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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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: “engagement analysis module” and “optimization module” found in claims 1 and 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (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) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 1-17 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 claim(s) contains 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Independent claims 1 and 9 recite “an engagement analysis module” and “an optimization module” without structural support. The instant application’s Specification is devoid of a structural definition to these two modules. The functions of the module are not supported with structure to perform the functions.
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 1-17 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 limitation “an engagement module” and “an optimization module” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. There is no structural support for these modules to explain how they function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 1 and 9 recite the limitations “an engagement module” and “an optimization module.” It is unclear if these modules are software or hardware. Claims 2-8 and 10-17 depend from claims 1, and 9 respectively and are rejected to on the same grounds.
Claim 8 recites the limitation “the risk of cognitive overload is eliminated” in lines 2-3 of the claim. It is unclear how the overload is eliminated and a person having ordinary skill in the art would not know the metes and bounds for the risk of cognitive overload being eliminated aby the optimization of the application. For the purposes of examination, the Examiner will interpret this to mean that if there is any adaptive learning in the art, the risk of cognitive overload is eliminated because the lessons are being adapted to the user.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 16 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 16 does not further claim any limitation nor is it dependent on any claim. Applicant may cancel the claim, amend the claim to place the claim in proper dependent form, rewrite the claim in independent form, or present a sufficient showing that the dependent claim complies with the statutory requirements.
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 1-17 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.
Claim 1 is directed to “a method” (i.e. a process) and claim 9 is directed to “a system” (i.e. a machine), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 1 of the subject-matter eligibility analysis: Yes.
However, the claims are drawn to an abstract idea of “determining sequence of content items to be presented to the user during the online learning session, shuffling the content items, and adjusting the difficulty levels of the content items based on user's proficiency level and historical performance data,” either in the form of either in the form of “certain methods of organizing human activity” which is defines as fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II) or reasonably in the form of “mental processes” which is defined as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III) which are “performed on a computer” (per MPEP 2106(III)(C) “A Claim That Requires a Computer May Still Recite a Mental Process”).
The claims are reasonably understood as either “certain methods of organizing human activity” and/or “mental process.” Independent claim 1, analyzed as the representative of the claimed subject matter, is reproduced below. The limitations determined to be abstract ideas are in italics. The additional elements recited at a high level of generality are shown in bold. The limitation(s) determined to be extra-solution activity are underlined.
Independent Claim 1: A method to present a variety of content items during an online learning session to enhance user engagement, the method comprising:
executing code using one or more processors of a computer system to cause the computer system to perform operations comprising:
accessing user engagement data stored in a user database via an engagement analysis module integrated with an optimized content delivery system, wherein the engagement data include user's interaction with the content during the online learning session, user's content interaction history, session duration, and content type;
processing the user engagement data via the engagement analysis module to predict user engagement patterns during the online learning session;
optimizing the content items to be presented during the online learning session via an optimization module, wherein optimizing the content items includes determining sequence of content items to be presented to the user during the online learning session, shuffling the content items, and adjusting the difficulty levels of the content items based on user's proficiency level and historical performance data;
receiving the optimized content items customized based on the user's learning history, preferences, and real-time engagement to create a personalized learning experience.
These limitations describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea.
Step 2A, Prong 1 of the subject-matter eligibility analysis: Yes.
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim a practical application because to the extent that, e.g., “one or more processors,” “a computer system,” “a user database,” “an engagement analysis module,” and “an optimization module,” are claimed, as these are merely claimed to add insignificant extra-solution activity to the judicial exception (e.g., data gathering) and/or do no more than generally link the use of a judicial exception to a particular technological environment or field of use. In other words, the claimed “determining sequence of content items to be presented to the user during the online learning session, shuffling the content items, and adjusting the difficulty levels of the content items based on user's proficiency level and historical performance data,” is not providing a practical application.
Step 2A, Prong 2 of the subject-matter eligibility analysis: No.
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g. “one or more processors,” “a computer system,” “a user database,” “an engagement analysis module,” and “an optimization module,” are claimed these are all generic, well-known, and conventional computing elements. As evidence that these are generic, well-known, and conventional computing elements, Applicant’s specification discloses them in a manner that indicates that 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), per MPEP § 2106.07(a) III (a), which satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Specifically, the Applicant’s claimed, “one or more processors,” “a computer system,” “a user database,” “an engagement analysis module,” and “an optimization module,” are described in the following paragraphs:
“[0095] The processor 1313, in one embodiment, is a microprocessor manufactured by Motorola Inc. of Illinois, Intel Corporation of California, or Advanced Micro Devices of California. However, any other suitable single or multiple microprocessors or microcomputers may be utilized. Main memory 1315 is comprised of dynamic random access memory (DRAM).”
“[0091] The type of computer system that can be specially programmed to implement and utilize the integrated user engagement and content delivery system 100 and process 200 includes a mainframe, a mini-computer, a personal computer system including notebook computers, a wireless, mobile computing device (including personal digital assistants, smartphones, and tablet computers).”
“[0025] The Integrated user engagement and content delivery system 100 includes a user database 106 operatively coupled to the online learning platform 102 for storing the user data associated with the user engagement on the online learning platform 102.”
“[0026] The engagement analysis module 108 is used to access and collect user profile details in real-time.”
“[0007] The system includes optimizing the content items to be presented during the online learning session via an optimization module, wherein optimizing the content items includes determining sequence of content items to be presented to the user during the online learning session, shuffling the content items, and adjusting the difficulty levels of the content items based on user's proficiency level and historical performance data.”
These elements are reasonably interpreted as a generic computer which provides no details of anything beyond ubiquitous standard equipment. As such, the claimed limitation of “one or more processors,” “a computer system,” “a user database,” “an engagement analysis module,” and “an optimization module,” are reasonably understood as not providing anything significantly more.
Step 2B, of the subject-matter eligibility analysis: No.
In addition, dependent claims 2-8, and 10-17 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-8, and 10-17 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to independent claims 1 and 9.
Therefore, claims 1-17 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 6-13, 15, and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by G. Venkatasubramanyam (US20210264804A1; hereinafter Venkatasubramanyam).
Regarding independent Claim 1, Venkatasubramanyam discloses a method to present a variety of content items during an online learning session to enhance user engagement (see Abstract), the method comprising: executing code using one or more processors of a computer system to cause the computer system to perform operations (“executable by at least one processor configured to execute computer program instructions” (recited in at least: Venkatasubramanyam)) comprising: accessing user engagement data stored in a user database via an engagement analysis module integrated with an optimized content delivery system, wherein the engagement data include user's interaction with the content during the online learning session, user's content interaction history, session duration, and content type; optimizing the content items to be presented during the online learning session via an optimization module, wherein optimizing the content items includes determining sequence of content items to be presented to the user during the online learning session, shuffling the content items, and adjusting the difficulty levels of the content items based on user's proficiency level and historical performance data; (“The client 301 refers to a browser, application, device, etc., of a knowledge seeker who registers and once registered signs into an account in the SLKRS 201e. The sign in process of the client 301 and subsequent user authentication to make sure the client 301 requesting access to the SLKRS 201e is indeed representing the knowledge seeker who had registered using the concerned sign in information is done by an identity provider 302” (recited in at least: Venkatasubramanyam paragraph [0067]); “ the method teaches applied computer science through an integrated, interdisciplinary curriculum designed to engage each knowledge seeker's unique interests. The method and the SLKRS utilize artificial intelligence and machine learning to adapt and personalize the learning processes to each knowledge seeker. The method, using the SLKRS, generates personalized knowledge concept graphs for each knowledge seeker. Through assessments and interactions, the method captures what a knowledge seeker knows and how well he or she can apply and understand concepts” (recited in at least: Venkatasubramanyam paragraph [0006])); processing the user engagement data via the engagement analysis module to predict user engagement patterns during the online learning session (“ the smart-learning and knowledge retrieval system (SLKRS) focuses on other learner characteristics. Prior knowledge is indisputably the biggest factor in predicting a learner's initial success in almost every learning situation” (recited in at least: Venkatasubramanyam paragraph [0044])); receiving the optimized content items customized based on the user's learning history, preferences, and real-time engagement to create a personalized learning experience (“uses knowledge concept graphs to provide personalized learning paths. The SLKRS combines knowledge concept graphs with an identified learning style of the knowledge seeker to curate information related to concepts in an e-learning course that the knowledge seeker undertakes. The SLKRS also uses the knowledge concept graphs to bring additional examples, content, and interventions to improve learning outcomes of the knowledge seeker” (recited in at least: Venkatasubramanyam paragraph [0046])).
Regarding Claim 2, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses wherein the content items include a combination of academic, non-academic, interactive, and non-interactive content (see FIG. 9 and at least: paragraph [0078] describing FIG 9).
Regarding Claim 3, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses wherein the content items include one or more of multiple-choice questions, fill-in-the-blanks, matching pairs, and innovative formats such as truth or lies, what's my name, controversial conversations, did you know segments, and explanatory videos to cater to different learning styles (“The chatbot platform 403 provides quizzes and assessments 704 related to the e-learning course to the knowledge seekers. These assessments are interactive in nature compared to plain assignments or tests that students take up in a course. The chatbot platform 403 sources the quizzes and assessments 704 from the microservices platform 407 through the concept mastery detection 603 service under general microservices 405 as disclosed in the detailed descriptions of FIG. 4 and FIG. 6” (recited in at least: Venkatasubramanyam paragraph [0074])).
Regarding Claim 4, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses wherein the sequence of content items is customized based on user's learning history, preferences, and real-time engagement to create a personalized learning experience comprising: updating the user profiles based on the user interactions; analyzing the user's preferences, including preferred content types, learning styles, and difficulty levels to tailor the content sequence to the user's individual needs and preferences; customizing the sequence of content items in real-time based on the user's current engagement metrics, performance, and feedback (“The smart-learning and knowledge retrieval system (SLKRS) also computes a knowledge concept graph for the knowledge seeker to define understanding and mastery over concepts in an e-learning course. Knowledge concept graphs can replace other measures of evaluation, for example, a cumulative grade point average (CGPA), of the knowledge seekers performance in the e-learning course. Starting from a high level of the knowledge concept graph, one can drill down to particular subjects or concepts and sub-concepts to gauge the mastery of the knowledge seeker in the particular concepts within the e-learning course. Hence, the subjects can be interdisciplinary and still its understanding evaluated using the knowledge concept graphs. Moreover, the SLKRS computes a knowledge concept graph in real-time deriving the same based on multiple underlying algorithms. In an embodiment, the SLKRS builds a knowledge concept graph that has nodes and edges. The SLKRS builds this knowledge concept graph for each e-learning course that a knowledge seeker undertakes. In the embodiment, the SLKRS also aggregates and cumulatively starts developing a knowledge concept graph that shows dots and connectors that, for example, look like synapses and neurons” (recited in at least: Venkatasubramanyam paragraph [0045])).
Regarding Claim 6, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses wherein one of the content items include non- interactive content segments including informative videos, explanatory text, or visual aids that provide supplementary information related to the learning objectives and are informative and engaging, offering users valuable insights into the subject matter without requiring active user participation (“Concept mastery detection 603 corresponds to regular assessments of the knowledge seekers' understanding of concepts in an e-learning course. User engagement 604 refers to the engagement of the knowledge seekers in the e-learning course. The SLKRS 201e generates interventions to maintain interest levels of the knowledge seekers to ensure their optimum performance in learning. The SLKRS 201e also provides dictionary and language 605 support to ensure knowledge seekers are supported regardless of the language they speak, except maybe in the case of esoteric languages” (recited in at least: Venkatasubramanyam paragraph [0073])).
Regarding Claim 7, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses wherein processing the collected engagement data and determining the sequence of content items to be presented during the online learning session comprises: processing the collected real-time user engagement data to understand user engagement patterns and learning behaviors and selecting content items from a pre-defined content database based on the processed data, considering the type of content, difficulty level, and relevance to the user's learning objectives; sequencing the selected content items in an order that optimizes the user's learning experience by shuffling through different content types and varying difficulty levels; adjusting the content items in real-time based on real-time user interactions and feedback during the online learning session, ensuring content relevance and engagement throughout the online learning session (“The content analysis service 612 analyzes e-learning content assimilated by the SLKRS 201e and responses of the knowledge seeker to assessments in light of any feedback from the knowledge seeker that the messaging service 611 receives to enable the SLKRS 201e to provide improved experiences to the knowledge seeker. The artificial intelligence as a service (AIaaS) platform 613 offers the general microservices 405 and artificial intelligence (AI) 406 microservices to the other components of the SLKRS 201e for using those services to provide adaptive and personalized e-learning to the knowledge seekers. In an embodiment, the AIaaS platform 613 is available to an enterprise that uses the SLKRS 201e for e-learning. In the embodiment, the enterprise can use the AIaaS platform 613 of the SLKRS 201e to run or refine its legacy applications and services, and also to offer new customized services to its clients.” (recited in at least: Venkatasubramanyam paragraph [0073])).
Regarding Claim 8, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses wherein by balancing the ratio of interactive to non- interactive content segments, the user engagement is optimized and the risk of cognitive overload is eliminated, thus providing an engaging and adaptive learning environment (“method and smart-learning and knowledge retrieval system (SLKRS) disclosed herein address the above recited need for providing adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker” (recited in at least: Venkatasubramanyam paragraph [0006])).
Regarding Claim 9 Venkatasubramanyam discloses, a system to present a variety of content items during an online learning session to enhance user engagement (see Abstract), the system comprising: one or more processors (“The SLKRS understands the received query using artificial intelligence and machine learning programs executable by at least one processor configured to execute computer program instructions” (recited in at least: Venkatasubramanyam paragraph [0008])); memory, operatively coupled to the one or more processors consisting of one or more codes that when executed cause the one or more processors to perform operations (“As exemplarily illustrated in FIG. 2, the smart-learning and knowledge retrieval system (SLKRS) 201e comprises a non-transitory computer readable storage medium, for example, a memory unit 201f for storing programs and data, and at least one processor 201a communicatively coupled to the non-transitory computer readable storage medium” (recited in at least: Venkatasubramanyam paragraph [0054])) comprising: steps similar in scope to independent claim 1.
Regarding Claim 10, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses a user interface integrated within an online learning platform that displays the generated content items (“The smart-learning and knowledge retrieval system (SLKRS) receives a query from the knowledge seeker through one of multiple interfaces comprising text boxes, chat interfaces, voice interfaces, phone applications, interactive chat bots, virtual reality interfaces and augmented reality interfaces” (recited in at least: Venkatasubramanyam paragraph [0008])).
Regarding Claim 11, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses wherein the relevance of the content items is evaluated to the user's learning objectives and the content sequence is dynamically adjusted based on relevance scores, ensuring that the learning experience remains focused and aligned with user goals (“The GUI 201c provided by the SLKRS 201e includes a teacher bot 1201 and conversational help bots 1202 for on-demand contextual help 1203. The SLKRS 201e provides for sophisticated navigation tools 1204, exemplarily illustrated by clickable links for “lessons” and “my courses”. Furthermore, there are navigation tools provided to switch between a programming tool, indicated as “scratch editor” and a tutorial. The SLKRS 201e also provides a personalized experience by providing notifications and messaging 1205. Additionally, there is rich visualization that makes the course highly interactive and immersive to enable a dynamic learning environment” (recited in at least: Venkatasubramanyam paragraph [0082])).
Regarding Claim 12, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses wherein user's preference is updated in real-time based on ongoing interactions and user feedback during the online learning session, ensuring that the received content items remain aligned with user's evolving learning needs and interests (“uses knowledge concept graphs to provide personalized learning paths. The SLKRS combines knowledge concept graphs with an identified learning style of the knowledge seeker to curate information related to concepts in an e-learning course that the knowledge seeker undertakes. The SLKRS also uses the knowledge concept graphs to bring additional examples, content, and interventions to improve learning outcomes of the knowledge seeker” (recited in at least: Venkatasubramanyam paragraph [0046])).
Regarding Claim 13, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses a machine learning module configured to train predictive models using aggregate user data to recognize engagement patterns and optimize content variety (“a fourth enabler 905 is being brand driven, which refers to the personality and reliability of the platform. These comprise branding, providing interactive chatbots and characters, an interaction patterns library that captures patterns in interaction between knowledge seekers and the SLKRS 201e, and using reusable communication templates that are immediately recognizable as being part of the SLKRS 201e” (recited in at least: Venkatasubramanyam paragraph [0078])).
Regarding Claim 15, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses wherein the distribution of content items, including interactive and non-interactive segments, is balanced, to provide a diverse and engaging learning experience while avoiding user fatigue and cognitive overload (“method and smart-learning and knowledge retrieval system (SLKRS) disclosed herein address the above recited need for providing adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker” (recited in at least: Venkatasubramanyam paragraph [0006])).
Regarding Claim 17, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses a feedback module configured to collect user feedback on content relevance, engagement levels, and perceived difficulty, and use this feedback to iteratively improve the content sequencing capabilities (“The messaging service 611 handles messages that the SLKRS 201e sends to a knowledge seeker by way of interventions and feedback that the knowledge seeker gives to the SLRKS 201e. The content analysis service 612 analyzes e-learning content assimilated by the SLKRS 201e and responses of the knowledge seeker to assessments in light of any feedback from the knowledge seeker that the messaging service 611 receives to enable the SLKRS 201e to provide improved experiences to the knowledge seeker.” (recited in at least: Venkatasubramanyam paragraph [0073])).
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 5, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Venkatasubramanyam in view of Jeffery Matthew Smith (US 20180285461 A1; hereinafter Smith).
Claim 5, Venkatasubramanyam discloses the claimed subject matter as stated above, and Venkatasubramanyam further discloses wherein sequence of interactive content with non- interactive content is provided to prevent mental fatigue to the user comprising: identifying one or more breakpoints within the online learning session to introduce non-interactive content segments based on factors such as session duration, user engagement metrics, and content complexity; providing the non-interactive content segments to the user and determining the frequency of interruptions made by the user based on user preferences, session goals, and learning objectives; monitoring user's response to the non-interactive content segments and gathering feedback for alleviating mental fatigue and enhancing the overall learning experience Nevertheless, such modification would have been obvious to one of ordinary skill in the art. In an analogous art to dynamically deliver content, Smith teaches “to not cause information fatigue, certain display limits may be in place. For example, limits may be based on topics/issues (e.g., the diverse content section may be displayed at most once per day for a particular topic or issue). As another example, limits may be particular to specific entities or candidates (e.g., diverse content may be displayed twice per day for each candidate for the same issue). As yet another example, the total number of diverse content sections displayed in a predetermined period of time may be limited (e.g., five per day)” (recited in at least: Smith paragraph [0020])). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have included breakpoints in lessons because it gives students necessary time for their brains to process information, reduce stress and help restore focus.
Regarding Claim 14, Venkatasubramanyam discloses the claimed subject matter as stated above; however, Venkatasubramanyam does not explicitly disclose wherein the optimized content delivery system is configured to dynamically adjust the sequence of content items based on engagement analysis, ensuring continuous user engagement and preventing content fatigue. Nevertheless, such modification would have been obvious to one of ordinary skill in the art. In an analogous art to dynamically deliver content, Smith teaches “to not cause information fatigue, certain display limits may be in place. For example, limits may be based on topics/issues (e.g., the diverse content section may be displayed at most once per day for a particular topic or issue). As another example, limits may be particular to specific entities or candidates (e.g., diverse content may be displayed twice per day for each candidate for the same issue). As yet another example, the total number of diverse content sections displayed in a predetermined period of time may be limited (e.g., five per day)” (recited in at least: Smith paragraph [0020])). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have included breakpoints in lessons because it gives students necessary time for their brains to process information, reduce stress and help restore focus.
Conclusion
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/SELWA A ALSOMAIRY/Examiner, Art Unit 3715
/Jay Trent Liddle/Primary Examiner, Art Unit 3715
