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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
item 211 (FIGS. 2A-2D, designating the ‘Profile’ icon)
item 302c (FIG. 3B)
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
¶[0025] recites “an ability to leverage community knowledge and interaction data,” which appears, in the context of the recited list of system deficiencies, to be a typographical error for “an inability to leverage…”
¶[0029] uses reference numeral “100” to designate both the “system” and the “platform” (“an exemplary system 100 comprising an exemplary interactive data modeling and communication platform 100”), while the platform is designated “110” throughout the remainder of the disclosure
¶[0029] recites “memory 112 of platform 110,” but reference numeral “112” designates the processor — the memory is designated “114”
¶[0035] recites “application program interfaces (APIs), now shown,” which appears to be a typographical error for “not shown”
¶[0064] recites “the use may access,” which appears to be a typographical error for “the user”
¶[0066] recites “the use has previously established,” which appears to be a typographical error for “the user”
¶[0103] recites “a columniation of four phases,” which appears to be a typographical error for “a culmination of four phases.”
Appropriate correction is required.
Claim Objections
Claims 1 and 16 are objected to because of the following informalities: claim 1 recites “the one or more user device” (in the “monitor at least one among the one or more external data systems and the one or more user device” limitation), which should be “the one or more user devices”; and claim 16 recites “wherein the platform configured to generate and transmit,” which should be “wherein the platform is configured to generate and transmit.” Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that meet the three-prong test set forth in MPEP § 2181, subsection I, and are therefore being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: "via one or more communication means" in claim 16 (and by dependency, claim 18). This claim limitation uses a term that does not include the word "means," but the word "means" is nonetheless used in the limitation. Because this claim limitation uses the word "means" without a structural modifier, and because the term "communication" describes the functional type rather than the structure, the three-prong test is satisfied. For Prong A, the limitation uses the word "means," creating a rebuttable presumption of § 112(f) invocation per Williamson v. Citrix Online, LLC, 792 F.3d 1339, 1348 (Fed. Cir. 2015) (en banc). No structural modifier precedes "means," and the presumption is not rebutted. For Prong B, the "communication means" performs the function of transmitting the user-personalized strategy, alerts, predictions, insights, suggestions, community groups activities, and forecasts to the one or more user devices. For Prong C, the claim recites no structure for performing the communication function; "communication" is a functional descriptor, not a structural one.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof. The claimed function of the "communication means" limitation is: transmitting one or more of the user-personalized strategy, alerts, predictions, insights, suggestions, community groups activities, and forecasts to the one or more user devices. The corresponding structure disclosed in the specification for performing this function is: the communication engine 128 of the platform server 110a (spec. ¶[0034]; ¶[0056]), operating via communication network(s) 106 (spec. ¶[0030]) through I/O interfaces 116 (spec. ¶[0034]), using communication protocols and services such as SMS text messaging and email (spec. ¶[0079]), and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function, such as specifying the type of communication network, protocol, or interface); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f).
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 1-26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "said re-execute" in the final limitation of the claim ("dynamically update the interactive GUI to display changes to the user-personalized strategy and the one or more tools and resources resulting from said re-execute"). There is insufficient antecedent basis for this limitation in the claim. The term "re-execute" is introduced in claim 1 only as a verb infinitive in the preceding limitation: "re-execute the one or more machine learning models responsive to the monitored data and information." The claim does not introduce "re-execute" (or any noun form thereof, such as "re-execution" or "re-executing") as a noun prior to the use of "said re-execute." The article "said" requires a prior noun antecedent; using "said" to refer to a previously-recited verb phrase does not satisfy the antecedent basis requirement of § 112(b). Claims 2–26, which all depend from claim 1 directly or through a chain of dependencies, are likewise indefinite.
Claims 2–26 are rejected as being dependent upon a rejected based claim.
For purposes of examination, "said re-execute" in claim 1 is interpreted under BRI to refer to the re-execution of the one or more machine learning models as recited in the immediately preceding limitation of claim 1, consistent with spec ¶[0042].
Claim 17 recites the limitation "bite-sized action items" ("the user-personalized strategy is presented as a compilation of a plurality of bite-sized action items"). The term "bite-sized" is a relative, subjective adjective that renders the claim indefinite because the specification does not provide an objective standard for ascertaining whether a given action item qualifies as "bite-sized." The specification's definitional guidance at ¶[0022] characterizes "bite-sized" action items as those that are "routine, easy to understand, easy to implement, have a small time commitment and/or are otherwise easily achievable" — itself a collection of subjective, unmeasurable characterizations.
Claim 19 is rejected as being dependent upon a rejected base claim.
For purposes of examination, "bite-sized action items" in claims 17 and 19 is interpreted under BRI to encompass any discrete, individually performable action or task forming part of the user-personalized strategy, without objective restriction on complexity, duration, or difficulty, as the specification provides only subjective characterizations.
Claim 18 recites "the bite-sized action items" ("wherein the bite-sized action items comprise a combination of time-based action items and task-based action items"). There is insufficient antecedent basis for this limitation in the claim. Claim 18 depends from claim 16 (→15→1). None of claims 1, 15, or 16 recites or introduces the term "bite-sized action items." The term "bite-sized action items" was first introduced in claim 17 — a sibling claim that depends from claim 1 but is not in the dependency chain of claim 18. Because claim 18 does not depend from claim 17, "bite-sized action items" is not available as an antecedent for "the bite-sized action items" in claim 18.
For purposes of examination, "the bite-sized action items" in claim 18 is interpreted under BRI to refer to the action items forming the user-personalized strategy.
Claim 23 recites the limitation "said evaluate" ("at least one of update and re-train at least one among the one or more machine learning models based on said evaluate"). There is insufficient antecedent basis for this limitation in the claim. The term "evaluate" is introduced in claim 23 only as a verb infinitive: "evaluate a performance of the one or more machine learning models over time based on one or more performance metrics." No noun form of "evaluate" (e.g., "evaluation" or "evaluating") is introduced prior to the use of "said evaluate." This deficiency is directly analogous to the "said re-execute" deficiency in claim 1. The proper formulation would be "based on said evaluation."
Claim 24 is rejected as being dependent upon a rejected base claim.
For purposes of examination, "said evaluate" in claims 23 and 24 is interpreted under BRI to refer to the evaluation of ML model performance.
Claim 24 additionally recites "user sentiment" as a component of machine learning model performance ("at least one of a measure of accuracy, user sentiment and a utilization..."). The term "user sentiment" is not defined by the claim, and the specification does not provide an objective standard for measuring user sentiment as a performance metric. Specification ¶[0054] references "user sentiment" parenthetically but provides no methodology, scale, or threshold. A PHOSITA cannot determine the metes and bounds of this limitation.
For purposes of examination, "user sentiment" in claim 24 is interpreted under BRI to encompass any measure of user attitude, opinion, or satisfaction regarding the platform's outputs, including survey scores, natural language analysis of feedback, or behavioral signals, as no objective standard is specified in the specification.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 13-16, 20, 23, 25 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over US Pat. Pub. No. 2021/0303973 to Edwards et al. (hereinafter Edwards) in view of US Pat. Pub. No. 2022/0223241 to Straatman et al. (hereinafter Straatman).
Per claim 1, Edwards discloses A system (Edwards: Abstract…Edwards describes a computer system implementing an AI-based personalized financial recommendation assistant that integrates user devices, a server platform, and external data sources, "Provided are a computer system and method for generating and providing intelligent recommendations using artificial intelligence ("AI")") comprising:
one or more user devices (Edwards: ¶[0076] …Edwards expressly discloses a server platform that communicates with a plurality of user devices, which constitutes one or more user devices, "The system 10 includes a server platform 12 which communicates with a plurality of user devices 14, 16, 18 via a network 20");
one or more external data systems (Edwards: ¶[0076]…Edwards discloses a second server platform separate from the recommendation server with which the recommendation server communicates, which constitutes one or more external data systems, "The server platform 12 may communicate with a second server platform 22 via the network 20"); and
a platform in communication with the one or more user devices and the one or more external data systems via one or more communication networks, (Edwards: ¶[0076]…Edwards's server platform 12 communicates with both the user devices and the second (external) server platform over the same network 20, which constitutes the platform being in communication with both elements via communication networks, "The system 10 includes a server platform 12 which communicates with a plurality of user devices 14, 16, 18 via a network 20. The server platform 12 may communicate with a second server platform 22 via the network 20")
the platform comprising one or more servers, the one or more servers comprising one or more processors and a memory storing computer-readable instructions that (Edwards: ¶[0078]…Edwards expressly discloses that the server platform devices include processors and memory that store applications executable by the processor, which directly reads on the claimed server-processor-memory-instructions arrangement, "The devices 12, 14, 16, 18, 22 may include one or more of a memory, a secondary storage device, a processor, an input device, a display device, and an output device. Memory may include random access memory (RAM) or similar types of memory. Also, memory may store one or more applications for execution by processor"), when executed by the one or more processors, cause the platform to:
generate and train one or more machine learning models (Edwards: ¶[0007]…Edwards expressly discloses a continually-training AI observing user behavior, which under BRI constitutes generate and train one or more machine learning models, "user-specific optimizations that can be captured by a continually-training AI observing the user's behaviour"); ¶[0064]…Edwards further discloses the conventional ML training pipeline of creating structured data, labeling, and training a network, confirming the generate-and-train step, "steps typically include creating structured data, labeling the data, training a network, and then storing the trained network");
receive, from among the one or more external data systems and the one or more user devices, data and information relating to a plurality of users (Edwards: ¶[0082]…Edwards expressly discloses receiving a plurality of information from each of the user devices and the second (external) server, which corresponds directly to the receive-data-from-external-systems-and-user-devices step, "Server platform 12 may be configured to receive a plurality of information from each of the user devices 14, 16, 18, and the second server 22");
convert the data and information into a format that is suitable for use by one or more machine learning models (Edwards: ¶[0064]…Edwards discloses creating structured and labeled data prior to training a network, which is the conventional data-conversion-to-ML-input step under BRI, "steps typically include creating structured data, labeling the data, training a network, and then storing the trained network"); (
execute the one or more machine learning models using the converted data and information as input, the one or more machine learning models (Edwards: Abstract…Edwards expressly describes generating and providing intelligent recommendations using AI models that take the user's data as input, which is the execute-the-ML-models step, "Provided are a computer system and method for generating and providing intelligent recommendations using artificial intelligence ("AI")") configured to:
generate, for at least one among the plurality of users, a user-personalized strategy (Edwards: ¶[0027]-[0028]…Edwards executes mutually-updating AI models that produce personalized financial recommendations for an individual user, which under BRI constitutes a user personalized strategy, "A computer system for generating and providing intelligent recommendations using artificial intelligence (“AI”) is provided. The system includes a memory for storing user feedback data, user financial assets data, and user financial goal data, and a processor in communication with the memory…a user interface generator module for generating a user interface for outputting the intelligent recommendations and receiving the user feedback data. The first Al model and the second Al model communicate and mutually update each other through a functional mapping"), and
identify one or more tools and resources for completing one or more aspects of the user-personalized strategy; (Edwards: ¶[0107]-[0109]…Edwards's transaction-optimizing network expressly maps a user's initial resources (financial assets, account balances, investments) to intermediate states that connect those resources to the user's goals, which under BRI identifies tools and resources for completing the strategy, " The transaction optimizing network 400 is configured to model future changes in the state 202 of a user's initial financial assets 201. The user's initial financial assets 201 may be considered user resources or user resource data. The user's financial assets may include user financial data. User financial data may include asset values, account balances, investments, anticipated financial inputs and outputs, etc.");
generate an interactive graphical user interface (GUI) that displays the user-personalized strategy and the one or more tools and resources for completing the one or more aspects of the user-personalized strategy (Edwards: ¶[0027]…Edwards discloses a user-interface-generator module that outputs the intelligent recommendations and receives user feedback, which is generating an interactive GUI displaying the strategy under BRI, "a user interface generator module for generating a user interface for outputting the intelligent recommendations and receiving the user feedback data");
Edwards does not disclose, but in combination with Straatman does teach:
monitor at least one among the one or more external data systems and the one or more user device for changes to the data and information; (Straatman: ¶[0016]…Straatman teaches an application modifying the user interface to display the recommendation, confirming the generate-interactive-GUI-displaying-strategy step, "The method includes modifying, by the application, the user interface to display the recommendation (114)"); ¶[0020]…Straatman expressly discloses continuously receiving data from external data sources (EMR, weather/air-quality external data, user sensors, self-reported data, etc.) and using this data to drive the ML model execution, which is the monitor-for-changes step under BRI, "the system 200 may receive data from sources such as electronic medical records, external data (e.g., weather and air quality and other environmental factors), user sensors, self-reported data from the user, and other data as described herein"; Edwards: ¶[0073]…Edwards also discloses the model incorporating detected user actions/state changes (such as receiving funds), which is detection of changes in the monitored data, "When a user makes a choice, or receives funds that move them closer to the goal, the model may incorporate this change by applying a Bayesian update to the first hidden layer of the network")
re-execute the one or more machine learning models responsive to the monitored data and information (Straatman: ¶[0020]…Straatman discloses that continuously received data is used to drive the execution of the machine-learning models, i.e., the models are re-executed responsive to the monitored data, "the platform 210 may use this continuously received data to improve time series analyses, identification of health dynamics, and the execution of the machine learning models that select recommendations for users"; Edwards: ¶[0073]…Edwards's Bayesian update to the network in response to monitored events is a re-execution of the ML model responsive to the monitored change, "the model may incorporate this change by applying a Bayesian update to the first hidden layer of the network"); and
dynamically update the interactive GUI to display changes to the user-personalized strategy and the one or more tools and resources resulting from said re-execute (Straatman: ¶[0016]…Straatman expressly modifies the user interface to display the updated recommendation, which is dynamically updating the interactive GUI to reflect re-execution output, "modifying, by the application, the user interface to display the recommendation (114)"; Edwards: ¶[0073]…Edwards's recursion of the code-effectiveness model causes the recommendation assistant to be reactive to the user's ongoing interactions, which is dynamic updating of the UI based on re-executed model output under BRI, "The recursion of the code effectiveness model's selection of effective interaction strategies also allows the recommendation system (assistant) to demonstrate behaviour that is reactive to the user's ongoing interactions to it").
Edwards and Straatman are analogous art because they are from the same field of endeavor, specifically machine-learning-driven personalized recommendation platforms that ingest data from user devices and external data sources and present recommendations through an interactive user interface. They are also reasonably pertinent to the same problem of generating, updating, and presenting user-personalized strategies on the basis of continuously-monitored data. Edwards cites continual learning from user behavior (Edwards: ¶[0064], [0114]), which Straatman expressly operationalizes through stream-based external/sensor data ingestion (Straatman: ¶[0020]).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the continually-training mutual-update AI model architecture of Edwards with Straatman's explicit teachings of streaming external-data ingestion and dynamic UI modification driven by the re-executed model so as to provide a more responsive personalized recommendation that automatically reflects changes in external-data sources and user state.
The suggestion/motivation for doing so would have been Straatman's explicit teaching that continuously received data improves time-series analyses and the execution of the machine learning models that select recommendations (Straatman: ¶[0020]), combined with Edwards's express invitation that user-specific optimizations be captured by a continually-training AI observing the user's behaviour (Edwards: ¶[0064], [0114]). A PHOSITA would have been motivated to apply Straatman's streaming-data-driven re-execution and UI modification to Edwards's continually-training AI to realize a system in which the recommendation engine reacts in real time to changes in both user and external-system data. Furthermore, this is a combination of prior-art elements according to known methods to yield predicable results (MPEP 2143.01(A)).
Per claim 2, Edwards combined with Straatman discloses claim 1. Edwards further teaches the data and information comprises a combination of historic and real-time data and information, and wherein the input to the one or more machine learning models comprises modeling output generated by at least one among the one or more machine learning models (Edwards: ¶[0073]...Edwards expressly discloses that the recommendation network uses both the user's historical state and current ongoing interactions, and that the code-effectiveness model selects interaction strategies based on outputs of the recommendation model — i.e., model output is itself input to another model, "The recursive nature of the construction guarantees that if a person does in fact work towards their goal , the algorithmic modelling of the problem space is tractable and converges. The recursion of the code effectiveness model's selection of effective interaction strategies also allows the recommendation system (assistant) to demonstrate behaviour that is reactive to the user's ongoing interactions to it").
Per claim 3, Edwards combined with Straatman discloses claim 2. Edwards further teaches the data and information further comprises data submitted by at least one among the user devices via the interactive GUI in response to one or more platform-generated prompts and questions (Edwards: Abstract…Edwards's user-interface generator receives user feedback data via the same GUI that presents recommendations, i.e., data submitted via the interactive GUI in response to platform-generated content, "a user interface generator module for generating a user interface for outputting the intelligent recommendations and receiving the user feedback data").
Per claim 4, Edwards combined with Straatman discloses claim 3. Edwards further teaches one or more storage devices accessible by the platform, the one or more storage devices storing the historic and real-time data and information, the machine learning modeling output, and the data submitted via the interactive GUI (Edwards: ¶[0078]…Edwards expressly discloses the server-platform devices including a secondary storage device alongside memory, which constitutes storage devices for the recited data, "The devices 12, 14, 16, 18, 22 may include one or more of a memory, a secondary storage device, a processor, an input device, a display device, and an output device").
Per claim 5, Edwards combined with Straatman discloses claim 1. Edwards combined with Straatman further teaches at least one among the one or more user devices accesses and interacts with the platform via a software application downloaded onto the at least one user device (Straatman: ¶[0025]…Straatman expressly discloses an application on the user device that authenticates and interacts with the platform, which is a software application on the user device under BRI, "The application 204 may receive authorization to access data from the user's account(s) with one or more third party providers, for example, and without limitation, from social media accounts, online music accounts, calendaring data, computer programming accounts, bank accounts, credit card accounts, email accounts, and so on. The user's computing device may collect location data (mobility data, local weather, local air quality, etc.) and provide that data to the application 204"). The rationale to combine Straatman with Edwards is the same as the parent claim.
Per claim 6, Edwards combined with Straatman discloses claim 1. Edwards further teaches at least one among the one or more user devices accesses and interacts with the platform online as one or more cloud-based subscription services (Edwards: ¶[0076]-[0082]…Edwards's recommendation system is implemented on a server platform that user devices access over a network, which under BRI is a cloud-based service and the machine learning models being online/cloud-delivered, this also being a cloud-based service, "The system 10 includes a server platform 12 which communicates with a plurality of user devices 14, 16, 18 via a network 20").
Per claim 7, Edwards combined with Straatman discloses claim 1. Edwards further teaches determine that the monitored data and information meets or exceeds one or more predetermined parameters; and re-execute the one or more machine learning models based on said determination (Edwards: ¶[0073]…Edwards's Bayesian update is triggered when the user's actions move them closer to a goal — i.e., when monitored data meets a parameter (proximity to goal) — and the network is re-executed in response, "When a user makes a choice, or receives funds that move them closer to the goal, the model may incorporate this change by applying a Bayesian update to the first hidden layer of the network").
Per claim 13, Edwards combined with Straatman discloses claim 1. Edwards combined with Straatman further teaches the platform is further configured to retrieve, from one or more data sources among the external data systems, user-personalized information and content based on modeling output from the one or more machine learning models, and wherein the interactive GUI displays the user-personalized information and content, simultaneously with the user-personalized strategy and the one or more tools and resources (Straatman: ¶[0025]…Straatman expressly retrieves user-personalized account data from external third-party providers (banks, calendar, social media, etc.) into the application, then displays it with the recommendation; the displayed recommendation is the personalized strategy, "The application 204 may receive authorization to access data from the user's account(s) with one or more third party providers, for example, and without limitation, from social media accounts, online music accounts, calendaring data, computer programming accounts, bank accounts, credit card accounts, email accounts, and so on"). The rationale to combine Straatman with Edwards is the same as the parent claim.
Per claim 14, Edwards combined with Straatman discloses claim 1. Edwards further teaches the interactive GUI comprises a plurality of dedicated display regions comprising a combination of one or more user input regions, one or more notification regions, one or more display regions, and one or more community interaction regions (Edwards: Abstract…Edwards's user interface generator outputs intelligent recommendations (display region) and receives user feedback (user input region), and a PHOSITA would understands the conventional implementation of such a multi-function GUI to comprise dedicated regions, "a user interface generator module for generating a user interface for outputting the intelligent recommendations and receiving the user feedback data").
Per claim 15, Edwards combined with Straatman discloses claim 1. Edwards combined with Straatman further teaches the platform is further configured to generate, simultaneously display and dynamically update one or more alerts, predictions, insights, suggestions, community groups activities, and forecasts based on modeling output from the one or more machine learning models (Straatman: ¶[0026]…Straatman expressly discloses generation of insights and recommendations by the machine-learning services and modification of the UI to display them — i.e., model-output-driven insights/suggestions/predictions simultaneously displayed and dynamically updated, "The application 204 may use a stream processing infrastructure to capture the gathered and stored data; as will be understood by those of skill in the art, stream processing allows for transmitting and exchanging data in a high data density architecture. Stream processing may include collection of data from the user, mapping/reducing the data to logic, and generation of insights and recommendations by one or more machine learning services"). The rationale to combine Straatman with Edwards is the same as the parent claim.
Per claim 16, Edwards combined with Straatman discloses claim 15. Edwards further teaches the platform configured to generate and transmit one or more of the user-personalized strategy, alerts, predictions, insights, suggestions, community groups activities, and forecasts to the one or more user devices via one or more communication means (Edwards: ¶[0076]…Edwards's server platform transmits recommendations and outputs to the user devices over a network, which is transmission via communication means under BRI, "The system 10 includes a server platform 12 which communicates with a plurality of user devices 14, 16, 18 via a network 20").
Per claim 20, Edwards combined with Straatman discloses claim 1. Edwards combined with Straatman further teaches the platform is configured to re-execute the one or more machine learning models according to one or more pre-determined schedules (Straatman: ¶[0020]…Straatman's platform receives data continuously and re-executes the ML models accordingly; a PHOSITA would understand a continuous/periodic execution as encompassing scheduled re-execution under BRI, "the platform 210 may use this continuously received data to improve time series analyses, identification of health dynamics, and the execution of the machine learning models that select recommendations for users"). The rationale to combine Straatman with Edwards is the same as the parent claim.
Per claim 23, Edwards combined with Straatman discloses claim 1. Edwards combined with Straatman further teaches evaluate a performance of the one or more machine learning models over time based on one or more performance metrics; and at least one of update and re-train at least one among the one or more machine learning models based on said evaluate (Straatman: ¶[0041]…Straatman expressly discloses training the recommendation engine along multiple dimensions to identify which recommendations are effective — i.e., performance evaluation and re-training driven by an effectiveness metric, "the recommendation engine 214 may be trained along one or more dimensions. The recommendation engine 214 may be trained to identify which recommendations are effective"; Edwards: ¶[0007]…Edwards also expressly contemplates continually-training the AI based on the user's behavior, confirming the over-time evaluation and re-training step, "user-specific optimizations that can be captured by a continually-training AI observing the user's behaviour"). The rationale to combine Straatman with Edwards is the same as the parent claim.
Per claim 25, Edwards combined with Straatman discloses claim 1. Edwards combined with Straatman further teaches the platform is configured to transmit the interactive GUI to one or more user devices for rendering and display thereon (Straatman: ¶[0016]…Straatman discloses the application on the user device (via the platform) modifying the user interface to display the recommendation — under BRI the platform causes the GUI to be transmitted/rendered/displayed at the user device, "The method includes modifying, by the application, the user interface to display the recommendation (114)"). The rationale to combine Straatman with Edwards is the same as the parent claim.
Per claim 26, Edwards combined with Straatman discloses claim 1. Edwards further teaches the platform is configured to render the GUI at said platform, and transmit the rendered GUI to one or more user devices for display thereon (Edwards: ¶[0027]…Edwards's server platform generates the user interface and transmits the recommendations to user devices over the network for display, which under BRI is rendering at the platform and transmitting the rendered GUI to user devices, "a user interface generator module for generating a user interface for outputting the intelligent recommendations and receiving the user feedback data"). The rationale to combine Straatman with Edwards is the same as the parent claim.
Claims 8-12, 17-19, 21, 22 and 24 are rejected under 35 USC 103 as being unpatentable over Edwards in view of Straatman in further view of US Pat. Pub. No. 2022/0296966 to Asikainen et al. (hereinafter Asikainen).
Per claim 8, Edwards combined with Straatman discloses claim 1. Edwards combined with Straatman does not expressly disclose, but with Asikainen does teach:
monitor and capture interaction data; utilize the interaction data as input to the one or more machine learning models; and re-execute the one or more machine learning models responsive to the interaction data, the interaction data defining one or more user interactions made between the one or more user devices and the platform (Asikainen: ¶[0096]-[0097]…Asikainen's recommendation engine processes user interaction data — specifically user repetitions and fatigue state captured via the user device — through the ML model and generates on-the-fly recommendations in response, which under BRI is monitoring/capturing user interaction data and re-executing the ML models responsive to it, "The recommendation engine 210 generates on-the-fly recommendation to modify or alter the user exercise workout based on the state or level of fatigue of the user").
Edwards combined with Straatman and Asikainen are analogous art because they are from the same field of endeavor, specifically machine-learning-driven personalized recommendation platforms that ingest data from user devices and external systems and present recommendations through an interactive user interface. They are also reasonably pertinent to the same problem of generating user-personalized strategies that can be continuously updated through monitored user-interaction data, including community-derived interaction data.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply Asikainen's gamification/community/subscription/rewards features to Edwards's continually-training recommendation system to broaden the platform's user engagement and monetization channels while leveraging the same continually-training AI for both individualized recommendations and community-driven recommendations.
The suggestion/motivation for doing so would have been Asikainen's express teaching that community accountability improves adherence to the user-personalized workout program (Asikainen: ¶[0112]), combined with Edwards's invitation to capture user-specific optimizations via continually-training AI (Edwards: ¶[0114]). A PHOSITA would have been motivated to apply Asikainen's community/subscription/gamification overlay to Edwards's continually-training AI to improve user retention and the ML model's training data by capturing richer community-interaction signals. Furthermore, this is a combination of prior-art elements according to known methods to yield predictable results (MPEP 2143.01(A)).
Per claim 9, Edwards combined with Straatman and Asikainen discloses claim 8. Asikainen further teaches the interaction data comprises community interaction data, and wherein the one or more machine learning models are configured to generate one or more community interaction groups and activities, the community interaction data defining user interactions in connection with one or more online community networking groups (Asikainen: ¶[0112]…Asikainen's gamification engine generates recommendations for the user to join an online community of other users and share progress; the engine's output is the community interaction group/activity recommendation under BRI, "the gamification engine 212 generates a recommendation for the user to join an online community of other users of interactive personal training device 108 or friends to stay accountable in adhering to their workout program"). The rationale to combine Asikainen with Edward in view of Straatman is the same as the parent claim.
Per claim 10, Edwards combined with Straatman and Asikainen discloses claim 9. Asikainen further teaches the community interaction data comprises one or more of conversations, messages, clicked links, user reaction indications, user posts, and user commentary associated with one or more members of the one or more online community networking groups (Asikainen: ¶[0112]…Asikainen's online-community feature captures user progress sharing with the community — i.e., user posts and reactions among community members under BRI, "the gamification engine 212 generates a recommendation for the user to join an online community of other users of interactive personal training device 108 or friends to stay accountable in adhering to their workout program"). The rationale to combine Asikainen with Edward in view of Straatman is the same as the parent claim.
Per claim 11, Edwards combined with Straatman and Asikainen discloses claim 9. Asikainen further teaches the one or more online community networking groups are accessible via the interactive GUI, simultaneously with the user-personalized strategy and the one or more tools and resources for completing the one or more aspects of the user-personalized strategy (Asikainen: ¶[0097]…Asikainen's interactive screen displays the recommendation (the personalized workout strategy) and, per the gamification engine's community recommendation, also exposes access to the community group — i.e., simultaneously accessible community access under BRI, "the recommendation engine 210 instructs the user interface engine 216 to display the recommendation on the interactive screen of the interactive personal training device 108"). The rationale to combine Asikainen with Edward in view of Straatman is the same as the parent claim.
Per claim 12, Edwards combined with Straatman and Asikainen discloses claim 9. Asikainen further teaches during a live community interaction, the platform captures live community interaction data, re-executes the one or more machine learning models using the live community interaction data as input, and dynamically updates the user-personalized strategy and the one or more tools and resources based on modeling output generated by the re-executed one or more machine learning models (Asikainen: ¶[0096]-[0097]…Asikainen's recommendation engine generates on-the-fly modifications to the workout responsive to live interaction data (user repetitions / state), which under BRI satisfies the live-community-interaction → re-execute → dynamic-update sequence when combined with the community feature of ¶[0112], "The recommendation engine 210 generates on-the-fly recommendation to modify or alter the user exercise workout based on the state or level of fatigue of the user"). The rationale to combine Asikainen with Edward in view of Straatman is the same as the parent claim.
Per claim 17, Edwards combined with Straatman discloses claim 1. Edwards combined with Straatman does not expressly disclose, but with Asikainen does teach the user-personalized strategy is presented as a compilation of a plurality of bite-sized action items, and wherein each of the one or more tools and resources corresponds to one or more of the bite-sized action items (Asikainen: ¶[0109]…Asikainen's gamification engine generates a set of personalized workout recommendations for the user — each a discrete exercise/activity targeted at the user's fitness goal — which is a plurality of bite-sized action items each corresponding to a specific resource (the exercise tool) under BRI, "the gamification engine 212 generates a set of workout recommendations for a user to maximize and/or balance development in one or more fitness areas of strength, mobility, and conditioning. For example, the gamification engine 212 uses a fitness goal (e.g., triathlon training, CrossFit training, etc.) of a user to maximize and/or balance development").
Edwards combined with Straatman and Asikainen are analogous art because they are from the same field of endeavor, specifically machine-learning-driven personalized recommendation platforms that ingest data from user devices and external systems and present recommendations through an interactive user interface. They are also reasonably pertinent to the same problem of generating user-personalized strategies that can be continuously updated through monitored user-interaction data, including community-derived interaction data.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply Asikainen's gamification/community/subscription/rewards features to Edwards's continually-training recommendation system to broaden the platform's user engagement and monetization channels while leveraging the same continually-training AI for both individualized recommendations and community-driven recommendations.
The suggestion/motivation for doing so would have been Asikainen's express teaching that community accountability improves adherence to the user-personalized workout program (Asikainen: ¶[0112]), combined with Edwards's invitation to capture user-specific optimizations via continually-training AI (Edwards: ¶[0114). A PHOSITA would have been motivated to apply Asikainen's community/subscription/gamification overlay to Edwards's continually-training AI to improve user retention and the ML model's training data by capturing richer community-interaction signals. Furthermore, this is a combination of prior-art elements according to known methods to yield predictable results (MPEP 2143.01(A)).
Per claim 18, Edwards combined with Straatman discloses claim 16. Edwards combined with Straatman does not expressly disclose, but with Asikainen does teach the bite-sized action items comprise a combination of time-based action items and task-based action items (Asikainen: ¶[0109]…Asikainen's workout recommendations are explicitly tied to specific tasks (exercises in strength/mobility/conditioning) and to time-based goals (e.g., triathlon training schedules), which under BRI is a combination of time-based and task-based action items, "the gamification engine 212 uses a fitness goal (e.g., triathlon training, CrossFit training, etc.) of a user to maximize and/or balance development").
Edwards combined with Straatman and Asikainen are analogous art because they are from the same field of endeavor, specifically machine-learning-driven personalized recommendation platforms that ingest data from user devices and external systems and present recommendations through an interactive user interface. They are also reasonably pertinent to the same problem of generating user-personalized strategies that can be continuously updated through monitored user-interaction data, including community-derived interaction data.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply Asikainen's gamification/community/subscription/rewards features to Edwards's continually-training recommendation system to broaden the platform's user engagement and monetization channels while leveraging the same continually-training AI for both individualized recommendations and community-driven recommendations.
The suggestion/motivation for doing so would have been Asikainen's express teaching that community accountability improves adherence to the user-personalized workout program (Asikainen: ¶[0112]), combined with Edwards's invitation to capture user-specific optimizations via continually-training AI (Edwards: ¶[0114). A PHOSITA would have been motivated to apply Asikainen's community/subscription/gamification overlay to Edwards's continually-training AI to improve user retention and the ML model's training data by capturing richer community-interaction signals. Furthermore, this is a combination of prior-art elements according to known methods to yield predictable results (MPEP 2143.01(A)).
Per claim 19, Edwards combined with Straatman and Asikainen discloses claim 17. Asikainen further teaches at least one of completion, expiration and rejection of one or more among the plurality of bite-sized action items causes the platform to re-execute the one or more machine learning models, and update a remainder of the plurality of the bite-sized action items (Asikainen: ¶[0097]…Asikainen's UI engine displays an updated recommendation on the interactive screen after the user completes a set of repetitions, which is precisely completion-driven re-execution and update of remaining recommendations under BRI, "the recommendation engine 210 instructs the user interface engine 216 to display the recommendation on the interactive screen of the interactive personal training device 108 after the user completes a set of repetitions or at the end"). The rationale to combine Asikainen with Edward in view of Straatman is the same as the parent claim.
Per claim 21, Edwards combined with Straatman discloses claim 1. Edwards combined with Straatman discloses does not expressly disclose, but with Asikainen does teach the platform is configured to lock and unlock the one or more tools and resources according to a subscription level of a user (Asikainen: ¶[0100]…Asikainen expressly conditions access to particular tools/resources (e.g., a personal trainer's workout feed and apparel) on the user's subscription to that trainer, which is subscription-level-conditioned lock/unlock of tools and resources under BRI, "the recommendation engine 210 may recommend to the user the fitness apparel worn by a personal trainer to whom the user subscribes for daily workouts").
Edwards combined with Straatman and Asikainen are analogous art because they are from the same field of endeavor, specifically machine-learning-driven personalized recommendation platforms that ingest data from user devices and external systems and present recommendations through an interactive user interface. They are also reasonably pertinent to the same problem of generating user-personalized strategies that can be continuously updated through monitored user-interaction data, including community-derived interaction data.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply Asikainen's gamification/community/subscription/rewards features to Edwards's continually-training recommendation system to broaden the platform's user engagement and monetization channels while leveraging the same continually-training AI for both individualized recommendations and community-driven recommendations.
The suggestion/motivation for doing so would have been Asikainen's express teaching that community accountability improves adherence to the user-personalized workout program (Asikainen: ¶[0112]), combined with Edwards's invitation to capture user-specific optimizations via continually-training AI (Edwards: ¶[0114). A PHOSITA would have been motivated to apply Asikainen's community/subscription/gamification overlay to Edwards's continually-training AI to improve user retention and the ML model's training data by capturing richer community-interaction signals. Furthermore, this is a combination of prior-art elements according to known methods to yield predictable results (MPEP 2143.01(A)).
Per claim 22, Edwards combined with Straatman and Asikainen discloses claim 8. Asikainen further teaches the platform is configured to generate and offer rewards, based on one or more of the interaction data, progression through the user-personalized strategy, and utilization of the one or more tools and resources, said rewards configured to unlock access to one or more features and functions of the platform (Asikainen: ¶[0105]-[0107]…Asikainen expressly awards badges based on user performance/interactions, and the badges unlock access to particular features (peer competitions, personal trainer access) — i.e., interaction-data-based rewards that unlock access to features under BRI, "unlocking and winning a peer competition with other users of similar competence and performance levels, a badge for unlocking access to a particular personal trainer, etc."). The rationale to combine Asikainen with Edward in view of Straatman is the same as the parent claim.
Per claim 24, Edwards combined with Straatman discloses claim 23. Edwards combined with Straatman does not expressly disclose, but with Asikainen does teach the performance of the one or more machine learning models comprises at least one of a measure of accuracy, user sentiment and a utilization associated with one or more of the user-personalized strategy, the one or more tools and resources, and one or more of the alerts, predictions, insights, suggestions, community groups activities, and forecasts generated by the platform (Asikainen: ¶[0097]…Asikainen's recommendation engine's performance is gauged on the basis of the user's state (fatigue), which under BRI is a measure of user sentiment toward the recommended strategy, combined with utilization (workout completion data), "The recommendation engine 210 generates on-the-fly recommendation to modify or alter the user exercise workout based on the state or level of fatigue of the user").
Edwards combined with Straatman and Asikainen are analogous art because they are from the same field of endeavor, specifically machine-learning-driven personalized recommendation platforms that ingest data from user devices and external systems and present recommendations through an interactive user interface. They are also reasonably pertinent to the same problem of generating user-personalized strategies that can be continuously updated through monitored user-interaction data, including community-derived interaction data.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply Asikainen's gamification/community/subscription/rewards features to Edwards's continually-training recommendation system to broaden the platform's user engagement and monetization channels while leveraging the same continually-training AI for both individualized recommendations and community-driven recommendations.
The suggestion/motivation for doing so would have been Asikainen's express teaching that community accountability improves adherence to the user-personalized workout program (Asikainen: ¶[0112]), combined with Edwards's invitation to capture user-specific optimizations via continually-training AI (Edwards: ¶[0114]). A PHOSITA would have been motivated to apply Asikainen's community/subscription/gamification overlay to Edwards's continually-training AI to improve user retention and the ML model's training data by capturing richer community-interaction signals. Furthermore, this is a combination of prior-art elements according to known methods to yield predictable results (MPEP 2143.01(A)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Patents and/or related publications are cited in the Notice of References Cited (Form PTO-892) attached to this action to further show the state of the art with respect to a machine learning platform for user-personalized strategies.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN CHEN whose telephone number is (571)272-4143. The examiner can normally be reached M-F 10-7.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALAN CHEN/Primary Examiner, Art Unit 2125
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