DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the application filed 30 June 2023. Claims 1-12 are pending and have been examined.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) 1-12 as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The claim(s) 1-12 is/are directed to the abstract idea of conducting anonymous intelligent surveys. The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself. Claim(s) (1-12) is/are directed to an abstract idea without significantly more.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes (from the January 2019 §101 Examination Guidelines), claim(s) (1-6) is/are directed to a method, and claims(s) (7-12) is/are directed to a system and therefore the claims recites a series of steps and, therefore the claims are viewed as falling in statutory categories.
Step 2A Prong 1
The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process. Specifically, the independent claims 1 and 7 recite a mental process as drafted, the claim recites the limitation of creating ontologies, receiving information and based on analysis creating surveys that generate new questions for clarification which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a processor,” nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for a processor language, the claim encompasses the user manually creating a survey based on collected and analyzed information. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. It has been established by ongoing guidance that claims that contain a generic processor are still viewed as mental process when they contain limitations that can practically be performed in the human mind, however this is different for instance when the human mind is not equipped to perform the claim limitations (network monitoring, data encryption for communication, and rendering images). Therefore, these limitations are viewed a mental process. Additionally, with regard to the instant application the Examiner has reviewed the disclosure and determined that the underlying claimed invention is described as a concept that is performed in the human mind and/or with the aid of a pen and paper, and thus it is viewed that the applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment or 3) is merely using a computer as a tool to perform the concept, and therefore is considered to recite a mental process.
Note to the Applicant per the 2019 October Guidance: The 2019 PEG sets forth a test that distills the relevant case law to aid in examination, and does not attempt to articulate each and every decision. As further explained in the 2019 PEG, the Office has shifted its approach from the case-comparison approach in determining whether a claim recites an abstract idea and instead uses enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent. By grouping the abstract ideas, the 2019 PEG shifts examiners’ focus from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. In sum, the 2019 PEG synthesizes the holdings of various court decisions to facilitate examination.
Step 2A Prong 2
Specifically, the determined judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and additionally that data accepting, creating, receiving, assigning, selecting, providing, inviting, enabling, recording, applying, answering, and enabling steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity (including post solution activity).
The claim recites the additional element(s): that a processor is used to perform the associating, creating, generating, building, identifying, transforming, and determining steps. The processor in the steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (conducting anonymous intelligent surveys). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim recites the additional element(s): creating, accepting, composing, receiving, assigning, provisioning, selecting, providing, inviting, enabling, recording, applying, answering, updating, and enabling performs the associating, creating, generating, building, identifying, transforming, and determining steps. The creating, accepting, composing, receiving, assigning, provisioning, selecting, providing, inviting, enabling, recording, applying, answering, updating, and enabling steps are recited at a high level of generality (i.e., as a general means of creating, accepting, composing, receiving, assigning, provisioning, selecting, providing, inviting, enabling, recording, applying, answering, updating, and enabling for use in the associating, creating, generating, building, identifying, transforming, determining steps), and amounts to mere data management, which is a form of insignificant extra-solution activity. The processor that performs the associating, creating, generating, building, identifying, transforming, determining steps are also recited at a high level of generality, and merely automates the associating, creating, generating, building, identifying, transforming, determining steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the processor).
The Examiner has further determined that the claims as a whole does not integrate a judicial exception into a practical application in order to provide an improvement in the functioning of a computer or an improvement to other technology or technical field. It has been determined that based on the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. It has not been provided clearly in the disclosure that the alleged improvement would be apparent to one of ordinary skill in the art, but is instead in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art, and therefore does not improve the technology. Second, in the instance, where it is not clear that the specification sets forth an improvement in technology, the claim must reflect the disclosed improvement (the claims must include components or steps of the invention that provide the improvement described in the specification).
Note to the Applicant from the October 2019 Guidance: Generally, examiners are not expected to make a qualitative judgment on the merits of the asserted improvement. If the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology. Any such evidence submitted under 37 C.F.R. § 1.132 must establish what the specification would convey to one of ordinary skill in the art and cannot be used to supplement the specification. For example, in response to a rejection under 35 U.S.C. § 101, an applicant could submit a declaration under § 1.132 providing testimony on how one of ordinary skill in the art would interpret the disclosed invention as improving technology and the underlying factual basis for that conclusion.
For further clarification the Examiner points out that the claim(s) 1-12 recite(s) associating, creating, generating, building, identifying, transforming, and determining which are viewed as an abstract idea in the form of a mental process. This judicial exception is not integrated into a practical application because the use of a computer for obtaining, classifying, quantifying, generation, identifying, creating, accepting, composing, receiving, assigning, provisioning, selecting, providing, inviting, enabling, recording, applying, answering, updating, and enabling performs the associating, creating, generating, building, identifying, transforming, determining which is the abstract idea steps of valuing an idea (conducting anonymous intelligent surveys) in the manner of “apply it”.
Thus, the claims recites an abstract idea directed to a mental process (i.e. to conducting anonymous intelligent surveys). Using a computer to creating, accepting, composing, receiving, assigning, provisioning, selecting, providing, inviting, enabling, recording, applying, answering, updating, enabling performs the associating, creating, generating, building, identifying, transforming, and determining the data resulting from this kind of mental process merely implements the abstract idea in the manner of “apply it” and does not provide 'something more' to make the claimed invention patent eligible. The claimed limitations of a computing device is not constraining the abstract idea to a particular technological environment and do not provide significantly more.
The conducting anonymous intelligent surveys would clearly be to a mental activity that a company would go through in order to decide how to create surveys. The specification makes it clear that the claimed invention is directed to the mental activity data gathering and data analysis to determine how to generate intelligent surveys:
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’.
The dependent claims do not remedy these deficiencies.
Claims 5, 6, 11, and 12 recite limitations which further limit the claimed analysis of data.
Claims 2, 3, 8 and 9 recites limitations directed to claim language viewed insignificantly extra solution activity.
Using a computer to perform the data processing as claimed is merely implementing the abstract idea in the manner of “apply it” and does not provide significantly more. Additionally with respect to the Berkheimer the Examiner points out that the steps of the claim are viewed to be to nothing more than spell out what it means to apply it on a computer and cannot confer patent-eligibility as there are no additional limitations beyond applying an abstract idea, restricted to a computer. As the claims are merely implementing the abstract idea in the manner of “Apply It” the need for a Berkheimer analysis does not apply and is not required. With respect to the currently filed claims the implementing steps can be found in Maruo which discloses how the claims alone and in combination are viewed to be well understood, routine and conventional based on point 3 of the Berkheimer memo and subsequent evidence, complying with and providing evidence.
Claims 4 and 10 recites limitations directed to claim language viewed non-functional data labels.
Thus, the problem the claimed invention is directed to answering the question based on gathered and analyzed information about the conducting anonymous intelligent surveys. This is not a technical or technological problem but is rather in the realm of survey generation and therefore an abstract idea.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. This is the case because in order for the claims to be viewed as significantly more the claims must incorporate the integral use of a machine to achieve performance of a method, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more in order for a machine to add significantly more, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly. Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more. Additionally, another consideration when determining whether a claim recites significantly more is whether the claim effects a transformation or reduction of a particular article to a different state or thing. "[T]ransformation 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. All together the above analysis shows there is not improvement in computer functionality, or improvement to any other technology or technical field. The claim is ineligible.
Additionally, with respect to the Berkheimer as noted above the same analysis applies to the 2B where the claims are viewed as applying it and as such no further analysis is required. However, with respect to the current claims creating, accepting, composing, receiving, assigning, provisioning, selecting, providing, inviting, enabling, recording, applying, answering, updating, enabling that are viewed as extra solution or post solution activity the Examiner notes that the claims are viewed as well-understood, routine, and conventional because a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s). An appropriate publication such as the currently cited prior art Maruo provides those extra solution activities and is viewed as a form of publication which also includes a book, manual, review article, or other source that describes the state of the art and discusses what is well-known and in common use in the relevant industry. The claim is ineligible.
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. Specifically, the dependent claims do not remedy these deficiencies of the independent claims.
With respect to the legal concept of prima facie case being a procedural tool of patent examination, which allocates the burdens going forward between the examiner and the applicant. MPEP § 2106.07 discusses the requirements of a prima facie case of ineligibility. In particular, the initial burden was on the Examiner and believed to be properly provided as to explain why the claim(s) are ineligible for patenting because of the above provided rejection which clearly and specifically points out in accordance with properly providing the requirement satisfying the initial burden of proof based on the Guidance from the United States Patent and Trademark Office and the burden now shifts to the applicant.
Therefore, based on the above analysis as conducted based on the Guidance from the United States Patent and Trademark Office the claims are viewed as a court recognized abstract idea, are viewed as a judicial exception, does not integrate the claims into a practical application, and does not provide an inventive concept, therefore the claims are ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1, 4-7, and 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maruo et al. (JP 3844976 B2) (hereafter Maruo) in view of Briancon et al. (U.S. Patent Publication 2020/0126126 A1) (hereafter Briancon) in further view of Ciravegna et al. (GB 2449501 A) (hereafter Ciavegna).
Referring to Claim 1, Mauro teaches a method of conducting anonymous intelligent surveys, said method comprising:
creating an anonymized people registry corresponding to a set of users, wherein each user from the set of users is unique (see; par. [0018] of Maruo teaches anonymizing a group of users).
receiving non-personally identifiable demographic information corresponding to each user from the set of users (see; par. [0004] of Maruo teaches receiving non-personal information including demographic style information).
receiving sets of preferences corresponding to each user from the set of users, wherein the sets of preferences are non-personally identifiable, wherein each set of preferences is assigned one or more domains (see; par. [0061] of Maruo teaches a user’s preferences are taken into account for the questions and use non-personal identifiable information).
provisioning an organization, from the set of organizations, to generate a questionnaire by (see; par. [0061] of Mauro teaches based on preferences of users of the organization create a questionnaire),
providing an interface for the organization to create a questionnaire by selecting one or more domains, from the set of domains, associated with the questionnaire (see; par. [0061] of Mauro teaches providing an interface for the users of an organization to handle a questionnaire),
generating sets of ontology-based questions, wherein the questions are generated based on one or more inputs, received from the organization (see; par. [0032] of Mauro teaches relationships between attributes and users (i.e. ontology) used to, par. [0061] generate a question),
building one or more surveys, corresponding to the organization (see; par. [0032] of Mauro teaches relationships between attributes and users (i.e. ontology) used to, par. [0061] generate a question),
wherein each survey comprises at least one set of ontology-based questions from the sets of ontology-based questions (see; par. [0061] of Mauro generate a question with respect to, par. [0032] the relationship between attributes and users (i.e. ontology)),
providing an interface for the organization to configure a target set of non-personally identifiable parameters; selecting candidate respondents to a survey from the one or more surveys by (see; par. [0026] of Mauro teaches collecting user data and convert it into non-personally identification information, par. [0030] utilizing a user interface for identifying users),
identifying a set of target users, from the set of users, based on comparison of the target set of non-personally identifiable parameters with the demographic information and the sets of preferences associated with each of the set of users (see; par. [0026] of Mauro teaches predicting privacy while collecting user data),
inviting the set of target users to take the survey (see; par. [0061] of Mauro teaches deciding how to send a questionnaire to a user),
enabling the set of target users to access the survey; capturing responses from the set of target users by (see; par. [0030] of Mauro teaches allowing access based on personal information, par. [0061] this can then provide a question),
recording responses received from at least one target user, from the set of target users, corresponding to at least one ontology-based question from the at least one set of ontology-based questions associated with the survey (see; par. [0032] of Mauro teaches an ontology-based questions, par. [0026] that has recorded from received data from specific users (i.e. targeted users)).
Mauro does not explicitly disclose the following limitations, however,
Briancon teaches transforming the responses into ontology-based RDF triples (see; par. [0058] and par. [0130] of Briancon teaches utilizing information that has been collected, par. [0037] where this is in the form of a RDF and generates questions), and
determining a confidence level corresponding to the one or more facts based on a set of predefined parameters (see; par. [0042] of Briancon teaches determine if a confidence level meets a threshold), and
when the confidence level is above a predefined threshold level (see; par. [0042] of Briancon teaches determine if a confidence level meets a threshold), and
applying the one or more facts to the remaining questions in the survey (see; par. [0107] of Briancon teaches dynamically updated questions based on attributes), and
automatically answer the remaining questions when applicable, or update the remaining questions (see; par. [0107] of Briancon teaches dynamically (i.e. automatically) update questions based on attributes), and
when the confidence level is below the predefined threshold level (see; par. [0059] of par. [0042] of Briancon teaches par. [0042] of Briancon teaches determining if a confidence level meets a threshold), and
enabling the user to preview and to approve or reject the automatically generated answer, or generating a new question for seeking further clarification (see; par. [0059] of Briancon teaches reviewing of questions, par. [0107] dynamically update new questions).
The Examiner notes that Mauro teaches similar to the instant application teaches marketing support system. Specifically, Mauro discloses the effective and efficient marketing information allowing for business operations it is therefore viewed as analogous art in the same field of endeavor. Additionally, Briancon teaches customer journey management engine and as it is comparable in certain respects to Mauro which searching method and system for managing data regarding a user as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mauro discloses the providing search results using keyword search on documents using semantic search on the document. However, Mauro fails to disclose transforming the responses into ontology-based RDF triples, determining a confidence level corresponding to the one or more facts based on a set of predefined parameters, when the confidence level is above a predefined threshold level, applying the one or more facts to the remaining questions in the survey, automatically answer the remaining questions when applicable, or update the remaining questions, when the confidence level is below the predefined threshold level, and enabling the user to preview and to approve or reject the automatically generated answer, or generating a new question for seeking further clarification.
Briancon discloses transforming the responses into ontology-based RDF triples, determining a confidence level corresponding to the one or more facts based on a set of predefined parameters, when the confidence level is above a predefined threshold level, applying the one or more facts to the remaining questions in the survey, automatically answer the remaining questions when applicable, or update the remaining questions, when the confidence level is below the predefined threshold level, and enabling the user to preview and to approve or reject the automatically generated answer, or generating a new question for seeking further clarification.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mauro transforming the responses into ontology-based RDF triples, determining a confidence level corresponding to the one or more facts based on a set of predefined parameters, when the confidence level is above a predefined threshold level, applying the one or more facts to the remaining questions in the survey, automatically answer the remaining questions when applicable, or update the remaining questions, when the confidence level is below the predefined threshold level, and enabling the user to preview and to approve or reject the automatically generated answer, or generating a new question for seeking further clarification. as taught by Briancon since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Mauro, and Briancon teach the collecting and analysis of data in order to manage data necessary for further utilization and they do not contradict or diminish the other alone or when combined.
Mauro in view of Briancon does not explicitly disclose the following limitations, however,
Ciravegna teaches creating ontologies by accepting a set of inputs corresponding to each domain from a set of domains, wherein the set of inputs comprise a set of concepts and a set of categories corresponding to each domain, wherein the set of inputs further comprise a set of properties and correlations between the set of properties associated with each domain (see; col. 7, lines (22-28) of Ciravegna teaches creating ontologies, col. 10, lines (9-13) which accepts inputs from the users, and Abstract where the domains are searched), and
associating the set of domains with a set of related ontologies based on the set of inputs (see; pg. 38, Sec. 2 of Ciravegna teaches associating documents with a set of ontology which are then associated with domains), and
composing Resource Description Framework (RDF) triples comprising data entities in subject-predicate-object structures based on the set of related ontologies, wherein the RDF triples constitute a knowledge graph; registering a set of users by (see; col. 9, lines (19-24) of Ciravegna teaches a RDF triple that includes data that is structured to the ontologies, where the RDF can utilize a graph for users), and
registering a set of organizations by creating an organization registry corresponding to a set of organizations (see; pg. 15, col. 1, par. 5 of Ciravegna teaches an organizational model that provides access to domains of different communities (i.e. organization), and the domain information is accessed (i.e. registered) taking communities into account (i.e. organization)), and
receiving domain information corresponding to each organization from the set of organizations (see; pg. 15, col. 1, par. 5 of Ciravegna teaches an organizational model that provides access to domains of different communities (i.e. organization)), and
assigning at least one target domain to the organization in the organization registry based on the domain information (see; pg. 27, col. 1, sec. 4.0 of Ciravegna teaches assigning instances to different concepts to the ontology), and
corresponding to the RDF triples (see; pg. 26, col. 2, sec. 3, par. 2 of Ciravegna teaches RDF triples representing the semantic knowledgebase for easier use of future data use), and
inferring one or more facts based on the ontology-based RDF triples (see; pg. 26, col. 1, sec. 2.2, par. 4 of Ciravegna teaches ontology used to annotate and search using synonyms (i.e. inferring similar concepts)).
The Examiner notes that Mauro teaches similar to the instant application teaches marketing support system. Specifically, Mauro discloses the effective and efficient marketing information allowing for business operations it is therefore viewed as analogous art in the same field of endeavor. Additionally, Briancon teaches customer journey management engine and as it is comparable in certain respects to Mauro which searching method and system for managing data regarding a user as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ciravegna teaches customer journey management engine and as it is comparable in certain respects to Mauro and Briancon which searching method and system for managing data regarding a user as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mauro and Briancon discloses the providing search results using keyword search on documents using semantic search on the document. However, Mauro and Brian con fails to disclose creating ontologies by accepting a set of inputs corresponding to each domain from a set of domains, wherein the set of inputs comprise a set of concepts and a set of categories corresponding to each domain, wherein the set of inputs further comprise a set of properties and correlations between the set of properties associated with each domain, associating the set of domains with a set of related ontologies based on the set of inputs, composing Resource Description Framework (RDF) triples comprising data entities in subject-predicate-object structures based on the set of related ontologies, wherein the RDF triples constitute a knowledge graph; registering a set of users by, registering a set of organizations by creating an organization registry corresponding to a set of organizations, receiving domain information corresponding to each organization from the set of organizations, assigning at least one target domain to the organization in the organization registry based on the domain information, corresponding to the RDF triples, and inferring one or more facts based on the ontology-based RDF triples.
Ciravegna discloses creating ontologies by accepting a set of inputs corresponding to each domain from a set of domains, wherein the set of inputs comprise a set of concepts and a set of categories corresponding to each domain, wherein the set of inputs further comprise a set of properties and correlations between the set of properties associated with each domain, associating the set of domains with a set of related ontologies based on the set of inputs, composing Resource Description Framework (RDF) triples comprising data entities in subject-predicate-object structures based on the set of related ontologies, wherein the RDF triples constitute a knowledge graph; registering a set of users by, registering a set of organizations by creating an organization registry corresponding to a set of organizations, receiving domain information corresponding to each organization from the set of organizations, assigning at least one target domain to the organization in the organization registry based on the domain information, corresponding to the RDF triples, and inferring one or more facts based on the ontology-based RDF triples.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mauro and Briancon creating ontologies by accepting a set of inputs corresponding to each domain from a set of domains, wherein the set of inputs comprise a set of concepts and a set of categories corresponding to each domain, wherein the set of inputs further comprise a set of properties and correlations between the set of properties associated with each domain, associating the set of domains with a set of related ontologies based on the set of inputs, composing Resource Description Framework (RDF) triples comprising data entities in subject-predicate-object structures based on the set of related ontologies, wherein the RDF triples constitute a knowledge graph; registering a set of users by, registering a set of organizations by creating an organization registry corresponding to a set of organizations, receiving domain information corresponding to each organization from the set of organizations, assigning at least one target domain to the organization in the organization registry based on the domain information, corresponding to the RDF triples, and inferring one or more facts based on the ontology-based RDF triples as taught by Ciravegna since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Mauro, Briancon, and Ciravegna teach the collecting and analysis of data in order to manage data necessary for further utilization and they do not contradict or diminish the other alone or when combined.
Referring to Claim 4, see discussion of claim 1 above, while Mauro in view of Briancon in further of Ciravegna teaches the method above, Mauro further discloses a method having the limitations of, however,
the sets of preferences are stored on the user device (see; par. [0031] of Mauro teaches user preferences stored)
Referring to Claim 5, see discussion of claim 1 above, while Mauro in view of Briancon in further of Ciravegna teaches the method above, Mauro does not explicitly discloses a method having the limitations of, however,
Briancon teaches steps for optimizing at least one survey from the one or more surveys based on the responses received from the at least one target user corresponding to the questionnaire associated with a previously conducted survey by (see; par. [0023] of Briancon teaches the targeting of people, par. [0021] based on surveys, par. [0095] that is utilized for follow up (i.e. previously conducted), and par. [0079] then presenting future questions to maximize the customer journey (i.e. optimization)), and
identifying a set of related questions between the previously conducted survey and the at least one survey from the one or more surveys (see; par. [0057]-[0058] of Briancon teaches the identifying interrelatedness between events affecting questions), and
optimizing the at least one survey from the one or more surveys by removing the set of related questions, from the at least one survey (see; par. [0006] of Briancon teaches determining the next best question, par. [0079] and presenting a future question to maximize the customer journey (i.e. optimization)), and
identify the relationship between any two or more responses from one or more previous surveys (see; par. [0007] of Briancon teaches identifying based on responses from a sequence of questions that are similar between members), and
generating inferences from the relationship between the two or more responses (see; par. [0079] of Briancon teaches making inferences based on question responses), and
determining a confidence level corresponding to the one or more inferences based on a set of predefined parameters (see; par. [0042] of Briancon teaches determining a confidence level regarding questions, par. [0079] and making inferences regarding events), and
when the confidence level is above a predefined threshold level, applying the one or more inferences to remaining questions in the survey, to automatically answer the remaining questions when applicable, or update the remaining questions (see; par. [0042] of Briancon teaches determining a confidence level regarding questions, par. [0077] then determine the next question to ask (i.e. update the remaining questions), par. [0107] dynamically updating the questions), and
when the confidence level is below the predefined threshold level, enabling the user to preview and to approve or reject the automatically generated answer or generating a new question for seeking further clarification (see; par. [0031] of Briancon teaches that based on the use of models (i.e. measured value… level is below) dynamically select future questions to avoid uncertainness (i.e. clarification)).
The Examiner notes that Mauro teaches similar to the instant application teaches marketing support system. Specifically, Mauro discloses the effective and efficient marketing information allowing for business operations it is therefore viewed as analogous art in the same field of endeavor. Additionally, Briancon teaches customer journey management engine and as it is comparable in certain respects to Mauro which searching method and system for managing data regarding a user as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mauro discloses the providing search results using keyword search on documents using semantic search on the document. However, Mauro fails to disclose steps for optimizing at least one survey from the one or more surveys based on the responses received from the at least one target user corresponding to the questionnaire associated with a previously conducted survey by, identifying a set of related questions between the previously conducted survey and the at least one survey from the one or more surveys, optimizing the at least one survey from the one or more surveys by removing the set of related questions, from the at least one survey, identify the relationship between any two or more responses from one or more previous surveys, generating inferences from the relationship between the two or more responses, determining a confidence level corresponding to the one or more inferences based on a set of predefined parameters, when the confidence level is above a predefined threshold level, applying the one or more inferences to remaining questions in the survey, to automatically answer the remaining questions when applicable, or update the remaining questions, and when the confidence level is below the predefined threshold level, enabling the user to preview and to approve or reject the automatically generated answer or generating a new question for seeking further clarification.
Briancon discloses steps for optimizing at least one survey from the one or more surveys based on the responses received from the at least one target user corresponding to the questionnaire associated with a previously conducted survey by, identifying a set of related questions between the previously conducted survey and the at least one survey from the one or more surveys, optimizing the at least one survey from the one or more surveys by removing the set of related questions, from the at least one survey, identify the relationship between any two or more responses from one or more previous surveys, generating inferences from the relationship between the two or more responses, determining a confidence level corresponding to the one or more inferences based on a set of predefined parameters, when the confidence level is above a predefined threshold level, applying the one or more inferences to remaining questions in the survey, to automatically answer the remaining questions when applicable, or update the remaining questions, and when the confidence level is below the predefined threshold level, enabling the user to preview and to approve or reject the automatically generated answer or generating a new question for seeking further clarification.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mauro steps for optimizing at least one survey from the one or more surveys based on the responses received from the at least one target user corresponding to the questionnaire associated with a previously conducted survey by, identifying a set of related questions between the previously conducted survey and the at least one survey from the one or more surveys, optimizing the at least one survey from the one or more surveys by removing the set of related questions, from the at least one survey, identify the relationship between any two or more responses from one or more previous surveys, generating inferences from the relationship between the two or more responses, determining a confidence level corresponding to the one or more inferences based on a set of predefined parameters, when the confidence level is above a predefined threshold level, applying the one or more inferences to remaining questions in the survey, to automatically answer the remaining questions when applicable, or update the remaining questions, and when the confidence level is below the predefined threshold level, enabling the user to preview and to approve or reject the automatically generated answer or generating a new question for seeking further clarification as taught by Briancon since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Mauro, and Briancon teach the collecting and analysis of data in order to manage data necessary for further utilization and they do not contradict or diminish the other alone or when combined.
Referring to Claim 6, see discussion of claim 1 above, while Mauro in view of Briancon in further of Ciravegna teaches the method above, Mauro in view of Briancon does not explicitly discloses a method having the limitations of, however,
Ciravegna teaches the responses from the target user are processed to compute findings, and the findings are injected back into the knowledge graph, wherein the knowledge graph is updated with the latest findings, wherein a feedback loop generated by tracking and recording changes in the knowledge graph over time results in a self-learning RDF triplestore (see; pg. 8, lines (19-24) of Ciravegna teaches an RDF triplestore model used to store metadata regarding the storage and analysis of data, pg. 36 sec 6 conclusions where the data is automatically stored in a graph form (i.e. updated graph form)).
The Examiner notes that Mauro teaches similar to the instant application teaches marketing support system. Specifically, Mauro discloses the effective and efficient marketing information allowing for business operations it is therefore viewed as analogous art in the same field of endeavor. Additionally, Briancon teaches customer journey management engine and as it is comparable in certain respects to Mauro which searching method and system for managing data regarding a user as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ciravegna teaches customer journey management engine and as it is comparable in certain respects to Mauro and Briancon which searching method and system for managing data regarding a user as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mauro and Briancon discloses the providing search results using keyword search on documents using semantic search on the document. However, Mauro and Brian con fails to disclose creating ontologies by accepting a set of inputs corresponding to each domain from a set of domains, wherein the set of inputs comprise a set of concepts and a set of categories corresponding to each domain, wherein the set of inputs further comprise a set of properties and correlations between the set of properties associated with each domain, associating the set of domains with a set of related ontologies based on the set of inputs, composing Resource Description Framework (RDF) triples comprising data entities in subject-predicate-object structures based on the set of related ontologies, wherein the RDF triples constitute a knowledge graph; registering a set of users by, registering a set of organizations by creating an organization registry corresponding to a set of organizations, receiving domain information corresponding to each organization from the set of organizations, assigning at least one target domain to the organization in the organization registry based on the domain information, corresponding to the RDF triples, and inferring one or more facts based on the ontology-based RDF triples.
Ciravegna discloses creating ontologies by accepting a set of inputs corresponding to each domain from a set of domains, wherein the set of inputs comprise a set of concepts and a set of categories corresponding to each domain, wherein the set of inputs further comprise a set of properties and correlations between the set of properties associated with each domain, associating the set of domains with a set of related ontologies based on the set of inputs, composing Resource Description Framework (RDF) triples comprising data entities in subject-predicate-object structures based on the set of related ontologies, wherein the RDF triples constitute a knowledge graph; registering a set of users by, registering a set of organizations by creating an organization registry corresponding to a set of organizations, receiving domain information corresponding to each organization from the set of organizations, assigning at least one target domain to the organization in the organization registry based on the domain information, corresponding to the RDF triples, and inferring one or more facts based on the ontology-based RDF triples.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mauro and Briancon creating ontologies by accepting a set of inputs corresponding to each domain from a set of domains, wherein the set of inputs comprise a set of concepts and a set of categories corresponding to each domain, wherein the set of inputs further comprise a set of properties and correlations between the set of properties associated with each domain, associating the set of domains with a set of related ontologies based on the set of inputs, composing Resource Description Framework (RDF) triples comprising data entities in subject-predicate-object structures based on the set of related ontologies, wherein the RDF triples constitute a knowledge graph; registering a set of users by, registering a set of organizations by creating an organization registry corresponding to a set of organizations, receiving domain information corresponding to each organization from the set of organizations, assigning at least one target domain to the organization in the organization registry based on the domain information, corresponding to the RDF triples, and inferring one or more facts based on the ontology-based RDF triples as taught by Ciravegna since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Mauro, Briancon, and Ciravegna teach the collecting and analysis of data in order to manage data necessary for further utilization and they do not contradict or diminish the other alone or when combined.
Referring to Claim 7, Mauro in view of Briancon in further view of Ciravegna teaches a system for conducting anonymous intelligent surveys. Claim 7 recites the same or similar limitations as those addressed above in claim 1, Claim 7 is therefore rejected for the same reasons as set forth above in claim 1, except for the following noted exceptions:
Briancon teaches a memory; and a processor coupled to the memory, wherein the processor is configured to execute programmed instructions stored in the memory for (see; par. [0009] of Briancon teaches a process and memory that utilizes the instructions from memory).
The Examiner notes that Mauro teaches similar to the instant application teaches marketing support system. Specifically, Mauro discloses the effective and efficient marketing informatio