Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
In the preliminary amendment dated 04/17/2025, the following occurred: claims 1-20 were canceled. Claims 21-40 were added.
This is the first action on the merits. Claims 21-40 are currently pending.
Priority
This application claims priority from Provisional Application Nos. 63108735 dated 11/02/2020.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 10/31/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 21-40 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.
Claims 21, 32 and 35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method for a therapeutic platform.
Regarding claims 21, 32 and 35, the limitation of (claim 21 being representative) receiving one or more user inputs of a user and feedback based on a user consumption of one or more prior therapeutic digital content; updating […], based on the feedback, to output an updated […]; generating a script, based on the one or more user inputs; determining therapeutic and/or educational digital content by applying the script as an input to […]; and receiving the therapeutic and/or educational digital content as drafted, is a process that, under the broadest reasonable interpretation, covers a method organizing human but for the recitation of generic computer components. That is other than reciting one or more processors, the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). For example, but for the one or more processors, the claims encompass receiving one or more user inputs and feedback, updating based on the feedback, generating a script, determining therapeutic and/or educational digital content and receiving the therapeutic and/or educational digital content in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The claim further recites “a decision model”, “updated decision model” and “a therapeutic and/or educational machine learning platform”. When given their broadest reasonable interpretation in light of the disclosure, the decision model, the updated decision model and the therapeutic and/or educational machine learning platform represents the creation of mathematical interrelationships between data. See Specification at Para. 0047, 0073, 0095, 0099, 00107-00111, 00117, 00120-00127, 00131 and 00147. As such, the decision model of a script generator, the updated decision model and the therapeutic and/or educational machine learning platform represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
This judicial exception is not integrated into a practical application. In particular, claims 21, 32 and 35 recite the additional elements of one or more processors. This additional element is not exclusively defined by the applicant and is recited at a high-level of generality (i.e., a generic computer components for enabling access to medical information or for performing generic computer functions. See Spec at para. 0045, 0065) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, even in combination, these additional elements do 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 an abstract idea.
Claims 21, 32 and 35 further recite the additional element of at a script generator, at least one of a therapeutic and/or educational content database and a content generator. These additional element are recited at a high level of generality (i.e. a general means to output/receive/transmit data) and amount to extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
The claims further recite the additional elements of a decision model, an updated decision model and a therapeutic and/or educational machine learning platform. As explained above these additional elements represents a mathematical concept. This mathematical concept represents (“apply it’) the abstract idea. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of one or more processors to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element a script generator, at least one of a therapeutic and/or educational content database and a content generator were considered extra-solution activity. This has been re-evaluated under “significantly more” analysis and determined to be well-understood, routine and conventional activity in the field. MPEP 2016.05(d)(II) indicates that receiving and/or transmitting data over a network has been held by the courts to be well-understood, routine and conventional activity (citing Symantec, TLI Communications, OIP Techs., and buySAFE). Well-understood, routine and conventional activity cannot provide an inventive concept (“significantly more”). Therefore when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a decision model, an updated decision model and a therapeutic and/or educational machine learning platform were determined to be the application of mathematical relationships to the identified abstract idea. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP2106.05(1)(A) indicates that merely saying “apply it’ or equivalent to the abstract idea cannot provide an inventive concept (“significantly more’). As such the claim is not patent eligible.
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 22-31, 33-34 and 36-40 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 22 further merely describe(s) generating the script. Claim(s) 22 also include the additional element of “a script machine learning model” which is interpreted to be (“apply it") the abstract idea. MPEP 2106.04(d)(I) and MPEP2106.05(1)(A) indicate that merely saying (“apply it") or equivalent to the abstract idea cannot provide a practical application or significantly more. As such the claim is not patent eligible. Claim(s) 23 further merely describe(s) the therapeutic and/or educational machine learning platform. Claim(s) 24 further merely describe(s) the thematic engine requests. Claim(s) 23 also include the additional elements of “one or more engines including one or more of a scripting engine, a generational engine, and a thematic engine” and claim(s) 24 includes the additional elements of “the thematic engine” which are interpreted the same as the script machine learning model above and do not provide practical application or significantly more for the same reason. Claim(s) 25 and 26 further merely describe(s) the content generator. Claim(s) 26 also include the additional elements of “a generative adversarial network (GAN) engine” which is interpreted the same as the script machine learning model above and does not provide practical application or significantly more for the same reason. Claim(s) 27 and 28 further merely describe(s) the feedback. Claim(s) 27 also include the additional elements of “one or more sensors” which is recited at a high level of generality (i.e. a general means to generate data) and does not provide practical application or significantly more. Claim(s) 28 also include the additional elements of “a wearable device, a medical device, a patch sensor, a biometric sensor, or a motion sensor.” which is interpreted the same as “the one or more sensors” above and does not provide a practical application or significantly more for the same reason. Claim(s) 29 further merely describe(s) the one or more user inputs. Claim(s) 30 further merely describe(s) providing holotropic breathwork guidance, relaxation breathing, mindfulness breathwork, and/or yogic breathing, the holotropic breathwork guidance. Claim(s) 31 further merely describe(s) determining a dosage amount of the therapeutic and/or educational digital content. Claim(s) 33 and 34 further merely describe(s) the one or more user states. Claim(s) 36 further merely describe(s) determining whether the attributes include three-dimensional objects, two-dimensional objects, or a combination of two-dimensional objects and three-dimensional objects and generating three-dimensional objects, two-dimensional objects, or a combination of the two-dimensional and three- dimensional objects. Claim(s) 37 further merely describe(s) selecting a scenery generative adversarial network. Claim(s) 38 further merely describe(s) the generative adversarial network engine. Claim(s) 37 and 38 also include the additional elements of “a scenery generative adversarial network” which is not exclusively defined by the applicant, is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component, and does not provide practical application or significantly more. Claim(s) 39 further merely describe(s) the terrain component. Claim(s) 40 further merely describe(s) the character component.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 21-40 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Abrahami (US 2017/0039045).
REGARDING CLAIM 21
Abrahami discloses a computer-implemented method for treating a medical condition or disruption in well-being via a digital therapeutic platform, the method comprising: receiving, by one or more processors, one or more user inputs of a user and feedback based on a user consumption of one or more prior therapeutic digital content (at [0033] teaches a pre-session questionnaire, allowing the user to provide some input data to the script (interpreted by examiner as receiving one or more user inputs of a user) and a post-session questionnaire, allowing the user to provide some feedback to the system on its effectiveness (interpreted by examiner as receiving feedback based on a user consumption of one or more prior therapeutic digital content) [0045] teaches providing output that is at least one of: a stimulus, a media element and an instruction (interpreted by examiner as therapeutic digital content)); updating, by the one or more processors, a decision model of a script generator, based on the feedback, to output an updated decision model ([0041] teaches the script handler includes a script updater to update the scripts from updates from the machine learner, the decision maker and the integrator (interpreted by examiner as updating a decision model of a script generator, based on the feedback, to output an updated decision model)); generating, by the one or more processors, a script at the updated decision model of the script generator, based on the one or more user inputs ([0033] teaches script customization (interpreted by examiner as script generator) and allowing the user to provide input data to the script interpreted by examiner as user input and generating script based on user input) and [0108] teaches a script designer system may communicate with system server to upload scripts, and script updates); determining, by the one or more processors, therapeutic and/or educational digital content by applying the script as an input to a therapeutic and/or educational machine learning platform ([0090] teaches media content. [0602] teaches employing multiple machine learning approaches and data representation in each script computation round. The approaches and data representations may include (for example) any of the following or a combination thereof: decision tree learning or regression trees, a supervised learning method that is trained with the most effective feature vector, where each decision tree node tests a feature and eventually used to build the model to predict the target values that are used to discriminate and classify the best script's path [0616] teaches ML engine may further locate additional users and draw from their experiences to train system for current users. [0617] teaches that system may employ multiple inter-related learning systems. For example, ML engine may train a learning system at the user level and a different one at the script level. ML engine may train the script level learning system which correlates session results from multiple users of the same script (interpreted by examiner as determining therapeutic and/or educational digital content by applying the script as an input to a therapeutic and/or educational machine learning platform); and receiving, by the one or more processors, the therapeutic and/or educational digital content, determined by the therapeutic and/or educational machine learning platform, from at least one of a therapeutic and/or educational content database or a content generator ([0085] The system may use an accumulating database (interpreted by the examiner as the therapeutic and/or educational content database or a content generator) of information about the current user (as well as other users), so to accurately assess the user's state at any time. This assessment may provide the scripts and the script creators with information helpful in delivering the relevant instructions correctly and with the right timing. The system may also provide information about the user's state and activities which can be used by the script to find the best way to reach the user's goals—therapeutic, behavior modification or otherwise.).
REGARDING CLAIM 22
Abrahami discloses the limitation of claim 21.
Abrahami further discloses:
The computer-implemented method of claim 21, wherein generating the script comprises: receiving a paradigm intervention as an output of a script machine learning model (Abrahami at [0112] teaches all script running and information processing is performed by system server and providing output to the user. [0302] teaches modeling the collected data. [0602] teaches building a model to predict the target values that are used to discriminate and classify the best script's path (interpreted by examiner as receiving a paradigm intervention as an output of a script machine learning model)).
REGARDING CLAIM 23
Abrahami discloses the limitation of claim 21.
Abrahami further discloses:
The computer-implemented method of claim 21, wherein the therapeutic and/or educational machine learning platform comprises one or more engines including one or more of a scripting engine, a generational engine, and a thematic engine, wherein one or more engines are configured to: determine therapeutic and/or educational digital content that is personalized and adapted to a user based on the one or more user inputs, wherein the therapeutic and/or educational digital content includes a virtual game, and wherein the therapeutic and/or educational digital content is one of updated based on the feedback or predicted based on the one or more user inputs (Abrahami at [0084] teaches an intelligent script engine (interpreted by examiner as the scripting engine) [0102] teaches an event handler generator (interpreted by examiner as the generational engine) and [0090] teaches server ML engine (interpreted by examiner as the thematic engine) [0090] teaches media content. [0602] teaches employing multiple machine learning approaches and data representation in each script computation round. The approaches and data representations may include (for example) any of the following or a combination thereof: decision tree learning or regression trees, a supervised learning method that is trained with the most effective feature vector, where each decision tree node tests a feature and eventually used to build the model to predict the target values that are used to discriminate and classify the best script's path [0616] teaches ML engine may further locate additional users and draw from their experiences to train system for current users. [0617] teaches that system may employ multiple inter-related learning systems. For example, ML engine may train a learning system at the user level and a different one at the script level. ML engine may train the script level learning system which correlates session results from multiple users of the same script (interpreted by examiner as determine therapeutic and/or educational digital content that is personalized and adapted to a user based on the one or more user inputs) [0268] teaches output may also include virtual reality (VR) and augmented reality (AR), which may be displayed (for example) using specialized VR/AR glasses technology (interpreted by examiner as the therapeutic and/or educational digital content includes a virtual game) [0091] teaches gathered user responses to scripts and content segments. [0499] teaches post session debriefing maybe be performed by post session de-briefer which may gather user feedback on system in general as well feedback on the specific script and session. Post session de-briefer may forward this collected post-session feedback to script handler to affect follow-up sessions accordingly and analyzer/server ML engine to be gathered into a per-script profile collected by system (e.g. are users satisfied with the script? Does it help them?) [0501] teaches the de-briefing may include in particular any problem or distress conditions (as described herein below) encountered during the session possibly inquiring user about them and determining if they should affect the session planning or future script use (interpreted by examiner as wherein the therapeutic and/or educational digital content is one of updated based on the feedback or predicted based on the one or more user inputs)).
REGARDING CLAIM 24
Abrahami discloses the limitation of claim 23.
Abrahami further discloses:
The computer-implemented method of claim 23, wherein the thematic engine requests the therapeutic and/or educational digital content from at least one of the therapeutic and/or educational content database or the content generator (Abrahami at [0090] teaches that system server may also be connected to a database. Database may store user information, scripts and media content for use by server ML engine (interpreted by examiner as the therapeutic and/or educational digital content from at least one of the therapeutic and/or educational content database or the content generator)).
REGARDING CLAIM 25
Abrahami discloses the limitation of claim 1.
Abrahami further discloses:
The computer-implemented method of claim 24, wherein the content generator is configured to generate content or modify content received from the therapeutic and/or educational content database (Abrahami at [0085] The system may use an accumulating database of information about the current user (as well as other users), so to accurately assess the user's state at any time. This assessment may provide the scripts and the script creators with information helpful in delivering the relevant instructions correctly and with the right timing. The system may also provide information about the user's state and activities which can be used by the script to find the best way to reach the user's goals—therapeutic, behavior modification or otherwise. [0268] teaches media deliverer may instruct media presenter to deliver output which may include visual content (interpreted by examiner as the content generator is configured to generate content or modify content received from the therapeutic and/or educational content database)).
REGARDING CLAIM 26
Abrahami discloses the limitation of claim 25.
Abrahami further discloses:
The computer-implemented method of claim 25, wherein the content generator comprises a generative adversarial network (GAN) engine and one or more machine learning agents configured to identify content for the user, wherein the GAN engine is configured to apply quantum mechanics to process user data (Abrahami at [0098] teaches a communication medium (interpreted by examiner as the GAN engine) [0038] teaches a script handler and a script runner (interpreted by examiner as the one or more machine learning agents configured to identify content for the user) [0032] teaches therapeutic scripts which can be played over any audio playback device and [0648] teaches therapeutic sessions and interacting with a user (interpreted by examiner as wherein the GAN engine is configured to apply quantum mechanics to process user data)).
REGARDING CLAIM 27
Abrahami discloses the limitation of claim 21.
Abrahami further discloses:
The computer-implemented method of claim 21, wherein the feedback is generated using one or more sensors (Abrahami at [0096] teaches feedback from the wearable elements and [0132] teaches wearable elements may include sensors (interpreted by examiner as the feedback is generated using one or more sensors)).
REGARDING CLAIM 28
Abrahami discloses the limitation of claim 27.
Abrahami further discloses:
The computer-implemented method of claim 27, wherein the feedback is collected using one or more of a wearable device, a medical device, a patch sensor, a biometric sensor, or a motion sensor (Abrahami at [0096] teaches feedback from the wearable elements (interpreted by examiner as the feedback is collected using one or more of a wearable device,)).
REGARDING CLAIM 29
Abrahami discloses the limitation of claim 21.
Abrahami further discloses:
The computer-implemented method of claim 21, wherein the one or more user inputs include a medication and wherein the therapeutic digital content is selected, at least in part, based on the medication (Abrahami at [0091] teaches receiving data about user and his state during multiple in session and out of session periods as collected by numerous user client hubs, and may analyze and integrate this information to make decisions regarding the current treatment (interpreted by examiner as the one or more user inputs include a medication) and [0303] teaches suggesting different treatments for behavioral difficulties and matching appropriate treatment methods in the form of specific scripts, script parameters or the use of human therapists (interpreted by examiner as wherein the therapeutic digital content is selected, at least in part, based on the medication)).
REGARDING CLAIM 30
Abrahami discloses the limitation of claim 21.
Abrahami further discloses:
The computer-implemented method of claim 21, further comprising providing holotropic breathwork guidance, relaxation breathing, mindfulness breathwork, and/or yogic breathing, the holotropic breathwork guidance being generated based at least in part on the therapeutic digital content provided to the user (Abrahami at [0197] teaches breath analysis and [0621] teaches a capability that allows a designer to specify that a given segment of music will be synchronized with breathing patterns of user (interpreted by examiner as providing holotropic breathwork guidance being generated based at least in part on the therapeutic digital content provided to the user)).
REGARDING CLAIM 31
Abrahami discloses the limitation of claim 21.
Abrahami further discloses:
The computer-implemented method of claim 21, further comprising determining a dosage amount of the therapeutic and/or educational digital content by the script generator, the dosage amount being determined based on at least one of the one or more user inputs or the feedback (Abrahami at [0277] teaches media deliverer determine which media files to play, to determine how to play given media files and how to provide alternative media selection i.e. replace a given media file in a script with another one (interpreted by examiner as means to determine a dosage amount of the therapeutic and/or educational digital content by the script generator, the dosage amount being determined based on at least one of the one or more user inputs or the feedback)).
REGARDING CLAIMS 32 and 35
Claims 32 and 35 are analogous to Claims 21-31 thus Claims 32 and 35 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 21-31. Abrahami further teaches [0047]-[0048] teach the script runner transitions to a new state upon receipt of an analysis based on at least one of: user profile information, explicit subject feedback, subject's cognitive or neurological state, neurological state reading, physiological sensors reading, subject behavior detection, post-session feedback from the subject and information from other subjects. [0054] teaches cognitive state monitoring. Moreover, [0037] teaches sense an attribute of a subject or to provide an output to the subject.
REGARDING CLAIM 33
Abrahami discloses the limitation of claim 32.
Abrahami further discloses:
The computer-implemented method of claim 32, wherein the one or more user states is one or more of metabolic disease, immunologic disease, neoplastic disease, endocrine disease, cardiovascular disease, neurodegenerative disease, bone disease Alzheimer's disease, cancer, panic disorder, stroke, Generalized Anxiety Disorder (GAD), Post-traumatic Stress Disorder (PTSD), corona phobia depression, selective mutism, agoraphobia, bipolar disorder, Attention Deficit Hyperactivity Disorder (ADHD), Obsessive-compulsive Disorder (OCD), or social anxiety (Abrahami at [0048] teaches subject's cognitive or neurological state, neurological state reading, physiological sensors reading, subject behavior detection, [0290] teaches the user's emotional state, mental/physiological state information (e.g. anxiety, stress, fatigue)).
REGARDING CLAIM 34
Abrahami discloses the limitation of claim 1.
Abrahami further discloses:
The computer-implemented method of claim 32, wherein the one or more user states is based on one or more of biometric data, electroencephalogram (EEG), heart rate, average heart rate, heart rate viability (HRV), resting heart rate (RHR), respiratory rate, pulse, eye tracking, pupil dilation, facial recognition, pulse oxygen, air quality, user temperature, functional MRI, brain blood flow, Magnetic Resonance Imaging (MRI), gray matter volume, white matter volume, blood flow, eye attribute, mouth attributes, smell attributes, blood attributes, muscle or bone attributes heart attributes, lung attributes, liver attributes, family history of risks, genetic history, genetic risks, digital body representation, digital body scans, sleep patterns, taste attributes, hormone levels, or gastrointestinal tract attributes (Abrahami at [0035] teaches physiological sensor is EEG (Electroencephalography)).
REGARDING CLAIM 36
Abrahami discloses the limitation of claim 1.
Abrahami further discloses:
The computer-implemented method of claim 35, further comprising: determining via the generative adversarial network engine whether the attributes of the therapeutic digital content include three-dimensional objects, two-dimensional objects, or a combination of two-dimensional objects and three-dimensional objects; and generating, via the generative adversarial network, three-dimensional objects, two-dimensional objects, or a combination of the two-dimensional and three- dimensional objects (Abrahami at [0135] teaches the wearable elements to implement virtual reality (VR)/augmented reality (AR) (interpreted by examiner as content include three-dimensional objects, two-dimensional objects, or a combination of two-dimensional objects and three-dimensional objects)).
REGARDING CLAIM 37
Claim 37 is analogous to Claims 21-31, 35 and 36 thus Claim 37 is similarly analyzed and rejected in a manner consistent with the rejection of Claims 21-31, 35 and 36.
REGARDING CLAIM 38
Abrahami discloses the limitation of claim 1.
Abrahami further discloses:
The computer-implemented method of claim 35, wherein the generative adversarial network engine includes a scenery generative adversarial network, wherein the scenery generative adversarial network includes a terrain component and a character component (Abrahami at [0186] teaches a hologram-based UI including such technologies as virtual keyboard/touch-screen or other UI, avatar based UI etc. may also be used. Such a hologram-based UI may be highly customizable to the specific preferences of user 5, including those gathered from any in session information recording and other calibration information and [0268] teaches the output may also include virtual reality (VR) and augmented reality (AR), which may be displayed (for example) using specialized VR/AR glasses technology or using regular screens (similar to regular video delivery). [0509] teaches the system may also modify AR-based content based on what is viewed (e.g. indoors, outdoors, or specific scene. For example, script controller 2013 may move AR content elements so not to hide important parts of the viewed scene (interpreted by examiner as the generative adversarial network engine includes a scenery generative adversarial network, wherein the scenery generative adversarial network includes a terrain component and a character component)).
REGARDING CLAIM 39
Abrahami discloses the limitation of claim 1.
Abrahami further discloses:
The computer-implemented method of claim 38, wherein the terrain component generates a terrain for a user interaction (Abrahami at [0083] teaches integrated interactions spanning both in session and out of session periods and [0118] teaches the interaction of user 5 with the system 100 is conducted during in session and out of session periods (interpreted by examiner as means to generate a terrain for a user interaction)).
REGARDING CLAIM 40
Abrahami discloses the limitation of claim 1.
Abrahami further discloses:
The computer-implemented method of claim 38, wherein the character component generates one or more characters into a scenery (Abrahami at [0269] teaches media deliverer may also use hologram based delivery which may include specific avatars, and may also encompass user (interpreted by examiner as one or more characters)).
Conclusion
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Erpenbach (US 2020/0035359) discloses cognitive systems for generating prospective medical treatment guidance.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIZA TONY KANAAN whose telephone number is (571)272-4664. The examiner can normally be reached on Mon-Thu 7:30am-5:30pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LIZA TONY KANAAN/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683