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 .
Status
This action is in response to the amendment filed on 12/17/2025. Claims 1-2, 4-11, 13-19 are pending. Claims 1, 2, 4, 8, 10, 11, 13, 17, 19 are amended. No claims have been added. Claims 3, 12, 20 have been cancelled.
Response to Arguments
Applicant's arguments filed 12/17/2025 have been fully considered but they are not persuasive. The applicant has argued the previous 101 rejection “Contrary to the Office Action's assertion, the claims do not recite and are not directed to any abstract idea. The USPTO publication July 2015 Update: Subject Matter Eligibility ("July 2015 Update") clarified that "the courts have declined to define abstract ideas, other than by example." July 2015 Update at page 3. However, the USPTO clarified in the 2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG") released on January 4, 2019 that "abstract ideas can be grouped as e.g., mathematical concepts, certain methods of organizing human activity, and mental processes." 2019 PEG at page 1. Mathematical concepts are defined as "mathematical relationships, mathematical formulas or equations, mathematical calculations;" certain methods of organizing human activity are defined 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);" and mental processes are defined as "concepts performed in the human mind (including an observation, evaluation, judgment, opinion)." Id. at pages 9-11.” The examiner respectfully disagrees. The claims are specifically direct to evaluating education data to derive goals and suggest accomplishments which is a mental process. A mental processes as "concepts performed in the human mind (including an observation, evaluation, judgment, opinion)." The steps of the claims involve taking a user's education data, derive a goal from that data, and suggest an accomplishment to pursue to achieve that goal. This the type of evaluation and judgment-based process that a human counselor, advisor, or coach performs mentally every day. A career counselor or academic advisor routinely reviews a student's educational background, identifies appropriate goals, and recommends steps to achieve those goals. Even though the mental steps are implemented using AI models this does not remove them from the mental processes category. The AI models in the claims are simply tools to perform the steps of the invention. The claims are also directed to a method of organizing human activity specifically, managing personal behavior and goal-setting. Certain methods of organizing human activity to include "managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions)." Claim 1 is directed to deriving personal goals from a user's education data and suggesting accomplishments to pursue to achieve those goals. This is a method of managing personal behavior specifically guiding a user toward self-improvement objectives based on their educational background. Goal-setting and achievement planning are fundamental human activities that have been performed by educators, coaches, and counselors long before the advent of computers or AI. The claims merely automate this human activity using generic AI models and a display interface.
The applicant has argued “Applicant respectfully submits that the claimed features, which relate to, among other things, addresses issues with accessing files in shared folders of a file storage system, is not similar to any of the three groupings of abstract ideas enumerated in the 2019 PEG. For example, while some of the claimed features may be based on mathematical concepts, the mathematical concepts are not recited in the claims. The claims also do not recite a certain method of organizing human activity given that the claim features relate to file folder access and using artificial intelligence to process data stored therein. The claims further do not recite a mental process because the claims, under their broadest reasonable interpretation, do not cover performance in the mind. Not only are the claims not similar to any of the three groupings of abstract ideas enumerated in the 2019 PEG, the claims compare favorably to cases and examples of non-abstract ideas.” The examiner respectfully disagrees. Applicant’s claims are directed to taking a user's education data, derive a goal from that data, and suggest an accomplishment to pursue to achieve that goal. There is no recitation of shared folders, file storage systems, or file access in claim 1. The argument about how "some of the claimed features may be based on mathematical concepts" is not persuasive because mathematical formulas or equations are not explicitly recited in the claims, the mathematical concepts grouping does not apply. This argument, while potentially valid for the mathematical concepts category standing alone, is irrelevant because the claims independently fall within the mental processes and methods of organizing human activity categories. A claim only fall within one abstract idea grouping to be identified as reciting an abstract idea.
The applicant has argued “For example, the claims are similar to the patent-eligible claims at issue in McRO, Inc. dba Planet Blue v. Bandai Namco Games America Inc., 120 USPQ2d 1091 (Fed. Cir. 2016) ("McRO"). As explained in the November 2016 Memo, the Federal Circuit held the claimed methods in McRO "of automatic lip synchronization and facial expression animation using computer-implemented rules patent eligible under 35 U.S.C. § 101, because they were not directed to an abstract idea." November 2016 Memo at page 2. The basis for the McRO court's decision "was that the claims were directed to an improvement in computer-related technology (allowing computers to produce 'accurate and realistic lip synchronization and facial expressions in animated characters" that previously could only be produced by human animators), and thus did not recite a concept similar to previously identified abstract ideas." Id. In particular, the court found that the claimed invention improved "computer animation through the use of specific rules" and that "human artists did not use the claimed rules, and instead relied on subjective determinations." Id. The incorporation of the particular claimed rules improved the existing technological process, unlike Alice in which a computer "was merely used as a tool to perform an existing process." Id.” The examiner respectfully disagrees. In McRO the Federal Circuit held claimed methods of automatic lip synchronization and facial expression animation using computer-implemented rules to be patent eligible under 35 U.S.C. 101, because they were not directed to an abstract idea. The Federal Circuit emphasized that it was the particular rules that drove the improvement, and that those rules were explicitly recited in the claims with sufficient specificity to define a concrete technical implementation. Here, claim 1 recites no specific rules, parameters, algorithms, or technical mechanisms by which the goal derivation AI model derives a goal from education data or by which the goal booster AI model generates a suggested accomplishment. The AI models are recited purely in functional terms they receive an input and produce an output without any specification of the rules, logic, or technical processes by which they operate. In applicant’s claims the claimed AI models do not improve the computer's technical capabilities. Deriving goals from educational background and suggesting accomplishments to achieve those goals is not a task that computers previously struggled to perform, it is a human advisory and counseling function that the claims are putting on a general purpose computer. The computer is being used as a tool to perform an existing human process. The applicant appears to be arguing that using AI to derive goals and suggest accomplishments produces a better result for users than prior methods. But an improvement to the usefulness of the output is not the same as an improvement to computer technology. McRO's improvement was to the computer animation process itself.
The applicant cited various paragraphs in the specification and has argued “Like the claims in McRO, amended Claim 1 is directed to an improvement in computer- related technology-namely, enabling a centralized storage platform for users that addresses the technical issues with accessing files in shared folders of a file storage system. For example, paragraphs [0031]-[0034] of the specification explain the technical issues as follows: [0031] As described above, the filing system in typical operating systems may allow users to organize stored files in one or more folders and/or to share access to the folder(s) with one or more other users and/or computing devices. Often, however, the files stored in a shared folder local to a user's computing device may only be accessible if the user's computing device is powered on and connected to a network. The user's computing device may also have memory limitations that could prevent large files from being stored in a shared folder.” The examiner respectfully disagrees. The applicant has argued the "technical issues with accessing files in shared folders of a file storage system" and points to paragraphs 31-34 of the specification to support this characterization. However, the claim language does not recite shared folders, file storage systems, file uploads, file access, network accessibility, or any of the technical file storage deficiencies described in those specification paragraphs. The amended claim recites training a goal derivation AI model on accomplishment/goal data, applying education data to derive a predicted goal, applying the predicted goal to suggest an accomplishment, and displaying the suggestion. None of these limitations are directed to solving file storage access problems. Applicant cannot rely on specification language to supply claim limitations that are not actually recited in the claims (See Intellectual Ventures.) Even accepting that paragraphs 31-34 describe genuine technical problems with file storage systems, those problems are not solved by the limitations of amended claim 1. The claim is directed to training an AI model on user data, predicting goals from education data, and suggesting accomplishments. None of the claimed limitations address file upload failures, network disruptions, memory limitations, or shared folder access deficiencies described in the specification. McRO defined rules that were themselves the technical mechanism of improvement. The amended claim still does not recite any specific rules, algorithms, parameters, or technical mechanisms by which the AI models operate. The addition of reciting that the goal derivation AI model is trained on accomplishment/goal data labeled with user goals may arguably to an improvement to the abstract idea of goal prediction but does not supply a specific technical implementation analogous to the rules in McRO. The claimed training of an AI model is a generic, conventional machine learning operation that is not itself a technical improvement to computer technology.
The applicant has argued “Thus, amended Claim 1 recites features directed to an improvement in file storage and file processing. Accordingly, Applicant respectfully submits that amended Claim 1 is not directed to an abstract idea and therefore is directed to patent eligible subject matter. Amended independent Claims 10 and 19 are different in scope than Claim 1, but include certain similar features and, thus, are also directed to patent eligible subject matter.” The examiner respectfully disagrees. The claim recites training a goal derivation AI model on labeled accomplishment/goal data, applying education data to predict a goal, applying the predicted goal to suggest an accomplishment, and displaying the suggested accomplishment. Not one of these limitations recites file storage, file processing, shared folders, file uploads, file access, network file systems, or any other file-related operation. Applicant's assertion that the claim recites features directed to file storage and file processing is not supported by the actual claim language.
The applicant has argued “Even if it were assumed that the claims recite an abstract idea, Applicant respectfully submits that the claims recite a practical application, and thus are not "directed to" an abstract idea. The 2019 PEG clarifies that a claim is not "directed to" a judicial exception, "and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception." 2019 PEG at page 13. In particular, a "claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception." Id. at pages 13-14….Furthermore, the USPTO memorandum Reminders on Evaluating Subject Matter Eligibility of Claims under 35 U.S.C. 101 dated August 4, 2025 ("2025 Memo") noted that "[i]n computer-related technologies, examiners can conclude that claims are eligible in Step 2A Prong Two by finding that a claim reflects an improvement to the functioning of a computer or to another technology or technical field, integrating a recited judicial exception into a practical application of the exception." 2025 Memo at page 4. An "important consideration in determining whether a claim improves technology or a technical field is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome," and the "examiner is reminded to consult the specification to determine whether the disclosed invention improves technology or a technical field, and evaluate the claim to ensure it reflects the disclosed improvement." Id. Moreover, the "claim itself does not need to explicitly recite the improvement described in the specification." Id.” The examiner respectfully disagrees. The claims do not reflect an improvement to computer technology or any other technical field. Applicant argues the August 2025 Memorandum's guidance that claims reflecting an improvement to the functioning of a computer or another technology or technical field can be found eligible at Prong 2. However, applicant’s claims are directed to AI-based goal prediction and accomplishment recommendation something that is currently and has previously been done by a human but implemented on a generic computer. The computer components recited in the claims (a processor, memory, and user interface display) perform their conventional functions without any modification or improvement. The AI models are recited in purely functional terms without any specific technical architecture, training methodology beyond conventional supervised learning, or operational mechanism that would constitute an improvement to computer technology. The claims do not improve how computers process data, store information, or execute instructions. The claims merely use a computer to automate an abstract advisory process. The August 2025 Memo states that the examiner must "evaluate the claim to ensure it reflects the disclosed improvement." However, applicant’s specification described technical improvement relates to file storage access and file processing capabilities but the claims do not recite any file storage, file access, or file processing limitations whatsoever. The August 2025 Memo emphasizes that an important consideration is "the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome." Claim 1 uses AI models to predict goals from education data and suggest accomplishments without reciting any particular technical implementation of that solution. The claim does not specify the architecture of the AI models, the specific features of the education data used for prediction, the technical mechanism by which the goal derivation model processes inputs, or any specific technical constraints on the training process beyond conventional supervised learning with labeled data. Here, the additional elements impose no meaningful technical limit on the abstract idea of goal prediction and accomplishment recommendation. The recitation of a generic processor, memory, and display does not confine the claims to any particular technical implementation it simply requires that the abstract idea be performed on a computer.
The applicant has argued “The 2025 Memo guidance is similar to the language found in MPEP § 2106.05(a), which states that "[i]n determining patent eligibility, examiners should consider whether the claim 'purport(s) to improve the functioning of the computer itself' or 'any other technology or technical field."' MPEP § 2106.05(a). If "it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification." Id. An "indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art." Id. After "the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology." Id. This section of the MPEP further clarifies that "[d]uring examination, the examiner should analyze the 'improvements' consideration by evaluating the specification and the claims to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement," and "examiners are not expected to make a qualitative judgement on the merits of the asserted improvement." Id... Thus, amended Claim 1 as a whole is directed to a particular improvement in file storage access and file processing. Accordingly, Applicant respectfully submits that amended Claim 1 as a whole integrates the alleged abstract idea into a practical application.” The examiner respectfully disagrees. Applicant has cited the claim language specially reciting the AI model training step, the goal derivation step, and the accomplishment suggestion step but does not make any attempt to explain how any of these limitations address the file storage access problems described in paragraphs 31-34. Applicant simply quotes the claim and then concludes, without analysis, that it is directed to a particular improvement in file storage access and file processing. The examiner has evaluated the claim and finds that none of the recited limitations specifically the training a goal derivation AI model, predicting a goal from education data, suggesting an accomplishment, and displaying the result address, solve, or reflect any of these file storage problems. The claim does not recite any file storage mechanism, file access protocol, network communication improvement, memory management technique, or file processing capability. Claim 1 mentions of goal tracking which shows that the claim involves goals but the claims AI-based goal prediction from education data is categorically different from the file-associated goal tracking described in the specification. The specification describes tracking goals associated with uploaded files the claim predicts entirely new goals from a user's educational background with no connection to any file or folder.
The applicant has argued “Even if it were assumed that amended Claim 1 is directed to an abstract idea, Applicant respectfully submits that amended Claim 1 is directed to "significantly more" than the alleged abstract idea. The Office Action alleged in its step two analysis that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Applicant respectfully disagrees. For example, the claims of the instant application can further be analogized to claim 4 of example 23 of the USPTO July 2015 Update Appendix 1: Examples ("July 2015 Examples"). July 2015 Examples at 9. In that example, the USPTO stated that the claim "limitations are not merely attempting to limit the [judicial exception] to a particular technological environment," but rather "[improve] the functioning of the basic display function of the computer itself' (i.e., "the ability of the computer to display information and interact with the user"). Id. Similarly, the limitations of amended Claim 1 are not merely attempting to limit the alleged abstract idea to a particular technological environment. Instead, the limitations recited in amended Claim 1 relate to improving a conventional data processing pipeline preview mode by using machine learning to select a more representative sampling of data output by a node of the data processing pipeline so that anomalous node behavior can be detected. In particular, amended Claim 1 recites "train a goal derivation artificial intelligence model using training data, wherein the training data comprises one or more training data items, and wherein an individual training data item comprises a set of accomplishments recorded by a second user and is labeled with an indication of a second goal pursued by the second user," "in response to a lack of information from a user on a goal to be pursued, apply the education data as an input to the goal derivation artificial intelligence model, wherein application of the education data as the input to the goal derivation artificial intelligence model causes the goal derivation artificial intelligence model to output a predicted goal," and "apply the predicted goal as an input to a goal booster artificial intelligence model, wherein application of the predicted goal as the input to the goal booster artificial intelligence model causes the goal booster artificial intelligence model to output an indication of a suggested accomplishment to pursue to achieve the predicted goal." These features can enable improvements over typical file storage systems. Accordingly, Applicant respectfully submits that even if it were assumed that amended Claim 1 is directed to an abstract idea, amended Claim 1 is directed to "significantly more" than the alleged abstract idea. Amended independent Claims 10 and 19 are different in scope than Claim 1, but include certain similar features and, thus, are also directed to "significantly more" than the alleged abstract idea.” The examiner respectfully disagrees. The applicant quotes the amended claim 1 limitations directed to AI-based goal derivation and accomplishment suggestion, and then concludes that these features "can enable improvements over typical file storage systems." The claim limitations are directed to goal prediction and accomplishment recommendation, not to file storage. The applicant has not explained how training a goal derivation AI model on accomplishment/goal data, predicting a goal from education data, and suggesting an accomplishment improves a file storage system in any technically specific or meaningful way. At Step 2B, the question is whether the additional elements considered individually and in combination amount to significantly more than the abstract idea itself. The additional elements in amended claim 1 beyond the abstract idea of goal prediction and accomplishment recommendation are: a processor, memory, a user device, and a display interface. These are generic computer components that perform their conventional functions of executing instructions, storing data, and displaying output. Applicant points to the training step as a key additional element. However, training an AI model using supervised learning with labeled training data is a well-understood, routine, and conventional machine learning technique. The claims do not recite any specific, unconventional training methodology, neural network architecture, optimization approach, or technical mechanism that would distinguish the claimed training from generic supervised machine learning. Amended claim 1 recites applying education data to the goal derivation model "in response to a lack of information from a user on a goal to be pursued." This specifies when the goal prediction step is performed but does not supply any additional technical mechanism or improvement. Conditional triggers for performing abstract steps do not amount to significantly more. Even considering the additional elements in combination the AI model training step, the conditional trigger, the processor, memory, user device, and display, the combination does not amount to significantly more than the abstract idea. The elements work together to implement the abstract idea of goal prediction and accomplishment recommendation on a generic computer system.
The applicant analogizes the claims to claim 4 of Example 23 of the July 2015 Examples, which was found eligible because it improved the basic display function of a computer specifically, improving the computer's ability to display information and interact with the user. However, amended claim 1 does not improve any basic computer function. The display limitation in claim 1 specifically causing the user device to display a suggested accomplishment is a conventional display operation that performs exactly the function displays have always performed. There is no improvement to the display function itself, to the user interaction mechanism, or to any other basic computer function. Example 23's claim 4 was eligible because the display improvement was intrinsic to and inseparable from the claim's technical innovation. This is not the same as applicant’s claims where the display is merely the output mechanism for the abstract idea. The 101 rejection is maintained and updated below.
Applicant’s arguments, with respect to the previous 112(b), second paragraph have been fully considered and are persuasive. The previous 112(b), second paragraph rejections of claims 19-20 have been withdrawn.
Applicant’s arguments with respect to the previous 103 rejection of claim(s) 1-2, 4-11, 13-19 have been considered but are moot, because in view of the amendments an updated search was conducted and the previous 103 rejection has been updated and modified.
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 1-2, 4-11, 13-19 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1, 2, 4-9, are directed to a system, claims 10-11, 13-18 are directed to a method, and claims 19 are directed to a computer readable medium. Therefore, claims 1-2, 4-11, 13-19 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 10, 19 recite using a model to output and display a suggested accomplishment to a user idea based on “Certain Methods of Organizing Human Activity” related to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Claim 1 recites abstract limitations including “obtain education data… train …model using training data, wherein the training data comprises one or more training data items, and wherein an individual training data item comprises a set of accomplishments recorded by a second user and is labeled with an indication of a second goal pursued by the second user, in response to a lack of information from a user on a goal to be pursued, apply the education data as an input …, wherein application of the education data as the input … causes the goal … to output a predicted goal; apply the predicted goal as an input …, wherein application of the predicted goal as the input …causes … output an indication of a suggested accomplishment to pursue to achieve the goal; and cause … display the suggested accomplishment… in response to a user selection of a goal booster …element ...” Claim 10 recites abstract limitations including “obtaining the user data…; training a … model using training data, wherein the training data comprises one or more training data items, and wherein an individual training data item comprises a set of accomplishments recorded by a second user and is labeled with an indication of a second goal pursued by the second user; in response to a lack of information from a user on a goal to be pursued applying the user data as an input …, wherein application of the user data as the input… causes …output a goal; applying the goal as an input …, wherein application of the predicted goal as the input …causes the.. output an indication of a suggested accomplishment to pursue to achieve the predicted goal; and causing…display the suggested accomplishment … in response to a user selection of a predicted goal booster … element ….” Claim 19 recites abstract limitations including “obtain user data…; train a … model using training data, wherein the training data comprises one or more training data items, and wherein an individual training data item comprises a set of accomplishments recorded by a second user and is labeled with an indication of a second goal pursued by the second user; in response to a lack of information from a user on a predicted goal to be pursued, apply the user data as an input …, wherein application of the user data as the input … causes the …output a goal; apply the predicted goal as an input …, wherein application of the goal as the input …causes the … output an indication of a suggested accomplishment to pursue to achieve the predicted goal; and cause …display the suggested accomplishment … in response to a user selection of a goal booster …element depicted ...”
These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “a processor,” “computer” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “a processor,” “computer” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “a processor,” “computer” language, the claim steps in the context of the claim encompass an abstract idea directed to a mental process and “Certain Methods of Organizing Human Activity.”
Dependent claims 2, 4, 7-9, 11, 13, 16-18, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 5-6, 14-15, will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 10, and 19 do not integrate the judicial exception into a practical application. Claim 1 is a system comprising “memory that stores computer-executable instructions; and a processor in communication with the memory, wherein the computer- executable instructions, when executed by the processor, cause the processor to: … a user device; …goal derivation artificial intelligence model…; a goal booster artificial intelligence model … user device … a user interface.” Claim 10 recites the additional elements of “A computer-implemented method for centralizing storage of user data, the computer-implemented method comprising: … a user device; … a goal derivation artificial intelligence model…; a goal booster artificial intelligence model …user device …a user interface.” Claim 19 recites the additional elements of ”A non-transitory, computer-readable medium comprising computer-executable instructions for retrieving property information, wherein the computer-executable instructions, when executed by a computer system, cause the computer system to: … a user device; a goal booster artificial intelligence model …a goal derivation artificial intelligence model, …the user device … a user interface.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to obtain, apply, display data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2, 4, 7-9, 11, 13, 16-18, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claims 5, 14 includes the additional element of “wherein the goal derivation artificial intelligence model comprises one of a machine learning model or a neural network.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claims 6, 15, includes the additional element of “wherein the user interface comprises a second user interface element that allows a user of the user device to capture the education data.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 10, and 19 do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, Claim 1 is a system comprising “memory that stores computer-executable instructions; and a processor in communication with the memory, wherein the computer- executable instructions, when executed by the processor, cause the processor to: a goal booster artificial intelligence model … a user device; …goal derivation artificial intelligence model…; a goal booster artificial intelligence model … user device … a user interface.” Claim 10 recites the additional elements of “A computer-implemented method for centralizing storage of user data, the computer-implemented method comprising: … a user device; … a goal derivation artificial intelligence model…; …user device …a user interface.” Claim 19 recites the additional elements of ”A non-transitory, computer-readable medium comprising computer-executable instructions for retrieving property information, wherein the computer-executable instructions, when executed by a computer system, cause the computer system to: … a user device; …a goal derivation artificial intelligence model, a goal booster artificial intelligence model …the user device … a user interface.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
The additional elements of the independent claims, when considered both individually and in combination, do not comprise anything significantly more than the judicial exception.
Dependent claims 2, 4, 7-9, 11, 13, 16-18, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claims 5, 14 includes the additional element of “wherein the goal derivation artificial intelligence model comprises one of a machine learning model or a neural network.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claims 6, 15, includes the additional element of “wherein the user interface comprises a second user interface element that allows a user of the user device to capture the education data.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 1-2, 4-11, 13-19 are rejected under 35 USC 101.
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.
Claim 1-2, 4-11, 13-19 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. The applicant has amended the claims to include the limitation of in response to a lack of information from a user on a goal to be pursued, apply the education data as an input to the goal derivation artificial intelligence model. The applicant has support in the originally filed disclosure for a lack of network access but does not have support for a lack of information from a user. Appropriate correction is required.
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 1-2, 4-11, 13-19rejected 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 10 and 19 recites the limitation “the predicted goal.” There is insufficient antecedent basis for this limitation in the claim.
The claims that depends upon the previously rejected claims are also rejected.
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.
Claim(s) 1-2, 4-11, 13-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miller (US 20200302296 A1) in view of Mohler et al. (US 20160321935 A1) in view of Terhark et al. (US 20180232751 A1).
Regarding claim 1, Miller discloses memory that stores computer-executable instructions (¶ 72, discloses a memory and configured to execute code stored therein. ¶ 78, 79, 86); and a processor in communication with the memory, wherein the computer- executable instructions, when executed by the processor, cause the processor to (¶ 72, discloses includes one or more suitably configured processors having a memory and configured to execute code stored therein. ¶ 78-80);
obtain education data from a user device (See Fig. 1, ¶ 72, discloses one or more suitably configured processors having a memory and configured to execute code stored therein.. the evaluation server 102 is configured to access student assessments and other data relating to the educational assessments, professional activities, and personal metrics of present and former student. ¶ 122, discloses the evaluation server 102 to access a specific educational dataset for a given learner. ¶ 65, 109);
train the goal derivation artificial intelligence model using training data, wherein the training data comprises one or more training data items, and wherein an individual training data item comprises a set of accomplishments recorded by a second user and is labeled with an indication of a second goal pursued by the second user (¶ 66, discloses training predictive models including a second cohort (user), ¶ 109-111, discloses increasing long term goals. ¶ 122, discloses goals for a specific user. ¶ 223, discloses a first and second learning cluster, ¶ 137-140, includes status identifiers for each member which would include one or more. ¶ 133, 240);
apply the education data as an input to a goal derivation artificial intelligence model, wherein application of the education data as the input to the goal derivation artificial intelligence model causes the goal derivation artificial intelligence model to output a predicted goal (¶ 66, discloses products described herein extract learner data from a collection of discrete educational databases and utilize AI based principals to construct predictive models relating to likelihood of educational or career success. Once generated, such predictive models are used to evaluate individual students for likelihood of success in future efforts or endeavors…the AI based evaluation modules can be used to review and interpret the likelihood that a given selection of evaluative materials (e.g. tests) are accurate predictors or future academic or career success. ¶ 122, discloses a model that can be used to evaluate a specific learner's academic checkpoint progress and career planning. Here, the value(s) output by the model indicates a score relating to the likelihood the specific learner will meet the desired milestone or career goal. ¶ 111, discloses utilizing AI based systems to parse such structured data (e.g. school rankings) and unstructured data (e.g. personal learner fit) to identify the institution that presents the highest probability of achieving both the educational and career goals of the learner. ¶ 109, discloses a generated model that outputs a score indicative of a short-term goal. ¶ 203);
apply the goal as an input to a goal booster artificial intelligence model, wherein application of the goal as the input to the goal booster artificial intelligence model causes the goal booster artificial intelligence model to output an indication of a suggested accomplishment to pursue to achieve the predicted goal (¶ 90, discloses how different educational steps lead to achieving key milestones. ¶ 66, discloses products described herein extract learner data from a collection of discrete educational databases and utilize AI based principals to construct predictive models relating to likelihood of educational or career success. Once generated, such predictive models are used to evaluate individual students for likelihood of success in future efforts or endeavors…the AI based evaluation modules can be used to review and interpret the likelihood that a given selection of evaluative materials (e.g. tests) are accurate predictors or future academic or career success. ¶ 122, discloses a model that can be used to evaluate a specific learner's academic checkpoint progress and career planning. Here, the value(s) output by the model indicates a score relating to the likelihood the specific learner will meet the desired milestone or career goal. ¶ 111-112, discloses utilizing AI based systems to parse such structured data (e.g. school rankings) and unstructured data (e.g. personal learner fit) to identify the institution that presents the highest probability of achieving both the educational and career goals of the learner. ¶ 109, discloses a generated model that outputs a score indicative of a short-term goal. ¶ 70, 141, 203);
Miller does not specifically teach displaying a suggested accomplishment.
However, Mohler teaches cause the user device to display the suggested accomplishment in a user interface in response to a user selection of a goal booster user interface element depicted in the user interface (abstract, ¶ 89, discloses generating life recommendation decisions for goals, ¶ 102, discloses suggested action for the participant to take that will affect and help achieve goals. ¶ 111-114, disclose further steps to achieve goals. ¶ 98, disclose displaying data related to goals. ¶ 46, Fig. 5-6, ¶ 96, 79, 54-55, disclose outputs).
Mohler also teaches apply the goal as an input to a goal booster artificial intelligence model, wherein application of the goal as the input to the goal booster artificial intelligence model causes the goal booster artificial intelligence model to output an indication of a suggested accomplishment to pursue to achieve the predicted goal (¶ 16, 46, 54, 89, 102-107, 112-113).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Miller to include/perform displaying a suggested accomplishment, as taught/suggested by Mohler. This known technique is applicable to the system of Miller as they both share characteristics and capabilities, namely, they are directed to making recommendations to meet goals. One of ordinary skill in the art would have recognized that applying the known technique of Mohler would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mohler to the teachings of Miller would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such goal features into similar systems. Further, applying displaying a suggested accomplishment would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user the ability to see the determined suggestions.
Miller does not specifically teach in response to a lack of information from a user on a goal to be pursued apply the education data as an input to the goal derivation artificial intelligence model.
However, Terhark also teaches
wherein an individual training data item comprises a set of accomplishments recorded by a second user and is labeled with an indication of a second goal pursued by the second user (¶ 22, discloses receiving user profiles and using that in a predictive modeling system, ¶ 114-115, 119, 208);
in response to a lack of information from a user on a goal to be pursued, apply the education data as an input to the goal derivation intelligence model, wherein application of the education data as the input to the goal derivation intelligence model causes the goal derivation intelligence model to output a predicted goal (¶ 120, discloses allowing the system platform to recommend career paths based on users' skills, education, and experiences. ¶ 122, discloses a Job Recommendation feature will use user's profile details as well as their preferences, and will determine the best jobs where they have the best opportunity of success. ¶ 191-193, disclose a proposed career path for the user. ¶ 195-199, discloses matching a user’s skills, education, certifications, licenses, and experience to return a job recommendation.).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Miller to include/perform displaying a lack of information from a user on a goal to be pursued, as taught/suggested by Terhark. This known technique is applicable to the system of Miller as they both share characteristics and capabilities, namely, they are directed to making career recommendations. One of ordinary skill in the art would have recognized that applying the known technique of Terhark would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Terhark to the teachings of Miller would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such career features into similar systems. Further, applying a lack of information from a user on a goal to be pursued would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user to use their talents, skills, and ability to choose a potential job.
Regarding claims 2 and 11, Miller teaches further cause the processor to apply the goal and the education data as the input to the predicted goal booster artificial intelligence model (¶ 110-111, discloses various inputs including goals, experiences, and education. ¶ 194, discloses various data points to include in a repository including goals and educational variables. Abstract, discloses assessment modeling. ¶ 65-66, 122, 109, 203).
Regarding claims 4, 13, Miller teaches wherein the computer-executable instructions, when executed, further cause the processor to train the goal booster artificial intelligence model using second training data, wherein the second training data comprises one or more second training data items, and wherein an individual second training data item comprises a set of accomplishments recorded by a third user and is labeled with an indication of whether a third goal pursued by the third user was achieved by the third user (¶ 66, discloses training predictive models including a second cohort (user), ¶ 111, discloses increasing long term goals. ¶ 122, discloses goals for a specific user. ¶ 223, discloses a first and second learning cluster, ¶ 137, includes status identifiers for each member which would include one or more. ¶ 129-130, discloses use by one or more users, ¶ 133, 240). Also taught by Terhark.
Regarding claims 5, 14, Miller teaches wherein the goal derivation artificial intelligence model comprises one of a machine learning model or a neural network (¶ 108-110, discloses goals and machine learning. ¶ 67-68, 112-113, 119, 143, disclose the use of machine learning and neural networks).
Regarding claims 6, 15, Miller teaches wherein the user interface comprises a second user interface element that allows a user of the user device to capture the education data (¶ 126, discloses providing an interface to a user and uploading educational data. ¶ 136, discloses custom content for a user on the interface, including educational content. ¶ 227, discloses generating an interface. ¶ 66, 111, 137, 223, disclose users including one or more.
Regarding claims 7, 16, Miller teaches wherein the user interface comprises a library of stored second education data organized by category (¶ 68, discloses a grouping of educational and academic categories. ¶ 26, 234).
Regarding claims 8, 17, Miller teaches achieving a predicted goal but does not compromise an indication of progress. However, Mohler teaches wherein the user interface comprises an indication of a progress of a user in achieving the goal (¶ 30, discloses unique ways to show progress toward goals, ¶ 52-56, discloses an indication of progress toward a goal. ¶ 99, discloses a progress report toward a goal.).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Miller to include/perform displaying an indication of a progress of a user in achieving the goal, as taught/suggested by Mohler. This known technique is applicable to the system of Miller as they both share characteristics and capabilities, namely, they are directed to goal management. One of ordinary skill in the art would have recognized that applying the known technique of Mohler would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mohler to the teachings of Miller would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such goal features into similar systems. Further, applying an indication of a progress of a user in achieving the goal would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user the ability to increase motivation, reinforce confidence, and facilitate better decision-making.
Regarding claims 9, 18, Miller teaches wherein the education data comprises at least one of an image of an academic award won or academic achievement earned by a user, an image of a trophies earned by the user, data representing an athletic performance or achievement by the user, a report card received by the user, a transcript received by the user, a test score earned by the user, a writing sample written by the user, an image of artwork created by the user, a resume, video of a performance by the user, or audio of a second performance by the user, an indication of a degree earned by the user, or proof of a skill learned by the user. (¶ 90, 109-110, 116, 124, 137, 140, 174-175, discloses test scores, ¶ 93, discloses transcripts, ¶ 97, discloses report cards). See also, Table 1-2, which discloses test scores, awards, and transcripts.
Regarding claim 10, Miller discloses a computer-implemented method for centralizing storage of user data, the computer-implemented method comprising (¶ 72, discloses includes one or more suitably configured processors having a memory and configured to execute code stored therein. ¶ 78-80, 86);
obtaining the user data from a user device (See Fig. 1, ¶ 72, discloses one or more suitably configured processors having a memory and configured to execute code stored therein.. the evaluation server 102 is configured to access student assessments and other data relating to the educational assessments, professional activities, and personal metrics of present and former student. ¶ 122, discloses the evaluation server 102 to access a specific educational dataset for a given learner. ¶ 65, 109);
training the goal derivation artificial intelligence model using training data, wherein the training data comprises one or more training data items, and wherein an individual training data item comprises a set of accomplishments recorded by a second user and is labeled with an indication of a second goal pursued by the second user (¶ 66, discloses training predictive models including a second cohort (user), ¶ 109-111, discloses increasing long term goals. ¶ 122, discloses goals for a specific user. ¶ 223, discloses a first and second learning cluster, ¶ 137-140, includes status identifiers for each member which would include one or more. ¶ 133, 240);
applying the user data as an input to a predicted goal derivation artificial intelligence model, wherein application of the user data as the input to the predicted goal derivation artificial intelligence model causes the goal derivation artificial intelligence model to output a goal (¶ 66, discloses products described herein extract learner data from a collection of discrete educational databases and utilize AI based principals to construct predictive models relating to likelihood of educational or career success. Once generated, such predictive models are used to evaluate individual students for likelihood of success in future efforts or endeavors…the AI based evaluation modules can be used to review and interpret the likelihood that a given selection of evaluative materials (e.g. tests) are accurate predictors or future academic or career success. ¶ 122, discloses a model that can be used to evaluate a specific learner's academic checkpoint progress and career planning. Here, the value(s) output by the model indicates a score relating to the likelihood the specific learner will meet the desired milestone or career goal. ¶ 111, discloses utilizing AI based systems to parse such structured data (e.g. school rankings) and unstructured data (e.g. personal learner fit) to identify the institution that presents the highest probability of achieving both the educational and career goals of the learner. ¶ 109, discloses a generated model that outputs a score indicative of a short-term goal. ¶ 203);
applying the predicted goal as an input to a goal booster artificial intelligence model, wherein application of the predicted goal as the input to the goal booster artificial intelligence model causes the booster artificial intelligence model to output an indication of a suggested accomplishment to pursue to achieve the predicted goal (¶ 90, discloses how different educational steps lead to achieving key milestones. ¶ 66, discloses products described herein extract learner data from a collection of discrete educational databases and utilize AI based principals to construct predictive models relating to likelihood of educational or career success. Once generated, such predictive models are used to evaluate individual students for likelihood of success in future efforts or endeavors…the AI based evaluation modules can be used to review and interpret the likelihood that a given selection of evaluative materials (e.g. tests) are accurate predictors or future academic or career success. ¶ 122, discloses a model that can be used to evaluate a specific learner's academic checkpoint progress and career planning. Here, the value(s) output by the model indicates a score relating to the likelihood the specific learner will meet the desired milestone or career goal. ¶ 111-112, discloses utilizing AI based systems to parse such structured data (e.g. school rankings) and unstructured data (e.g. personal learner fit) to identify the institution that presents the highest probability of achieving both the educational and career goals of the learner. ¶ 109, discloses a generated model that outputs a score indicative of a short-term goal. ¶ 70, 141, 203);
Miller does not specifically teach displaying a suggested accomplishment.
However, Mohler teaches causing the user device to display the suggested accomplishment in a user interface in response to a user selection of a goal booster user interface element depicted in the user interface (abstract, ¶ 89, discloses generating life recommendation decisions for goals, ¶ 102, discloses suggested action for the participant to take that will affect and help achieve goals. ¶ 111-114, disclose further steps to achieve goals. ¶ 98, disclose displaying data related to goals. ¶ 46, Fig. 5-6, ¶ 96, 79, 54-55, disclose outputs). Mohler also teaches applying the goal as an input to a goal booster artificial intelligence model, wherein application of the goal as the input to the goal booster artificial intelligence model causes the goal booster artificial intelligence model to output an indication of a suggested accomplishment to pursue to achieve the goal (¶ 16, 46, 54, 89, 102-107, 112-113).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Miller to include/perform displaying a suggested accomplishment, as taught/suggested by Mohler. This known technique is applicable to the system of Miller as they both share characteristics and capabilities, namely, they are directed to making recommendations to meet goals. One of ordinary skill in the art would have recognized that applying the known technique of Mohler would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mohler to the teachings of Miller would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such goal features into similar systems. Further, applying displaying a suggested accomplishment would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user the ability to see the determined suggestions.
Miller does not specifically teach in response to a lack of information from a user on a goal to be pursued apply the education data as an input to the goal derivation artificial intelligence model.
However, Terhark also teaches
wherein an individual training data item comprises a set of accomplishments recorded by a second user and is labeled with an indication of a second goal pursued by the second user (¶ 22, discloses receiving user profiles and using that in a predictive modeling system, ¶ 114-115, 119, 208);
in response to a lack of information from a user on a goal to be pursued, apply the education data as an input to the goal derivation intelligence model, wherein application of the education data as the input to the goal derivation intelligence model causes the goal derivation intelligence model to output a predicted goal (¶ 120, discloses allowing the system platform to recommend career paths based on users' skills, education, and experiences. ¶ 122, discloses a Job Recommendation feature will use user's profile details as well as their preferences, and will determine the best jobs where they have the best opportunity of success. ¶ 191-193, disclose a proposed career path for the user. ¶ 195-199, discloses matching a user’s skills, education, certifications, licenses, and experience to return a job recommendation.).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Miller to include/perform displaying a lack of information from a user on a goal to be pursued, as taught/suggested by Terhark. This known technique is applicable to the system of Miller as they both share characteristics and capabilities, namely, they are directed to making career recommendations. One of ordinary skill in the art would have recognized that applying the known technique of Terhark would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Terhark to the teachings of Miller would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such career features into similar systems. Further, applying a lack of information from a user on a goal to be pursued would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user to use their talents, skills, and ability to choose a potential job.
Regarding claim 19, Miller discloses a non-transitory, computer-readable medium comprising computer-executable instructions for retrieving property information, wherein the computer-executable instructions, when executed by a computer system, cause the computer system to (¶ 72, discloses includes one or more suitably configured processors having a memory and configured to execute code stored therein. ¶ 78-80, 86);
obtain the user data from a user device (See Fig. 1, ¶ 72, discloses one or more suitably configured processors having a memory and configured to execute code stored therein.. the evaluation server 102 is configured to access student assessments and other data relating to the educational assessments, professional activities, and personal metrics of present and former student. ¶ 122, discloses the evaluation server 102 to access a specific educational dataset for a given learner. ¶ 65, 109);
train the goal derivation artificial intelligence model using training data, wherein the training data comprises one or more training data items, and wherein an individual training data item comprises a set of accomplishments recorded by a second user and is labeled with an indication of a second goal pursued by the second user (¶ 66, discloses training predictive models including a second cohort (user), ¶ 109-111, discloses increasing long term goals. ¶ 122, discloses goals for a specific user. ¶ 223, discloses a first and second learning cluster, ¶ 137-140, includes status identifiers for each member which would include one or more. ¶ 133, 240).
apply the user data as an input to a goal derivation artificial intelligence model, wherein application of the user data as the input to the goal derivation artificial intelligence model causes the goal derivation artificial intelligence model to output a goal (¶ 66, discloses products described herein extract learner data from a collection of discrete educational databases and utilize AI based principals to construct predictive models relating to likelihood of educational or career success. Once generated, such predictive models are used to evaluate individual students for likelihood of success in future efforts or endeavors…the AI based evaluation modules can be used to review and interpret the likelihood that a given selection of evaluative materials (e.g. tests) are accurate predictors or future academic or career success. ¶ 122, discloses a model that can be used to evaluate a specific learner's academic checkpoint progress and career planning. Here, the value(s) output by the model indicates a score relating to the likelihood the specific learner will meet the desired milestone or career goal. ¶ 111, discloses utilizing AI based systems to parse such structured data (e.g. school rankings) and unstructured data (e.g. personal learner fit) to identify the institution that presents the highest probability of achieving both the educational and career goals of the learner. ¶ 109, discloses a generated model that outputs a score indicative of a short-term goal. ¶ 203);
apply the predicted goal as an input to a goal booster artificial intelligence model, wherein application of the predicted goal as the input to the goal booster artificial intelligence model causes the goal booster artificial intelligence model to output an indication of a suggested accomplishment to pursue to achieve the predicted goal (¶ 90, discloses how different educational steps lead to achieving key milestones. ¶ 66, discloses products described herein extract learner data from a collection of discrete educational databases and utilize AI based principals to construct predictive models relating to likelihood of educational or career success. Once generated, such predictive models are used to evaluate individual students for likelihood of success in future efforts or endeavors…the AI based evaluation modules can be used to review and interpret the likelihood that a given selection of evaluative materials (e.g. tests) are accurate predictors or future academic or career success. ¶ 122, discloses a model that can be used to evaluate a specific learner's academic checkpoint progress and career planning. Here, the value(s) output by the model indicates a score relating to the likelihood the specific learner will meet the desired milestone or career goal. ¶ 111-112, discloses utilizing AI based systems to parse such structured data (e.g. school rankings) and unstructured data (e.g. personal learner fit) to identify the institution that presents the highest probability of achieving both the educational and career goals of the learner. ¶ 109, discloses a generated model that outputs a score indicative of a short-term goal. ¶ 70, 141, 203).
Miller does not specifically teach displaying a suggested accomplishment.
However, Mohler teaches cause the user device to display the suggested accomplishment in a user interface in response to a user selection of a goal booster user interface element depicted in the user interface (abstract, ¶ 89, discloses generating life recommendation decisions for goals, ¶ 102, discloses suggested action for the participant to take that will affect and help achieve goals. ¶ 111-114, disclose further steps to achieve goals. ¶ 98, disclose displaying data related to goals. ¶ 46, Fig. 5-6, ¶ 96, 79, 54-55, disclose outputs). Mohler also teaches apply the goal as an input to a goal booster artificial intelligence model, wherein application of the goal as the input to the goal booster artificial intelligence model causes the goal booster artificial intelligence model to output an indication of a suggested accomplishment to pursue to achieve the goal (¶ 16, 46, 54, 89, 102-107, 112-113).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Miller to include/perform displaying a suggested accomplishment, as taught/suggested by Mohler. This known technique is applicable to the system of Miller as they both share characteristics and capabilities, namely, they are directed to making recommendations to meet goals. One of ordinary skill in the art would have recognized that applying the known technique of Mohler would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mohler to the teachings of Miller would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such goal features into similar systems. Further, applying displaying a suggested accomplishment would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user the ability to see the determined suggestions.
Miller does not specifically teach in response to a lack of information from a user on a goal to be pursued apply the education data as an input to the goal derivation artificial intelligence model.
However, Terhark also teaches
wherein an individual training data item comprises a set of accomplishments recorded by a second user and is labeled with an indication of a second goal pursued by the second user (¶ 22, discloses receiving user profiles and using that in a predictive modeling system, ¶ 114-115, 119, 208);
in response to a lack of information from a user on a goal to be pursued, apply the education data as an input to the goal derivation intelligence model, wherein application of the education data as the input to the goal derivation intelligence model causes the goal derivation intelligence model to output a predicted goal (¶ 120, discloses allowing the system platform to recommend career paths based on users' skills, education, and experiences. ¶ 122, discloses a Job Recommendation feature will use user's profile details as well as their preferences, and will determine the best jobs where they have the best opportunity of success. ¶ 191-193, disclose a proposed career path for the user. ¶ 195-199, discloses matching a user’s skills, education, certifications, licenses, and experience to return a job recommendation.).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Miller to include/perform displaying a lack of information from a user on a goal to be pursued, as taught/suggested by Terhark. This known technique is applicable to the system of Miller as they both share characteristics and capabilities, namely, they are directed to making career recommendations. One of ordinary skill in the art would have recognized that applying the known technique of Terhark would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Terhark to the teachings of Miller would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such career features into similar systems. Further, applying a lack of information from a user on a goal to be pursued would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user to use their talents, skills, and ability to choose a potential job.
Other pertinent prior art includes Martin et al. (US 20180247549 A1) discloses using responses and post-recommendation data as feedback to further train the machine learning-based system. Depaolo et al. (US 10984666 B1) discloses goal planning based on user needs and priorities over as the user's life stages evolve. Varga et al. (US 20200302564 A1) discloses multidimensional predictive modeling.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/ Primary Examiner, Art Unit 3625