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 .
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 – “Statutory Category Identification”
Claims 1 and 18 are directed to “a method” (i.e. “a process”), and claim 17 is directed to “a system” (i.e. “a machine”), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 1 “Abstract Idea Identification”
However, the claims are drawn to an abstract idea of “computerized human interaction,” either in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), or reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion). Regardless, the claims are reasonably understood as either “certain methods of organizing human activity” or “mental processes,” which require the following limitations:
Per claim 1:
“causing …at least one artificial intelligence ("AI")/machine learning ("ML") -driven persona to interact with a user…the interaction including a conversation between the at least one AI/ML-driven persona and the user during which the at least one AI/ML-driven persona engages in assisting the user in learning one or more social skills to interact with other people;
analyzing… and using one of at least one AI/ML model, the interaction to determine a first skill level of the user in at least one set of social skills among the one or more social skills, the first skill level comprising a level of competence in the at least one set of social skill that the user possesses;
generating… and using one of the at least one AI/ML model, one or more first conversational threads configured to achieve a first goal of developing the first skill level of the user in the at least one set of social skill, based on the analysis of the interaction and based on social or behavioral therapy data for the at least one set of social skills that is accessible from a database; and
causing…the at least one AI/ML-driven persona to continue the conversation with the user using the one or more first conversational threads to work toward achieving the first goal.”
Per claim 17:
“cause at least one artificial intelligence ("AI")/machine learning ("ML") - driven persona to interact with a user …the interaction including a conversation between the at least one AI/ML- driven persona and the user during which the at least one AI/ML- driven persona engages in assisting the user in learning one or more social skills to interact with other people;
analyze, using one of at least one AI/ML model, the interaction to determine a first skill level of the user in at least one set of social skills among the one or more social skills, the first skill level comprising a level of competence in the at least one set of social skill that the user possesses;
generate, using one of the at least one AI/ML model, one or more first conversational threads configured to achieve a first goal of developing the first skill level of the user in the at least one set of social skill, based on the analysis of the interaction and based on social or behavioral therapy data for the at least one set of social skills that is accessible from a database; and cause the at least one AI/ML-driven persona to continue the conversation with the user using the one or more first conversational threads to work toward achieving the first goal.”
Per claim 18:
“causing…at least one artificial intelligence ("AI")/machine learning ("ML") -driven persona to interact with a user … the interaction including a conversation between the at least one AI/ML-driven persona and the user during which the at least one AI/ML-driven persona engages in assisting the user in learning one or more social skills to interact with multiple people at a time;
analyzing…and using one of at least one AI/ML model, the interaction to determine a first skill level of the user in the one or more social skills, the first skill level comprising a level of competence in the one or more social skills that the user possesses;
generating…and using one of the at least one AI/ML model, one or more first conversational threads configured to achieve a first goal of developing the first skill level of the user in the one or more social skills, based on the analysis of the interaction and based on social or behavioral therapy data for the one or more social skills that is accessible from a database; and
causing…the at least one AI/ML-driven persona to continue the conversation with the user using the one or more first conversational threads to work toward achieving the first goal.”
These limitations simply describe a process of data gathering and manipulation, which is analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 2 – “Practical Application”
Furthermore, the applicants claimed elements of “a computing system, comprising: at least one first processor; and a first non-transitory computer readable medium” and “a user interface,” are merely claimed to generally link the use of a judicial exception (e.g., pre-solution activity of data gathering and post-solution activity of presenting data) to (1) a particular technological environment or (2) field of use, per MPEP §2106.05(h); and are applying the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, per MPEP §2106.05(f). In other words, the claimed “computerized human interaction,” is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Step 2B – “Significantly More”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g. “a computing system, comprising: at least one first processor; and a first non-transitory computer readable medium” and “a user interface,” are claimed, these are generic, well-known, and conventional data gather computing elements. As evidence that these are generic, well-known, and a conventional data gathering computing elements (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known, the Applicant’s specification discloses these in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a). As such, this satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Specifically, the Applicant’s claimed “a computing system, comprising: at least one first processor; and a first non-transitory computer readable medium” as described in paras. [0168], [0171], [0175], and [0176] of the Applicant’s written description as originally filed, provides the following:
“[0168] The computer or hardware system 600—which might represent an embodiment of the computer or hardware system (i.e., computing systems 105, 105a, and 105b, user interactive systems 115a and 115b, AI/ML systems 120a and 120b, user device 140, gateway device 160, provider server(s) 175a, training server(s) 190a, and therapy server(s) 195a, etc.), described above with respect to FIGS. 1-5—is shown comprising hardware elements that can be electrically coupled via a bus 605 (or may otherwise be in communication, as appropriate). The hardware elements may include one or more processors 610, including, without limitation, one or more general-purpose processors and/or one or more special-purpose processors (such as microprocessors, digital signal processing chips, graphics acceleration processors, and/or the like); one or more input devices 615, which can include, without limitation, a mouse, a keyboard, and/or the like; and one or more output devices 620, which can include, without limitation, a display device, a printer, and/or the like.”
“[0171] The computer or hardware system 600 also may comprise software elements, shown as being currently located within the working memory 635, including an operating system 640, device drivers, executable libraries, and/or other code, such as one or more application programs 645, which may comprise computer programs provided by various embodiments (including, without limitation, hypervisors, virtual machines (“VMs”), and the like), and/or may be designed to implement methods, and/or configure systems, provided by other embodiments, as described herein. Merely by way of example, one or more procedures described with respect to the method(s) discussed above might be implemented as code and/or instructions executable by a computer (and/or a processor within a computer); in an aspect, then, such code and/or instructions can be used to configure and/or adapt a general purpose computer (or other device) to perform one or more operations in accordance with the described methods.”
“[0175] The terms “machine readable medium” and “computer readable medium,” as used herein, refer to any medium that participates in providing data that causes a machine to operate in a specific fashion. In an embodiment implemented using the computer or hardware system 600, various computer readable media might be involved in providing instructions/code to processor(s) 610 for execution and/or might be used to store and/or carry such instructions/code (e.g., as signals). In many implementations, a computer readable medium is a non-transitory, physical, and/or tangible storage medium. In some embodiments, a computer readable medium may take many forms, including, but not limited to, non-volatile media, volatile media, or the like. Non-volatile media includes, for example, optical and/or magnetic disks, such as the storage device(s) 625. Volatile media includes, without limitation, dynamic memory, such as the working memory 635. In some alternative embodiments, a computer readable medium may take the form of transmission media, which includes, without limitation, coaxial cables, copper wire, and fiber optics, including the wires that comprise the bus 605, as well as the various components of the communication subsystem 630 (and/or the media by which the communications subsystem 630 provides communication with other devices). In an alternative set of embodiments, transmission media can also take the form of waves (including without limitation radio, acoustic, and/or light waves, such as those generated during radio-wave and infra-red data communications).”
“[0176] Common forms of physical and/or tangible computer readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read instructions and/or code.”
As such, this is broadly described and reasonably interpreted as a generic computer which includes generic computer components that are commonly provided in commercially available computers.
Likewise, the Applicant’s claimed “a user interface,” as described in paras. [0085] and [0090] of the Applicant’s written description as originally filed, provides the following:
“[0085] In the non-limiting embodiment of FIG. 1, system 100 may include a computing system(s) 105, which may include computing system(s) 105a and/or computing system(s) 105b. In some cases, computing system(s) 105a and/or 105b may each include, without limitation, at least one of a processor(s) 110, a user interactive system(s) 115, and/or an artificial intelligence (“AI”)/machine learning (“ML”) system(s) 120, and/or the like. Each AI/ML system(s) 120 may include, but is not limited to, one or more AI/ML models 125a and one or more AI/ML-driven personas 125b. System 100 may further include a database(s) 130 (including database(s) 130a communicatively coupled to computing system(s) 105a and/or database(s) 130b communicatively coupled to computing system(s) 105b), a user interface (“UI”) 135a for a user device 140 that may be associated with a user 145, and the like. In some instances, the computing system(s) 105a, database(s) 130a, and user device 140 may be located at location 150, at which a local network(s) 155 may be established that communicates, via gateway device 160, to external network(s) 165a and/or 165b, or the like.”
“[0090] In some instances, the user device 140 may each include, but is not limited to, one of a desktop computer, a laptop computer, a tablet computer, a smart phone, a mobile phone, or any suitable device capable of communicating with network(s) 155 or with computing system(s) 105a, gateway device 160, or other network devices within network(s) 155, or via any suitable device capable of communicating with at least one of the computing system(s) 105b, the user interactive system(s) 115b, the AI/ML system(s) 120b, the web portal 170, the provider server(s) 175a, the training server(s) 190a, and/or the therapy server(s) 195a, and/or the like, via a web-based portal (e.g., web portal 170, or the like), an application programming interface (“API”), a server, a software application (“app”), or any other suitable communications interface, or the like (not shown), over network(s) 155, 165a, and/or 165b, via gateway device 160, or the like.”
As such, “a user interface,” is reasonably interpreted to be a mere display device which is physically combined with ubiquitous, standard off-the-shelf equipment that is commercially available today.
Therefore, the Applicant’s own specification discloses ubiquitous standard equipment that is (1) generic, routine, conventional, and/or commercially available; and (2) does not provide anything significantly more. Thus, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 2-16 and 19-20 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-16 and 19-20 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to claim 1 or 18. Therefore, claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject-matter.
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.
Claims 1-4,8-11, 14, 15 17 and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hayes-Roth (US 2003/0028498).
Regarding claim 1, and substantially similar limitations in claims 17 and 18, Hayes-Roth discloses a method, comprising: causing, by a computing system, at least one artificial intelligence ("AI")/machine learning ("ML") -driven persona to interact with a user via a user interface ("UI")
(see para. [0015]: The present invention advantageously utilizes the patented AI technologies to create customizable expert agents that can have personalized conversational interactions with learners/customers much like human expert agents. The customizable expert agent of the present invention combines natural language conversation, animated gestures, subject expertise, and access to various electronic resources to create enjoyable and effective online experiences in a variety of contexts. The preferred embodiment of the present invention operates over a computer network, e.g., a World Wide Web (web), utilizing client-server technologies. However, it will be apparent to one skilled in the art that the invention could operate entirely within a single computer or computer-enabled device as well. See para. [0041]: The expert sales agent would communicate with the customer in natural language dialogue. This dialogue may be exchanged via various interface input/output (I/O) technologies, including but not limited to text, speech/voice/audio, and graphics/images modalities.), the interaction including a conversation between the at least one AI/ML-driven persona and the user during which the at least one AI/ML-driven persona engages in assisting the user in learning one or more social skills to interact with other people (see para. [0051]: Although the embodiment above teaches a customizable expert sales agent for a class of retail clothing applications, the customizable expert agent can be realized for many other forms of expertise and classes of applications, including, but not limited to the following: expert sales agents for autos, real estate, computers, consumer products and electronics; expert coaches for soft skills, hard skills, athletic skills, game skills, management skills, sales skills, customer service skills, team skills, negotiation, ethics, parenting, peer interactions, partner interactions; expert tutors for a variety of subject matter; expert agents for advising, influencing, interviewing, persuading, or learning about users; expert agents for entertaining or role playing with users.
See para. [0052]: The customizable expert agent can be particularly useful to business entities that need to provide online training (hereinafter referred to as "Target Customers"). In a preferred embodiment, an Expert Coach provides application-independent coaching expertise for helping users to acquire behaviors or skills. Like the expert sales agent described above, the Coach's expertise can be customized with application-specific information for use in a particular application, potentially including parameter value specifications, dialogue, actions, flow of control logic, etc. In addition, the Coach may have expertise involving the use of learning content objects (hereinafter referred to as "Learning Objects"), which may be authored by training application content authors or acquired from a third party, and provided along with related application-specific information for use by the Coach. The preferred embodiment of the Coach and the associated preferred authoring tool are described herein in detail in a later section.
See para. [0056]: A specific embodiment of the present invention will now be described in which the expert agent is a coach (Expert Coach) having general (function/category) expertise in coaching and application-specific (sub-function/sub-category) expertise in coaching "people skills." That is, skills for interacting effectively with other people.
See paras. [0200]-[0209]:
[0200] The Coach can be implemented with different sets of parameterized Autonomous Dialogue which can contain application-independent content (coaching, motivation, etc.), possibly augmented with application-specific dialogue, and be instantiated and delivered contingently at runtime. This dialogue is used to give runtime information, guide the Learner, offer appropriate encouragement, find out global user preferences or learning styles, etc. The following exemplifies how such dialogue can be delivered, with application-specific dialogue in italics.
[0201] 1) Situation: Start of Learner's session with a STOW application.
[0202] Dialogue Determiner(s): New Learner/ length of time since last session (<1, 2-7, or >7 days)
[0203] Sample Dialog: "Hola Joan, welcome back to Spanish for Au Pairs!"
[0204] "Buenos dias, Joan, it's nice to see you again."
[0205] "Hi Joan, it's been a long time--I missed you!"
[0206] 2) Situation: Feedback Dialogue after a Learner has attempted an Assessment Object, before detailed Autonomous Dialogue describing scores is given.
[0207] Dialogue Determiner(s): Previous replies to this question
[0208] Sample Dialog: "I'd like to review your correct performance, as well as your errors, OK?"
[0209] "I guess you don't like me to give feedback on your correct performance, so I'll just review your errors for the rest of the session, OK?");
analyzing, by the computing system and using one of at least one AI/ML model, the interaction to determine a first skill level of the user in at least one set of social skills among the one or more social skills, the first skill level comprising a level of competence in the at least one set of social skill that the user possesses; generating, by the computing system and using one of the at least one AI/ML model, one or more first conversational threads configured to achieve a first goal of developing the first skill level of the user in the at least one set of social skill, based on the analysis of the interaction and based on social or behavioral therapy data for the at least one set of social skills that is accessible from a database (see para. [0049]: As illustrated with the earlier types of services, the expert sales agent performs its learning, adaptation, personalization, and relationship-building services by instantiating, elaborating, or refining application-independent dialogue and behavior with application-specific information.
See para. [0053]: An objective of the preferred embodiment of the present invention is to create an integrated software system as well as program products for Target Customers' online training application authoring and corresponding web deployment. Specifically, an application shell presents (unlimited) online learning contents to a learner/student/trainee through a web browser interface. The web browser displays appropriate existing Learning Objects as specified by the application content author. The system mediates the student's interaction with Learning Objects via an expert agent acting as a Coach. The Coach interacts with the student in a mixed-initiative conversation and human-like manner. It tracks and evaluates an individual student's performance, provides personalized feedback, and recommends learning objects for study to remedy the student's weaknesses. Overall, the Coach guides each student along an individually optimized path to mastery of the learning goals specified by the application author. In each case, the Coach's application-independent expertise may include decision criteria for recommending a next learning object to the user, along with dialogue for introducing, explaining, or concluding the user's interaction with a type of learning object, alternative dialogue to use in various special circumstances, such as first time vs. repeat with a learning object, first error versus repeat error on a learning goal, fast learner versus slow learner, etc., and personalization of dialogue for warmth and motivation. Complementary application-specific information may be provided to identify learning objects to be used in various circumstances and to provide dialogue for use with particular learning objects or in particular circumstances. For example, for a slow learner making a second error on a particular learning objective, the Coach might deliver the following dialogue instantiating application-independent dialogue with application-specific information (in italics): "You got one out of two communication goals, John. You told Nina that the problem was late reports. But you forgot to tell her that the consequence was that she could lose her job. Knowing the consequence will help motivate Nina to improve her performance. You missed this one last time too, John. But not to worry! You've only had two tries and most people take three ties to get this right. Here is a tip: Next time, try to the tell Nina the consequences immediately after you tell her the problem."
See para. [0060]: The Expert Coach displays application-independent coaching expertise in her pedagogical strategy: provide the user an overview of target skills, assess the user's current skills in a role play, give the user feedback on performance of component skills in the role play, tutor the user on weak skills identified in the role play, repeat the assessment-feedback-tutoring loop until all skills are perfect in role play, repeat the assessment-feedback-coaching loop on a second role play until all skills are perfect in that role play. The Expert Coach also displays application-independent content in some of her motivational dialogue, for example, "Excellent! You mastered all skills on your first try." The Expert Coach displays application-specific content in her use of particular learning objects, such as the "Introduction," "Examples," and "Study Material" objects. She uses application-specific learning objects for the "Linda" and "Ed" role play objects. She also uses application-specific dialogue, for example, "You need to work more on Communication."
See para. [0061]: The functionality demonstrated in this embodiment is implemented in a generalizable form where the number of learning objects presented is unlimited.); and causing, by the computing system, the at least one AI/ML-driven persona to continue the conversation with the user using the one or more first conversational threads to work toward achieving the first goal (see para. [0053]: An objective of the preferred embodiment of the present invention is to create an integrated software system as well as program products for Target Customers' online training application authoring and corresponding web deployment. Specifically, an application shell presents (unlimited) online learning contents to a learner/student/trainee through a web browser interface. The web browser displays appropriate existing Learning Objects as specified by the application content author. The system mediates the student's interaction with Learning Objects via an expert agent acting as a Coach. The Coach interacts with the student in a mixed-initiative conversation and human-like manner. It tracks and evaluates an individual student's performance, provides personalized feedback, and recommends learning objects for study to remedy the student's weaknesses. Overall, the Coach guides each student along an individually optimized path to mastery of the learning goals specified by the application author. In each case, the Coach's application-independent expertise may include decision criteria for recommending a next learning object to the user, along with dialogue for introducing, explaining, or concluding the user's interaction with a type of learning object, alternative dialogue to use in various special circumstances, such as first time vs. repeat with a learning object, first error versus repeat error on a learning goal, fast learner versus slow learner, etc., and personalization of dialogue for warmth and motivation. Complementary application-specific information may be provided to identify learning objects to be used in various circumstances and to provide dialogue for use with particular learning objects or in particular circumstances. For example, for a slow learner making a second error on a particular learning objective, the Coach might deliver the following dialogue instantiating application-independent dialogue with application-specific information (in italics): "You got one out of two communication goals, John. You told Nina that the problem was late reports. But you forgot to tell her that the consequence was that she could lose her job. Knowing the consequence will help motivate Nina to improve her performance. You missed this one last time too, John. But not to worry! You've only had two tries and most people take three ties to get this right. Here is a tip: Next time, try to the tell Nina the consequences immediately after you tell her the problem."
see paras. [0231]-[0232]: [0231] Sample Dialog: "I'd like to coach you on the goals you missed-situation and consequences. OK?" [0232] "You don't seem to like me coaching you, but I really think it would help you. May I give you a little coaching this time?")
Per claim 17, a system, comprising: a computing system, comprising: at least one first processor; and a first non-transitory computer readable medium communicatively coupled to the at least one first processor, the first non-transitory computer readable medium having stored thereon computer software comprising a first set of instructions (see para. [0004] This invention relates generally to a digital character with artificial intelligence and improvisational behaviors and other life-like qualities. More particularly, it relates to a computer-based customizable expert agent as well as software system and corresponding program products for customizing the expert agent's expertise for use in particular applications. The software system and program products utilize existing technology to enable conversational as well as other sorts of interactions between the customizable expert agent and real people. The customizable expert agent can operate over a local or global computer network, over a wireless network, or locally on a computer or a computer-enabled device.
see para. [0134] Pentium-class processor;
see para. [0143]: STOW CAT can install and perform acceptably on a Pentium III class system with 600 MHz processor, 128 MB RAM, at least 100 MB free hard drive space, TCP/IP-capable network connection and Windows 2000/XP operating system. Because it is written in Java, STOW CAT also can install and perform acceptably on most Unix and Linux platforms).
Regarding claim 2, Hayes-Roth discloses wherein the computing system comprises at least one of a server, an Al system, a ML system, an AI/ML system, a deep learning ("DL") system, a user interactive system, a customer interface server, a social skills training system, a behavioral skills training system, a behavioral therapy system, an education server, an education facility computing system, a cloud computing system, or a distributed computing system, wherein the UI comprises one of a voice-only UI, a telephone communication UI, a video-only UI, a video with voice UI, a chat UI, a software application ("app") UI, a holographic UI, a virtual reality ("VR") -based UI, an augmented reality ("AR") -based UI, a mixed reality ("MR") -based UI, or a web- portal -based UI. (see para. [0015]: The present invention advantageously utilizes the patented AI technologies to create customizable expert agents that can have personalized conversational interactions with learners/customers much like human expert agents. The customizable expert agent of the present invention combines natural language conversation, animated gestures, subject expertise, and access to various electronic resources to create enjoyable and effective online experiences in a variety of contexts. The preferred embodiment of the present invention operates over a computer network, e.g., a World Wide Web (web), utilizing client-server technologies. However, it will be apparent to one skilled in the art that the invention could operate entirely within a single computer or computer-enabled device as well.)
Regarding claim 3, Hayes-Roth discloses wherein the one or more social skills comprise at least one of social coordination skills, mentoring skills, negotiation skills, persuasion skills, psychosocial service orientation skills, social perceptiveness skills, active listening skills, delegation skills, decision-making skills, problem-solving skills, creative thinking skills, critical thinking skills, communication skills, interpersonal skills, self-awareness skills, empathy skills, assertiveness skills, equanimity skills, psychological resilience skills, or coping skills (see para. [0051]: Although the embodiment above teaches a customizable expert sales agent for a class of retail clothing applications, the customizable expert agent can be realized for many other forms of expertise and classes of applications, including, but not limited to the following: expert sales agents for autos, real estate, computers, consumer products and electronics; expert coaches for soft skills, hard skills, athletic skills, game skills, management skills, sales skills, customer service skills, team skills, negotiation, ethics, parenting, peer interactions, partner interactions; expert tutors for a variety of subject matter; expert agents for advising, influencing, interviewing, persuading, or learning about users; expert agents for entertaining or role playing with users.
see para. [0056]: A specific embodiment of the present invention will now be described in which the expert agent is a coach (Expert Coach) having general (function/category) expertise in coaching and application-specific (sub-function/sub-category) expertise in coaching "people skills." That is, skills for interacting effectively with other people.).
Regarding claim 4, Hayes-Roth discloses further comprising at least one of: using a first set of AI/ML-driven personas to assist the user in learning a first set of social skills among the one or more social skills; and using a second set of AI/ML-driven personas that is different from the first set of AI/ML-driven personas to assist the user in learning a second set of social skill among the one or more social skills that is different from the first set of social skills; or using a first set of social training strategies among a plurality of social training strategies for assisting the user in learning the first set of social skills; and using a second set of social training strategies among the plurality of social training strategies that is different from the first set of social training strategies for assisting the user in learning the second set of social skills,
wherein the plurality of social training strategies includes encouragement of use of at least one of self-reflection, meditation, social setting simulation, empathetic interaction, behavioral adjustment, conversation training, psychotherapy treatment, cognitive behavioral therapy ("CBT") or computerized CBT, dialectical behavior therapy, simulated hypnotherapy, art therapy, or learning using a combination of two or more of said social training strategies (see para. [0060] The Expert Coach displays application-independent coaching expertise in her pedagogical strategy: provide the user an overview of target skills, assess the user's current skills in a role play, give the user feedback on performance of component skills in the role play, tutor the user on weak skills identified in the role play, repeat the assessment-feedback-tutoring loop until all skills are perfect in role play, repeat the assessment-feedback-coaching loop on a second role play until all skills are perfect in that role play. The Expert Coach also displays application-independent content in some of her motivational dialogue, for example, "Excellent! You mastered all skills on your first try." The Expert Coach displays application-specific content in her use of particular learning objects, such as the "Introduction," "Examples," and "Study Material" objects. She uses application-specific learning objects for the "Linda" and "Ed" role play objects. She also uses application-specific dialogue, for example, "You need to work more on Communication.").
Regarding claim 8, Hayes-Roth discloses further comprising: analyzing, by the computing system and using one of the at least one AI/ML model, the interaction to identify one or more observable characteristics of the user, the one or more observable characteristics including at least one of one or more speech patterns of the user, a language used by the user, whether the user has an accent, what accent the user has, one or more non-verbal cues of the user, a demeanor of the user, a sentiment of the user, or an emotional state of the user (see paras. [0043]; [0201] to [0205]: e.g., the Al already identifies one or more observable characteristics of the user, including language preferred by the user and/or providing training regarding a language that the user is using, etc.);
accessing and analyzing, by the computing system and using one of the at least one AI/ML model, stored information associated with the user to identify one or more conversation points, the stored information including at least one of account information associated with the user, contact information associated with the user, order history data associated with the user, previous interactions with the user, historical data associated with the user, demographic information about the user, personal information about the user, user-volunteered information regarding general interests of the user, information regarding a market segment within which the user is classified, or societal information for a societal segment to which the user belongs (see paras. ([0181]; [0188] to [0190]; [0202] to [0205]: e.g., the system already comprises a database-such as, a Learner Profile Database; and thereby, stores the learning activities or events of the user. In this regard, the learner profile database above corresponds to the account information associated with the user. In addition, when launching a training, the Al agent generates a dialog thar reflects the state of the previous training session-such as, "Ho/a Joan, welcome back to Spanish for Au Pairs!':· and this also indicates that the Al agent assesses and analyzes the stored information to identify one or more conversation points); and
causing, by the computing system, the at least one AI/ML-driven persona to adapt by modifying the interaction with the user, based at least in part on at least one of the identified one or more observable characteristics of the user or the identified one or more conversation points, to enhance or improve the interaction with the user (see paras. ([0202] to [0205]: e.g., per the scenario discussed above, the Al agent greets the user using a pertinent dialog, "Ho/a Joan, welcome back to Spanish for Au Pairs!", when the user returns to that training session. Accordingly, the above already indicates the process of adapting the Al driven persona by modifying the interaction with the user based at least on the identified one or more conversation points; and this archives the intended purpose of enhancing/improving the interaction with the user).
Regarding claim 9, Hayes-Roth discloses wherein the at least one AI/ML-driven persona is among a plurality of AI/ML-driven personas comprising at least one of one or more personas based on a fictional literary character, one or more personas based on a non-fictional literary character, one or more personas based on a comic-book character, one or more personas based on a cartoon character, one or more personas based on an anime character, one or more personas based on a manga character, one or more personas based on a television character, one or more personas based on a movie character, one or more personas based on a character from an advertisement, one or more personas based on a mascot, one or more personas based on a meme, one or more personas based on an athlete, one or more personas based on a sports personality, one or more personas based on a news personality, one or more personas based on a political personality, one or more personas based on a reality television personality, one or more personas based on a social media influencer, one or more personas based on a living celebrity, one or more personas based on a deceased celebrity, one or more personas based on a historical figure, one or more person as based on a fictionalization of a historical figure, one or more personas based on a character played by an actor or actress, one or more personas based on a bespoke character, or one or more personas simulating average humans in a geographical area within which the user is currently located or was previously residing (See paras. [00014]; [0015]; [0039]: e.g., one or more of the personas of the Al agent is based on at least a fictional/literary character).
Regarding claim 10, Hayes-Roth discloses wherein the UI is a visual-based UI, wherein the method further comprises: generating, by the computing system, an avatar for each of the at least one AI/ML-driven persona; displaying, by the computing system and within the UI, the avatar for each of the at least one AI/ML-driven persona (see paras. [0014]; [0015]; [0041]; also see FIG 1A to FIG 1P: e.g., the system generates one or more Al agent in the form of a digital character; the digital character is customizable according to one or more desired personalities to interact and/or coach the user; and wherein, during interaction with the user, the digital character is displayed to the user via a user-interface; see FIG 1A to FIG 1P. Thus, the digital character above corresponds to an avatar); and animating, by the computing system and within the UI, the avatar in synchronization with the conversation with the user, wherein the interaction further comprises the animation of the avatar (see paras. ([0015]; [0053]; [0184]: e.g., the Al agent, which is in the form of the digital character/avatar, is interacting with the user in a human-like manner; and wherein the character is also animated during interactions such as, the character gestures and/or performs actions during interaction with the user, etc. Accordingly, the above indicates the process of animating the avatar in synchronization with the conversation with the user; wherein the interaction comprises the animation of the avatar).
Regarding claim 11, Hayes-Roth discloses wherein each AI/ML-driven persona has a set personality, the set personality including at least one of a set speech pattern, a set mannerism, a set command of one or more languages, a set accent, a set collection of non-verbal cues, or a set collection of emotional demeanors (see paras. ([0014]; [0015]; [0043]: e.g., the Al agent already possesses different personalities, moods, and other life-like qualities; and wherein the Al agent also interacts with the user according to the user's preferred language. Thus, Al/ML driven persona already has a set personality that includes at least one of a set mannerism, a set command of one or more languages, etc.), wherein each of the interaction, the conversation, the one or more first conversational threads, and the animation of each AI/ML-driven persona is performed in a manner consistent with the set personality of said AI/ML-driven persona (see paras. ([0015]; [0040]; [0046]; [0050]; [0184]: e.g., during interaction with the user, the Al agent converses with the user in a human-like manner; such as, expressing empathy, initiating a topic/question relevant to the user's current activity, etc., and furthermore, the Al agent, which is already in the form of a human digital character/avatar, gestures and/or performs relevant actions during interaction with the user, etc. Accordingly, each of the interaction, the conversation, the one or more first conversational threads, and the animation of each Al/ML-driven persona is performed in a manner consistent with the set personality of said Al/ML-driven persona).
Regarding claim 14, Hayes-Roth discloses further comprising at least one of:
adapting or adjusting, by the computing system and using one of the at least one AI/ML model, one or more interaction characteristics of one or more of the at least one AI/ML-driven persona to match to a determined corresponding interaction characteristic of the user, the one or more interaction characteristics including at least one of speech pattern, language, accent, cultural mannerisms, cultural phraseology, general mannerisms, general phraseology, slang, jargon, or sentiment (see paras. [0015]; [0042]; [0043]; [0046] to [0050]: e.g., the Al agent learns, from one or more interactions it performed with the user, one or more attributes related to the user-such as, the user's interaction style and/or interests, etc., and thereby, the Al agents personalizes its interaction and/or services accordingly. Thus, the above indicates the process of adapting or adjusting (i) a personality of one or more of the at least one Al/ML-driven persona to mold to or match a determined personality of the user, which is determined based on at least one of analysis of: (a) the interaction with the user, (b) a previous interaction with the user, or (c) known information about the user, or (ii) one or more interaction characteristics of the at least one Al/ML-driven persona to match to a determined corresponding interaction characteristic of the user-such as, characteristic relating to language, general mannerisms, or sentiment).
Regarding claim 15, Hayes-Roth discloses further comprising: determining, by the computing system and using one of the at least one AI/ML model, a structure of the interaction with the user, based at least in part on the first goal (see para. ([0042]: e.g., based on at least one goal-such as, at least one product that the user is trying to purchase, the Al agent determines a relevant structure for interacting with the user in order to achieve the goal; such as, a structure that involves engaging the user with one or more relevant questions, etc. The above indicates cates the process of determining, by the computing system and using one of the at least one Al/ML model, a structure of the interaction with the user, based at least in part on the first goal); and determining, by the computing system and using one of the at least one AI/ML model, one or more first parameters for the determined structure of the interaction with the user, the one or more first parameters defining conversational guardrails for steering the interaction away from conversational tangents and toward achieving the first goal; wherein the one or more first conversational threads are generated based on the one or more first parameters (see para. ([0045]: e.g., when the Al agent determines, based on the interaction with the user, that the user is steering away from the goal of purchasing the product, the Al agent determines one or more conversational parameters that it must use in order to steer the interaction towards achieving the goal. For instance, when the Al agent determines that the user is hesitating to buy the product, the Al agent promptly provides one or more pertinent remarks that encourages the user to buy the product, etc. Accordingly, the teaching above indicates the process of determining one or more first parameters for the determined structure of the interaction with the user, the first parameter(s) define conversational guardrails for steering the interaction away from conversational tangents and toward achieving the first goal; and thereby it generates one or more first conversational threads based on the above parameter(s)).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Hayes-Roth (US 2003/0028498).
Regarding claim 12, Hayes-Roth teaches the claimed limitations as discussed above per claim 10. Hayes-Roth further discloses further comprising, in response to user selection of a gamification mode, performing the following: generating, by the computing system, a visual representation of a list of one or more goals for learning the at least one set of social skills, the one or more goals for learning the at least one set of social skills including the first goal (see paras. ([0354] to [0357]: e.g., the system provides a user interface that displays to the user/learner one or more goals;