1 and 7-10)
training module (claims 2 and 4)
The specification provides that “modules or components of the recommendation system 145 can be implemented with a combination of software (e.g., executable instructions, or computer code) and hardware (e.g., at least a memory and processor)” (Detailed Description in ¶ 39). Therefore, the claim limitations will be interpreted to be a hardware AND software computer program product stored on a memory (see MPEP § 2181(II)(B) wherein when the supporting disclosure for a computer-implemented invention discusses the implementation of the functionality of the invention through hardware, software, or a combination of both, a question can arise as to which mode of implementation supports the means-plus-function limitation. The language of 35 U.S.C. 112(f) requires that the recited "means" for performing the specified function shall be construed to cover the corresponding "structure or material" described in the specification and equivalents thereof. Therefore, by choosing to use a means-plus-function limitation and invoke 35 U.S.C. 112(f) applicant limits that claim limitation to the disclosed structure, i.e., implementation by hardware or the combination of hardware and software, and equivalents thereof. Therefore, the examiner should not construe the limitation as covering pure software implementation). Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-20 are drawn to a system, method, and device, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a system for a connected fitness platform in part performing the steps of generate a recommendation score for a target exercise class with respect to a potential user of the target exercise class by receiving user characteristics and workout sequence data for the potential user; applying a behavioral sequence transformer (BST) to the user characteristics and workout sequence data, wherein the BTS is adapted from a token that represents a start state of the potential user; and generating the recommendation score based on a comparison of an embedding generated by applying the BST to the user characteristics and the workout sequence data and an embedding associated with the target exercise class; and present a recommendation to the potential user for the target exercise class when the recommendation score is above a threshold score.
Independent claim 11 recites a method in part performing the steps of generating a recommendation score for a target exercise class with respect to a potential user of the target exercise class by receiving user characteristics and workout sequence data for the potential user; applying a behavioral sequence transformer (BST) to the user characteristics and workout sequence data; and generating the recommendation score based on a comparison of an embedding generated by applying the BST to the user characteristics and the workout sequence data and an embedding associated with the target exercise class; and presenting a recommendation to the potential user for the target exercise class when the recommendation score is above a threshold score.
Independent claim 20 recites a non-transitory, computer-readable medium in part performing the steps of training a behavioral sequence transformer (BST) using a context-augmented start token (CAST) that represents a start state for a new user; applying the trained BST to user characteristics and workout sequence data associated with the new user; generating, for multiple exercise classes available to the new user, a recommendation score based on a comparison of an embedding generated by applying the BST to the user characteristics and the workout sequence data and an embedding associated with the multiple exercise classes; and presenting recommendations to the new user for a subset of exercise classes of the multiple exercise classes on the generated recommendation scores.
These steps of analyzing a user’s workout behaviors to recommend a new workout via a prediction model amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people – similar to i. filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016)).
Dependent claims 2 & 12 recite, in part, train the BST using a context-augmented start token (CAST) that represents the start state for the potential user.
Dependent claims 3 & 13 recite, in part, wherein the CAST includes seed values for multiple metadata types associated with exercise classes provided.
Dependent claims 4 & 14 recite, in part, trains the BST using a next item prediction task and an initial workout sequence represented by the CAST.
Dependent claims 5 & 15 recite, in part, wherein the scoring module generates the recommendation score for the target exercise class by performing a dot product operation on the embedding generated by applying the BST to the user characteristics and the workout sequence data and the embedding associated with the target exercise class to determine a similarity.
Dependent claim 6 recites, in part, wherein the scoring module utilizes a multi-layer perceptron (MLP) to encode the embedding generated by applying the BST to the user characteristics and the workout sequence data and to encode the embedding associated with the target exercise class.
Dependent claims 7 & 17 recite, in part, presents the recommendation to the potential user for the target exercise class.
Dependent claims 8 & 18 recite, in part, presents the recommendation to the potential user for the target exercise class.
Dependent claims 9 & 19 recite, in part, presents the recommendation to the potential user for the target exercise class.
Dependent claim 10 recites, in part, presents the recommendation to the potential user for the target exercise class as a row of similar exercise classes.
Dependent claim 16 recites, in part, encoding the embedding generated by applying the BST to the user characteristics and the workout sequence data and the embedding associated with the target exercise class via a multi-layer perceptron (MLP).
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 10 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Examiner notes that the training of the behavioral sequence transformer algorithm is described in the specification as a mathematical algorithm wherein the feature vector weights are determined (Detailed Description in ¶ 43-48) and has no particular hardware or software requirements for implementation. In light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claims recite both a mental process and mathematical concept and is not subject matter eligible. The use a training module as a computer hardware component to train a behavioral sequence transformer also amounts to applying data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) and therefore are mere instructions to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014) consistent with Example 47 claim 2. The techniques outlined, and Examiner notes the known methods of training to one of ordinary skill in the art, are mathematical algorithms or mental processes of labeling and fitting data to a particular model representation and therefore have been analyzed under Step 2A Prong 1.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 1, 11, and 20 recite a connected fitness platform. Claims 1-10 recite a scoring module, output module, and training module. Claim 11 recites a recommendation system of a connected fitness platform.
Claims 7 and 17 recite a user interface associated with an exercise machine upon which the potential user views exercise classes streamed by the connected fitness platform. Claims 9, 10, and 19 recite a home screen displayed by an exercise machine upon which the potential user views exercise classes streamed by the connected fitness platform. Claims 8 and 18 recite a mobile application via which the potential user views exercise classes streamed by the connected fitness platform.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claims 1, 11, and 20 recite a connected fitness platform. Claims 1-10 recite a scoring module, output module, and training module. Claim 11 recites a recommendation system of a connected fitness platform. Claims 7 and 17 recite a user interface associated with an exercise machine upon which the potential user views exercise classes streamed by the connected fitness platform. Claims 9, 10, and 19 recite a home screen displayed by an exercise machine upon which the potential user views exercise classes streamed by the connected fitness platform. Claims 8 and 18 recite a mobile application via which the potential user views exercise classes streamed by the connected fitness platform.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Examiner also notes that claims 7 and 17 recite a user interface associated with an exercise machine upon which the potential user views exercise classes streamed by the connected fitness platform; claims 9, 10, and 19 recite a home screen displayed by an exercise machine upon which the potential user views exercise classes streamed by the connected fitness platform; and claims 8 and 18 recite a mobile application via which the potential user views exercise classes streamed by the connected fitness platform. The exercise machine and mobile phone (see USAA v. PNC Bank, N.A., No. 2023-1639 at 8 (Fed. Cir. June 12, 2025) (finding that a mobile handheld device is a general-purpose computing device) are not positively recited, only the displays of said devices with the location of the display being intended uses of the displays. The limitations are only recited as a tool which only serves as display/output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to post-solution output on a well-known display device) and is therefore not a practical application of the recited judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-20 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over King et al. (US Patent Application No. 2018/0036591)[hereinafter King] in view of Huang et al., Multi-scale modeling temporal hierarchical attention for sequential recommendation, 641 Information Sciences (May 10, 2023)[hereinafter Huang].
As per claim 1, King teaches on the following limitations of the claim:
a recommendation system for a connected fitness platform, the recommendation system comprising is taught in the Detailed Description in ¶ 0056, ¶ 0071, ¶ 0101, and ¶ 0106 (teaching on behavior based learning model for recommending workout video resources to a user of a fitness platform hosted on a central server computer with a processor, memory, and corresponding hardware)
a scoring module that is configured to generate a recommendation score for a target exercise class with respect to a potential user of the target exercise class by is taught in the Detailed Description in ¶ 0071, ¶ 0105, ¶ 0128, and ¶ 0197 (teaching on scoring via a historical behavior based machine learning model, a score for each candidate workout resource)
receiving user characteristics and workout sequence data for the potential user is taught in the Detailed Description in ¶ 0054-56, ¶ 0094-95, ¶ 0105, and ¶ 0135 (teaching on receiving user profile and candidate workout resource attribute data)
wherein the BST is adapted from a token that represents a start state for a potential user is taught in the Detailed Description in ¶ 0105, ¶ 0124-125, ¶ 0152, and ¶ 0157-158 (teaching on recommendation determination model being trained on historical user attribute data including historical usage data wherein a scoring tag for a new user (treated as synonymous to a context-augmented start token (CAST)) indicated as "little experience")
generating the recommendation score based on a comparison of an embedding generated by applying the BST to the user characteristics and the workout sequence data and an embedding associated with the target exercise class; and is taught in the Detailed Description in ¶ 0105, ¶ 0124-125, and ¶ 0152-156 (teaching on utilizing a machine learning model to determine a recommendation score for a video resource based on input vectors (treated as synonymous to embedding data) created from the user profile and candidate workout resource attribute data)
an output module that is configured to present a recommendation to the potential user for the target exercise class is taught in the Detailed Description in ¶ 0069 and ¶ 0101 (teaching on outputting the recommendation for a video resource for a new user)
when the recommendation score is above a threshold score is taught in the Detailed Description in ¶ 0152-156 (teaching on comparing the video resource score to a plurality of thresholds for suitability)
King fails to teach the following limitation of claim 1. Huang, however, does teach the following:
applying a behavioral sequence transformer (BST) to the user characteristics and workout sequence data; and is taught in the § Abstract on p. 1, in Figure 2 on p. 7, and in § Introduction on p. 2 ¶ 5 (teaching on a behavior sequence transformer model for predicting a video recommendation to a user - Examiner notes that the BTS will hereinafter be any model for predicting the video recommendations relying on Huang to teach on the particular model type)
One of ordinary skill in the art before the effective filing date of the instant claims would combine the machine learning algorithm for recommending fitness video content to a user of King with the behavioral sequence multi-head attention model of Huang with the motivation of solving the cold start problem associated with new user scenario (in the § 4.1 Overview on p. 6) and “improve[] the recommendation performance in general” (Huang in the § 2.3. Micro-video recommendation on p. 5).
As per claim 2, the combination of King and Huang discloses all of the limitations of claim 1. King also discloses the following:
the recommendation system of claim 1, further comprising: a training module that is configured to train the BST using a context-augmented start token (CAST) that represents the start state for the potential user within the connected fitness platform is taught in the Detailed Description in ¶ 0105, ¶ 0124-125, ¶ 0152, and ¶ 0157-158 (teaching on recommendation determination machine learning model being trained on historical user attribute data including historical usage data wherein a scoring tag for a new user (treated as synonymous to a context-augmented start token (CAST)) indicated as "little experience")
Claim 12 is rejected under the same rational.
As per claim 3, the combination of King and Huang discloses all of the limitations of claim 2. King also discloses the following:
the recommendation system of claim 2, wherein the CAST includes seed values for multiple metadata types associated with exercise classes provided by the connected fitness platform is taught in the Detailed Description in ¶ 0152-158 (teaching on recommendation determination machine learning model being trained on historical user attribute data including historical usage data wherein a scoring tag for a new user (treated as synonymous to a context-augmented start token (CAST)) indicated as "little experience" to the focus, intensity, discipline, proficiency, etc. scoring tag "seed values")
Claim 13 is rejected under the same rational.
As per claim 4, the combination of King and Huang discloses all of the limitations of claim 2. King also discloses the following:
the recommendation system of claim 2, wherein the training module trains the BST using a next item prediction task and an initial workout sequence represented by the CAST is taught in the Detailed Description in ¶ 0112-114, ¶ 0124-125, and ¶ 0152-158 (teaching on a recommending a first workout block appropriate to the user scoring tag (treated as synonymous to an initial workout sequence) and future workout blocks in response to the feedback from the first block (treated as synonymous to a next item prediction task) and other historical user workout outcomes)
Claim 14 is rejected under the same rational.
As per claim 5, the combination of King and Huang discloses all of the limitations of claim 1. King fails to teach the following; Huang, however, does disclose:
the recommendation system of claim 1, wherein the scoring module generates the recommendation score for the target exercise class by performing a dot product operation on the embedding generated by applying the BST to the user characteristics and the workout sequence data and the embedding associated with the target exercise class to determine a similarity is taught in the § (a) Collaborative Filtering-based Modeling Methods on p. 11 and in Figure 2 on p. 7 (teaching on utilizing a multi-layer perceptron for generating vector embedding representations for training data examiner notes that in a MLP requires the use of a dot product calculate the weighted sum of inputs for each neuron)
One of ordinary skill in the art would have recognized that applying the known technique of utilizing a multi-layer perceptron to encode data into a vector embedding would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the MLP technique of Huang to the teachings of King would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied and would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow vectorization of the video items into embeddings (Huang in § (a) Collaborative Filtering-based Modeling Methods on p. 11).
Claim 15 is rejected under the same rational.
As per claim 6, the combination of King and Huang discloses all of the limitations of claim 1. King fails to teach the following; Huang, however, does disclose:
the recommendation system of claim 1, wherein the scoring module utilizes a multi-layer perceptron (MLP) to encode the embedding generated by applying the BST to the user characteristics and the workout sequence data and to encode the embedding associated with the target exercise class is taught in the § (a) Collaborative Filtering-based Modeling Methods on p. 11 (teaching on utilizing a multi-layer perceptron for generating vector embedding representations for training data)
One of ordinary skill in the art would have recognized that applying the known technique of utilizing a multi-layer perceptron to encode data into a vector embedding would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the MLP technique of Huang to the teachings of King would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied and would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow vectorization of the video items into embeddings (Huang in § (a) Collaborative Filtering-based Modeling Methods on p. 11).
As per claim 7, the combination of King and Huang discloses all of the limitations of claim 1. King also discloses the following:
the recommendation system of claim 1, wherein the output module presents the recommendation to the potential user for the target exercise class via a user interface associated with an exercise machine upon which the potential user views exercise classes streamed by the connected fitness platform is taught in the Detailed Description in ¶ 0069 and ¶ 0101 (teaching on displaying the recommended video resource to the user via the fitness platform application on gym-equipment-specific display)
Claim 17 is rejected under the same rational.
As per claim 8, the combination of King and Huang discloses all of the limitations of claim 1. King also discloses the following:
the recommendation system of claim 1, wherein the output module presents the recommendation to the potential user for the target exercise class via a mobile application via which the potential user views exercise classes streamed by the connected fitness platform is taught in the Detailed Description in ¶ 0101 (teaching on displaying the recommended video resource to the user on via the fitness platform application on a tablet display (treated as synonymous to a mobile application))
Claim 18 is rejected under the same rational.
As per claim 9, the combination of King and Huang discloses all of the limitations of claim 1. King also discloses the following:
the recommendation system of claim 1, wherein the output module presents the recommendation to the potential user for the target exercise class via a home screen displayed by an exercise machine upon which the potential user views exercise classes streamed by the connected fitness platform is taught in the Detailed Description in ¶ 0069 and ¶ 0101 (teaching on displaying the first recommended video resource (treated as synonymous to a home screen) to the user on a display device via the fitness platform application on gym-equipment-specific display)
Claim 19 is rejected under the same rational.
As per claim 10, the combination of King and Huang discloses all of the limitations of claim 1. King also discloses the following:
the recommendation system of claim 1, wherein the output module presents the recommendation to the potential user for the target exercise class as a row of similar exercise classes via a home screen displayed by an exercise machine upon which the potential user views exercise classes streamed by the connected fitness platform is taught in the Detailed Description in ¶ 0069 and ¶ 0101 (teaching on displaying the first recommended video resource (treated as synonymous to a home screen) to the user via the fitness platform application on gym-equipment-specific display wherein the video resource is a plurality of ordered related videos)
As per claim 11, King teaches on the following limitations of the claim:
a method performed by a recommendation system of a connected fitness platform, the method comprising is taught in the Detailed Description in ¶ 0056, ¶ 0071, ¶ 0101, and ¶ 0106 (teaching on behavior based learning model for recommending workout video resources to a user of a fitness platform hosted on a central server computer with a processor, memory, and corresponding hardware)
generating a recommendation score for a target exercise class with respect to a potential user of the target exercise class by is taught in the Detailed Description in ¶ 0071, ¶ 0105, ¶ 0128, and ¶ 0197 (teaching on scoring via a historical behavior based machine learning model, a score for each candidate workout resource)
receiving user characteristics and workout sequence data for the potential user is taught in the Detailed Description in ¶ 0054-56, ¶ 0094-95, ¶ 0105, and ¶ 0135 (teaching on receiving user profile and candidate workout resource attribute data)
generating the recommendation score based on a comparison of an embedding generated by applying the BST to the user characteristics and the workout sequence data and an embedding associated with the target exercise class; and is taught in the Detailed Description in ¶ 0105, ¶ 0124-125, and ¶ 0152-156 (teaching on utilizing a machine learning model to determine a recommendation score for a video resource based on input vectors (treated as synonymous to embedding data) created from the user profile and candidate workout resource attribute data)
presenting a recommendation to the potential user for the target exercise class is taught in the Detailed Description in ¶ 0069 and ¶ 0101 (teaching on outputting the recommendation for a video resource for a new user)
when the recommendation score is above a threshold score is taught in the Detailed Description in ¶ 0152-156 (teaching on comparing the video resource score to a plurality of thresholds for suitability)
King fails to teach the following limitation of claim 11. Huang, however, does teach the following:
applying a behavioral sequence transformer (BST) to the user characteristics and workout sequence data; and is taught in the § Abstract on p. 1, in Figure 2 on p. 7, and in § Introduction on p. 2 ¶ 5 (teaching on a behavior sequence transformer model for predicting a video recommendation to a user - Examiner notes that the BTS will hereinafter be any model for predicting the video recommendations relying on Huang to teach on the particular model type)
One of ordinary skill in the art before the effective filing date of the instant claims would combine the machine learning algorithm for recommending fitness video content to a user of King with the behavioral sequence multi-head attention model of Huang with the motivation of solving the cold start problem associated with new user scenario (in the § 4.1 Overview on p. 6) and “improve[] the recommendation performance in general” (Huang in the § 2.3. Micro-video recommendation on p. 5).
As per claim 16, the combination of King and Huang discloses all of the limitations of claim 11. King fails to teach the following; Huang, however, does disclose:
the method of claim 11, further comprising: encoding the embedding generated by applying the BST to the user characteristics and the workout sequence data and the embedding associated with the target exercise class via a multi-layer perceptron (MLP) is taught in the § (a) Collaborative Filtering-based Modeling Methods on p. 11 (teaching on utilizing a multi-layer perceptron for generating vector embedding representations for training data)
One of ordinary skill in the art would have recognized that applying the known technique of utilizing a multi-layer perceptron to encode data into a vector embedding would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the MLP technique of Huang to the teachings of King would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied and would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow vectorization of the video items into embeddings (Huang in § (a) Collaborative Filtering-based Modeling Methods on p. 11).
As per claim 20, King teaches on the following limitations of the claim:
a non-transitory, computer-readable medium whose contents, when executed by a computing system, cause the computing system to perform a method, the method comprising is taught in the Detailed Description in ¶ 0056, ¶ 0071, ¶ 0101, and ¶ 0106 (teaching on behavior based learning model for recommending workout video resources to a user of a fitness platform hosted on a central server computer with a processor, memory, and corresponding hardware)
using a context-augmented start token (CAST) that represents a start state for a new user within a connected fitness platform is taught in the Detailed Description in ¶ 0112-114, ¶ 0124-125, and ¶ 0152-158 (teaching on a recommending a first workout block appropriate to the user scoring tag (treated as synonymous to an initial workout sequence) and future workout blocks in response to the feedback from the first block (treated as synonymous to a next item prediction task) and other historical user workout outcomes)
generating, for multiple exercise classes available to the new user via the connected fitness platform, a recommendation score based on a comparison of an embedding generated by applying the BST to the user characteristics and the workout sequence data and an embedding associated with the multiple exercise classes; and is taught in the Detailed Description in ¶ 0105, ¶ 0124-125, and ¶ 0152-156 (teaching on utilizing a machine learning model to determine a recommendation score for a video resource based on input vectors (treated as synonymous to embedding data) created from the user profile and candidate workout resource attribute data)
presenting recommendations to the new user for a subset of exercise classes of the multiple exercise classes on the generated recommendation scores is taught in the Detailed Description in ¶ 0069 and ¶ 0101 (teaching on displaying the first recommended video resource (treated as synonymous to a home screen) to the user via the fitness platform application on gym-equipment-specific display wherein the video resource is a plurality of ordered related videos)
King fails to teach the following limitation of claim 20. Huang, however, does teach the following:
training a behavioral sequence transformer (BST) is taught in the § Abstract on p. 1, in Figure 2 on p. 7, and in § Introduction on p. 2 ¶ 5 (teaching on training a behavior sequence transformer model for predicting a video recommendation to a user - Examiner notes that the BTS will hereinafter be any model for predicting the video recommendations relying on Huang to teach on the particular model type)
applying the trained BST to user characteristics and workout sequence data associated with the new user is taught in the § Abstract on p. 1, in Figure 2 on p. 7, and in § Introduction on p. 2 ¶ 5 (teaching on a behavior sequence transformer model for predicting a video recommendation to a user - Examiner notes that the BTS will hereinafter be any model for predicting the video recommendations relying on Huang to teach on the particular model type)
One of ordinary skill in the art before the effective filing date of the instant claims would combine the machine learning algorithm for recommending fitness video content to a user of King with the behavioral sequence multi-head attention model of Huang with the motivation of solving the cold start problem associated with new user scenario (in the § 4.1 Overview on p. 6) and “improve[] the recommendation performance in general” (Huang in the § 2.3. Micro-video recommendation on p. 5).
Response to Arguments
Applicant's arguments filed 19 February 2026 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserts that the claims are directed towards a particular application of training and implementing a machine learning model to address a cold start problem – stating that this is a practical application and not merely abstract consistent with Ex parte Desjardins and Enfish. Examiner is not persuaded. Applicant has not invented a new model or method for managing said model, but has merely applied a known model to a new context similar to that in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). Neither enhancing a connected fitness platform nor solving a cold start video recommendation is a technological problem and the use of a behavioral sequence transformer model any more than applying an abstract idea to a general purpose computer algorithm (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology).
Applicant's arguments filed 19 February 2026 with respect to 35 USC § 103 have been fully considered but they are not persuasive. Applicant asserts that the instant claims are distinguishable from King as King aligns resources with a known user experience while the instant claims are directed towards a token representing a start state of a potential user for the BST model to solve a “cold start” problem. Examiner is not persuaded. There is no evidence in the claim that a potential user must be a user that has never before utilized a device – that is, the broadest reasonable interpretation of “potential” user includes a user considering doing a new workout or a user trying a new workout type. Furthermore, Examiner notes that Applicant’s assertion that “King tags resources to align the resources with its knowledge of user’s experiences” including a “little experience” level – this is analogous with the broadest reasonable interpretation of “new” as something recently created, discovered, or introduced wherein “new” is not limited to “zero” OR never experienced.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857