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 .
DETAILED ACTION
Claims 1-20 are pending and are considered in this Non-Final Office action.
Continuation-in-Part
This application is a continuation-in-part (“CIP”) application of U.S. application no. 17/886,573 filed on 8/21/2022 (“Parent Application”). See MPEP §201.08. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also, in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/29/2023 is acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The initialed and dated copy of Applicant’s IDS form 1449 is attached to the instant Office action.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-10 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.
Claims 2-10 recite the limitation "the apparatus." Claims 2-10 depend on independent claim 1 directly or indirectly. Independent claim 1 discloses “a system for developing a personalized and interactive curriculum, wherein the system comprises: at least a processor; a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor.” Independent claim 1 is completely silent to an apparatus. Therefore, there is insufficient antecedent basis for this limitation in the claim.
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 2-10 are 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. Independent claim 1 discloses “a system for developing a personalized and interactive curriculum, wherein the system comprises: at least a processor; a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor.” The dependent claims 2-10 recite an “apparatus of claim 1” which fails to reference a claim previously set forth and specify a further limitation of the subject matter claimed in independent claim. Specifically, the structure of the system in independent claim 1 was replaced by the structure of an apparatus in claims 2-10, therefore the dependent claims are improper. See MPEP 608.01(n). Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) 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 therefore, 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 non-patentable subject matter. The claims are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In accordance with Step 1, it is first noted that the claimed system in claim 1; apparatus of claims 2-10 and method in claims 11-20 is directed to a potentially eligible category of subject matter (i.e., processes, machine etc.). Thus, Step 1 is satisfied with respect to claims 1-20.
In accordance with Step 2A, Prong One, claims 1-20, the claimed invention recites an abstract idea. Specifically, the independent claim(s) recite(s) (abstract idea recited in italics and additional elements recited in bold):
Claim 1:
A system for developing a personalized and interactive curriculum, wherein the system comprises: at least a processor; a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive user data from a user, wherein the user data comprises scheduling data and domain-specific data, wherein domain-specific data comprises health data; generate a plurality of candidate schedules as a function of the at least a domain target and the scheduling data; select an optimal user schedule from the plurality of candidate schedules; track a user's progress with regard to the optimal user schedule, wherein tracking the user's progress comprises periodically scanning a user device for medical data; iteratively update the optimal user schedule as a function of the user's progress; and display an updated optimal user schedule using a remote device.
Claim 11:
A method for developing a personalized and interactive curriculum, wherein the method comprises: receiving, using at least a processor, user data from a user, wherein the user data comprises scheduling data and domain-specific data, wherein domain-specific data comprises health data; generating, using at least a processor, a plurality of candidate schedules as a function of the at least a domain target and the scheduling data; selecting, using at least a processor, an optimal user schedule from the plurality of candidate schedules; tracking, using at least a processor, a user's progress with regard to the optimal user schedule, wherein tracking the user's progress comprises periodically scanning a user device for medical data; iteratively updating, using at least a processor, the optimal user schedule as a function of the user's progress; displaying an updated optimal user schedule using a remote device.
The above-recited italicized limitations viewed as an abstract idea are certain methods of organizing
human activity (i.e., 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)) and mental processes (i.e., concepts performed in the human mind (including an observation, evaluation, judgment, opinion). The claimed invention is directed to observing user data and progress to evaluate an optimal user schedule, which is a mental process of observing and evaluating user’s personal behavior. Accordingly, the claims recite mental processes and certain methods of organizing human activity.
According to Step 2A, prong two, this judicial exception is not integrated into a practical application
because the use of bolded additional elements for receiving/transmitting data (e.g., receive user data from a user, wherein the user data comprises scheduling data and domain-specific data, wherein domain-specific data comprises health data; track a user's progress with regard to the optimal user schedule, wherein tracking the user's progress comprises periodically scanning a user device for medical data; iteratively update the optimal user schedule as a function of the user's progress; etc.); ; processing data in the form of evaluating/observing (e.g., generate a plurality of candidate schedules as a function of the at least a domain target and the scheduling data; select an optimal user schedule from the plurality of candidate schedules; etc.); storing data; and displaying data (e.g., display an updated optimal user schedule using a remote device; etc.) and repeating steps is merely implementing the abstract idea steps of valuing an idea in the manner of “apply it”. The claim(s) does/do not include additional elements that are sufficient to practically apply the judicial exception because they, whether taken separately or as a whole, merely use conventional computer components or technology to receive, process, store and display data and thus do not provide an inventive concept in the claims.
In accordance with Step 2B, the claims only recite the above bolded additional elements. The additional elements are recited at a high-level of generality (i.e., as a generic computer for evaluating user data to generate an optimal schedule) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, as evidence of generic computer implementation and an indication that the claimed invention does not amount to significantly more, it is first noted in the Applicant’s Specification, in ¶0093, that “computer system 1000 includes a processor 1004 and a memory 1008 that communicate with each other, and with other components, via a bus 1012.Bus 1012 may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. Processor 1004 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 1004 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor 1004 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating-point unit (FPU), and/or system on a chip (SoC).” As additional evidence of conventional computer implementation, it is noted in the MPEP, the courts have recognized that “receiving or transmitting data over a network, e.g., using the Internet to gather data” (See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) and “storing and retrieving information in memory”, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93” (e.g. receiving user data to store as a function of progress) to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (See MPEP 2106.05(d)). From the interpretation of the MPEP and the Specification, one would reasonably deduce that the additional elements are merely embodies generic computers and generic computing functions.
Dependent claims 2-4 and 12-14 recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. The dependent claims do not remedy these deficiencies. Specifically, the dependent claims 2-4 and 12-14 further describe the domain of data that is observed in the abstract idea of evaluating user data to generate an optimal schedule. These dependent claims do not recite any additional elements and recite mental processes and methods of organizing human activity as described above.
Dependent claims 5 and 15 recite the abstract idea in italics and additional elements in bold): wherein generating the plurality of candidate schedules comprises: receiving scheduling training data correlating domain-specific data to scheduling data; training a scheduling machine-learning model as a function of the scheduling training data, wherein the scheduling machine-learning model includes a neural network, wherein the scheduling training data further comprises at least a historical domain target input and outputs at least a plurality of candidate schedules, wherein outputting the at least a plurality of candidate schedules further comprises applying weighted values to the at least a historical domain target input and correlating the weighted values of the at least a historical datum target input to adjacent layers of at least a plurality of candidate schedules; and generating a plurality of candidate schedules as a function of the scheduling machine-learning model. Examiner notes that the abstract idea recited in these dependent claims further recites how the observed user data is received in generating a user schedule, which is a mental process of observing and evaluating user’s personal behavior. The additional elements describe the training of a scheduling machine learning model that includes a neural network. The training as claimed describes the general training process of inputting historical inputs and outputs and applying mathematical weights to those mathematical inputs and outputs. Therefore, the machine learning model, as claimed, values an idea in the manner of “apply it,” does not practically apply the abstract idea, such that the observed data is merely used as data inputs to the generic machine learning model to generate a user schedule. Further, Applicant’s Specification recites that “a "machine-learning model," as used in this disclosure, is a mathematical and/or algorithmic representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above and stored in memory; an input is submitted to a machine-learning model once created, which generates an output based on the relationship that was derived… As a further non-limiting example, a machine-learning model may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training data set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes.” It is clear from Applicant’s Specification that the claimed training is performed through the use of any generic or suitable mathematical algorithm. Applicant’s disclosure fails to provide a preponderance of evidence that the training of the scheduling machine-learning model constitutes a technological improvement. Therefore, the claimed generic implementation of a machine learning model does not amount to significantly more.
Dependent claims 6, 8-9, 16 and 18-19 recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. The dependent claims do not remedy these deficiencies. Dependent claims 6, 8-9, 16 and 18-19 recite the receiving and transmitting of update data for further evaluation to the user’s schedule. Therefore, the dependent claims further recite observing user data and progress to evaluate an optimal user schedule, which is a mental process of observing and evaluating user’s personal behavior.
Dependent claim 7 and 17 recite an additional element “wherein generating evaluation results comprises generating evaluation results using an evaluation machine learning model.” This claim merely recites the abstract idea being applied generically on machine learning technology. The claims are silent to what the machine learning model is or how it implements learning in the technology, such that it constitutes an improvement. Rather the claim recites that the abstract idea step of “generating evaluation results” is performed using the evaluation machine learning model. The mere implementation of abstract idea steps of valuing an idea in the manner of “apply it,” does not practically apply the abstract idea. Additionally, it is noted in Applicant’s Specification, ¶0069, that “a "machine-learning model," as used in this disclosure, is a mathematical and/or algorithmic representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above and stored in memory; an input is submitted to a machine-learning model once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum.” It is clear from Applicant’s Specification that the used machine learning model can be any well‐understood, routine, and conventional machine-learning process that represents a mathematical and/or algorithmic representation. With respect to Applicant’s Specification, the claimed generic implementation of a machine learning model does not amount to significantly more.
Dependent claims 10 and 20 recite the use of an additional abstract idea of mathematical functions, such that it recites the use of “generating a score associated with each candidate schedule of the plurality of candidate schedules using an objective function.” The claimed score is produced through a mathematical function of an objective function, which is used to evaluate schedules for a user. Accordingly, these dependent claims do not recite any additional elements and recite an abstract idea that further narrows the abstract idea described in detail above.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 5-9, 11 and 15-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mason (United States Patent Application Publication, 2022/0415469).
As per Claim 1, Mason discloses a system for developing a personalized and interactive curriculum, wherein the system comprises:
at least a processor; a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor (Mason: ¶0168-0169); to:
receive user data from a user, wherein the user data comprises scheduling data and domain-specific data, wherein domain-specific data comprises health data (Mason: ¶0040 and 0045: A multitude of user data may be received for scheduling data specific to a cohort. Also, user data may be received describing user characteristics such as health data, exercise data, age, weight, etc.);
generate a plurality of candidate schedules as a function of the at least a domain target and the scheduling data (Mason: ¶0114-0117: Candidate scheduled plans are recommended to the user as a function of the machine learning model that inputted the domain and scheduling data.);
select an optimal user schedule from the plurality of candidate schedules (Mason: ¶0119: A candidate schedule plan is selected from the recommended optimal plans.);
track a user's progress with regard to the optimal user schedule, wherein tracking the user's progress comprises periodically scanning a user device for medical data (Mason: ¶0122: A user’s progress in the current scheduled plan, optimized from the machine learning model is tracked. See ¶0158-0160 where the exercise apparatus and/or device is scanned for measurement data of the user to track a user’s progress.);
iteratively update the optimal user schedule as a function of the user's progress (Mason: ¶0146: According to the tracking of a user’s progress and depending if a variance in the user data and cohort data is acceptable, the user’s scheduled plan is dynamically updated.); and
display an updated optimal user schedule using a remote device (Mason: See ¶0119 and Fig. 7 where the selected optimal scheduled plan is displayed on a patient interface. See ¶0081 where the patient interface is on a remote device.).
Claim 11 recites the limitations already addressed by the rejection of claim 1; therefore, the same rejection applies.
As per Claim 5, Mason discloses the apparatus of claim 1, wherein generating the plurality of candidate schedules comprises: receiving scheduling training data correlating domain-specific data to scheduling data (Mason: ¶0066-0068: User-specific attribute, differential and measurement data is received an used as training data to match patterns of characteristics of a user to recommend scheduled plans for desired results); training a scheduling machine-learning model as a function of the scheduling training data, wherein the scheduling machine-learning model includes a neural network (Mason: ¶0069-0070: Training data is inputted into a neural network machine learning model.), wherein the scheduling training data further comprises at least a historical domain target input and outputs at least a plurality of candidate schedules, wherein outputting the at least a plurality of candidate schedules further comprises applying weighted values to the at least a historical domain target input and correlating the weighted values of the at least a historical datum target input to adjacent layers of at least a plurality of candidate schedules (Mason: ¶0127: The one or more machine learning models may be iteratively retrained to select different features capable of enabling output optimization. The features that may be modified may include a quantity of nodes included in each layer of the machine learning models, an objective function executed at each node, a quantity of layers, various weights associated with outputs of each node, and the like. See ¶0060-0061 where the domain for the machine learning model includes historical data correlations to various initial scheduled plans.); and generating a plurality of candidate schedules as a function of the scheduling machine-learning model (Mason: ¶0049-0051: The machine learning model is trained to generate recommended scheduled plans that are optimal for the user.).
Claim 15 recites the limitations already addressed by the rejection of claim 5; therefore, the same rejection applies.
As per Claim 6, Mason discloses the apparatus of claim 1, wherein iteratively updating the optimal user schedule comprises: determining objective update data as a function of the user's progress; generating evaluation results as a function of evaluating the objective update data; iteratively updating the optimal user schedule as a function of the evaluation results. (Mason: ¶0121-0123: A user’s progress in the current scheduled plan, optimized from the machine learning model is tracked. The tracked performance information provides updated characteristics on the users. Specifically, it is noted whether the user is ahead or behind schedule. In the evaluation of the performance results information, a machine learning model can determine an adjustment to the scheduled plan.).
Claim 16 recites the limitations already addressed by the rejection of claim 6; therefore, the same rejection applies.
As per Claim 7, Mason discloses the apparatus of claim 6, wherein generating evaluation results comprises generating evaluation results using an evaluation machine learning model (Mason: ¶0122: Determining the patient is on track for the current scheduled plan may cause the trained machine learning model to adjust a parameter. For example, the machine learning model may optimize one or more exercises of the scheduled plan the user is performing. To further improve the performance of the user, the adjustment may be based on a next step of the scheduled plan.).
Claim 17 recites the limitations already addressed by the rejection of claim 7; therefore, the same rejection applies.
As per Claim 8, Mason discloses the apparatus of claim 6, wherein tracking the user's progress comprises sending one or more notifications as a function of the evaluation results (Mason: ¶0146-0147: When the scheduled plan is dynamically updated with respect to a user’s performance, a message may be sent to a user’s interface.).
Claim 18 recites the limitations already addressed by the rejection of claim 8; therefore, the same rejection applies.
As per Claim 9, Mason discloses the apparatus of claim 1, wherein tracking the user's progress comprises comparing a geographic location of the user to lesson location data (Mason: ¶0036-0037 The determined scheduled plans compare the user’s location to the exercise protocol’s location, as a measure to monitor the user’s progress.).
Claim 19 recites the limitations already addressed by the rejection of claim 9; therefore, the same rejection applies.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2-4 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Mason (United States Patent Application Publication, 2022/0415469) in view of Anders (United States Patent Application, 2020/0234606).
As per Claim 2, Mason discloses the apparatus of claim 1.
Mason does not explicitly disclose; however, Anders discloses wherein the plurality of candidate schedules comprises a plurality of lessons related to a domain corresponding to the domain-specific data, wherein the plurality of lessons comprises online lessons (Anders: ¶0069-0071: Comparable candidates schedules are recommended according to domain-specific data. See Fig. 7 where the lessons comprise online and livestream lessons.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Mason with Anders’ candidate course plan for recommendation to a user because the references are analogous/compatible, since each is directed toward features of scheduling a recommended plan for a user, and because incorporating Anders’ candidate course plan for recommendation to a user in Mason would have served Mason's pursuit of dynamically including or excluding recommended scheduled plans (See Mason, ¶0051); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 12 recites the limitations already addressed by the rejection of claim 2; therefore, the same rejection applies.
As per Claim 3, Mason in view of Anders discloses the apparatus of claim 2, wherein the plurality of lessons comprises exercise lessons (Mason: See ¶0120 where the scheduled plan includes exercise sessions.).
Claim 13 recites the limitations already addressed by the rejection of claim 3; therefore, the same rejection applies.
As per Claim 4, Mason in view of Anders discloses the apparatus of claim 2, wherein the plurality of lessons comprises nutritional lessons (Mason: See ¶0120 where the scheduled plan includes nutritional regimen sessions.).
Claim 14 recites the limitations already addressed by the rejection of claim 4; therefore, the same rejection applies.
Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mason (United States Patent Application Publication, 2022/0415469) in view of Ni et al. (United States Patent Application, 2018/0060500, hereinafter referred to as Ni).
As per Claim 10, Mason discloses the apparatus of claim 1.
Mason does not explicitly disclose; however, Ni discloses wherein the memory instructs the processor to generate a score associated with each candidate schedule of the plurality of candidate schedules using an objective function (Ni: ¶0040: Candidate recommendations for scheduled dates a scored in an objective function that computes a composite score.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Mason with Ni’s composite score for comparing candidate schedules because the references are analogous/compatible, since each is directed toward features of scheduling a recommended plan for a user, and because incorporating Ni’s composite score for comparing candidate schedules in Mason would have served Mason's pursuit of dynamically including or excluding recommended scheduled plans (See Mason, ¶0051); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 20 recites the limitations already addressed by the rejection of claim 10; therefore, the same rejection applies.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chevalier (US 2019/0385470): A method of automatically providing personalized learning activities to users of an online learning platform is described. An input of an educational objective and a finite duration of time for completion of the educational objective is received. An effective time available to the user for rendering a playlist of educational content files is calculated. A plurality of educational content files that meet the educational objective is selected from an educational content repository. A subset of educational content files is selected from the plurality based on a learning profile associated with the user and time needed for rendering. An ordered playlist of the educational content files in the subset is generated, such that the ordered playlist is estimated to be rendered by the user within the effective time and is optimized to achieve the educational objective for the user.
Qiu (US 2022/0114900): Generating a personalized learning experience using artificial intelligence and disparate data sources is described. A computing device is directed to identify a plurality of objectives for a user profile to meet in association with a learning course, identify a plurality of learning resources associated with the learning course, and identify information associated with the user profile. The computing device then executes an artificial intelligence routine using the information associated with the user profile, the plurality of learning resources, and the plurality of objectives that generates a customized course for the user profile. The customized course for the user profile is different than courses customized for other user profiles. The customized course is presented in a user portal for the user profile. The customized course as generated includes a subset of the learning modules curated for the user profile and an order assigned to the user profile for completion.
Deng et al. (US 2014/0172455): A method for providing a health visualization model includes receiving user data comprising characteristics corresponding to a user, and adherence data comprising adherence history corresponding to the user, determining a relationship between an adherence level of the user and an expected health outcome based on the user data and the adherence data, generating the health visualization model based on the determined relationship, and outputting the health visualization model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLISON MICHELLE NEAL whose telephone number is (571)272-9334. The examiner can normally be reached 9-2pm ET, M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at 5712705389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALLISON M NEAL/Primary Examiner, Art Unit 3625
Prosecution Insights