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 .
Response to Amendment
This Action is in response to the Amendment filed May 11, 2026. Claims 5, 12 and 19 are canceled. Claims 1, 6, 8, 13, 15 and 20 are amended. Claims 1-4, 6-11, 13-18 and 20 are currently pending and have been examined in the application.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-4, 6-11, 13-18 and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In claims 1, 8 and 15 the limitation “distributing, to the plurality of ML models, the plurality of user features and event features corresponding to different video events of the one or more video events” is not supported by the original disclosure. The specification discloses “a system 500 for identifying the optimal model for event recommendation using machine learning. In the system 500 shown, user event data 500 can be processed using one of four models, model A 520, model B 530, model C 540, or model D 550” [0061]. This does not describe that the plurality of user features and event features distributed to the ML models correspond to different video events of the one or more video events. Accordingly, the limitation is directed to impermissible new matter. Claims 2-4, 6-7; 9-11, 13-14 and 16-18, 20 by being dependents of Claims 1, 8 and 15 respectively are also rejected.
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 1-4, 6-11, 13-18 and 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.
Claims 1-4, 6-11, 13-18 and 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-20 are directed toward at least one abstract ideas without significantly more. In accordance with MPEP § 2106, the rationale for this determination is explained below.
Representative claim 1 is directed towards a method, claim 8 is directed towards a system, claim 15 is directed towards a non-transitory medium, which are statutory categories of invention.
Although, claim 1 is directed toward a statutory category of invention, the claim however, is directed towards at least one abstract idea. The limitations that set forth the abstract idea recites: distributing, the plurality of user features and event features corresponding to different video events of the one or more video events; determining a predicted favorite score for each of the one or more events personalized for each user, the predicted favorite score based at least in part on the plurality of user features and the plurality of event features, wherein the event features comprise numeric and non-numeric features, and wherein each of the numeric and non-numeric features is associated with a weight; displaying at least one of the one or more events to the user based at least in part on the personalized predicted favorite score associated with each of the one or more events determine one or more user interactions with the one or more video events; and remove a first model of the plurality of ML models from the plurality of ML models based on the one or more user interactions. These limitations, describe commercial interactions including, marketing or sales activities or business relations; as well as managing personal behavior including following rules or instructions. As such, the limitations are directed towards the abstract grouping of Certain Methods of Organizing Human Activity in prong one of step 2A of the Alice/Mayo test (see MPEP 2106.04(a)(2) II), and/or Mathematical Concepts (see MPEP 2106.04(a)(2) I).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (see 2019 MPEP 2106.04(d)), the additional elements provided by the claim as a whole amount to insignificant extra-solution activity and merely using a computer as a tool to perform an abstract idea. In particular the claim recites the additional elements: receiving, by a system comprising a plurality of machine-learning ("ML") models, a plurality of user features associated with a user; receiving, by the system, a plurality of event features, each of said event features associated with one or more events, which amounts to necessary data gathering, apparent in implementing the judicial exception. See MPEP 2106.05(g). While, the limitations, to the plurality of ML models; by the system using the plurality of ML models; providing to a client device, are recited at a high level of generality and are the mere use of a computer as a tool to perform the abstract ideas. See MPEP 2106.05(f). Simply adding insignificant extra-solution activities and using a computer to apply the abstract idea are not practical applications of the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites extra-solution activity and generic computer components. Viewing the limitation individually, the receiving, by a system comprising a plurality of machine-learning ("ML") models, a plurality of user features associated with a user; the receiving, by the system, a plurality of event features, each of said event features associated with one or more events, are used only for insignificant extra-solution activity because such activities amount to necessary data gathering used to implement the aforementioned abstract concept, see MPEP 2106.05(g). The courts have recognized performing repetitive calculations; receiving, processing; automating mental tasks and receiving or transmitting data over a network to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity. See MPEP 2106.05(d)II; Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015). Moreover, the limitations generically, referring to a plurality of machine-learning ("ML") models, a client device, a processor and memory (claim 8) also do not constitute significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment1. Viewing these limitations as a combination, the additional elements amount to no more than merely applying the exception using generic computer system. Merely applying an exception using generic computer components cannot provide an inventive concept. Therefore, the limitations of the claim as a whole, when viewed individually and as an ordered combination, do not amount to significantly more than the abstract idea.
An analysis of dependent claims 2-4, 6-7, likewise, do not provide any limitations that would remedy the deficiencies outlined above. The claims only further add to the abstract idea, with no elements which integrate the abstract idea into a practical application or constitute significantly more. For instance, claims 4-6 provide for machine learning models to calculate a score. This amounts to merely using a computer algorithm as a tool, to perform an abstract idea. Thus, while the dependent claims may slightly narrow the abstract idea by further describing it, they do not make it less abstract and are rejected accordingly. Further still, claims 8-11, 13-18 and 20 suffer from substantially the same deficiencies as outlined with respect to claims 1-4, 6-7 and are also rejected accordingly.
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 of this title, 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 1-4, 7-11 and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wadhawan (US Publication 2020/0160961) in view of Singh (US Publication 2020/0334641) in further view of Wang (US Publication 2019/0114689)..
Regarding Claims 1, 8 and 15, Wadhawan teaches a method, system and non-transitory computer-readable medium, comprising:
receiving, by a system comprising a plurality of machine-learning ("ML") models, a plurality of user features associated with a user; Wadhawan [0029: user first installs the native application on her mobile device or navigates to a webpage associated with the fitness platform via a web browser on her mobile device, the computer system can present a survey—via the native application or webpage—to the user, such as including a prompt to indicate: the user's current fitness level; demographic information of the user; the user's music preferences; the user's preferred exercise types; and/or a fitness-related goal; system can then store these user data in a user profile associated with the user; 0030: the computer system can implement machine learning techniques to identify other users who previously completed at least one class on the fitness platform];
receiving, by the system, a plurality of event features, each of said event features associated with one or more video events; Wadhawan [0010: computer system can include a remote server that hosts or interfaces with a class database containing a corpus of prerecorded classes; computer system can host or interface with a native application or web browser executing on a user's computing device (e.g., a smartphone, a tablet, a smart television) to serve selected classes to the user; 0013: upon selection of the first class by the user at the native application, the native application can retrieve (e.g., download, stream) this first class from the class database and initiate playback of this first class—such as an audio track and/or a video track; 0042: when the user accesses the fitness platform via the native application at the start of an exercise session, the native application can: access a list of difficulties of classes completed by the user prior to the exercise session; estimate a target difficulty for the exercise session based on this list of difficulties; and query the user—via the native application—for an available exercise time for the exercise session];
determining, a predicted favorite score for each of the one or more video events personalized for each user, the predicted favorite score based at least in part on the plurality of user features and the plurality of event features; Wadhawan [0032: system can: aggregate an initial list of highest-ranking classes (e.g., classes that the user is predicted to complete with greatest probability) that fulfill the user's music preferences and that are of class types preferred by the user; and present this initial list of classes to the user's computing device; 0037: computer system can rank, or score classes in the class database based on: proximity of their difficulty levels to the target difficulty level calculated for the user; proximity of song titles to derived music preferences of the user; proximity of trainer styles and trainer support scores in these classes to the derived trainer preferences of the user; 0042: computer system can then calculate a ranking of classes based on: proximity of difficulty levels associated with these classes to the target difficulty calculated for the user; and proximity of durations of these classes to the available exercise time specified by the user];
providing to a client device for displaying at the client device, at least one of the one or more events to the user based at least in part on the personalized predicted favorite score associated with each of the one or more video events. Wadhawan [0032: present this initial list of classes to the user's computing device; 0042: computer system can calculate a ranking of classes; computer system can then interface with the native application to present classes to the user according to this ranking in (near) real-time]
determining one or more user interactions with the one or more video events; Wadhawan [0013: native application can retrieve (e.g., download, stream) first class from the class database and initiate playback of this first class—such as a video track—as shown in FIG. 1; the user may follow along to trainer instructions and trainer guidance thus presented in this first class];
Wadhawan does not specifically disclose, a system comprising a plurality of machine-learning ("ML") models; this is disclosed by Singh [0056: interest parser operate in conjunction with one or more machine learning models to process user data];
the system comprising the plurality of machine-learning ("ML") models, wherein the event features comprise numeric and non-numeric features, and wherein each of the numeric and non-numeric features is associated with a weight; this is disclosed by Singh [0043: for each of the categories of data, embodiments may be configured to assign a score to each piece a data and sum the score wherein the score is a measure of how far the user may be willing to travel; 0052: interest weighting factors [of an event] may comprise ordered pairs of interests coupled with their strengths; a strength may be a number between 1 and 10; 0097: determining interest weighting factors based at least in part on the user data and event data];
before the effective filing date of the claimed invention, it would have been obvious for those skilled in the art to modify the teachings of Wadhawan with the teachings from Singh with the motivation to generate event recommendations for user by determining interest weighting factors based at least in part on user data, calendar data, map data, and event data. Singh [0113];
Wadhawan does not specifically disclose, distributing, to the plurality of ML models, the plurality of user features and event features corresponding to different video events of the one or more video events; Wang [0043: the individual user's reactions, sentiment, and intent are fed back to the information exchange system in real-time to adding additional parameters to recommendation models; 0047: recorded data is used to identify existing customer profiles and/or create new unique customer profiles using various specialized artificial intelligence methods; for existing customers, profiles can be identified through facial recognition based on image streams (videos); 0070: information exchange system activates respective input streams (e.g., image streams or periodic samples from the image streams) from one or more on-site cameras located at a current deployment location];
and removing a first model of the plurality of ML models from the plurality of ML models based on the one or more user interactions. This is disclosed by Wang [0043: individual user's reactions, sentiment, and intent are fed back to the information exchange system in real-time to reselect the set of models (e.g., removal of models) that are used to perform the analysis and recommendations].
Before the effective filing date of the claimed invention, it would have been obvious for those skilled in the art to modify the teachings of Wadhawan with the teachings from Wang with the motivation to generate system knowledge and conclusions from different data sources and analysis methods, such as various machine learning algorithms and specially engineered decision logic and algorithms, and/or combinations thereof to determine key characteristics of the user that are the most influential to his/her purchasing decision-making, and identify a subset of models and analysis tools to generate suitable responses to the user and provide the most relevant recommendations. Wang [0044].
Regarding Claims 2, 9 and 16, Wadhawan discloses, wherein the plurality of user features comprises a measure of user involvement associated with a past event. Wadhawan [0036: estimate the user's preferences for trainer style and trainer support based on trainer demographics and trainer support scores of classes previously completed by the user; 0042: computer system (or the native application) can access a list of difficulties of classes completed by the user prior to the exercise session].
Regarding Claims 3, 10 and 17, Wadhawan discloses, wherein the measure of user involvement with the past event comprises at least one of a purchase, a selection, or a rating. Wadhawan [0011: the computer system ranks classes by probability that the user will complete these classes based on historical class completion data of the user; 0036: fitness platform can store metadata of classes previously started, exited, completed, and skipped by the user; 0057: collects additional user data as the user selects and completes classes on the fitness platform over time].
Regarding Claims 4, 11 and 18, Wadhawan discloses, wherein the predicted favorite score is determined based on at least one of a plurality of machine learning models based on the measure of user involvement. Wadhawan [0030: computer system can implement clustering, regression, artificial intelligence, or machine learning techniques to identify a cohort (or cluster, group) of other users who previously completed at least one class on the fitness platform; 0036: implement regression techniques to derive a trajectory of difficulty levels of classes completed by the user; and estimate the user's preferences for trainer style and trainer support based on trainer demographics and trainer support scores of classes previously completed by the user].
Regarding Claims 7 and 14, Wadhawan discloses, wherein the event features comprise at least one of a text feature and a non-text feature. Wadhawan [0012: user may review titles and textual descriptions of classes before selecting a first class; 0021: class includes an audio track containing guided oral instruction from a trainer narrating an exercise routine, music, and metadata describing characteristics of the exercise routine].
Claims 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wadhawan (US Publication 2020/0160961) in view of Singh (US Publication 2020/0334641) in further view of Polish (US Publication 2022/0014571).
A. In regards to Claims 6, 13 and 20, Wadhawan does not specifically disclose, further comprising selecting by the system, at the client device, an optimal machine learning model of the plurality of machine learning models based on an evaluation of the performance of each of the plurality of machine learning models. This is disclosed by Polish [0335: next process is parameter tuning, which involves developing a model to represent the best effort].
Before the effective filing date of the claimed invention, it would have been obvious for those skilled in the art to modify the teachings of Wadhawan with the teachings from Polish with the motivation to provide artificial intelligence models that keep track of participant and meeting or event metrics and anticipate next action and make recommendations that facilitate completion of the next actions. Polish [0103].
Response to Applicant’s Arguments
Applicant's filed arguments have been fully considered but have not been found persuasive.
A. Applicant argues regarding the 35 U.S.C. § 101 rejection that the amended independent claims recite a practical application of the abstract idea recited in the claim because it involves a technological solution to a technological problem. In particular, the evaluation and reconfiguration of a computing system based on its performance to improve its performance on future tasks. The Examiner respectfully disagrees. Applicant’s claims do not proffer any reconfiguration or improvements. Merely removing a ML model based on user interactions is performing the same task with one less algorithm. Furthermore, there is no technical support/technical evidence in Applicant's Specification of how the system is reconfigured, and what technological problem it provides a solution for by using one less algorithm. As attested to by Applicant’s specification the problem addressed is for “providing event recommendation using a machine learning model” [0062]. Merely applying an abstract idea by computer components does not integrate the abstract idea into a practical application.
As such, the claims as a whole, in view of Alice, do not connote an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment. Therefore, the 35 U.S.C. § 101 rejection is maintained.
B. Applicant’s 35 U.S.C. § 103 arguments in regards to claims 1, 8 and 15 are moot in light of the new grounds of rejections. Applicant’s other arguments regarding the dependent claims are rejected accordingly to independent claims 1, 8 and 15.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Errol CARVALHO whose telephone number is (571)272-9987. The Examiner can normally be reached on M-F 9:30-7:00 Alt Fri
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached on 571- 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E CARVALHO/
Primary Examiner, Art Unit 3622
1 See, Alice Corp. Pty Ltd. v. CLS Bank lnt'l, 134 S. Ct. 2347, 2360 (2014) (noting that none of the hardware recited “offers a meaningful limitation beyond generally linking ‘the use of the [method] to a particular technological environment,’ that is, implementation via computers” (citing Bilski v. Kappos, 561 U.S. 593, 610-11 (2010))).
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