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 Final Office Action is responsive to Applicant's amendment filed on 2 June 2025. Applicant’s amendment on 2 June 2025 amended Claims 1, 8, and 15. Currently Claims 1-5, 8-12, 15-19 are pending and have been examined. Claims 6, 7, 13, 14, 20 and 21 were previously canceled. The Examiner notes that the 101 rejection for claims have been maintained.
Response to Arguments
Applicant's arguments filed 2 June 2025 have been fully considered but they are not persuasive.
The Applicant argues on pages 9-13 that “claims 1, 8, and 15 recite a practical application of the alleged abstract idea… the claim includes the components or steps of the invention that provide the improvement described in the specification… one of ordinary skill in the art would recognize the claimed invention as providing an improvement… and of the subject application as filed describe the technical problem solved by and the technical solution realized by the claimed invention… the specification describes the invention such that the improvement… is apparent to one of ordinary skill in the art”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner has carefully considered this amendment in light of the Applicant’s arguments and current USPTO subject matter eligibility guidance. However, the amendment is not viewed to overcome the rejection. It is viewed that the new limitation continues to fall within the abstract idea groupings of data analysis and mental process as described in MPEP 2106.04(a)(2). Specifically, with respect to collection of data, the sept of inferred information about the one or more users amounts to collecting and characterizing information, which courts have consistently found to be an abstract idea. With respect to analysis of data, the step of generating a representative set personas is viewed as a mental process that could be performed in the human mind (i.e. grouping people into categories). Such inference and categorization of expressly identified as a mental process under the USPTO’s abstract idea groupings. With respect to the displaying of results, the step of deploying an actor for each persona in a virtual cyber space amounts to displaying results of the data analysis in a simulation environment. This is a mere visualization of data, which routs have found ineligible when performed on a generic computer. Taken together, these steps reflect the idea of observing social media users creating categories of personas, and showing them in a simulation. While the amendment adds detail, it is still framed as data collection, data analysis, and output of results – operations that fall squarely within abstract ideas when implemented on a general purpose computer.
The amended claim does not solve a technical problem in a computer functionality or another field of technology. Instead, it applies conventional computing tools to perform social analysis and display the results in a simulation. The “virtual cyber space” and “actors” are recited at a high level of generality without technical implementation details. It is not viewed that there is no improvement to computer operation, such as a new data structure, memory handling technique, or network protocol. Rather, the computer is merely being used as a tool to carry out the abstract idea. This distinguishes the present claims from existing court cases that show eligible because the claims recited a specific data structure that improved database operation and places them closer to cases where the courts held that claims reciting the collection, analysis, and display of information, even in the context of computer networks, were ineligible because they did not recite a technical improvement. Although the amendment adds narrative detail, it does not change the fundamental character of the claim. The claim remain directed to the abstract idea of analyzing user information, categorizing it into personas, and displaying the results in a simulation, which is not a technical solution to a technical problem.
Additionally, it is noted that it is not clear that the amendment provides an improvement to the functioning of a computer itself or to another technology or technical field. The recited steps of generating personas, deploying actors, and simulating interactions are described at a high level of generality and rely upon generic computing components. No new computer architecture, data structure or processing technique is claimed. The claims do not recite use of a particular machine integral to the performance of the abstract idea. The virtual cyberspace and actors are functional descriptions of a computing environment and do not constitute a specific machine. The recitation of a generic computer is insufficient. The claims amount to instructions to apply the abstract idea using a computer to deploying personas in a simulation. Such instructions are not viewed to amount to the integration into a practical application. The claim as a whole is not viewed to integrate the recited judicial exception into a practical application. The only additional elements are generic computer implementation and output of results, which are insufficient to impose a meaningful limit on the abstract idea. At best the amendment is viewed as merely an improvement to the abstract idea and not an improvement in the technology or technical field. The rejection is therefore maintained.
The Applicant argues on pages 13-14 that “claims 1, 8, and 15 include the components or steps of the invention that provide the improvement described in the specification… identifying one or more keywords… using a word embedding machine learning model”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that as indicated in the previous Office Action 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, which in this case it is not clear based upon a review of the disclosure that the specification sets forth an improvement in technology, the claim must not reflect the disclosed improvement (the claims must include components or steps of the invention that provide the improvement described in the specification). However, while the disclosure goes into detail on the improvement of the abstract idea in the form of analysis it is not clear that the improvement improves the technology or technical field because as noted above the claims do not recite use of a particular machine integral to the performance of the abstract idea. The virtual cyberspace and actors are functional descriptions of a computing environment and do not constitute a specific machine. The claim is therefore maintained.
The Applicant argues on pages 14-15 that “the personas are automatically generated representative of users that are deployed as autonomous actors in a virtual cyber space simulation of social media where each actor are engages with users within the virtual cyber space simulation according to the inferred information by using the keywords of entire social posts to generate personality information for keywords present in a personality to word data structure and to generate personality information for keywords ”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that the Applicant argues that the amended claims are directed to patent-eligible subject matter because the automatically generated personas are deployed as autonomous actors in a virtual cyberspace simulation, mimicking real-world people in order to achieve the claimed desired goal. However, this argument is not persuasive, because the claim remains directed to the abstract idea of collecting data in the form of inferring information from social media posts, analyzing the data by generating representative personas, and presenting the results by deploying the personas as actors in a simulation. The additional recitation that the actors mimic real-world people or simulate real-world interactions does not integrate the exception into a practical application, but instead represents a restatement of the desired results of the abstract idea. The claim does not recite a specific technological improvement to computer functionality, simulation technology, or any other technical field. Rather, it is viewed to merely invoke a generic computing environment to implement the abstract idea. Courts have repeatedly held that claims reciting data analysis and presentation of results, even if framed as mimicking or modeling real world phenomena, remain ineligible absent a technological improvement. Therefore, the amendment does not overcome the existing rejection and the rejection is therefore maintained.
The remaining Applicant's arguments filed 2 June 2025 have been fully considered but they are moot in view of new grounds of rejection as necessitated by amendment.
Examiner’s Note
It is indicated that claims 1, 8, and 15 have all been amended, however there are only amendments made to claim 1. The Examiner for the purpose of compact prosecution is viewing that the amendment to claim 1 is also being amended in claims 8 and 15 and have been examined accordingly.
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.
Additionally, claims 1-5, 8-12, 15-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) 1-5, 8-12, 15-19 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) 1-5, 8-12, 15-19 is/are directed to the abstract idea of the collecting, comparing, and identifying information about one or more users based on social media posts. 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) (1-5, 8-12, 15-19) 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) (1-5) is/are directed to a method, claim(s) (8-12) is/ are directed to a computer readable medium, and claims(s) (15-19) 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) 1-5, 8-12, 15-19 recite(s) a mental process. Specifically, the independent claims 1, 8, and 15 recite a mental process: as drafted, the claim recites the limitation of comparing social media posts, identifying inferred information about one or more users, determining similarity scores, and generating social media post similarity scores 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 one or more processors, nothing in the claim precludes the determining and generating steps from practically being performed in the human mind. For example, but for the one or more processor language, the claim encompasses the user manually sorting through social media posts, which could have been previously collected, and printed so a user can sort through them. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. This limitation is a mental process. While the Guidance provides that claims do not recite a mental process when they contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations (GPS position calculation, network monitoring, data encryption for communication, rendering images). However 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 the data collecting step required to use the comparing, identifying, and determining, generating do not add a meaningful limitation to the method as they are insignificant extra-solution activity (including post solution activity).
The claims recites the additional element(s): that a processor is used to perform both the comparing, identifying, determining and generating steps. The processor in both steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (comparing, identifying, determining, and generating information about one or more users based on social media posts). 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): collecting a plurality of social media posts is used to perform the comparing, identifying, determining, and generating steps. The collecting step is recited at a high level of generality (i.e., as a general means of gathering social media posts for use in the comparing, identifying, determining, and generating steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The processor that is used to perform the comparing, identifying, determining, and generating steps is also recited at a high level of generality, and merely automates the comparing, identifying, determining, and generating 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, which in this case it is not clear that the specification sets forth an improvement in technology, the claim must not 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) 1-21 recite(s) collecting a plurality of social media posts, comparing each social media post to data structures, and identifying inferred information about the one or more users, determining similarity scores, and generating a social media post similarity score based on social media 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 collecting, comparing, identifying, determining, and generating which is the abstract idea steps of valuing an idea (collecting, comparing, identifying, determining, and generating information about one or more users based on social media posts) in the manner of “apply it”.
Thus, the claims recites an abstract idea directed to a mental process (i.e. to perform the collecting, comparing, identifying, determining, and generating information about one or more users based on social media posts). Using a computer to collecting, comparing, identifying, determining, and generating the data resulting from this 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 comparing, identifying, determining, and generating would clearly be to a mental activity that a company would go through in order to decide how to analyze social media posts in order to infer user information about users. The specification makes it clear that the claimed invention is directed to the mental activity of data gathering and data analysis to determine how to infer social data about users using social media posts:
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, 12, and 19 recite limitations which further limit the claimed analysis of data.
No Claims are viewed to recite limitations directed to claim language viewed insignificantly extra solution activity.
However, for clarification 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 Garcia 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 2, 3, 4, 10, 11, 17, and 18 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 collecting, comparing, identifying, determining, and generating inferred information about users based on social media posts. This is not a technical or technological problem but is rather in the realm of social media analysis and therefore an abstract idea.
Step 2B
The claim(s) 1-21 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.
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 claims 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 could include 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 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 October 2019 Guidance and the burden now shifts to the applicant.
Therefore, based on the above analysis as conducted based on the January 2019 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) 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 1-5, 8-12, and 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over GARCIA et al. (U.S. Patent Publication 2019/0379624 A1) (hereafter Garcia) in further view of Trillo Vargas et al. (U.S. Patent Publication 2021/0073255 A1) (hereafter Trillo).
Referring to Claim 1, Garcia teaches a computer-implemented method comprising:
collecting, by a computing device, a plurality of social media posts (see; par. [0050] of Garcia teaches collecting social media posts).
comparing each social media post to one or more data structures to determine a similarity score associated with one or more entries in the one or more data structures (see; par. [0094] and par. [0114] of Garcia teaches determining similarity between images from social media (posts) (i.e. comparing) based on the similarity of the posts or similarity score).
Garcia does not explicitly disclose the following limitations, however,
Oehrle teaches wherein comparing each social media post to the one or more data structures to determine the similarity score associated with the one or more entries in the one or more data structures includes (see; par. [0206]-[0210] of Oehrle teaches the determining of the similarity of words from multiple sources including from social media), and
The Examiner notes that Garcia teaches similar to the instant application teaches social media analysis and determination about content information. Specifically, Garcia discloses the social media analysis in order to identify second social media post as related to the first post utilizing scoring to understand relationship it is therefore viewed as analogous art in the same field of endeavor. Additionally, Oehrle teaches analyzing and predicting the future behavior of people and organizations utilizing social media meta data and as it is comparable in certain respects to Garcia which provides social media analysis and determination about content information 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.
Garcia discloses the social media analysis in order to identify second social media post as related to the first post utilizing scoring to understand relationship. However, Garcia fails to disclose comparing each social media post to the one or more data structures to determine the similarity score associated with the one or more entries in the one or more data structures includes and generating a representative set of personas from the inferred information about the one or more users of the plurality of social media posts.
Oehrle discloses comparing each social media post to the one or more data structures to determine the similarity score associated with the one or more entries in the one or more data structures includes and generating a representative set of personas from the inferred information about the one or more users of the plurality of social media posts.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Garcia comparing each social media post to the one or more data structures to determine the similarity score associated with the one or more entries in the one or more data structures includes and generating a representative set of personas from the inferred information about the one or more users of the plurality of social media posts as taught by Oehrle 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, Garcia and Oehrle teach the collecting and analysis of data associated with social media in order to determine connections with other data and they do not contradict or diminish the other alone or when combined.
Garcia in view of Oehrle does not explicitly disclose the following limitations, however,
Trillo teaches identifying one or more keywords in a respective social media post that are in Big-5 personality-to-word structure (see; par. [0044] of Trillo teaches identifying keywords in social media posts, including par. [0040] Big-5 personality to word structures), and
identifying one or more keywords from the plurality of keywords in a respective social media post that are not in the Big-5 personality-to-word structure (see; par. [0044] of Trillo teaches identifying keywords in social media posts, including par. [0040] Big-5 personality to word structures, par. [0047] as well as words that do not match Big-5 personality to word structures), and
identifying inferred information about one or more users of the plurality of social media posts based upon, at least in part, the social media post similarity score of each social media post (see; par. [0039]-[0040] determining inferences based similarity scores between word structures based on a percentile score, par. [0006] specifically related to the social media posts).
The Examiner notes that Garcia teaches similar to the instant application teaches social media analysis and determination about content information. Specifically, Garcia discloses the social media analysis in order to identify second social media post as related to the first post utilizing scoring to understand relationship it is therefore viewed as analogous art in the same field of endeavor. Additionally, Oehrle teaches analyzing and predicting the future behavior of people and organizations utilizing social media meta data and as it is comparable in certain respects to Garcia which provides social media analysis and determination about content information 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, Trillo teaches analyzing the tone of textual messages including social media and as it is comparable in certain respects to Garcia and Oehrle which provides social media analysis and determination about content information 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.
Garcia and Oehrle discloses the social media analysis in order to identify second social media post as related to the first post utilizing scoring to understand relationship. However, Garcia and Oehrle fails to disclose identifying one or more keywords in a respective social media post that are in Big-5 personality-to-word structure, identifying one or more keywords from the plurality of keywords in a respective social media post that are not in the Big-5 personality-to-word structure, and identifying inferred information about one or more users of the plurality of social media posts based upon, at least in part, the social media post similarity score of each social media post.
Trillo discloses identifying one or more keywords in a respective social media post that are in Big-5 personality-to-word structure, identifying one or more keywords from the plurality of keywords in a respective social media post that are not in the Big-5 personality-to-word structure, and identifying inferred information about one or more users of the plurality of social media posts based upon, at least in part, the social media post similarity score of each social media post.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Garcia and Oehrle identifying one or more keywords in a respective social media post that are in Big-5 personality-to-word structure, identifying one or more keywords from the plurality of keywords in a respective social media post that are not in the Big-5 personality-to-word structure, and identifying inferred information about one or more users of the plurality of social media posts based upon, at least in part, the social media post similarity score of each social media post as taught by Trillo 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, Garcia, Oehrle, and Trillo teach the collecting and analysis of data associated with social media in order to determine connections with other data and they do not contradict or diminish the other alone or when combined.
Garcia in view of Oehrle in further view of Trillo does not explicitly disclose the following limitations, however,
Chen teaches determining similarity scores between the one or more keywords that are in the Big-5 personality-to-word data structure and one or more entries in the Big-5 personality-to- word structure by weighting the one or more keywords in the plurality of social media posts that are in the Big-5 personality-to-word structure with a weight associated with a particular personality that the keyword maps (see; par. [0031]-[0033] of Chen teaches based on collected words from social media posts, par. [0023] and based on the collected words comparing it to Big 5 words that aid in the determination a personality score, par. [0040] where the personality score is then used as part of understanding the personality scoring is used in the determinate of a similarity analysis of traits using rules that are statistically based (i.e. similarity scoring)) and
determining similarity scores between the one or more keywords that are not in the Big-5 personality-to-word data structure and the one or more entries in the Big-5 personality-to-word structure using a word embedding machine learning model by determining a similarity score between each keyword that is not in the Big-5 personality-to-word structure and a most similar entry in the Big-5 personality-to-word structure for each keyword that is not in the Big-5 personality-to-word structure (see; Table 1 of Chen teaches the providing of a list of Big 5 words and words that have low level facets of Big 5 which are not the Big 5 but associated with the Big 5 personality word), where par. [0031]-[0033] based on collected words from social media posts, par. [0023] and based on the collected words comparing it to low level facets words related to the Big 5 words that aid in the determination a personality score, par. [0040] where the personality score is then used as part of understanding the personality scoring is used in the determinate of a similarity analysis of traits using rules that are statistically based (i.e. similarity scoring)), and
generating a social media post similarity score for the respective social media post by combining the similarity scores for keywords that that are in the Big-5 personality-to-word data structure and the similarity scores for the keywords that are not in the Big-5 personality-to-word data structure (see; Abstract of Chen teaches determining a social media scoring social media traits of the current influencer, par. [0023] were the Big 5 correction from the social media, par. [0021] including similarities).
The Examiner notes that Garcia teaches similar to the instant application teaches social media analysis and determination about content information. Specifically, Garcia discloses the social media analysis in order to identify second social media post as related to the first post utilizing scoring to understand relationship it is therefore viewed as analogous art in the same field of endeavor. Additionally, Oehrle teaches analyzing and predicting the future behavior of people and organizations utilizing social media meta data and as it is comparable in certain respects to Garcia which provides social media analysis and determination about content information 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, Trillo teaches analyzing the tone of textual messages including social media and as it is comparable in certain respects to Garcia and Oehrle which provides social media analysis and determination about content information 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, Chen teaches trait based early detection of influencer on social media and as it is comparable in certain respects to Garcia, Oehrle, and Trillo which provides social media analysis and determination about content information 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.
Garcia, Oehrle, and Trillo discloses the social media analysis in order to identify second social media post as related to the first post utilizing scoring to understand relationship. However, Garcia, Oehrle, and Trillo fails to disclose determining similarity scores between the one or more keywords that are in the Big-5 personality-to-word data structure and one or more entries in the Big-5 personality-to- word structure by weighting the one or more keywords in the plurality of social media posts that are in the Big-5 personality-to-word structure with a weight associated with a particular personality that the keyword maps, determining similarity scores between the one or more keywords that are not in the Big-5 personality-to-word data structure and the one or more entries in the Big-5 personality-to-word structure using a word embedding machine learning model by determining a similarity score between each keyword that is not in the Big-5 personality-to-word structure and a most similar entry in the Big-5 personality-to-word structure for each keyword that is not in the Big-5 personality-to-word structure, and generating a social media post similarity score for the respective social media post by combining the similarity scores for keywords that that are in the Big-5 personality-to-word data structure and the similarity scores for the keywords that are not in the Big-5 personality-to-word data structure.
Chen discloses determining similarity scores between the one or more keywords that are in the Big-5 personality-to-word data structure and one or more entries in the Big-5 personality-to- word structure by weighting the one or more keywords in the plurality of social media posts that are in the Big-5 personality-to-word structure with a weight associated with a particular personality that the keyword maps, determining similarity scores between the one or more keywords that are not in the Big-5 personality-to-word data structure and the one or more entries in the Big-5 personality-to-word structure using a word embedding machine learning model by determining a similarity score between each keyword that is not in the Big-5 personality-to-word structure and a most similar entry in the Big-5 personality-to-word structure for each keyword that is not in the Big-5 personality-to-word structure, and generating a social media post similarity score for the respective social media post by combining the similarity scores for keywords that that are in the Big-5 personality-to-word data structure and the similarity scores for the keywords that are not in the Big-5 personality-to-word data structure.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Garcia, Oehrle, and Trillo determining similarity scores between the one or more keywords that are in the Big-5 personality-to-word data structure and one or more entries in the Big-5 personality-to- word structure by weighting the one or more keywords in the plurality of social media posts that are in the Big-5 personality-to-word structure with a weight associated with a particular personality that the keyword maps, determining similarity scores between the one or more keywords that are not in the Big-5 personality-to-word data structure and the one or more entries in the Big-5 personality-to-word structure using a word embedding machine learning model by determining a similarity score between each keyword that is not in the Big-5 personality-to-word structure and a most similar entry in the Big-5 personality-to-word structure for each keyword that is not in the Big-5 personality-to-word structure, and generating a social media post similarity score for the respective social media post by combining the similarity scores for keywords that that are in the Big-5 personality-to-word data structure and the similarity scores for the keywords that are not in the Big-5 personality-to-word data structure as taught by Chen 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, Garcia, Oehrle, Trillo, and Chen teach the collecting and analysis of data associated with social media in order to determine connections with other data and they do not contradict or diminish the other alone or when combined.
Garcia in view of Oehrle in further view of Trillo in further view of Chen does not explicitly disclose the following limitation, however,
Lai teaches generating a representative set of personas from the inferred information about the one or more users of the plurality of social media posts by deploying an actor for each persona in a virtual cyber space defining a simulation of social media, wherein the actor for each persona engages with users within the virtual cyber space according to the inferred information (see; par. [0039] of Lai teaches generating chats in social media in the form of an avatar (i.e. persona), par. [0033] that utilizes samples of user social media posts including, but not limited to the Big 5 personality, but gathered from inferred needs and values).
The Examiner notes that Garcia teaches similar to the instant application teaches social media analysis and determination about content information. Specifically, Garcia discloses the social media analysis in order to identify second social media post as related to the first post utilizing scoring to understand relationship it is therefore viewed as analogous art in the same field of endeavor. Additionally, Oehrle teaches analyzing and predicting the future behavior of people and organizations utilizing social media meta data and as it is comparable in certain respects to Garcia which provides social media analysis and determination about content information 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, Trillo teaches analyzing the tone of textual messages including social media and as it is comparable in certain respects to Garcia and Oehrle which provides social media analysis and determination about content information 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, Chen teaches trait based early detection of influencer on social media and as it is comparable in certain respects to Garcia, Oehrle, and Trillo which provides social media analysis and determination about content information 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, Lai teaches trait based early detection of influencer on social media and as it is comparable in certain respects to Garcia, Oehrle, Trillo, and Chen which provides automatic detection of user personality traits based on social media posts 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.
Garcia, Oehrle, Trillo, and Chen discloses the social media analysis in order to identify second social media post as related to the first post utilizing scoring to understand relationship. However, Garcia, Oehrle, Trillo, and Chen fails to disclose generating a representative set of personas from the inferred information about the one or more users of the plurality of social media posts by deploying an actor for each persona in a virtual cyber space defining a simulation of social media, wherein the actor for each persona engages with users within the virtual cyber space according to the inferred information.
Lai discloses generating a representative set of personas from the inferred information about the one or more users of the plurality of social media posts by deploying an actor for each persona in a virtual cyber space defining a simulation of social media, wherein the actor for each persona engages with users within the virtual cyber space according to the inferred information.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Garcia, Oehrle, Trillo, and Chen generating a representative set of personas from the inferred information about the one or more users of the plurality of social media posts by deploying an actor for each persona in a virtual cyber space defining a simulation of social media, wherein the actor for each persona engages with users within the virtual cyber space according to the inferred information as taught by Lai 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, Garcia, Oehrle, Trillo, Chen, and Lai teach the collecting and analysis of data associated with social media in order to determine connections with other data and they do not contradict or diminish the other alone or when combined.
Referring to Claim 2, see discussion of claim 1 above, while Garcia in view of Oehrle in further view of Trillo in further view of Chen in further view of Lai teaches t