Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This is a final rejection. Claims 1-4, 6-7, and 10-15 are pending.
Status of Claims
Applicant’s response date 12/02/2025, amending claims 1, 4,and 11-15.
Response to Amendment
The previously pending rejection under 35 USC 101, will be maintained. The 101 rejection is updated in light of the amendments.
Response to Arguments
Arguments regarding 35 USC 103 – the rejection is removed for the reason found in the “Allowable Subject Matter” section found below.
Applicant's arguments filed 03/01/2024 have been fully considered but they are not persuasive, moreover, any new grounds of rejection have been necessitated by applicant’s amendments to the claims.
Response to Argument under 35 USC 101:
Applicants argue: see applicant remarks pages 14-15 (with regard to prong 2)
The Applicant respectfully submits that amended claim I and its dependent
claims are patent eligible subject matter for at least one or more bases of subject matter
eligibility. First, claim 1 provides improvement technical in nature, e.g., the technical (not abstract) improvements of removing data skew and data collinearity and reducing server processing. Such technical improvements address technological issues identified by the Applicant: e.g., problems arising from inaccurate measurement and data unreliability and a need for a more reliable system and method for accurate measurement. (See, e.g., Applicant's specification at 2, 1st full paragraph.) In addressing these technical problems and needs, the technical steps of the claimed solution to these problems involve removing redundancy and data skew and also reducing server processing, and these have a technical nature as disclosed in the Applicant's specification. (Id, e.g., at 26-27, bridging paragraph.)
Examiner respectfully disagree:
First, independent claim 1 recites the abstract idea of determining reputation of an entity based on received data from a survey questions/ratings responses. which is considered certain methods of organizing human activity because the bolded claim limitations pertain to (i) commercial or legal interactions. See MPEP §2106.04(a)(2)(II). Applicant's claims as recited above provide a business solution of notifying a user to take an action. Applicant's claimed invention pertains to commercial/legal interactions because the limitations recite an abstract idea of determining reputation of an entity based on received data from a survey questions/ratings responses. which pertain to "agreements in the form of contracts; legal obligation; behaviors; business relations" expressly categorized under commercial/legal interactions. See MPEP §2106.04(a)(2)(II). Furthermore, the claim limitations are also directed towards mathematical concepts because consistent with this description, the limitations recite a method that applying weighting formulas to different data and converting the received rating from a raw scale to a zero to one-hundred scale, weighting, and aggregating the converted received ratings which is a mathematical formulas/equation and/or calculations. Data are analyzed using statistical and/mathematical techniques. Which pertain to “mathematical calculation/formula/calculations” expressly categorized under mathematical concepts. See MPEP §2106.04(a)(2)(II). Furthermore, the claim limitations are also recites towards mental processes because the limitations determining a level of emotional between users and a reputation recommendation to increase revenue/reputation of the entity, which is “observation, evaluations, judgments, and opinions,” expressly categorized under mental processes. See MPEP §2106.04(a)(2)(II).
Applicants argue: see applicant remarks pages 13-16 (with regard to prong 2)
claim 1 is also directed to a technological ordered combination including detailed usage of "unsupervised machine learning" and "supervised machine learning" in a manner that is significantly more than an alleged abstract idea of determining reputation or data collection to determine reputation and a practical application of determining reputation with such detailed machine learning techniques. Indeed, amended claim 1 no longer recites "determining reputation of an entity" at all.
Examiner respectfully disagree:
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional element, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use exception, such that it is more than a drafting effort designed to monopolize the exception.
The claims recites the additional limitation of “a software”, “a reputation server”, “a database”, “a user interface”, “system, “internet”, “electronic devices”, “a computer”, “a mobile device” are recited in a high level of generality and recited as performing generic computer functions routinely used in computer applications. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp. 134 S. Ct, at 2360,110 USPQ2d at 1984 (see MPEP 2106.05(f). The additional elements of a “unsupervised and supervised machine learning”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “unsupervised and supervised machine learning” is insufficient to show a practical application of the recited abstract idea.
The use of generic computer component to “…determine a sample size of population … determining a perception of those within the unique group … receive survey data … convert the received rating … applying a standardization formula … applying other standardization weighting …. Determining a level of rational connection … ” does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (step 2A-prong two: NO).
Second, The Alice framework, we turn to step 2B (Part 2 of Mayo) to determine if the claim is sufficient to ensure that the claim amounts to “significantly more” than the abstract idea itself. These additional elements recite conventional computer components and conventional functions of:
Claim 1 does not include my limitations amounting to significantly more than the abstract idea, along. Claim 1 includes various elements that are not directed to the abstract idea. These elements include “a software”, “a reputation server”, “a database”, “a user interface”, “system, “internet”, “electronic devices”, “a computer”, “a mobile device”. Examiner asserts that a “a software”, “a reputation server”, “a database”, “a user interface”, “system, “internet”, “electronic devices”, “a computer”, “a mobile device” are a generic computing element performing generic computing functions. (See MPEP 2106.05(f)).
Further, with data mining (i.e., searching over a network), receiving, processing, storing data, and parsing (i.e. extract, transform data) the courts have recognized the following computer function as well-understood, routing, and conventional functions when they are claimed in merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (i.e. “receiving, processing, transmitting, storing data”, etc.) are well-understood, routine, etc. (MPEP 2106.059d)).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices.
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 1-4, 6-7, and 10-15 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without a practical application or significantly more than the abstract idea.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Regarding Step 1
Claims 1-4, 6-7, and 10-15 are directed to a method (process). Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1]
Claims 1-4, 6-7, and 10-15 are directed toward the judicial exception of an abstract idea.
Regarding independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1. (Currently Amended) A method for removing skew from data, removing collinearity in data, and reducing server processing in a server of a network comprising the steps of:
removing skew from data of previously determined factor scores by removing one or more redundant factors and collinearity in data of the previously determined factor scores, the removing comprising:
the server as a reputation server performing redundancy analysis on the previously determined factor scores to remove the one or more redundant factors and remove the collinearity in the data of the previously determined factor scores, which removes the skew from the data of the previously determined factor scores, the redundancy analysis comprising the server projecting a plurality of factors including the one or more redundant factors onto linearly independent unique components, resulting in one or more less factors than originally used, removing the one or more redundant factors;
based on the redundancy analysis, removing one or more survey questions from a first survey to a previous group of one or more respondents, by the reputation server, wherein at least some of the data of the previously determined factor scores is based on one or more answers to the one or more survey questions to be removed from the first survey;
based on the removing of the one or more redundant factors, reducing server processing to be performed by the reputation server in a step of determining based on unsupervised machine learning, wherein said unsupervised-machine-learning-based determining step comprises determining, by the reputation server, to which reputation driver within a set of reputation drivers each factor belongs by using unsupervised machine learning on data of the remaining factors after the redundancy analysis is performed, where a reputation driver is an area that members of the unique group would tend to care about when assessing the reputation of the entity, wherein the unsupervised- machine-learning-based determining assigns the remaining factors among the set of reputation drivers, wherein the step of removing skew from data, the step of removing one or more survey questions, and the step of reducing server processing are steps included in using a single final reputation perception score and an aggregate factor score per factor to provide a reputation of an entity, wherein the using a single final reputation perception score and an aggregate factor score per factor to provide a reputation of an entity, further includes a step of averaging, by the reputation server, the scores of the factors assigned to a reputation driver within the set of reputation drivers to provide a reputation driver score for each reputation driver within the set of reputation drivers, resulting in multiple reputation driver scores, wherein the assigned factors are assigned based on the unsupervised- machine-learning-based determining performed based on the data of the remaining factors, and
wherein the using a single final reputation perception score and an aggregate factor score per factor to provide a reputation of an entity, further includes a step of determining, by the reputation server, a reputation driver weight of impact on the reputation of the entity for which reputation is being measured per reputation driver by using a supervised machine learning regression module on data of the reputation driver scores to predict the reputation perception score from the reputation driver scores, resulting in multiple reputation driver weights to guide the entity on actions to increasing its reputation based on the reputation driver weights, wherein the reputation driver scores are provided by the averaging performed by the reputation server,
wherein the single final reputation perception score, the multiple reputation driver scores, and the multiple reputation driver weights are to be presented via a user interface on an electronic device such as a computer or a mobile device,
wherein the electronic device such as the computer or the mobile device are configured to be used by users to interact with the reputation server via an internet through a user interface (UI), wherein the reputation server comprises software,
wherein a network comprises the electronic device, the reputation server, the internet, and a database, wherein the single final reputation perception score and the aggregate factor score per factor are provided when: a sample size of population is determined that is statistically representative of a desired survey population and which provides at least a ninety-five percent confidence interval for reputation metrics of the entity for which reputation is measured, where those within the sample size are referred to herein as a unique group, wherein the unique group includes the previous group of one or more respondents, the reputation server determines a perception of those within the unique group about the entity, referred to herein as a reputation perception score,
wherein reputation perception score determines the level of emotional evaluation of the entity by the unique group, which comprises:
the reputation server receives survey ratings from the unique group about their perception of the entity by rated emotional evaluation survey questions, where each survey rating is provided by a party within the unique group, and wherein the emotional evaluation survey questions are categorized into at least one category, where each category focuses on a different emotional evaluation, the reputation server activates reputation module engine software at the reputation server for converting the received rating from a raw scale to a zero to one-hundred scale, the reputation server applies a standardization formula of cultural weighting in each country or region by weighting the received survey ratings to normalize cultural bias where cultural bias is a tendency of individuals in different geographies to rate companies higher or lower based on local cultural norms or atmospheres resulting in an artificial skew in rating distribution, the reputation server applies other standardization weighting such as a data source weighting, and the reputation server aggregates the converted received ratings within each category, and averages the aggregated results within the different categories to provide the single final reputation perception score per entity that is stored in a storage device within the reputation server;
the reputation server determines a level of rational connection of those within the unique group with the entity, referred to herein as a reputation factor score, which comprises: the reputation server receives survey ratings from focused rational connection survey questions, where each survey rating is provided by a party within the unique group, and wherein the rational connection survey questions are each referred to as a factor;
the reputation server activates reputation factor score module engine software at the reputation server for the steps of converting the received survey ratings from rational connection survey questions from a raw scale to a zero to one- hundred scale;
the reputation server applies a standardization formula of cultural weighting in each country or region by weighting the received survey ratings to normalize cultural bias; and
the reputation server applies other standardization weighting such as a data source weighting;
the reputation server aggregates all converted survey ratings received for a single factor to provide the aggregate factor score per factor; and
the reputation server stores each factor score in the storage device within the reputation server, wherein the stored factor scores includes the previously determined factor scores.
The Applicant's Specification titled "SYSTEM AND METHOD FOR DETERMINING AND MANAGING REPUTATION OF ENTITIES AND INDUSTRIES" emphasizes the business need for data analysis, "In summary, the present disclosure relates to methods and systems for determining reputation of an entity based on received data from a survey questions/ratings responses " (Spec. [0024-0026]).
As the bolded claim limitations above demonstrate, independent claim 1 recites the abstract idea of determining reputation of an entity based on received data from a survey questions/ratings responses. which is considered certain methods of organizing human activity because the bolded claim limitations pertain to (i) commercial or legal interactions. See MPEP §2106.04(a)(2)(II).
Applicant's claims as recited above provide a business solution of notifying a user to take an action. Applicant's claimed invention pertains to commercial/legal interactions because the limitations recite an abstract idea of determining reputation of an entity based on received data from a survey questions/ratings responses. which pertain to "agreements in the form of contracts; legal obligation; behaviors; business relations" expressly categorized under commercial/legal interactions. See MPEP §2106.04(a)(2)(II).
Furthermore, the claim limitations are also directed towards mathematical concepts because consistent with this description, the limitations recite a method that applying weighting formulas to different data and converting the received rating from a raw scale to a zero to one-hundred scale, weighting, and aggregating the converted received ratings which is a mathematical formulas/equation and/or calculations. Data are analyzed using statistical and/mathematical techniques. Which pertain to “mathematical calculation/formula/calculations” expressly categorized under mathematical concepts. See MPEP §2106.04(a)(2)(II).
Furthermore, the claim limitations are also recites towards mental processes because the limitations determining a level of emotional between users and a reputation recommendation to increase revenue/reputation of the entity, which is “observation, evaluations, judgments, and opinions,” expressly categorized under mental processes. See MPEP §2106.04(a)(2)(II).
Dependent claims 2-15, further reiterate the same abstract ideas with further embellishments, such as
claim 2 further comprising the step of data cleaning by activating a data cleaning module within the reputation server and removing from the sample size data any corrupt data and responses of those not likely to provide true responses to survey questions based on pre-defined data cleaning conditions stored within the reputation server.
Claim 3 wherein the categories of emotional evaluation survey questions are categorized into more than one of the categories consisting of questions that determine a level of esteem that a party within the unique group who is surveyed associates with the entity to which a reputation measurement is desired, questions that determine a level of admiration that a party within the unique group who is surveyed associates with the entity to which a reputation measurement is desired, questions that determine a level of trust that a party within the unique group who is surveyed associates with the entity to which a reputation measurement is desired, and questions that determine a level of positive feeling that a party within the unique group who is surveyed associates with the entity to which a reputation measurement is desired.
claim 4 step of activating a reputation perception score module software within the reputation server and categorizing the single final reputation perception score into a nominative scale based on quantiles of a normal distribution to illustrate meaning to the entity for which reputation is being determined.
claim 5 Cancelled
claim 6 wherein the set of drivers includes products and services, innovation, workplace, governance, conduct, citizenship, leadership, social, environmental, and performance
claim 7 wherein the step of determining to which driver within a set of drivers each factor belongs is performed using unsupervised machine learning clustering.
claim 8 Cancelled
claim 9 Cancelled
claim 10 wherein the machine learning regression module is selected automatically by the reputation server by a pre- defined criteria from the group consisting of linear regression, multivariate linear regression, random forest, gradient boosting, and an ensemble of decision trees models.
claim 11 wherein the entity seeking its reputation is provided with a report via the user interface on the computer or the mobile device per period of time, that is containing its overall reputation perception score and a list of the reputation factor scores, and a list of drivers and associated reputation driver scores and reputation driver weights so as to provide guidance on which areas to invest additional time and money for maximum increase in reputation.
claim 12 wherein the entity seeking its reputation with a report via the user interface on the computer or the mobile device of overall reputation score of a different entity and a list of the reputation factor scores, and a list of drivers and associated reputation driver scores and reputation driver weights of the different entity, for comparison purposes.
claim 13 further comprising providing the entity seeking its reputation with the predicted reputation score provided by using the supervised machine learning regression module via the user interface on the computer or the mobile device.
claim 14 wherein the entity is provided with the single factor score per each factor via the user interface for the computer or the mobile device to provide a granular representation view of reputation of the entity.
claim 15 providing the entity seeking its reputation with a report via the user interface on the computer or the mobile device, per period of time, containing its overall reputation score and a list of the reputation factors and reputation factor scores, and associated reputation factor importance weights so as to provide guidance on which areas to invest additional time and money for maximum increase in reputation.
which are nonetheless directed towards fundamentally the same abstract ideas as indicated for independent claim 1.
Regarding Step 2A [prong 2]
Claims 1-4, 6-7, and 10-15 fail to integrate the abstract idea into a practical application. Independent claims 1 include the following additional elements which do not amount to a practical application:
Claim 1. (Currently Amended) using software, a reputation server, a database, and a user interface (UI) system through which users may interact with the reputation server via
The bolded limitations recited above in independent claim 1 pertain to additional elements which merely provide an abstract-idea-based-solution implemented with computer hardware and software components, including the additional elements of software, a reputation server, a database, a user interface, an electronic device, a computer, a mobile device, a unsupervised and supervised machine learning and reputation module engine software which fail to integrate the abstract idea into a practical application because there are (1) no actual improvements to the functioning of a computer, (2) nor to any other technology or technical field, (3) nor do the claims apply the judicial exception with, or by use of, a particular machine, (4) nor do the claims provide a transformation or reduction of a particular article to a different state or thing, (5) nor provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of MPEP §2106.04(d)(1) and §2106.05 (a-c & e-h), (6) nor do the claims apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, in view of MPEP §2106.04(d)(2). The Specification provides a high level of generality regarding the additional elements claimed without sufficient detail or specific implementation structure so as to limit the abstract idea, for instance,. (fig. 1). Nothing in the Specification describes the specific operations recited in claim 1 as particularly invoking any inventive programming, or requiring any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is somehow implemented using any specialized element other than all-purpose computer components to perform recited computer functions. The claimed invention is merely directed to utilizing computer technology as a tool for solving a business problem of data analytics. Nowhere in the Specification does the Applicant emphasize additional hardware and/or software elements which provide an actual improvement in computer functionality, or to a technology or technical field, other than using these elements as a computational tool to automate and perform the abstract idea. See MPEP §2106.05(a & e).
The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant's claimed invention which merely pertains to steps for determining reputation of an entity based on received data from a survey questions/ratings responses and the additional computer elements a tool to perform the abstract idea, and merely linking the use of the abstract idea to a particular technological environment. See MPEP §2106.04 and §21062106.05(f-h). Alternatively, the Office has long considered data gathering, analysis and data output to be insignificant extra-solution activity, and these additional elements do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.04 and §2106.05(g). Thus, the additional elements recited above fail to provide an actual improvement in computer functionality, or to a technology or technical field. See MPEP §2106.04(d)(1) and §2106§2106.05 (a & e).
Instead, the recited additional elements above, merely limit the invention to a technological environment in which the abstract concept identified above is implemented utilizing the computational tools provided by the additional elements to automate and perform the abstract idea, which is insufficient to provide a practical application since the additional elements do no more than generally link the use of the abstract idea to a particular technological environment. See MPEP §2106.04. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Alternatively, the Office has long considered data gathering and data processing as well as data output recruitment information on a social network to be insignificant extra-solution activity, and these additional elements used to gather and output recruitment information on a social network are insignificant extra-solution limitations that do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(g). The current invention determining reputation of an entity based on received data from a survey questions/ratings responses. when considered in combination, the claims do not amount to improvements of the functioning of a computer, or to any technology or technical field. Applicant's limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits.
Dependent claims 2-4, 6-7, and 10-15 merely incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claim 1 respectively, for example,
Claim 2 recite a data cleaning module, claim 4 recite a reputation perception score module software, claims 7, 10, and 13 recite a supervised machine learning regression modules, linear regression, multivariate linear regression, random forest, gradient boosting, and an ensemble of decision trees models. but these features only serve to further limit the abstract idea of independent claim 1, The additional elements of a “unsupervised and supervised machine learning”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “unsupervised and supervised machine learning” is insufficient to show a practical application of the recited abstract idea. Furthermore, merely using/applying in a computer environment such as merely using the computer as a tool to apply instructions of the abstract idea do nothing more than provide insignificant extra-solution activity since they amount to data gathering, analysis and outputting. Furthermore, they do not pertain to a technological problem being solved in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, and/or the limitations fail to achieve an actual improvement in computer functionality or improvement in specific technology other than using the computer as a tool to perform the abstract idea.
Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application.
Regarding Step 2B
Claims 1-4, 6-7, and 10-15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element(s) as described above with respect to Step 2A Prong 2, the additional element of claim 1 include software, a reputation server, a database, a user interface, an electronic device, a computer, a mobile device, a unsupervised and supervised machine learning nd reputation module engine software. Claim 2 recite a data cleaning module, claim 4 recite a reputation perception score module software, , claims 7, 10, and 13 recite a supervised machine learning regression modules, linear regression, multivariate linear regression, random forest, gradient boosting, and an ensemble of decision trees models. The displaying interface and storing data merely amount to a general purpose computer used to apply the abstract idea(s) (MPEP 2106.05(f)) and/or performs insignificant extra-solution activity, e.g. data retrieval and storage, as described above (MPEP 2106.05(g)) which are further merely well-understood, routine, and conventional activit(ies) as evidenced by MPEP 2106.06(05)(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser’s back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to determining reputation of an entity based on received data from a survey questions/ratings responses, applying weighting formulas to different data and converting the received rating from a raw scale to a zero to one-hundred scale, weighting, and aggregating the converted received ratings which is a mathematical formulas/equation and/or calculations, and determining a level of emotional between users and a reputation recommendation to increase revenue/reputation of the entity.
Claims 1-4, 6-7, and 10-15 is accordingly rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Allowable Subject Matter
Regarding the 35 USC 103 rejection, Examiner has fully considered applicant’s argument and amendments. See applicant remarks pages 16-19.
Closes prior art to the invention include RepTrak-2014 Slide 1 (khuatquanghung.com) (hereinafter RepTrak-2014), Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. " O'Reilly Media, Inc.", 2019. (hereinafter Geron), https://www.geopoll.com/blog/weighting-survey-data-raking-cell-weighting/ (hereinafter GeoPoll), Global CR RepTrak 100 2018, Reputation Institute.pdf (rankingthebrands.com) (hereinafter RepRak-2018), Bao, L., Xing, Z., Xia, X., Lo, D., & Li, S. (2017, May). Who will leave the company?: a large-scale industry study of developer turnover by mining monthly work report. In 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR) (pp. 170-181). IEEE. (hereinafter Bao) and Rantanen A, Salminen J, Ginter F, Jansen BJ. Classifying online corporate reputation with machine learning: a study in the banking domain. Internet Research. 2019 Nov 1. (hereinafter Rantanen). None of the prior art of record, taken individually or in combination, teach, inter allia, teaches the claimed invention as detailed in the independent claim 1, removing of the one or more redundant factors, reducing server processing to be performed by the reputation server in a step of determining based on unsupervised machine learning, wherein said unsupervised-machine-learning-based determining step comprises determining, by the reputation server, to which reputation driver within a set of reputation drivers each factor belongs … herein the step of removing skew from data, the step of removing one or more survey questions, and the step of reducing server processing are steps included in using a single final reputation perception score and an aggregate factor score per factor to provide a reputation of an entity,wherein the using a single final reputation perception score and an aggregate factor score per factor to provide a reputation of an entity, …. herein the single final reputation perception score and the aggregate factor score per factor are provided when: a sample size of population is determined that is statistically representative of a desired survey population and which provides at least a ninety-five percent confidence interval for reputation metrics of the entity for which reputation is measured, where those within the sample size are referred to herein as a unique group, wherein the unique group includes the previous group of one or more respondents, the reputation server determines a perception of those within the unique group about the entity, referred to herein as a reputation perception score, wherein reputation perception score determines the level of emotional evaluation of the entity by the unique group, which comprises: the reputation server receives survey ratings from the unique group about their perception of the entity by rated emotional evaluation survey questions, where each survey rating is provided by a party within the unique group, and wherein the emotional evaluation survey questions are categorized into at least one category, where each category focuses on a different emotional evaluation, the reputation server activates reputation module engine software at the reputation server for converting the received rating from a raw scale to a zero to one-hundred scale, the reputation server applies a standardization formula of cultural weighting in each country or region by weighting the received survey ratings to normalize cultural bias where cultural bias is a tendency of individuals in different geographies to rate companies higher or lower based on local cultural norms or atmospheres resulting in an artificial skew in rating distribution,7the reputation server applies other standardization weighting such as a data source weighting, and the reputation server aggregates the converted received ratings within each category, and averages the aggregated results within the different categories to provide the single final reputation perception score per entity that is stored in a storage device within the reputation server;the reputation server determines a level of rational connection of those within the unique group with the entity, referred to herein as a reputation factor score, which comprises:the reputation server receives survey ratings from focused rational connection survey questions, where each survey rating is provided by a party within the unique group, and wherein the rational connection survey questions are each referred to as a factor; … to normalize cultural bias; and the reputation server applies other standardization weighting such as a data source weighting”. The reason for withdrawn the art rejection under 35 USC 103 of Claims 1-4, 6-7, and 10-15 in the instant application is because the prior art of record fails to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Upon further searching the examiner could not identify any prior art to teach these limitations. The prior art on record, alone or in combination, neither anticipates, reasonably teaches, not renders obvious the Applicant’s claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HAMZEH OBAID/Primary Examiner, Art Unit 3624