DETAILED ACTION
Status of Claims
Claims 1-21 are pending in this application, with claims 1, 12 and 21 being independent.
Notice of 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 .
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.
Obligation Under 37 CFR 1.56 – Joint Inventors
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Drawings
The drawings were received on December 11, 2023. These drawings are acceptable.
Claim Objections
Claim 2 is objected to because of the following informalities: Claim 2 is misnumbered as claim 1 (e.g., “1. The method of claim 1,”). Appropriate correction is required.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 6-7, 9-10, 12-14, 17, 19 and 21 are rejected under 35 U.S.C. 102 (a)(1) and/or 102 (a)(2) as being anticipated by ABRAMSON et al. (US 2018/0293483, hereinafter “ABRAMSON”).
Regarding claim 1, ABRAMSON discloses a computer-implemented method (¶ [0014]: “systems and methods of creating a conversational chat bot of a specific person (or specific entity).” ¶ [0030]: “ FIG. 3 illustrates an example method of creating a conversational chat bot of a specific person as described herein. In aspects, method 300 may be executed by an exemplary system such as system 100 of FIG. 1. In examples, method 300 may be executed on a device comprising at least one processor configured to store and execute operations, programs or instructions. However, method 300 is not limited to such examples. In other examples, method 300 may be performed on an application or service for creating and/or implementing a conversational chat bot or LU model. In at least one example, method 300 may be executed (e.g., computer-implemented operations) by one or more components of a distributed network, such as a web service/distributed network service (e.g. cloud service).”) comprising:
receiving a user input (e.g., ¶ [0014]: “receive input via a user interface”) that includes a description (e.g., ¶ [0031]: “a description,”) of an artificially intelligent (AI) character (e.g., ¶ [0031]: “a chat bot or LU model.”) (¶ [0014]: “the user creating/training the chat bot” ¶ [0020]: “In aspects, client devices 102A-C may be configured to receive input via a user interface component or other input means. Examples of input may include voice, visual, touch, and text input.” ¶ [0031]: “Example method 300 begins at operation 302 where a request associated with a specific person or entity is received. In aspects, a computing device, such as input processing unit 200, may receive a request to generate, train or modify a chat bot or LU model. The request may comprise information associated with a specific person or entity, such as a name, a nickname, an occupation, an associated time period (e.g., lifetime, period in office, playing career, etc.), a description, etc. The information may be used to identify one or more data sources comprising (or potentially comprising) data related to the specific person or entity.” ¶ [0052]: “Aspects of the present disclosure provide a system comprising: at least one processor; and memory coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor, performs a method for creating a conversational chat bot of a specific entity, the method comprising: receiving a request associated with a specific entity; accessing social data associated with the specific entity, the social data comprising at least one of images of the specific entity, voice data for the specific entity, conversational data associated with the specific entity, and publicly available information about the specific entity; using the social data to create a personality index, wherein the personality index comprises personality information for the specific entity; and using the personality index to train a chat bot to interact conversationally using the personality of the specific entity.”);
in response to receiving the user input (e.g.,. ¶ [0032]: “cause the index-generation component to generate a personality index (or an instance thereof) as part of receiving the request received at operation 302.”), automatically constructing the AI character (¶ [0014]: “creating a conversational chat bot of a specific person (or specific entity).” ¶ [0030]: “FIG. 3 illustrates an example method of creating a conversational chat bot of a specific person as described herein. In aspects, method 300 may be executed by an exemplary system such as system 100 of FIG. 1. In examples, method 300 may be executed on a device comprising at least one processor configured to store and execute operations, programs or instructions. However, method 300 is not limited to such examples. In other examples, method 300 may be performed on an application or service for creating and/or implementing a conversational chat bot or LU model. In at least one example, method 300 may be executed (e.g., computer-implemented operations) by one or more components of a distributed network, such as a web service/distributed network service (e.g. cloud service).” ¶ [0031]: “Example method 300 begins at operation 302 where a request associated with a specific person or entity is received. In aspects, a computing device, such as input processing unit 200, may receive a request to generate, train or modify a chat bot or LU model. The request may comprise information associated with a specific person or entity, such as a name, a nickname, an occupation, an associated time period (e.g., lifetime, period in office, playing career, etc.), a description, etc. The information may be used to identify one or more data sources comprising (or potentially comprising) data related to the specific person or entity. At operation 304, social data for the specific person/entity may be accessed.” ¶ [0032]: “At operation 306, a personality index may be created using social data. In aspects, a computing device may have access to index-generation component, such as index engine 206. The index-generation component may have access to one or more sources of social data, such as data store(s) 204. In examples, the computing device may cause the index-generation component to generate a personality index (or an instance thereof) as part of receiving the request received at operation 302. The computing device may provide the index-generation component information identifying a specific person or entity. As a result, the index-generation component may identify and/or collect data related to the identified specific person/entity from the one or more sources of social data. The identified/collected data may then be processed and applied to the personality index (e.g., a generic personality index); thereby, creating a personalized personality index in the theme of the specific person/entity. For example, a machine learning model may analyze a set of social data to identify and categorize content, content attributes, content authors/contributors, data sources, etc. Such an analysis may include categorizing the social data by type (e.g., textual data, audio data, image data, etc.), determining the source/author(s) of the social data (e.g., a specific person/entity, one or more other persons similar to a specific person/entity, subject matter experts, random users, etc.), determining the degree of similarity between a specific person/entity and alternate sources/authors, identifying question and answer pairs, identifying dialogue expressions, etc.” ¶ [0052]: “Aspects of the present disclosure provide a system comprising: at least one processor; and memory coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor, performs a method for creating a conversational chat bot of a specific entity, the method comprising: receiving a request associated with a specific entity; accessing social data associated with the specific entity, the social data comprising at least one of images of the specific entity, voice data for the specific entity, conversational data associated with the specific entity, and publicly available information about the specific entity; using the social data to create a personality index, wherein the personality index comprises personality information for the specific entity; and using the personality index to train a chat bot to interact conversationally using the personality of the specific entity.”) by:
generating a personality dataset based on the description (e.g., ¶ [0017]: “create a personalized personality index;” ¶ [0025]: “creation of a personalized personality index (e.g., a personality index corresponding to the personalized data for the specific person/entity).”), the personality dataset describing personality characteristics of the custom AI character (¶ [0052]: “wherein the personality index comprises personality information for the specific entity;” ) (¶ [0020]: “As an example, client devices 102A-C may provide access to social media data, user profile data, and image data for one or more people/entities. Such data may be locally stored on client devices 102A-C, or on one or more of server devices 106A-C. In some aspects, client devices 102A-C may have access to a personality index (or an instance thereof). The personality index may be a generic personality index or a personalized personality index. A generic personality index, as used herein, may comprise social data corresponding to a set of training data, a generic user, or a multitude of anonymous users. A personalized personality index, as used hereon, may comprise social data for one or more people/entities, one or more algorithms for processing social data and events (e.g., textual data, handwritten data, images, voice data, historical events, etc.), and processed data (e.g., dialogue slots and corresponding data, event and dialogue hypotheses, time period information, image tags and descriptions, voice font data, 2D/3D information, etc.). Client devices 102A-C may configure a generic personality index by applying social data to the generic personality index. For example, social data for a specific person may be applied to a generic personality index, thereby creating a personalized personality index for the specific person. In some aspects, the personalized personality index may change or evolve over time as social data and similar information is altered (e.g., added, modified, or removed) in the personalized personality index.” ¶ [0025]: “Index engine 206 may be configured to create a personality index. In aspects, index engine 206 may receive a request to generate a personality index. The request may be associated with one or more specific people or entities. In examples, a request may be transmitted to index engine 206 via interface 202, or received directly via an interface component accessible by a client or client device. In response to receiving the request, index engine 206 may access social data collected by interface 202 and/or stored by data store(s) 204. Index engine 206 may search for and collect social data associated with the one or more specific people or entities identified in the request. The social data associated with the one or more specific people or entities (“personalized data”) may be combined with a personality index (or a generic personality index) and processed to facilitate the creation of a personalized personality index (e.g., a personality index corresponding to the personalized data for the specific person/entity).” ¶ [0026]: “Processing the personalized data may further comprise determining and categorizing conversation data associated with people/entities similar to the specific person/entity identified in the request. In examples, determining similarities between a specific person/entity and another person/entity (e.g., the “other person”) may include using machine learned techniques and/or natural language processing techniques to analyze and compare the social data of the other person. Such an analysis/comparison may include the use of latent semantic indexing, latent Dirichlet processing, word and/or sentence embedding models, collaborative filtering techniques, entity graphs, Jaccard similarity, cosine similarity and/or translation models. Such an analysis/comparison may further include the use of approval indicators (e.g., “likes”/“dislikes,” display screen swipes, ratings, reviews, comments, watch lists, etc.) for social media data, music data, image data, etc. In at least one example, the analysis may include comparing one or more characteristics (e.g., traits, attributes, events, etc.) of the specific person/entity with the other person. Such characteristics may include demographic data (e.g., age, gender, income, employment, education, time period of lifetime, etc.), behavioral data (e.g., access dates/times, transaction trends, purchase history, frequented sites, dwell times, click data, etc.), stylistic content of data (e.g., style, diction, tone, voice, intent, sentence/dialogue length and complexity, etc.), psychographic data (e.g., user interests, opinions, likes/dislikes, values, attitudes, habits, etc.), and the like. In such an example, at least a subset of the characteristics may be provided to a scoring or comparison algorithm/model for evaluation. The scoring or comparison algorithm/model may generate and/or assign scores or labels to the evaluated characteristics. The scoring or comparison algorithm/model may use the generated scores/labels to determine a similarity score or metric for the other person. The similarity score/metric may represent the estimated similarity between a specific person/entity and the other person/entity. In aspects, the processed personalized data may be used to create, organize, populate or update a personalized personality index for the specific person/entity identified in the request.” ¶ [0052]: “Aspects of the present disclosure provide a system comprising: at least one processor; and memory coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor, performs a method for creating a conversational chat bot of a specific entity, the method comprising: receiving a request associated with a specific entity; accessing social data associated with the specific entity, the social data comprising at least one of images of the specific entity, voice data for the specific entity, conversational data associated with the specific entity, and publicly available information about the specific entity; using the social data to create a personality index, wherein the personality index comprises personality information for the specific entity; and using the personality index to train a chat bot to interact conversationally using the personality of the specific entity.”);
generating a voice dataset based on the description (e.g., ¶ [0022]: “access voice data (e.g., voice recordings, musical recordings, etc.)”), the voice dataset describing voice characteristics of the custom AI character (e.g., ¶ [0017]: “creating an accurate voice font using recordings of a specific person; applying a voice font of a specific person to a chat bot of the specific person;” ¶ [0022]: “create a voice font of a specific person.” ¶ [0022]: “The voice font may then be applied to a chat bot to enable the chat bot to converse in the voice of a specific person.”) (¶ [0022]: “In aspects, client devices 102A-C may provide for creating and/or applying a voice font to a chat bot. For example, client devices 102A-C may access voice data (e.g., voice recordings, musical recordings, etc.) comprised in social data, a personality index or other data sources. Speech recognition and/or speech synthesis techniques may be applied to the voice data to create a voice font of a specific person. The models and/or algorithms for implementing such techniques may be provided by client devices 102A-C, server devices 106A-C, or a separate device/service. The voice font may then be applied to a chat bot to enable the chat bot to converse in the voice of a specific person.” ¶ [0032]: “The analysis of social data may further include evaluating voice data in (or associated with) the social data. Such an evaluation may include using speech recognition and/or speech syntheses techniques to generate a voice font corresponding to a specific person/entity.” ); and
generating an appearance dataset (e.g., ¶ [0022]: “image data to create a 2D model of the specific person.” ¶ [0022]: “image data and/or 3D data”) based on the description (e.g.,), the appearance dataset describing a visual appearance of the custom AI character (¶ [0017]: “generating a 3D model of a specific person; and applying a 3D of a specific person to a chat bot of the specific person, among other examples.” ¶ [0022]: “image data and/or 3D data may be applied to a 3D modelling algorithm or service to create a 3D model of a person or entity.” ¶ [0022]: “a 2D or 3D model may be applied to a chat bot to enable immersive interactions with the likeness of a specific person/entity.”) (¶ [0022]: “In some aspects, client devices 102A-C may further provide for creating and/or applying a 2D or 3D model of a specific person to a chat bot. For example, client devices 102A-C may access image data to create a 2D model of the specific person. Additionally or alternatively, client devices 102A-C may access image data and/or 3D data (e.g., photos, images, depth information, color information, mapping information, etc.) comprised in social data, a personality index or other data sources. The image data and/or 3D data may be applied to a 3D modelling algorithm or service to create a 3D model of a person or entity. Alternately, client devices 102A-C may access a 3D modelling device. The 3D modelling device may be configured to perform a 3D scan of a person or entity, and/or access one or more previous 3D scans. In examples, a 2D or 3D model may be applied to a chat bot to enable immersive interactions with the likeness of a specific person/entity.” ¶ [0032]: “Further still, the analysis of social data may include generating and/or evaluating 2D/3D data in (or associated with) the social data. Such an evaluation may include using 2D/3D modelling techniques (and associated data) to generate a 2D or 3D model corresponding to a specific person/entity.”); and
providing the custom AI character (e.g., ¶ [0029]: “generate a personalized chat bot configured to interact conversationally in the personality of a specific person/entity.” ¶ [0034]: “At operation 308, a chat bot or LU model may be trained using a personality index (or personalized personality index).”) based on the personality dataset, the voice dataset, and the appearance dataset (¶ [0016]: “In aspects, a voice font of a specific person may be generated by applying speech recognition and/or a speech synthesis algorithm to one or more voice recordings in the social data. The voice recordings may be collected from the social data, one or more Internet of Things (“IoT”) data sources (such as personal digital assistants, natural language understanding systems, etc.), and the like. The voice font may be applied to the chat bot of a specific person. In aspects, a two-dimensional (“2D”) image of a specific person may be generated by applying a facial recognition/detection algorithm to one or more photos in the social data and/or collected from one or more other data sources. The 2D image may be applied to the chat bot of a specific person to create a more realistic, human-like chat experience. In some aspects, a 2D model (or a portion of the data used to create the 2D model) of a specific person may be used to generate a three-dimensional (“3D”) model of the specific person. For example, one or more images, depth information and/or color information may be provided as input(s) to a 3D modelling algorithm. The 3D modelling algorithm may generate a 3D model and facilitate the application of the 3D model to the chat bot of a specific person. The 3D model may provide for a more immersive and interactive experience (e.g., in a virtual reality, augmented reality, or mixed reality context) for a user interacting with the chat bot.” ¶ [0022]: “In aspects, client devices 102A-C may provide for creating and/or applying a voice font to a chat bot. For example, client devices 102A-C may access voice data (e.g., voice recordings, musical recordings, etc.) comprised in social data, a personality index or other data sources. Speech recognition and/or speech synthesis techniques may be applied to the voice data to create a voice font of a specific person. The models and/or algorithms for implementing such techniques may be provided by client devices 102A-C, server devices 106A-C, or a separate device/service. The voice font may then be applied to a chat bot to enable the chat bot to converse in the voice of a specific person. In some aspects, client devices 102A-C may further provide for creating and/or applying a 2D or 3D model of a specific person to a chat bot. For example, client devices 102A-C may access image data to create a 2D model of the specific person. Additionally or alternatively, client devices 102A-C may access image data and/or 3D data (e.g., photos, images, depth information, color information, mapping information, etc.) comprised in social data, a personality index or other data sources. The image data and/or 3D data may be applied to a 3D modelling algorithm or service to create a 3D model of a person or entity. Alternately, client devices 102A-C may access a 3D modelling device. The 3D modelling device may be configured to perform a 3D scan of a person or entity, and/or access one or more previous 3D scans. In examples, a 2D or 3D model may be applied to a chat bot to enable immersive interactions with the likeness of a specific person/entity.” ¶ [0029]: “Chat bot engine 208 may be configured to generate a chat bot or LU model. In aspects, input processing unit 200 may cause chat bot engine 208 to generate one or more chat bots (or instances thereof). Input processing unit 200 may then cause or facilitate the application of data from a personality index to the one or more generated chat bots. In examples, applying personalized data to a chat bot may generate a personalized chat bot configured to interact conversationally in the personality of a specific person/entity. Applying personalized data to a chat bot may also cause a voice font, a 2D image, or a 3D model of a specific person/entity to be applied to the chat bot.” ¶ [0034]: “The trained chat bot/LU model may be operable to interact conversationally in the personality of a specific person/entity associated with the personalized personality index. Interacting conversationally may include determining the a subject and/or intent for one or more expressions of a dialogue, identifying a data source comprising response data, determining whether response data is present in accessible data sources, generating and posing questions to supplement gaps and/or verify data in the data source data, etc.” ¶ [0034]: “In some aspects, training a chat bot/LU model may additionally include applying one or more visual or auditory characteristics or attributes to a chat bot/LU model. For example, a personality index may include (or have access to) a voice font, a 2D image and/or a 3D model of a specific person/entity associated with the personalized personality index. The voice font, a 2D image and/or a 3D model may be applied to the chat bot/LU model to provide a more immersive user experience for users interacting with the chat bot/LU model.” ¶ [0052]: “providing an index generation engine access to the stored social data. In some examples, the method further comprises: processing the social data using at least one of machine learning techniques and one or more rule sets; and applying the processed social data to the personality index to generate a personalized personality index.” ¶ [0052]: “In some examples, training the chat bot comprises applying to the chat bot at least one of a voice font of the specific entity, a 2D image of the specific entity, and a 3D image of the specific entity.” ¶ [0053]: “method for creating a conversational chat bot of a specific entity, the method comprising: receiving a request associated with a specific entity; accessing social data associated with the specific entity, the social data comprising at least one of images of the specific entity, voice data for the specific entity, conversational data associated with the specific entity, and publicly available information about the specific entity; using the social data to create a personality index, wherein the personality index comprises personality information for the specific entity; and using the personality index to train a chat bot to interact conversationally using the personality of the specific entity. In some examples, the specific entity corresponds to at least one of a friend, a relative, an acquaintance, a celebrity, a fictional character and a historical figure. In some examples, the personality index provides access to data from the specific entity and to a generalized chat index. In some examples, the method further comprises processing the social data using at least one of machine learning techniques and one or more rule sets, wherein processing the social data comprises identifying conversation data collected for the specific entity and identifying conversation data collected for one or more entities similar to the specific entity. In some examples, identifying conversation data collected for one or more entities similar to the specific entity comprises determining similarities between the one or more entities and the specific entity using at least one of expression analysis techniques, approval indicators, and characteristics comparisons. In some examples, the compared characteristics comprise at least one of demographic data, behavioral data, content style, and psychographic data.”).
Regarding claim 2 (depends on claim 1), ABRAMSON discloses:
wherein the personality characteristics comprise intelligence attributes (¶ [0026]: “demographic data (e.g., age, gender, income, employment, education, time period of lifetime, etc.),”), psyche attributes (¶ [0026]: “psychographic data (e.g., user interests, opinions, likes/dislikes, values, attitudes, habits, etc.),”), identity attributes (¶ [0026]: “demographic data (e.g., age, gender, income, employment, education, time period of lifetime, etc.),”), and skill attributes (¶ [0026]: “stylistic content of data (e.g., style, diction, tone, voice, intent, sentence/dialogue length and complexity, etc.),”).
Regarding claim 3 (depends on claim 1), ABRAMSON discloses:
generating a voice model for the custom AI character (¶ [0022]: “creating and/or applying a voice font to a chat bot.” ¶ [0029]: “a voice font,”) based on the voice dataset (e.g., ¶ [0022]: “voice data”; ¶ [0022]: “Speech recognition and/or speech synthesis techniques may be applied to the voice data to create a voice font of a specific person.”) (¶ [0022]: “In aspects, client devices 102A-C may provide for creating and/or applying a voice font to a chat bot. For example, client devices 102A-C may access voice data (e.g., voice recordings, musical recordings, etc.) comprised in social data, a personality index or other data sources. Speech recognition and/or speech synthesis techniques may be applied to the voice data to create a voice font of a specific person. The models and/or algorithms for implementing such techniques may be provided by client devices 102A-C, server devices 106A-C, or a separate device/service. The voice font may then be applied to a chat bot to enable the chat bot to converse in the voice of a specific person.” ¶ [0029]: “Chat bot engine 208 may be configured to generate a chat bot or LU model. In aspects, input processing unit 200 may cause chat bot engine 208 to generate one or more chat bots (or instances thereof). Input processing unit 200 may then cause or facilitate the application of data from a personality index to the one or more generated chat bots. In examples, applying personalized data to a chat bot may generate a personalized chat bot configured to interact conversationally in the personality of a specific person/entity. Applying personalized data to a chat bot may also cause a voice font, a 2D image, or a 3D model of a specific person/entity to be applied to the chat bot.” ¶ [0034]: “In some aspects, training a chat bot/LU model may additionally include applying one or more visual or auditory characteristics or attributes to a chat bot/LU model. For example, a personality index may include (or have access to) a voice font, a 2D image and/or a 3D model of a specific person/entity associated with the personalized personality index. The voice font, a 2D image and/or a 3D model may be applied to the chat bot/LU model to provide a more immersive user experience for users interacting with the chat bot/LU model.”); and
generating an image of the custom AI character(e.g., ¶ [0022]: “image data” ) based on the appearance dataset (e.g., ¶ [0022]: “image data and/or 3D data” ) (¶ [0022]: “In some aspects, client devices 102A-C may further provide for creating and/or applying a 2D or 3D model of a specific person to a chat bot. For example, client devices 102A-C may access image data to create a 2D model of the specific person. Additionally or alternatively, client devices 102A-C may access image data and/or 3D data (e.g., photos, images, depth information, color information, mapping information, etc.) comprised in social data, a personality index or other data sources. The image data and/or 3D data may be applied to a 3D modelling algorithm or service to create a 3D model of a person or entity. Alternately, client devices 102A-C may access a 3D modelling device. The 3D modelling device may be configured to perform a 3D scan of a person or entity, and/or access one or more previous 3D scans. In examples, a 2D or 3D model may be applied to a chat bot to enable immersive interactions with the likeness of a specific person/entity.” ¶ [0029]: “Chat bot engine 208 may be configured to generate a chat bot or LU model. In aspects, input processing unit 200 may cause chat bot engine 208 to generate one or more chat bots (or instances thereof). Input processing unit 200 may then cause or facilitate the application of data from a personality index to the one or more generated chat bots. In examples, applying personalized data to a chat bot may generate a personalized chat bot configured to interact conversationally in the personality of a specific person/entity. Applying personalized data to a chat bot may also cause a voice font, a 2D image, or a 3D model of a specific person/entity to be applied to the chat bot.” ¶ [0034]: “In some aspects, training a chat bot/LU model may additionally include applying one or more visual or auditory characteristics or attributes to a chat bot/LU model. For example, a personality index may include (or have access to) a voice font, a 2D image and/or a 3D model of a specific person/entity associated with the personalized personality index. The voice font, a 2D image and/or a 3D model may be applied to the chat bot/LU model to provide a more immersive user experience for users interacting with the chat bot/LU model.”).
Regarding claim 6 (depends on claim 1), ABRAMSON discloses:
parsing (¶ [0031]: “identifying keywords or terms in a request,”), using a parsing subsystem (e.g., ¶ [0032]: “index engine 206”), the description of the custom AI character (e.g., ¶ [0032]: “cause the index-generation component to generate a personality index (or an instance thereof) as part of receiving the request received at operation 302. The computing device may provide the index-generation component information identifying a specific person or entity. As a result, the index-generation component may identify and/or collect data related to the identified specific person/entity from the one or more sources of social data.”) (¶ [0031]: “Example method 300 begins at operation 302 where a request associated with a specific person or entity is received. In aspects, a computing device, such as input processing unit 200, may receive a request to generate, train or modify a chat bot or LU model. The request may comprise information associated with a specific person or entity, such as a name, a nickname, an occupation, an associated time period (e.g., lifetime, period in office, playing career, etc.), a description, etc. The information may be used to identify one or more data sources comprising (or potentially comprising) data related to the specific person or entity. At operation 304, social data for the specific person/entity may be accessed. In aspects, one or more queries may be generated and submitted to the one or more identified data sources. Generating queries may comprise identifying keywords or terms in a request, and formulating queries based thereon. In response to submitting a query, one or more result sets comprising social data may be generated and received by the computing device. In examples, the social data may comprise information relating to one or more specific people or entities. Such information may include images, image data, voice data, social media posts, written letters, user profile information, behavioral data, transactional data, geolocation data, and other forms of data. As an example, the social data for a current celebrity may include social media posts from (and about) the celebrity, voice and image data (e.g., recordings of interviews, performances, etc.), movies/televisions shows, electronic news and articles about the celebrity, web chatter relating to the celebrity, etc. As another example, the social data for a historical figure (such as Abraham Lincoln) may include handwritten letters and similar correspondences authored by the historical figure, books authored or about the historical figure, information related to the relevant time period associated with the historical figure, physical media comprising audio data and/or video data, photos, etc. In such aspects, the social data (or portions thereof) may be stored in a data store accessible to the computing device, such as data store(s) 204.” ¶ [0032]: “At operation 306, a personality index may be created using social data. In aspects, a computing device may have access to index-generation component, such as index engine 206. The index-generation component may have access to one or more sources of social data, such as data store(s) 204. In examples, the computing device may cause the index-generation component to generate a personality index (or an instance thereof) as part of receiving the request received at operation 302. The computing device may provide the index-generation component information identifying a specific person or entity. As a result, the index-generation component may identify and/or collect data related to the identified specific person/entity from the one or more sources of social data. The identified/collected data may then be processed and applied to the personality index (e.g., a generic personality index); thereby, creating a personalized personality index in the theme of the specific person/entity. For example, a machine learning model may analyze a set of social data to identify and categorize content, content attributes, content authors/contributors, data sources, etc. Such an analysis may include categorizing the social data by type (e.g., textual data, audio data, image data, etc.), determining the source/author(s) of the social data (e.g., a specific person/entity, one or more other persons similar to a specific person/entity, subject matter experts, random users, etc.), determining the degree of similarity between a specific person/entity and alternate sources/authors, identifying question and answer pairs, identifying dialogue expressions, etc.”), into
(i) personality features (e.g., ¶ [0015]: “conversational attributes”, ¶ [0015]: “behavioral attributes” and/or ¶ [0015]: “demographic information”) associated with the personality characteristics of the custom AI character (¶ [0014]: “create or modify a personalized chat index in the theme of the specific person's personality.”) (¶ [0014]: “Social data, as used herein, may refer to images, image data, voice data, emails, text messages, dialogue data/commands, social media posts, written letters, user profile information, behavioral data, transactional data, geolocation data, and other forms of data about a specific person. In examples, social data may be stored by, and/or collected from, various data sources. The social data (or portions thereof) may be used to create or modify a personalized chat index in the theme of the specific person's personality. A chat index, as used herein, may refer to a repository of conversational data. In examples, creating/modifying the personalized chat index may comprise applying one or more rule sets or machine learning to the social data of a specific person.” ¶ [0015]: “In aspects, a personalized chat index may be used to train a chat bot or language understanding (LU) model to converse and/or interact in the personality of the specific person. A model, as used herein, may refer to a predictive or statistical language model that may be used to determine a probability distribution over one or more word, character sequences or events, and/or to predict a response value from one or more predictors. In examples, a model may be a rule-based model, a machine-learning regressor, a machine-learning classifier, a neural network, or the like. In some aspects, conversing in the personality of a specific person may include determining and/or using conversational attributes of the specific person, such as style, diction, tone, voice, intent, sentence/dialogue length and complexity, topic, and consistency. Conversing in the personality of a specific person may additionally include determining and/or using behavioral attributes (e.g., user interests, opinions, etc.) and demographic information (e.g., age, gender, education, profession, income level, relationship status, etc.) of the specific person and/or persons determined to be similar to the specific person.” ),
(ii) voice features (¶ [0034]: “auditory characteristics or attributes”) associated with the voice characteristics of the custom AI character (¶ [0003]: “In some aspects, a voice font of the specific person may be generated using recordings and sound data related to the specific person.” ¶ [0016]: “In aspects, a voice font of a specific person may be generated by applying speech recognition and/or a speech synthesis algorithm to one or more voice recordings in the social data. The voice recordings may be collected from the social data, one or more Internet of Things (“IoT”) data sources (such as personal digital assistants, natural language understanding systems, etc.), and the like. The voice font may be applied to the chat bot of a specific person.” ¶ [0032]: “The analysis of social data may further include evaluating voice data in (or associated with) the social data. Such an evaluation may include using speech recognition and/or speech syntheses techniques to generate a voice font corresponding to a specific person/entity.”), and
(iii) appearance features (¶ [0034]: “visual or auditory characteristics or attributes”) associated with the visual appearance of the custom AI character (¶ [0003]: “In some aspects, a 2D or 3D model of the specific person may be generated using images, depth information, and/or video data associated with the specific person.” ¶ [0016]: “In aspects, a two-dimensional (“2D”) image of a specific person may be generated by applying a facial recognition/detection algorithm to one or more photos in the social data and/or collected from one or more other data sources. The 2D image may be applied to the chat bot of a specific person to create a more realistic, human-like chat experience. In some aspects, a 2D model (or a portion of the data used to create the 2D model) of a specific person may be used to generate a three-dimensional (“3D”) model of the specific person. For example, one or more images, depth information and/or color information may be provided as input(s) to a 3D modelling algorithm. The 3D modelling algorithm may generate a 3D model and facilitate the application of the 3D model to the chat bot of a specific person. The 3D model may provide for a more immersive and interactive experience (e.g., in a virtual reality, augmented reality, or mixed reality context) for a user interacting with the chat bot.” ¶ [0032]: “The analysis of social data may also include evaluating photo data in (or associated with) the social data. Such an evaluation may include using, for example, deep learning to detect tags in (and/or attributes of) the photo data, process (e.g., identify, annotate, summarize, etc.) the events in the photo data, and/or correlate the detected tags with the processed photo data.” ¶ [0032]: “Further still, the analysis of social data may include generating and/or evaluating 2D/3D data in (or associated with) the social data. Such an evaluation may include using 2D/3D modelling techniques (and associated data) to generate a 2D or 3D model corresponding to a specific person/entity.”); and
generating the personality dataset based on the personality features (¶ [0032]: “The index-generation component may have access to one or more sources of social data, such as data store(s) 204. In examples, the computing device may cause the index-generation component to generate a personality index (or an instance thereof) as part of receiving the request received at operation 302. The computing device may provide the index-generation component information identifying a specific person or entity. As a result, the index-generation component may identify and/or collect data related to the identified specific person/entity from the one or more sources of social data. The identified/collected data may then be processed and applied to the personality index (e.g., a generic personality index); thereby, creating a personalized personality index in the theme of the specific person/entity. For example, a machine learning model may analyze a set of social data to identify and categorize content, content attributes, content authors/contributors, data sources, etc. Such an analysis may include categorizing the social data by type (e.g., textual data, audio data, image data, etc.),” ¶ [0003]: “create or modify a special index in the theme of the specific person's personality.”), the voice dataset based on the voice features (¶ [0032]: “evaluating voice data in (or associated with) the social data. Such an evaluation may include using speech recognition and/or speech syntheses techniques to generate a voice font corresponding to a specific person/entity.”), and the appearance dataset based on the appearance features (¶ [0032]: “evaluating photo data in (or associated with) the social data. Such an evaluation may include using, for example, deep learning to detect tags in (and/or attributes of) the photo data, process (e.g., identify, annotate, summarize, etc.) the events in the photo data, and/or correlate the detected tags with the processed photo data.” ¶ [0032]: “generating and/or evaluating 2D/3D data in (or associated with) the social data. Such an evaluation may include using 2D/3D modelling techniques (and associated data) to generate a 2D or 3D model corresponding to a specific person/entity.”) (¶ [0003]: “In aspects, social data (e.g., images, voice data, social media posts, electronic messages, written letters, etc.) relating to the specific person may be accessed. The social data may be used to create or modify a special index in the theme of the specific person's personality. The special index may be used to train a chat bot to converse and interact in the personality of the specific person.” ¶ [0015]: “In aspects, a personalized chat index may be used to train a chat bot or language understanding (LU) model to converse and/or interact in the personality of the specific person. A model, as used herein, may refer to a predictive or statistical language model that may be used to determine a probability distribution over one or more word, character sequences or events, and/or to predict a response value from one or more predictors. In examples, a model may be a rule-based model, a machine-learning regressor, a machine-learning classifier, a neural network, or the like. In some aspects, conversing in the personality of a specific person may include determining and/or using conversational attributes of the specific person, such as style, diction, tone, voice, intent, sentence/dialogue length and complexity, topic, and consistency. Conversing in the personality of a specific person may additionally include determining and/or using behavioral attributes (e.g., user interests, opinions, etc.) and demographic information (e.g., age, gender, education, profession, income level, relationship status, etc.) of the specific person and/or persons determined to be similar to the specific person.” ).
Regarding claim 7 (depends on claim 6), ABRAMSON discloses:
wherein the parsing subsystem includes one or more trained models (e.g., ¶ [0032]: “a machine learning model”) (¶ [0031]: “Example method 300 begins at operation 302 where a request associated with a specific person or entity is received. In aspects, a computing device, such as input processing unit 200, may receive a request to generate, train or modify a chat bot or LU model. The request may comprise information associated with a specific person or entity, such as a name, a nickname, an occupation, an associated time period (e.g., lifetime, period in office, playing career, etc.), a description, etc. The information may be used to identify one or more data sources comprising (or potentially comprising) data related to the specific person or entity. At operation 304, social data for the specific person/entity may be accessed. In aspects, one or more queries may be generated and submitted to the one or more identified data sources. Generating queries may comprise identifying keywords or terms in a request, and formulating queries based thereon. In response to submitting a query, one or more result sets comprising social data may be generated and received by the computing device. In examples, the social data may comprise information relating to one or more specific people or entities. Such information may include images, image data, voice data, social media posts, written letters, user profile information, behavioral data, transactional data, geolocation data, and other forms of data. As an example, the social data for a current celebrity may include social media posts from (and about) the celebrity, voice and image data (e.g., recordings of interviews, performances, etc.), movies/televisions shows, electronic news and articles about the celebrity, web chatter relating to the celebrity, etc. As another example, the social data for a historical figure (such as Abraham Lincoln) may include handwritten letters and similar correspondences authored by the historical figure, books authored or about the historical figure, information related to the relevant time period associated with the historical figure, physical media comprising audio data and/or video data, photos, etc. In such aspects, the social data (or portions thereof) may be stored in a data store accessible to the computing device, such as data store(s) 204.” ¶ [0032]: “At operation 306, a personality index may be created using social data. In aspects, a computing device may have access to index-generation component, such as index engine 206. The index-generation component may have access to one or more sources of social data, such as data store(s) 204. In examples, the computing device may cause the index-generation compo