DETAILED ACTION
The following is a first office action upon examination of application number 18/827945. Claims 1-20 are pending in the application and have been examined on the merits discussed below.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/9/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date.
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 17/844747, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. The prior-filed applications lack disclosure/description for at least features related to applying a natural language processing algorithm to extract linguistic features and communication patterns from the emails and the social media posts and correlating behavioral types and social media posts with performance metrics of an organization to form a correlated analysis table. Therefore, claims 1-20 do not receive the benefit of the earlier filing date.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1) Claims 1-12 are directed to a device comprising a processor; thus the device is directed to a machine which is a statutory category of invention. Claims 13-20 are directed to a method; thus these claims are directed to a process, which is one of the statutory categories of invention.
(Step 2A) The claims recite an abstract idea instructing how to analyze word selection to model business performance, which is described by claim limitations reciting:
…collect electronic communications data from emails and social media posts …;
…access the emails and the social media posts,…extract linguistic features and communications patterns from the emails and the social media posts;
…access the linguistic features and the communications patterns,… classify the emails and the social media posts into behavioral types based on the linguistic features and the communications patterns;
…access the behavioral types,… correlate the behavioral types and the social media posts with performance metrics of an organization to form a correlated analysis table; and
…access the correlated analysis table,… generate a model of business performance based on the correlated analysis table, and present a prediction of the business performance based on the correlated analysis table.
The identified limitations in the claims describing analyzing word selection to model business performance (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices or, alternatively, the “Mental Processes” grouping of abstract ideas since the identified limitations can be performed by a human, mentally or with pen and paper. Dependent claims 2, 3, 4, 5, 6, 10, 11, 12, 14, 15, 16, 17, 18, and 20 recite limitations that further narrow the abstract idea (i.e., analyzing word selection to model business performance); therefore, these claims are also found to recite an abstract idea.
This judicial exception is not integrated into a practical application because additional elements such as the memory, communicatively connected to the processor; communications subsystem, communicatively connected to the processor and the memory; data retrieval module, the data retrieval module including non-transitory computer instructions for the processor; data analysis module, the data analysis module including non-transitory computer instructions for the processor; individual categorization module, the individual categorization module including non-transitory computer instructions for the processor; correlation analysis module, the correlation analysis module including non-transitory computer instructions for the processor; and outcome prediction module, the outcome prediction module including non-transitory computer instructions for the processor in claim 1; the processor with a data retrieval module; and communications subsystem communicatively connected to the processor in claim 13, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a computer/processor.
Additional elements such as a data retrieval module, the data retrieval module including non-transitory computer instructions for the processor to collect electronic communications data from emails and social media posts through the communications subsystem; a data analysis module, configured to access…; an individual categorization module, configured to access…; a correlation analysis module, configured to access…; and an outcome prediction module, configured to access… do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add insignificant extra-solution activities (data gathering). Additional elements reciting apply a natural language processing algorithm do not provide an improvement; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Similarly, additional elements in claims 7, 8, 9, and 19, related to a machine learning algorithm trained, Facebook interface, and LinkedIn interface do not yield an improvement and only generally link the abstract idea to a technological environment. Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component (see Spec. [0091]). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements such as a data retrieval module, the data retrieval module including non-transitory computer instructions for the processor to collect electronic communications data from emails and social media posts through the communications subsystem; a data analysis module, configured to access…; an individual categorization module, configured to access…; a correlation analysis module, configured to access…; and an outcome prediction module, configured to access… do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add insignificant extra-solution activities (data gathering). Additionally, with respect to data gathering limitations, the courts have recognized the use of computers to receive and transmit data as a well-understood, routine, and conventional, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Additional elements reciting apply a natural language processing algorithm do not provide an improvement; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Additional elements in claims 7, 8, 9, and 19, related to a machine learning algorithm trained, Facebook interface, and LinkedIn interface do not yield an improvement and only generally link the abstract idea to a technological environment. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 7, 13-15, and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Identifying Virtual Tribes by Their Language in Enterprise E-Mail Archives (Morgan, 2020).
As per claim 1, Morgan teaches: a computerized electronic device comprising:a processor; memory, communicatively connected to the processor; and a communications subsystem, communicatively connected to the processor and the memory; the memory comprising: a data retrieval module, the data retrieval module including non-transitory computer instructions for the processor to collect electronic communications data from emails and social media posts through the communications subsystem; ([Abstract] … Tribefinder tool to analyze two e-mail archives [Page 2] used with data from Twitter, it can also operate on other forms of media, like Email [Page 4] Emails have also been used as a source to mine data (Bird et al., 2006). In professional environments, Emails represent a typical social network and exhibit “long-tailed, small-world” traits (Bird et al., 2006))
a data analysis module, configured to access the emails and the social media posts, the data analysis module including non-transitory computer instructions for the processor to apply a natural language processing algorithm to extract linguistic features and communications patterns from the emails and the social media posts; ([Page 2] finds the different types of tribes and their leaders on Wikipedia, then looks at the language of the leaders on Twitte [Page 6] … automatically extracted keywords that would be associated with a certain tribe… deep learning being used to find the key patterns and ideas in the tweets of tribe leaders (De Oliveira & Gloor, 2018). This is used to identify textual patterns for each tribe and create a specific set of words for each tribe as well)
an individual categorization module, configured to access the linguistic features and the communications patterns, the individual categorization module including non-transitory computer instructions for the processor to classify the emails and the social media posts into behavioral types based on the linguistic features and the communications patterns; ([Page 2] … Tribefinder is a novel system that uses Artificial Intelligence and Machine Learning to identify the tribes of users based on social media data…People are assigned to tribes if their word usage is like that of the aggregate of all “leaders” of a tribe.. [Page 6] Tribe allocation assigns tribal affiliations to people based on their characteristics…classifications for specific users can be created. Specifically, one’s words in Emails or Tweets are converted into vectors which are then inputted into the long short term memory models)
a correlation analysis module, configured to access the behavioral types, the correlation analysis module including non-transitory computer instructions for the processor to correlate the behavioral types and the social media posts with performance metrics of an organization to form a correlated analysis table; and ([Page 2-3] . Each tribe has specific traits, and this paper will look at and compare them between the different tribes. The traits, or honest signals, that this paper uses are related to productivity, connectivity, complexity, and communication habits of each tribe [Page 8] … Tribefinder proved to be powerful as it can be used to discover nonobvious characteristics of employees in a firm… [Page 9] … These tribes have their notable traits identified through data mining Emails and social network analysis. This can be impactful as firms can identify employee tribes that need an increase in sentiment and those that speak with the highest complexity, meaning new ideas are coined [Page 14] Table 4. Differences in Honest Signals among Personality Tribes… The Stock-Trader tribe seems to be more productive to conversations than the Politician tribe, as it has a larger Contribution Index. However, the Politician tribe changes its position more in discussions than the Risk-Taker and Journalist tribes, with a high Rotating Leadership. The Journalists and Risk-Takers speak most positively in discussions, as they have the highest average sentiments. Moreover, the Journalists have the largest deviations in the positivity of their messages in comparison to the Politicians and Stock-traders, given that they have a high average emotionality.).
an outcome prediction module, configured to access the correlated analysis table, the outcome prediction module including non-transitory computer instructions for the processor to generate a model of business performance based on the correlated analysis table, and present a prediction of the business performance based on the correlated analysis table ([Page 8] employee tribes will be analyzed through the use of the honest signals (Gloor, 2017; Pentland, 2010). These honest signals identify differences in the activity and language of tribes, which firms can use to see which tribes are the most positive and active in the workplace [Page 15] … this paper utilizes a new tool developed by Gloor et al. (2019) in order to identify tribes. … These groups have their traits analyzed through honest signals (Gloor, 2017; Pentland, 2010), which demonstrates that
there are differences among the tribes that have impacts on communication habits. [Page 16] … measure moral values of employees, or their attitudes towards risk by creating the appropriate tribes. By comparing the tribal affiliations with the “honest signals of communication”, we illustrate the underlying traits of different groups of employees, thus providing valuable cues to managers about the characteristics of their employees. This paper is early research, but it clearly demonstrates the power of this approach to discover the underlying individual attributes and behavioral characteristics of members of an organization otherwise not accessible).
As per claim 2, Morgan teaches: wherein the memory further comprises a social network analysis module, configured to access the behavioral types and the communications patterns, the social network analysis module including non-transitory computer instructions for the processor to compute centrality and response time metrics from the behavioral types and the communications patterns ([Page 8] …The interconnectedness of a tribe is measured through its social network’s network centrality … [Page 15] … Average response time and nudges (the amount of Emails one sends in order to get a reply from another) could be used).
As per claim 3, Morgan teaches: wherein the correlation analysis module is further configured to access the centrality and response time metrics and incorporates the centrality and response time metrics in the forming of the correlated analysis table ([Page 8] …employee tribes will be analyzed through the use of the honest signals …The interconnectedness of a tribe is measured through its social network’s network centrality … The activity of users was measured through messages sent, contribution index, and rotating leadership. Messages sent is simply the amount of Emails sent by an individual. Rotating leadership is the oscillations in betweenness centrality in a specified time period (15 days). [Page 12] …. In the Enron dataset, the Spiritualists rotate their positions of leadership the least, which means that their betweenness centrality rarely oscillates. This suggests that in comparison to the Nerds and Treehuggers, Spiritualists rarely change their positions in a group and either stay as group leaders or followers. [Page 15] … Average response time and nudges (the amount of Emails one sends in order to get a reply from another) could be used).
As per claim 7, Morgan teaches: where the individual categorization module employs a machine learning algorithm trained on a dataset of known behavioral examples (Page 2 Tribefinder is a novel system that uses Artificial Intelligence and Machine Learning to identify the tribes of users based on social media data … Tribefinder works through the use of word embeddings and long short term memory (LSTM) (Hochreiter et al. 1997). It currently determines tribes using the words in their messages. More specifically, it finds the different types of tribes and their leaders on Wikipedia, then looks at the language of the leaders on Twitter (Gloor et al., 2019). People are assigned to tribes if their word usage is like that of the aggregate of all “leaders” of a tribe. (Gloor et al., 2019)).
As per claim 13, this claim recites limitations substantially similar to those addressed by the rejection of claim 1, above; therefore, the same rejection applies.
As per claim 14, this claim recites limitations substantially similar to those addressed by the rejection of claim 2, above; therefore, the same rejection applies.
As per claim 15, this claim recites limitations substantially similar to those addressed by the rejection of claim 3, above; therefore, the same rejection applies.
As per claim 19, this claim recites limitations substantially similar to those addressed by the rejection of claim 7, above; therefore, the same rejection applies.
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 for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4, 6, 8, 16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Identifying Virtual Tribes by Their Language in Enterprise E-Mail Archives (Morgan, 2020); in view of US 2024/0086641 (Coppersmith).
As per claim 4, Morgan teaches: an emotional analysis module configured to access the linguistic features and the communications patterns to determine … emotions in the emails and the social media posts ([Page 9] … Average sentiment is the measure of the positivity and negativity of a user’s Emails. It was calculated using a classifier algorithm and varies from 0 and 1, with 0 being the most negative and 1 the most positive (Gloor et al., 2019). Average emotionality is the measure of user’s deviation from the usual sentiment and is measured as the standard deviation from the mean sentiment (Gloor, 2017)).
Although not explicitly taught by Morgan, Coppersmith teaches: an emotional analysis module configured to access the linguistic features and the communications patterns to determine levels of specific emotions in the emails and the social media posts ([0007] … process the text messages and to assess at, least three emotions of the team members as reflected in the text messages, the three emotions drawn from the group consisting of anger, disgust, fear, happiness, sadness [0062] The software may evaluate Slack messages 110, other social media messages (including Twitter. Facebook, Instagram, Tumblr, Reddit, Fitbit. Runkeeper, and Jawbone), email, SMS text messages 112, work communications [0037] The emotional classifiers 132 and sentiment classifiers 134 discerned from text and sensors may include suicide risk, mental illness, joy, fear, sadness, anger, or frustration among team members, team effectiveness, language between two team members [0038] … level for several emotions [0083] For example, micropattern analysis may consider sets of consecutive messages, evaluate each message for sentiment, and reduce the sequence to a tuple of sentiment scores.).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Morgan with the aforementioned teachings of Coppersmith with the motivation of assessing emotions (Coppersmith [0007]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Coppersmith to the system of Morgan would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of specific emotions.
As per claim 6, although not explicitly taught by Morgan, Coppersmith teaches: where the specific emotions include the levels of anger, fear, happiness, and sadness ([0037] …sentiment classifiers 134 discerned from text and sensors may include suicide risk, mental illness, joy, fear, sadness, anger, or frustration among team members; joy/happiness. [0066] … emotion analysis 132 may analyze for emotions such as joy, sadness, surprise, anger, and fear).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Morgan with the aforementioned teachings of Coppersmith with the motivation of assessing emotions (Coppersmith [0007]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Coppersmith to the system of Morgan would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of specific emotions.
As per claim 8, although not explicitly taught by Morgan, Coppersmith teaches: where the communications subsystem includes a Facebook interface ([0036] … system 100 may collect “passive” data 110, 112, 114, that is, data that are created and exist independently of system 100. [0062] The software may evaluate Slack messages 110, other social media messages (including Twitter. Facebook, Instagram, Tumblr, Reddit, Fitbit. Runkeeper, and Jawbone), email, SMS text messages 112, work communications, design, project plan, and financial memoranda and other long-form written communications, non-work personal communications (such as team members' personal social media posts), issue tracking and project tracking software, and any other digital data 114, 116 that may reflect or influence emotional state and sentiment of team members, or interactions within the team [0151] … computer that is in communication (e.g., via a communications network) with one or more devices. The computer may communicate with the devices directly or indirectly, via any wired or wireless medium (e.g. the Internet. LAN, WAN or Ethernet).
One of ordinary skill in the art would have recognized that applying the teachings of Coppersmith to the system of Morgan would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for collection of Facebook data.
As per claim 16, this claim recites limitations substantially similar to those addressed by the rejection of claim 4, above; therefore, the same rejection applies.
As per claim 18, this claim recites limitations substantially similar to those addressed by the rejection of claim 6, above; therefore, the same rejection applies.
Claim(s) 5 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Identifying Virtual Tribes by Their Language in Enterprise E-Mail Archives (Morgan, 2020); in view of US 2024/0086641 (Coppersmith); in view of US 2023/0360067 (Dorrington).
As per claim 5, although not explicitly taught by Morgan, Dorrington teaches: the individual categorization module is configured to access the specific emotions and incorporate the specific emotions into the determination of the behavioral types ([0001] … understanding and predicting the behaviour of humans based on their psychological (emotional) state of mind. [0018] Preferably, the one of more emotions identified are one of more of: anger, anticipation, contempt, disgust, fear, joy, love, sadness, or surprise. [0042] … identify the role of differing emotion states on significant actions taken by the subject (customer, employer, or business partners, etc.); these are called ‘Business Outcomes’. For example, feelings of Anger and Distrust can be associated with some employees who resign unexpectedly (there may be other causes as well, with different emotional states). For example, business outcomes may include personnel changes, (as either employer or employee), decisions on purchasing, or choosing one product or brand over a similar alternative, etc.).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Morgan with the aforementioned teachings of Dorrington with the motivation of predicting behavior based on emotional states (Dorrington [0001]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Dorrington to the system of Morgan would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for use of specific emotions in the modeling of behavior.
As per claim 17, this claim recites limitations substantially similar to those addressed by the rejection of claim 5, above; therefore, the same rejection applies.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Identifying Virtual Tribes by Their Language in Enterprise E-Mail Archives (Morgan, 2020); in view of 11587172 (Guzman).
As per claim 9, although not explicitly taught by Morgan, Guzman teaches: where the communications subsystem includes a LinkedIn interface (Col 11 ln 59-65 In some embodiments, a method of generating a sentiment indicator, the method comprising: obtaining Twitter, Facebook, LinkedIn, other social media data, blog posts and comments, or news data directly via an API feed or from an aggregator; calculating the ratio of negative words to total words related to a certain subject or extracting sentiment using natural language processing and other computational techniques).
One of ordinary skill in the art would have recognized that applying the teachings of Coppersmith to the system of Morgan would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for collection of LinkedIn data.
Claim(s) 10-12 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Identifying Virtual Tribes by Their Language in Enterprise E-Mail Archives (Morgan, 2020); in view of Measuring ethical behavior with AI and natural language processing to assess business success (Gloor, 2022).
As per claim 10, Gloor teaches: where the behavioral types comprise "bee", "ant", and "leech" ([Page 1] … three different “tribes” of ethical, moral, and non-ethical people, based on Twitter feeds of people of known high and low ethics and morals: fair and modest collaborators codified as ethical “bees”; hard-working competitive workers as moral “ants”; and selfish, arrogant people as non-ethical “leeches”).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Morgan with the aforementioned teachings of Gloor with the motivation of dividing a population into ethical, moral, and non-ethical tribes (Gloor [page 1]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Gloor to the system of Morgan would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use a predetermined set of tribes.
As per claim 11, Gloor teaches: where the behavioral type is assigned a first behavior type when the linguistic features and the communications patterns substantially indicate ethical behavior, high interest in collaborative tasks, openness to new experiences, and a tendency to assist others ([Page 1] fair and modest collaborators codified as ethical “bees” … Human “bees”, just
like the real bees pollinating the plants on our planet, are doing good for everybody. However, just like real bees,
human “bees” frequently get little recognition for their essential contributions to the good of society. [Page 8] … “bees” tend to take more social risks and are open to trying new things [Page 9] …bees—i.e., individuals who are open to learning, try
new things, and care for others).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Morgan with the aforementioned teachings of Gloor with the motivation of dividing a population into ethical, moral, and non-ethical tribes (Gloor [page 1])Further, one of ordinary skill in the art would have recognized that applying the teachings of Gloor to the system of Morgan would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of an ethical tribe.
As per claim 12, Gloor teaches: where the behavioral type is assigned a second behavior type when the linguistic features and the communications patterns substantially indicate unethical behavior, self-promoting and self-absorbed behavior, and a tendency to prioritize personal gain over group welfare ([Page 1] … selfish, arrogant people as non-ethical “leeches”… , human “leeches” are egoists. Just like real leeches, which steal their victim’s blood for themselves, human “leeches” only care about their benefits with little regard for the welfare of others [Page 2] …while “leeches” only care about their own interests, with no concern for the welfare of others. In other words, bees are “ethical”, ants are “moral”, and leeches are “amoral”. Differently from “bees”, “ants” and “leeches” will thus stick to the moral value systems of their in-groups which might be ethical or unethical, with little compassion for the rest of society).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Morgan with the aforementioned teachings of Gloor with the motivation of dividing a population into ethical, moral, and non-ethical tribes (Gloor [page 1])Further, one of ordinary skill in the art would have recognized that applying the teachings of Gloor to the system of Morgan would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of an non-ethical tribe.
As per claim 20, Gloor teaches: where the behavioral type is assigned a third behavior type when the linguistic features and the communications patterns substantially indicate firm moral values within a group, competitive and hard work, and valuing tradition and loyalty ([Page 1] hard-working competitive workers as moral “ants” … human “ants” are competitive workers who are well-embedded in their in-group and work hard to get ahead. [Page 2] … “ants” highly value tradition and loyalty to other members of their in-group … ).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Morgan with the aforementioned teachings of Gloor with the motivation of dividing a population into ethical, moral, and non-ethical tribes (Gloor [page 1])Further, one of ordinary skill in the art would have recognized that applying the teachings of Gloor to the system of Morgan would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of an moral tribe.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2021/0097605 (Yeri) – discloses the use of NLP to analyze emails and model user behavior.
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/ALAN TORRICO-LOPEZ/Primary Examiner, Art Unit 3625