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 .
Claims 1-20 are presented for examination.
This Office action is Non-Final.
Claim Objections
Claim 9 is objected to because of the following informalities: the phrase “…select an first action…” appears grammatically incorrect and should be --…select a first action…--. See claim 9, 6th limitation. Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 – Statutory Category
The independent claims 1, 9 and 17 are directed to method, system and non-transitory computer readable storage media, respectively, and therefore fall within the statutory categories of process, machine, and manufacture. Eligibility analysis is required because the claims may be directed to a judicial exception.
Step 2A – Prong One
Independent claims 1, 9, and 17 recite limitations that, under their broadest reasonable interpretation, describe collecting information, analyzing the information, and making decisions based on the analysis.
For example, claim 1 recites:
receiving first and second scores associated with respective user characteristics;
evaluating the scores to determine a first trust metric;
detecting a criterion for reevaluating the trust metric;
selecting an action and timing for obtaining additional information;
generating a second trust metric using the obtained information; and
performing a second action based on the second trust metric.
These limitations describe evaluating information regarding a user, determining a trust assessment, deciding when additional information should be obtained, and making a subsequent decision based upon the updated assessment. Such limitations can practically be performed in the human mind or with pen and paper and therefore recite mental processes. Additionally, the claims are directed to managing relationships between an organization and a user by determining trustworthiness and deciding whether and when to obtain additional information, which also constitutes a certain method of organizing human activity, including commercial or legal interactions.
Accordingly, claims 1, 9, and 17 recite an abstract idea.
Dependent Claims
Claims 2-8 depend from claim 1.
Claims 10-16 depend from claim 9.
Claims 18-20 depend from claim 17.
These dependent claims merely further define the abstract idea by specifying:
types of user characteristics;
machine-learning models used to generate scores;
user profile characteristics;
threshold conditions;
target metrics;
identifying user features;
requesting identity verification or profile information; and
selecting actions using machine-learning models.
These additional limitations merely describe additional data, additional analysis, or particular decision criteria used in carrying out the abstract idea. They do not alter the character of the claims as being directed to the judicial exception.
Accordingly, claims 2-8, 10-16 and 18-20 also recite the abstract idea.
Step 2A – Prong Two
The claims do not integrate the abstract idea into a practical application.
The additional elements recite generic computer implementation, including one or more processors, memory, and computer-readable storage media performing conventional receiving, evaluating, generating, and storing operations.
Although the claims recite selecting an action and timing for obtaining information, the claims do not improve the functioning of a computer, machine-learning model, network, database, or other technology. Rather, the claims merely use generic computing technology as a tool to perform the abstract analysis.
Further, the claims do not recite any particular technological improvement to machine-learning techniques, model architecture, model training, feature extraction, computer security, network communications, or data storage. Instead, the claims merely employ machine learning as a tool for implementing the abstract decision-making process.
Accordingly, the claims do not integrate the judicial exception into a practical application.
Step 2B – Inventive Concept
The additional elements, individually and as an ordered combination, do not amount to significantly more than the judicial exception.
The recited processor, memory, computer-readable storage medium, receiving of information, evaluating data, generating trust metrics, selecting actions, and performing actions are described at a high level of generality and constitute well-understood, routine, and conventional computer functions.
The ordered combination merely automates the abstract process of evaluating user information, determining trust, requesting additional information, updating the trust determination, and acting on the updated determination.
Accordingly, claims 1-20 do not recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Therefore, claims 1-20 represents an abstract idea.
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 10 and 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 10 and 12 recites the limitation "characteristic" in the body of the claims. There is insufficient antecedent basis for this limitation in the claim. The Examiner believes the Applicant intended it to refer to “attribute” as recited by independent claim 9.Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
O’Malley discloses predictive analysis, scenario simulation, and decision optimization is provided. The system includes a prediction management system executed on a distributed computing infrastructure, and a prediction engine configured to receive input data, including event parameters, user-defined constraints, real-time data feeds, and historical trends. The prediction engine generates predictive models using algorithms trained on historical event outcomes, assigns probability scores and confidence intervals to potential outcomes, and dynamically updates the models based on new input data. Actionable insights are generated and ranked according to predefined success criteria. A non-transitory computer-readable medium is used to store the predictive models, outcome probabilities, and actionable insights for subsequent analysis and reporting. This system facilitates enhanced decision-making by offering real-time insights and continuously refined predictions, thereby optimizing responses to complex events and scenarios.
Cella et al. discloses a system includes a data pool system that is configured to receive data from a plurality of different data sources and maintain a training data set that is used to train a specific machine-learning model based on the data from the plurality of different data sources. In embodiments, the system further includes a data scoring system that determines a data reliability score corresponding to the new data based on a set of intrinsic features of the new data and a data scoring model, wherein the data pool system selectively adds the new data to the training data set based on the reliability score of the new data. The system also includes a machine learning system that trains the specific machine-learning model based on the training data set.
Tashman et al. discloses classifying a user and issuing actions are disclosed. One method may include receiving a first score for a first characteristic associated with a user and a second score for a second characteristic associated with the user. The first and second scores may be evaluated for determining a first metric for the user. A criterion may be detected for reevaluating the first metric. Based on detecting the criterion, a first action and a timing of the first action may be selected for obtaining information associated with the user. The first action and timing of the first action may be configured to maximize accuracy of a prediction of a second metric and minimize a cost associated with the first action. The second metric may be generated based on the information obtained via the first action. A second action may be performed based on the second metric.
Romero et al. discloses an interdependent series/suite of AI models. In one embodiment, a processor receives projection assumption inputs from a user device and executes a population builder machine learning model to predict a dynamic adjustment table. It applies the model to population data to generate a value population file, which simulates a subset of the population based on the predicted table. The file contains value cells representing instances of a product. The device then runs a mortality machine learning model to determine mortality data for the product using the simulated population. Finally, it executes a flow projection model to generate a projection report for the product, incorporating mortality data and projection assumptions.
O Conchuir et al. discloses forecasting techniques for forecasting holistic, categorical improvement predictions. The techniques may include generating a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group. The techniques include using an action-specific causal inference model to generate a metric-specific predictive impact measure. The techniques include generating a metric-level categorical improvement prediction and a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics. The techniques include initiating a performance of a prediction-based action based on the categorical improvement prediction.
Mattar et al. discloses intelligent priority evaluators configured to perform a method that prioritizes tasks submitted by various users, even if the tasks are similarly classified. The scheduling system can collect, calculate, and use various criteria to determine a reward score in order to prioritize one task over another, such as for dynamic scheduling purposes. This can be performed in addition to or as a replacement for receiving user designations of priority.
Jesneck et al. discloses determining competency scores and predicting outcomes for healthcare professionals. The platforms and methods utilize performance evaluations obtained from evaluator healthcare professionals for a target healthcare professional and matched peer group, and can be used for evaluating current performance and predicting future performance.
Cross et al. discloses dynamically discovering components of a computer network environment. The processing circuit of a data acquisition engine configured determine a domain name associated with an entity profile, determine an IP range, validate at the domain name, the IP range, and the IP address, collect additional device connectivity data, and provide the additional device connectively data.
Cmielowski et al. discloses receiving a selection of a desired value of the metric for predicting a value of the first label attribute based on a current training dataset. Previously obtained sets of training settings may be used for determining a set of training settings that corresponds to the desired value of the metric. A ML engine may be controlled to generate using the current training dataset a machine learning model according to the determined set of training settings. The generated machine learning model may be deployed for performing predictions of values of the first label attribute.
Hou discloses a metric based property rating and categorization system comprises data model, calculation and categorization engines and user interface logic. The data model describes the property attributes, categories and stats record information as well as their relationships. The calculation engine provides methods to generate metric scores and calculate property rating. The categorization engine is used to categorize a property to corresponding categories according to its characteristics. The user interface logic provides an interface accessible to a user to search and analyze property metrics, rating and category related information.
Pandian et al. discloses a machine learning-based network trained to perform risk assessment through device data. A service provider server receives device data of a user device associated with a merchant account registered with a merchant server for a merchant service, and receives, from the merchant server, a request containing a unique token identifier for initiating a risk assessment operation to generate a device assessment score for the user device, where the device assessment score indicates a level of risk between the user device and the merchant account. The service provider server selects a risk assessment engine to perform the risk assessment operation and generates the device assessment score and a narrative for the device assessment score, and sends, to the merchant server through an application programming interface, a message containing a unique device identifier for the user device, the device assessment score and the narrative.
Andrews et al. discloses determining if a Merchant should be provided transaction processing services by an Acquirer and/or continue to be provided such services by the Acquirer. In one embodiment, the inventive system and methods permit a more accurate and reliable determination of the risk to an Acquirer presented by a Merchant, based on a risk assessment engine and the described set of data sources.
Siddique et al. discloses online methods of collaboration in community environments. The methods and systems are related to an online apparel modeling system that allows users to have three-dimensional models of their physical profile created. Users may purchase various goods and/or services and collaborate with other users in the online environment.
Adler discloses a set of modeling and analysis tools is provided to help companies make informed strategic decisions in complex, rapidly changing market environments. Outcomes of candidate decisions are simulated over time, under different evolutionary scenarios that reflect assumptions about trends in a market and the overall economy, and the likely behavior of individual businesses. Detailed analyses are then generated, both qualitative and quantitative, of the different outcomes, helping users to identify the decision option with the most attractive rewards and tolerable risks. Users may revisit prior decisions, by periodically updating models with current market data and refining behavioral assumptions based on observations. Users can then re-run the simulations and analyses to determine if decisions remain valid and optimal, or whether circumstances have changed sufficiently to warrant modifying initial strategies. Applications include supporting strategic decision-making pertaining to business issues such as B2B channel strategies, mergers & acquisitions, creating (or dropping) products, business units, or production capacity, and to strategic decision making in military, legislative, healthcare, environmental, political, and other non-business domains.
Examiner’s Remarks
Upon resolution of the above rejections under 35 USC 101 and 112, the present record indicates that claims 1-20 would be in condition for allowance because the prior art of record does not teach or suggest the presently claimed subject matter.
Conclusions/Points of Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORGE A CASANOVA whose telephone number is (571)270-3563. The examiner can normally be reached M-F: 9 a.m. to 6 p.m. (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached at (571) 270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORGE A CASANOVA/Primary Examiner, Art Unit 2165
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