DETAILED ACTION
This is a Final Office Action in response to the amendment filed 10/17/2025.
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 of Claims
Claims 1-20 are currently pending in the application and have been examined.
Response to Amendment
The amendment filed 10/17/2025 has been entered.
Response to Arguments
Claim Rejections 35 U.S.C. 101:
Applicant submits on page 10 of the remarks that the claims are directed to the field of science for data management and an improvement to this scientific field and not an abstract idea of organizing human behavior. Examiner respectfully disagrees and notes that according to the 2019 Revised Patent Subject Matter Eligibility Guidance (PEG) and under step 2A of the analysis of claims per the Alice framework, if a claim limitation covers managing personal behavior or relationships or interactions between people, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas.
Applicant submits on page 10 of the remarks that the claims integrate the purported judicial exception into a practical application by providing data processing technology improvements. Examiner respectfully disagrees and notes that the present claims do not integrate the judicial exception into a practical application in a matter that imposes meaningful limit to the judicial exception. The claims as presented are merely linking the use of the judicial exception to a computer system.
Applicant submits on page 11 of the remarks that the claims are directed to significantly more than an abstract idea. Examiner notes that when determining whether a claim recites significantly more in Step 2B the analysis takes into consideration whether the claim effects a transformation or reduction of a particular article to a different state or thing. Transformation and reduction of an article ‘to a different state or thing’ is the clue to patentability of a process claim that does not include particular machines." Bilski v. Kappos, 561 U.S. 593, 658, 95 USPQ2d 1001, 1007 (2010) (quoting Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972)). See MPEP 2106.05(c). Furthermore, the additional elements recited in the claims merely recite the use of a generic computer to perform generic computer functions of storing and transmitting data. These generic computer functions do not integrate the abstract idea into a practical application and do not recite significantly more than the judicial exception.
Claim Rejections 35 U.S.C. 103:
Applicant submits on pages 13-14 that “Neilsen's generic "pre-processing" and "properly formatting" fails to disclose: The specific mapping step between extracted data elements, Normalization of terminology across different platforms to recognize similar information, The structured transformation from platform-dependent to platform independent formats.” Examiner notes that Nielsen [0274] discloses a data processing layer that is responsible for pre-processing the data received from the Social Data Aggregator. This pre-processing is mainly to extract relevant data from the raw data received from Data Integration Layer and to properly format this data for passing to the next layer.
Further, Applicant submits that “Neilsen does not teach claim 9's feature: wherein the MLP (multilayer perceptron machine learning system) implements a pattern recognition algorithm for the platform independent format to identify behavioral patterns not detectable in the platform dependent format.” Examiner respectfully disagrees and notes that Nielsen discloses using patterns and a machine learning model in at least [0270].
Applicant submits on page 14 of the remarks that “Nielsen does not teach identifying platform dependent formats. Second, Nielsen does not teach or suggest Applicant's feature, convert a plurality of data files into a platform independent format by: normalizing the data, e.g., recognizing similar underlying information and transforming, e.g., data structures from the different platform dependent formats into a common format, to generate standardized data files. Nielsen merely extracts relevant data from raw data and formats the data before passing it to a next layer.” Examiner respectfully disagrees with this interpretation and notes that as explained in the rejection, Nielsen [0274] discloses a data processing layer that is responsible for pre-processing the data received from the Social Data Aggregator. This pre-processing is mainly to extract relevant data from the raw data received from Data Integration Layer and to properly format this data for passing to the next layer. See also Nielsen [0274-0283].
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
With respect to claims 1-20, the independent claims (claims 1, 9 and 16) are directed, in part, to a method and a system for behavior assessment. Step 1 – First pursuant to step 1 in the January 2019 Guidance, claims 1-8 are directed to a method comprising a series of steps which falls under the statutory category of a process, claims 9-15 are directed to a system which falls under the statutory category of a machine, and claims 16-20 are directed to a tangible non-transitory computer-readable medium, which falls under the statutory category of an article of manufacture, therefore the claims are eligible under Step 1. However, these claim elements are considered to be abstract ideas because they are directed to a method of organizing human activity which includes managing personal behavior or relationships/interactions between people.
As per Step 2A - Prong 1 of the subject matter eligibility analysis, the claims are directed, in part, to obtaining…user data for the individual from a plurality of digital sources, the user data having a plurality of data files; identifying the plurality of data files have different platform dependent formats; converting… the plurality of data files into a platform independent format by normalizing and transforming data structures of the different platform dependent forms, the normalizing and transforming including: extracting data elements from each platform dependent format according to a semantic analysis to recognize similar underlying information; mapping the extracted data elements to a common data structure, and generating standardized data files with the common data structure; creating… categorized user data by organizing the standardized data files into a plurality of predefined content-based bins, the plurality of predefined content-based bins including at least one of a location bin, a purchases bin, an activity bin, a fitness bin, or a social network bin; determining… one or more behavioral insight categories from the categorized user data; determining… a plurality of behavioral metrics based on the one or more behavioral insight categories and the categorized user data; forming… a personality profile for the individual by converting the plurality of behavioral metrics into one or more scores; generating a risk assessment for the individual based on the personality profile; and presenting… an indication of the risk assessment. If a claim limitation, under its broadest reasonable interpretation covers managing personal behavior or relationships/interactions between people then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
As per Step 2A - Prong 2 of the subject matter eligibility analysis, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: “a computing device”; “data aggregator”; “digital sources”; “a system”; “processor”; “machine-learning algorithms”; “tangible non-transitory computer-readable storage media”; “a display”; “a multilayer perceptron machine-learning system”. These additional element in both steps are recited at a high-level of generality (i.e., as a generic device performing a generic computer function of receiving and storing data) such that these elements amount no more than mere instructions to apply the exception using a generic computer component. Examiner looks to Applicant’s specification in at least figure 1 and related text and [0065-0068] to understand that the invention may be implemented in a generic environment that “The computing device 1102 may be a computing system capable of executing a computer program product to execute a computer process. Data and program files may be input to the computing device 1102, which reads the files and executes the programs therein. Some of the elements of the computing device 1102 include one or more hardware processors 1104, one or more memory devices 1106, and/or one or more ports, such as input/output (IO) port(s) 1108 and communication port(s) 1110. Additionally, other elements that will be recognized by those skilled in the art may be included in the computing device 1102 but are not explicitly depicted in FIG. 11 or discussed further herein. Various elements of the computing device 1102 may communicate with one another by way of the communication port(s) 1110 and/or one or more communication buses, point-to-point communication paths, or other communication means. The processor 1104 may include, for example, a central processing unit (CPU), a microprocessor, a microcontroller, a digital signal processor (DSP), and/or one or more internal levels of cache. There may be one or more processors 1104, such that the processor 1104 comprises a single central-processing unit, or a plurality of processing units capable of executing instructions and performing operations in parallel with each other, commonly referred to as a parallel processing environment. The computing device 1102 may be a conventional computer, a distributed computer, or any other type of computer, such as one or more external computers made available via a cloud computing architecture. The presently described technology is optionally implemented in software stored on the data storage device(s) such as the memory device(s) 1106, and/or communicated via one or more of the I/O port(s) 1108 and the communication port(s) 1110, thereby transforming the computing device 1102 in FIG. 11 to a special purpose machine for implementing the operations described herein and generating the personality profile 102. Moreover, the computing device 1102, as implemented in the systems 100-1000, receives various types of input data (e.g., in different data formats) and transforms the input data through the stages of the data flow described herein into new types of data files (e.g., the categorized user data, the content hierarchy 116, and the activity timeline 118,). Moreover, these new data files are transformed further into the behavior metrics 124 the behavioral dimensions 126, the lifestyle index values 1002, and the brand personality values 1004, which enables the computing device 1102 to do something it could not do before-generate the personality profile 102 based on user data 104 from different digital sources 106. The one or more memory device(s) 1106 may include any non-volatile data storage device capable of storing data generated or employed within the computing device 1102, such as computer executable instructions for performing a computer process, which may include instructions of both application programs and an operating system (OS) that manages the various components of the computing device 1102. The memory device(s) 1106 may include, without limitation, magnetic disk drives, optical disk drives, solid state drives (SSDs), flash drives, and the like. The memory device(s) 1106 may include removable data storage media, non-removable data storage media, and/or external storage devices made available via a wired or wireless network architecture with such computer program products, including one or more database management products, web server products, application server products, and/or other additional software components. Examples of removable data storage media include Compact Disc Read-Only Memory (CD- ROM), Digital Versatile Disc Read-Only Memory (DVD-ROM), magneto-optical disks, flash drives, and the like. Examples of non-removable data storage media include internal magnetic hard disks, SSDs, and the like. The one or more memory device(s) 1106 may include volatile memory (e.g., dynamic random-access memory (DRAM), static random-access memory (SRAM), etc.) and/or non-volatile memory (e.g., read-only memory (ROM), flash memory, etc.). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they are mere instructions to implement the abstract idea on a computer.
As per Step 2B of the subject matter eligibility analysis, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are mere instructions to apply the abstract idea on a computer. When considered individually, these claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements and the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. Next, when the “machine learning” is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning does not add significantly more to the claims. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that amount to significantly more than the abstract idea itself. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
Dependent claims 2-8, 10-15, 17-20 further refine the abstract idea. These claims do not provide a meaningful linking to the judicial exception. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as by describing the nature and content of the data that is received/sent. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not significantly more than the abstract concepts at the core of the claimed invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub. No. 2015/0161738 (hereinafter; Stempora) in view of US Pub. No. 2016/0253688 (hereinafter; Nielsen).
Regarding claim 1, Stempora discloses:
A computer-implemented method, the method comprising: obtaining, at a data aggregator of a computing device, user data for the individual from a plurality of digital sources, the user data having a plurality of data files; (Stempora [0054] discloses information stored in multiple formats, […] information for a group of individuals is stored in a single cognitive map or a collection of cognitive maps[…] A cognitive map for a single individual, a collection of cognitive maps for a group of individuals, or a single cognitive map for a group of individuals comprises cognitive information that may be stored on one or more non-transitory computer-readable media that are connected or in communication with one or more devices (including portable devices, wearable devices, desktops, laptops, servers, etc.), or that are in operable communication via wired (internet protocol, etc.) or wireless formats (Wi-Fi, Bluetooth.TM., IEEE 802.11 formats, cellular communication data formats (GPRS, 3G, 4G (Mobile WiMAX, LTE, etc.), or optical, etc.) with one or more devices or processors. In one embodiment, one or more of the devices (such as a portable device for example) communicates cognitive information from one or more cognitive maps to another device (such as a server).)
creating, by the data aggregator at the computing device, categorized user data by organizing the data files into a plurality of predefined content- based bins, the plurality of predefined content-based bins including at least one of a location bin, a purchases bin, an activity bin, a fitness bin, or a social network bin; (Stempora [0052] discloses […] the plurality of cognitive maps may be used to classify one or more individuals into groups. The classification may be based on one or more selected from the group […] cognitive information, traits, physical or mental condition, personalities, preferences, personal characteristic information, level of the risk behavior from risk-seeking to risk-averse, social connections with other individuals, location, credit score, or other demographic information[…])
forming, at the computing device, a personality profile for the individual by converting the plurality of behavioral metrics into one or more scores; (Stempora [0015] discloses risk related score; [0061-0063] disclose In one embodiment, a method of generating the risk assessment, the risk score, the underwriting, or the cost of insurance for an individual comprises profiling the individual such that they are categorized on a scale from very risk-seeking individual to a very risk-averse individual. In one embodiment, decision information such as contextual information is used to determine the level of risk associated with one or more risk-related decisions made by the individual. In one embodiment, the individual risk profile includes risk-related information, such as a characterization of the individual on a scale from very risk-seeking to very risk-averse for one or more individuals and may be generated for different situation (where for example, the individual may be categorized on a risk scale differently for different situations or conditions).)
generating a risk assessment for the individual based on the personality profile; (Stempora [0013-0014] disclose risk related decision information; [0060-0061] disclose […]profiling the individual such that they are categorized on a scale from very risk-seeking individual to a very risk-averse individual…)
and presenting, at a display communicatively coupled to the computing device, an indication of the risk assessment. (Stempora [0082] discloses a portable device display; The form or delivery of the feedback may take many forms, such as an SMS text message; email, pop-up notification; an application changing the display to indicate a representation of feedback; a web based application or a report with results and/or analysis of recent risk-related behavior…)
Although Stempora discloses systems and methos for user behavior assessment, Stempora does not specifically disclose transforming and normalizing data or the use of a machine learning algorithm. However, Nielsen discloses the following limitations:
identifying the plurality of data files have different platform dependent formats; converting, by the data aggregator, the plurality of data files into a platform independent format by normalizing and transforming data structures of the different platform dependent forms, the normalizing and transforming including: extracting data elements from each platform dependent format according to a semantic analysis to recognize similar underlying information; (Nielsen discloses transforming data in at least [0058]; [0109]; [0265] discloses creating a structured document by denoting structural semantics for text and links[…])
mapping the extracted data elements to a common data structure, and generating standardized data files with the common data structure; (Nielsen [0274] discloses a data processing layer that is responsible for pre-processing the data received from the Social Data Aggregator. This pre-processing is mainly to extract relevant data from the raw data received from Data Integration Layer and to properly format this data for passing to the next layer; See Nielsen [0274-0283].)
determining, at the computing device by executing one or more machine-learning algorithms, one or more behavioral insight categories from the categorized user data; (Nielsen [0270] […] discloses a machine learning model, which is essentially an estimation of real behavior of users, is trained based on the different features, calculated to capture different aspects of user's behavior.)
determining, at the computing device by executing the one or more machine-learning algorithms, a plurality of behavioral metrics based on the one or more behavioral insight categories and the categorized user data; (Nielsen [0309]; [0244] disclose use metrics and using machine learning to predict user behavior.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method for risk scoring of an individual with the system for predicting churn propensity of an individual of Nielsen in order to predict likelihood of engagement with a service (Nielsen abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 2, Stempora discloses:
The method of claim 1, further comprising: generating an activity timeline based on the categorized user data; determining an activity pattern from the activity timeline; and determining the one or more behavioral insight categories based on the activity pattern. (Stempora [0009] discloses time intervals; [0020] discloses deviations from time and identifying patterns of behavior.)
Regarding claim 3, Stempora discloses:
The method of claim 2, wherein the activity pattern comprises at least one of: an action frequency; an action recurrence amount; an action periodicity; an action-reaction occurrence; a total number of action occurrences; or an action cluster. (Stempora [0042] discloses a decision making process algorithm that includes a vehicle operator frequently choosing a particular process under a set of conditions; identifying patterns in the decision making process.)
Regarding claim 4, Although Stempora discloses systems and methos for user behavior assessment, Stempora does not specifically disclose hierarchy of data. However, Nielsen discloses the following limitations:
The method of claim 3, further comprising: generating a content hierarchy from the categorized user data; determining a first behavioral insight category of the one or more behavioral insight categories using a first content hierarchy level of the content hierarchy in response to the first behavioral insight category having a first number of occurrences greater than a predetermined category threshold; and determining a second behavioral insight category of the one or more behavioral insight categories using a second content hierarchy level of the content hierarchy in response to the second behavioral insight category having a second number of occurrences greater than the predetermined category threshold. (Nielsen [0170] discloses order of connections; [0244] discloses hierarchy of connections.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method for risk scoring of an individual with the system for predicting churn propensity of an individual of Nielsen in order to predict likelihood of engagement with a service (Nielsen abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 5, Stempora discloses:
The method of claim 4, further comprising receiving an input selecting a timeline view representing the activity timeline or a content view representing the content hierarchy. (Stempora[0009] discloses different timeline views.)
Regarding claim 6, Stempora discloses:
The method of claim 1, wherein the plurality of digital sources comprise: a first type of digital source; a second type of digital source that is different than the first type of digital source; and a third type of digital source that is different than the first type of digital source and the second type of digital source. (Stempora [0033] discloses using information from one or more data sources selected from: the initial underwriting profile, external data sources, third-party data sources, a wearable device (smart watch, pulse monitor, contact lens, etc.), a portable device (cellphone, etc.), a telematics device, a medical device (magnetoencephalography (MEG) device, etc.), a computing device (tablet computer, laptop computer, desktop computer, etc.), and other electronic device.)
Regarding claim 7, Stempora discloses:
The method of claim 6, wherein: the first type of digital source provides the user data in a first data format; the second type of digital source provides the user data in a second data format that is different than the first data format; and the third type of digital source provides the user data in a third data format that is different than the first data format and the second data format. Stempora [0054] discloses A cognitive map for a single individual, a collection of cognitive maps for a group of individuals, or a single cognitive map for a group of individuals comprises cognitive information that may be stored on one or more non-transitory computer-readable media that are connected or in communication with one or more devices (including portable devices, wearable devices, desktops, laptops, servers, etc.), or that are in operable communication via wired (internet protocol, etc.) or wireless formats (Wi-Fi, Bluetooth.TM., IEEE 802.11 formats, cellular communication data formats (GPRS, 3G, 4G (Mobile WiMAX, LTE, etc.), or optical, etc.) with one or more devices or processors.)
Regarding claim 8, Stempora discloses:
The method of claim 7, wherein the plurality of digital sources are at least one of a social media application, a search application, an e-commerce application, a food ordering application, a credit card service, a bank record, or a wearable device application. (Stempora [0036] discloses gathering information from an individual’s social media website.)
Regarding claim 9, Stempora discloses:
A system, the system comprising: at least one processor configured to: receive, from a first digital source, first user data for the individual having a first data format; receive, from a second digital source, second user data for the individual having a second data format that is different from the first data format; (Stempora [0054] discloses information stored in multiple formats, […] information for a group of individuals is stored in a single cognitive map or a collection of cognitive maps[…] A cognitive map for a single individual, a collection of cognitive maps for a group of individuals, or a single cognitive map for a group of individuals comprises cognitive information that may be stored on one or more non-transitory computer-readable media that are connected or in communication with one or more devices (including portable devices, wearable devices, desktops, laptops, servers, etc.), or that are in operable communication via wired (internet protocol, etc.) or wireless formats (Wi-Fi, Bluetooth.TM., IEEE 802.11 formats, cellular communication data formats (GPRS, 3G, 4G (Mobile WiMAX, LTE, etc.), or optical, etc.) with one or more devices or processors. In one embodiment, one or more of the devices (such as a portable device for example) communicates cognitive information from one or more cognitive maps to another device (such as a server).)
create categorized user data by categorizing the standardized first user data and the standardized second user data into a plurality of predefined content-based bins, the plurality of predefined content- based bins including at least one of a location bin, a purchases bin, an activity bin, a fitness bin, or a social network bin; (Stempora [0052] discloses […] the plurality of cognitive maps may be used to classify one or more individuals into groups. The classification may be based on one or more selected from the group […] cognitive information, traits, physical or mental condition, personalities, preferences, personal characteristic information, level of the risk behavior from risk-seeking to risk-averse, social connections with other individuals, location, credit score, or other demographic information[…])
generate a personality profile for the individual by converting the plurality of behavioral dimensions into one or more scores; (Stempora [0015] discloses risk related score; [0061-0063] disclose In one embodiment, a method of generating the risk assessment, the risk score, the underwriting, or the cost of insurance for an individual comprises profiling the individual such that they are categorized on a scale from very risk-seeking individual to a very risk-averse individual. In one embodiment, decision information such as contextual information is used to determine the level of risk associated with one or more risk-related decisions made by the individual. In one embodiment, the individual risk profile includes risk-related information, such as a characterization of the individual on a scale from very risk-seeking to very risk-averse for one or more individuals and may be generated for different situation (where for example, the individual may be categorized on a risk scale differently for different situations or conditions).)
generate for the individual, based on the personality profile, one or more of: a risk assessment; a data privacy assessment for a data privacy service; or a product recommendation for a commerce website; (Stempora [0013-0014] disclose risk related decision information; [0060-0061] disclose […]profiling the individual such that they are categorized on a scale from very risk-seeking individual to a very risk-averse individual…)
and cause the risk assessment, data privacy assessment, or the product recommendation to be presented at a display of a computing device. (Stempora [0082] discloses a portable device display; The form or delivery of the feedback may take many forms, such as an SMS text message; email, pop-up notification; an application changing the display to indicate a representation of feedback; a web based application or a report with results and/or analysis of recent risk-related behavior…)
Although Stempora discloses systems and methos for user behavior assessment, Stempora does not specifically disclose transforming and normalizing data or the use of a machine learning algorithm. However, Nielsen discloses the following limitations:
identify the first user data and the second user data have different platform dependent formats; convert the first user data and the second user data into a platform independent format by normalizing terminology across the different platform dependent formats to recognize similar underlying information by extracting data elements from each platform dependent format according to a semantic analysis, transforming data structures from the first data format and the second to recognize similar underlying information by extracting data elements from each platform dependent format according to a semantic analysis, (Nielsen discloses transforming data in at least [0058]; [0109]; [0265] discloses creating a structured document by denoting structural semantics for text and links[…]; Nielsen [0274] discloses a data processing layer that is responsible for pre-processing the data received from the Social Data Aggregator. This pre-processing is mainly to extract relevant data from the raw data received from Data Integration Layer and to properly format this data for passing to the next layer; See Nielsen [0274-0283].)
transforming data structures from the first data format and the second data format by mapping the extracted elements to a common data structure and, generating standardized first user data and standardized second user data with the common data structure; Nielsen discloses transforming data in at least [0058]; [0109]; [0265] discloses creating a structured document by denoting structural semantics for text and links[…]; Nielsen [0274] discloses a data processing layer that is responsible for pre-processing the data received from the Social Data Aggregator. This pre-processing is mainly to extract relevant data from the raw data received from Data Integration Layer and to properly format this data for passing to the next layer; See Nielsen [0274-0283].)
determine one or more behavioral metrics from the categorized user data using a multilayer perceptron (MLP) machine-learning system and a first training data set of predetermined behavior metrics, wherein the MLP implements a pattern recognition algorithm for the platform independent format to identify behavioral patterns not detectable in the platform dependent format; (Nielsen [0270] […]discloses a machine learning model, which is essentially an estimation of real behavior of users, is trained based on the different features, calculated to capture different aspects of user's behavior.)
determine a plurality of behavioral dimensions based on the one or more behavioral metrics and the categorized user data; (Nielsen [0309]; [0244] disclose use metrics and using machine learning to predict user behavior.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method for risk scoring of an individual with the system for predicting churn propensity of an individual of Nielsen in order to predict likelihood of engagement with a service (Nielsen abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 10, Stempora discloses:
The system of claim 9, wherein the plurality of predefined content-based bins include two or more of: a location bin; a purchases bin; an activity bin; a fitness bin; a social network bin; or a personally identifiable information bin. (Stempora [0052] discloses […] the plurality of cognitive maps may be used to classify one or more individuals into groups. The classification may be based on one or more selected from the group […] cognitive information, traits, physical or mental condition, personalities, preferences, personal characteristic information, level of the risk behavior from risk-seeking to risk-averse, social connections with other individuals, location, credit score, or other demographic information[…])
Regarding claim 11, Although Stempora discloses systems and methos for user behavior assessment, Stempora does not specifically disclose action items. However, Nielsen discloses the following limitations:
The system of claim 10, wherein the at least one processor is further configured to: determine a plurality of content identifiers associated with a plurality of action items associated with the plurality of predefined content-based bins; determine a plurality of interest category identifiers associated with the plurality of action items in the plurality of predefined content-based bins; and determine one or more behavioral insight categories from the categorized user data by using the plurality of interest category identifiers across different predefined content- based bins, the one or more behavioral metrics being based on the one or more behavioral insight categories. (Nielsen [0028]; [0034] disclose performing an action based on a churn probability of a user; [0035] discloses performing an action based on a behavior of a customer.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method for risk scoring of an individual with the system for predicting churn propensity of an individual of Nielsen in order to predict likelihood of engagement with a service (Nielsen abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 12, Although Stempora discloses systems and methos for user behavior assessment, Stempora does not specifically disclose the use of a machine learning algorithm. However, Nielsen discloses the following limitations:
The system of claim 11, wherein the one or more behavioral insight categories are determined using the multilayer perceptron (MLP) machine-learning system and a second training data set of predetermined behavioral insight categories. (Nielsen [0110]; [0180] disclose training data sets.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method for risk scoring of an individual with the system for predicting churn propensity of an individual of Nielsen in order to predict likelihood of engagement with a service (Nielsen abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 13, Although Stempora discloses systems and methos for user behavior assessment, Stempora does not specifically disclose action items. However, Nielsen discloses the following limitations:
The system of claim 11, wherein: the plurality of content identifiers indicate a plurality of actions associated with the plurality of action items; and the plurality of interest category identifiers indicate types of interests associated with the plurality of action items. (Nielsen [0028]; [0034] disclose performing an action based on a churn probability of a user; [0035] discloses performing an action based on a behavior of a customer; [0172] discloses interest metrics and common interests between users.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method for risk scoring of an individual with the system for predicting churn propensity of an individual of Nielsen in order to predict likelihood of engagement with a service (Nielsen abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 14, Although Stempora discloses systems and methos for user behavior assessment, Stempora does not specifically disclose action items. However, Nielsen discloses the following limitations:
The system of claim 13, wherein the plurality of actions include at least one of a search for a title associated with a genre, a search for a type of food, a purchase of a consumer good, a purchase of digital media, joining a digital media service, joining a health service, or a performing physical activity. (Nielsen discloses predicting user’s predisposition to join a subscription-based service (i.e. joining a digital media service).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method for risk scoring of an individual with the system for predicting churn propensity of an individual of Nielsen in order to predict likelihood of engagement with a service (Nielsen abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 15, Although Stempora discloses systems and methos for user behavior assessment, Stempora does not specifically disclose action items. However, Nielsen discloses the following limitations:
The system of claim 9, wherein creating the categorized user data includes generating a plurality of category tags associated with a plurality of action items represented by the first user data and the second user data. (Nielsen [0277] discloses the use of tags.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method for risk scoring of an individual with the system for predicting churn propensity of an individual of Nielsen in order to predict likelihood of engagement with a service (Nielsen abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 16, Stempora discloses:
One or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing a computer process on a computing system, the computer process comprising: creating categorized user data by categorizing the standardized data files into a plurality of content-based bins, the plurality of content-based bins including at least one of a location bin, a purchases bin, an activity bin, a fitness bin, or a social network bin; (Stempora [0052] discloses […] the plurality of cognitive maps may be used to classify one or more individuals into groups. The classification may be based on one or more selected from the group […] cognitive information, traits, physical or mental condition, personalities, preferences, personal characteristic information, level of the risk behavior from risk-seeking to risk-averse, social connections with other individuals, location, credit score, or other demographic information[…])
generating an activity timeline representing the categorized user data; (Stempora [0009] discloses time intervals; [0020] discloses deviations from time and identifying patterns of behavior.)
converting the plurality of behavior metrics into a personality profile for the individual; (Stempora [0013-0014] disclose risk related decision information; [0060-0061] disclose […]profiling the individual such that they are categorized on a scale from very risk-seeking individual to a very risk-averse individual…)
and generating a risk assessment for the individual based on the personality profile. (Stempora [0013-0014] disclose risk related decision information; [0060-0061] disclose […]profiling the individual such that they are categorized on a scale from very risk-seeking individual to a very risk-averse individual…)
Although Stempora discloses systems and methos for user behavior assessment, Stempora does not specifically disclose transforming and normalizing data or the use of a machine learning algorithm. However, Nielsen discloses the following limitations:
receiving user data for an individual, the user data having a plurality of data files; identifying the plurality of data files have different platform dependent formats; converting the plurality of data files into a platform independent format by transforming and normalizing data structures of the different platform dependent formats, the transforming and normalizing includes: normalizing terminology across the different platform dependent formats to recognize similar underlying information, by extracting data elements according to a semantic analysis, (Nielsen discloses transforming data in at least [0058]; [0109]; [0265] discloses creating a structured document by denoting structural semantics for text and links[…])
mapping the data elements to a common data structure, and generating standardized data files with the common data structure; (Nielsen [0274] discloses a data processing layer that is responsible for pre-processing the data received from the Social Data Aggregator. This pre-processing is mainly to extract relevant data from the raw data received from Data Integration Layer and to properly format this data for passing to the next layer; See Nielsen [0274-0283].)
generating a content hierarchy representing the categorized user data; (Nielsen [0170] discloses order of connections; [0244] discloses hierarchy of connections.)
determining, by executing one or more machine-learning algorithms, one or more behavioral insight categories from the categorized user data using the activity timeline and the content hierarchy; (Nielsen [0270] […]discloses a machine learning model, which is essentially an estimation of real behavior of users, is trained based on the different features, calculated to capture different aspects of user's behavior.)
determining, by executing the one or more machine-learning algorithms, a plurality of behavior metrics based on the one or more behavioral insight categories and the categorized user data; (Nielsen [0309]; [0244] disclose use metrics and using machine learning to predict user behavior.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method for risk scoring of an individual with the system for predicting churn propensity of an individual of Nielsen in order to predict likelihood of engagement with a service (Nielsen abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 17, Stempora discloses:
The one or more tangible non-transitory computer-readable storage media of claim 16, wherein converting the plurality of behavior metrics into the personality profile includes associating the plurality of behavior metrics with one or more behavior dimensions including at least one of: a health dimension; a finance dimension; a mobility dimension; an interests dimension; a sociability dimension; or a personal identity dimension. (Stempora [0036] discloses information related to social networking websites.)
Regarding claim 18, Stempora discloses:
The one or more tangible non-transitory computer-readable storage media of claim 17, wherein converting the plurality of behavior metrics into the personality profile includes generating, based on the plurality of behavior metrics associated with the one or more behavior dimensions, a plurality of lifestyle index values and a plurality of brand personality values. (Stempora [0061] discloses profiling individuals.)
Regarding claim 19, Stempora discloses:
The one or more tangible non-transitory computer-readable storage media of claim 16, the computer process further comprising: generating an evidence confidence rating for the user data based on an origin-based interest scale indicating a level of interest associated with the user data based on how the user data originated; and weighing the user data or the categorized user data based on the evidence confidence rating. (Stempora [0046] discloses different confidence levels.)
Regarding claim 20, Stempora discloses:
The one or more tangible non-transitory computer-readable storage media of claim 19, the computer process further comprising: generating the evidence confidence rating for the user data based on a category-based interest scale indicating the level of interest associated with the user data based on a behavioral insight category associated with the user data; and weighing the user data or the categorized user data based on the evidence confidence rating. (Stempora [0046] discloses different confidence levels.)
Conclusion
THIS ACTION IS MADE FINAL. 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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/FRANCIS Z. SANTIAGO MERCED/Examiner, Art Unit 3625