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
This is a first office action in response to application filed, with the above serial number, on 24 April 2024 in which claims 1-20 are presented for examination. Claims 1-20 are therefore pending in the application.
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)(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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ahafonov et al (hereinafter “Ahafonov”, 12,602,842).
As per Claim 1, Ahafonov discloses a method of leveraging generative artificial intelligence (AI) to generate content corresponding to a persona, the method comprising:
receiving context from a user, the context comprising a persona and a prompt (at least col. 30:47-31:40; 23:51-24:15; receive a prompt as input, as well as contextual data from user conversation and user selecting persona);
providing the context to a generative AI model (at least col. 30:47-31:40; 23:51-24:15; generative machine learning models are trained to receive a prompt as input);
based on the context, receiving, from the generative AI model, content at least partially derived from one or more files in a dataset (at least col. 30:47-31:40; 23:51-24:15; the generative machine learning models are trained to receive a prompt as input (which can include any combination of text, images, audio, and/or videos) and to generate an output that responds to the prompt); and
providing the content corresponding to the context to the user (at least col. 30:47-31:40; 23:51-24:15; the user 638 specifically alters the “persona” of interactions with the personal AI agent system 600 by interacting with a personal AI configuration component 634 and specifically requesting that the personal AI agent system 600 respond or act in a specific way (e.g., “be funnier”, “answer in riddles” etc.) or provide certain type of content).
As per Claim 2. The method of claim 1, further comprising receiving a selection of the one or more files in the dataset (at least col. 30:47-31:40; 23:51-24:15; information gathered from the UI components 520 (representing a current real-world environment) and user database 504. Namely, the personal AI agent 502 generates a prompt that includes an image captured by the user system 102 and one or more vectors derived from the multimodal memory 508. This prompt can be provided as input to the neural network engine 514. The neural network engine 514 then accesses additional sources of data, such as external data sources 516 and/or feature APIs 518, to generate content that matches the inputs of the prompt).
As per Claim 3. The method of claim 2, further comprising extracting text from the one or more files (at least col. 30:47-31:40; 23:51-24:15; 27:39-57; response content is text, the content response component 612 uses the neural network 636 to identify and extract important information from unstructured text, such as a question of the user 638 or a request. This can be done using methodologies such as named entity recognition, part-of-speech tagging, sentiment analysis, and the like; the generative machine learning models are trained to receive a prompt as input (which can include any combination of text, images, audio, and/or videos) and to generate an output that responds to the prompt. In some cases, the generative machine learning models generate an artificial image/video and/or text that is responsive to the prompt).
As per Claim 4. The method of claim 3, further comprising converting the extracted text into vector representations describing content corresponding to the one or more files (at least col. 22:4-47; 23:51-24:15; personal AI agent 502 uses embeddings for the multimodal memory 508, which refers to a technique used in machine learning to represent and store data from multiple modalities (such as images, text, and audio) in a common vector space; if a user has stored an entity (e.g., a memory object) that includes an image, text description, and audio recording, embeddings can be used to represent each of these modalities in a common vector space. This allows for the efficient retrieval and integration of information from multiple modalities when accessing the memory object; the personal AI agent 502 generates a prompt that includes an image captured by the user system 102 and one or more vectors derived from the multimodal memory 508).
As per Claim 5. The method of claim 4, further comprising generating a database comprising the vector representations (at least col. 22:4-47; 23:51-24:15; personal AI agent 502 uses embeddings for the multimodal memory 508, which refers to a technique used in machine learning to represent and store data from multiple modalities (such as images, text, and audio) in a common vector space; if a user has stored an entity (e.g., a memory object) that includes an image, text description, and audio recording, embeddings can be used to represent each of these modalities in a common vector space. This allows for the efficient retrieval and integration of information from multiple modalities when accessing the memory object; the personal AI agent 502 generates a prompt that includes an image captured by the user system 102 and one or more vectors derived from the multimodal memory 508).
As per Claim 6. The method of claim 1, wherein the persona corresponds to a target demographic (at least col. 29:3-23; 13:18-20; 30:47-31:40; an overall intent of the user is built using various signals or data such as user demographics; the personal AI agent system 600 stores a conversation or interaction state for the user 638 as part of the user's profile stored in the user profile 616. The personal AI agent system 600 uses the conversation or interaction state and demographic or other information about the user 638 to determine a personality or tone for the user 638, such as by adopting a formal tone for an older user, and a more informal tone for a younger user).
As per Claim 7. The method of claim 1, wherein the context instructs the generative AI model to identify themes and topics of the one or more files (at least col. 30:47-31:40; 23:51-24:15; user-specific models 510 generate hashtags to categorize and organize user posts based on a particular theme or topic, which allow users to connect with other users who share similar interests or to participate in trending conversations; col. 27:12-43; content response component 612 uses the intent 622 received from the intent component 614 and any extracted information from the intent component 614 to determine the appropriate content item 610).
As per Claim 8. The method of claim 1, wherein the context instructs the generative AI model to distill and summarize the one or more files into an executive summary (at least col. 15:19-25; generative AI examples, the prediction/inference data that is output include trend assessment and predictions, translations, summaries, image or video recognition and categorization, natural language processing, face recognition, user sentiment assessments, advertisement targeting and optimization, voice recognition, or media content generation, recommendation, and personalization).
As per Claim 9. The method of claim 1, wherein the context instructs the generative AI model to pinpoint a specific file of the one or more files (at least col. 30:47-31:40; 23:51-24:15; the user 638 specifically alters the “persona” of interactions with the personal AI agent system 600 by interacting with a personal AI configuration component 634 and specifically requesting that the personal AI agent system 600 respond or act in a specific way (e.g., “be funnier”, “answer in riddles” etc.) or provide certain type of content more often than others).
As per Claim 10. The method of claim 1, wherein the context instructs the generative AI model to generate a predictive narrative (at least col. 15:19-25; generative AI examples, the prediction/inference data that is output include trend assessment and predictions, translations, summaries, image or video recognition and categorization, natural language processing, face recognition, user sentiment assessments, advertisement targeting and optimization, voice recognition, or media content generation, recommendation, and personalization).
As per Claim 11. The method of claim 1, wherein the content is an advertisement corresponding to the one or more files based on a perspective of the persona (at least col. 33:32-53; the personal AI agent system 600 assists advertisers by simplifying the process of creating advertising creatives and/or generating advertising creatives for the advertisers. By providing inputs such as website, target application, additional assets, and target keywords, the personal AI agent system 600 can automatically generate advertisement creatives).
As per Claim 12. The method of claim 1, wherein the content is a portion of a website corresponding to the one or more files based on a perspective of the persona (at least col. 30:47-31:40; 23:51-24:15; the personal AI agent system 600 assists advertisers by simplifying the process of creating advertising creatives and/or generating advertising creatives for the advertisers. By providing inputs such as website, target application, additional assets, and target keywords, the personal AI agent system 600 can automatically generate advertisement creatives).
As per Claim 13. The method of claim 1, wherein the content is a reaction to information corresponding to the one or more files based on a perspective of the persona (at least col. 30:47-31:40; 23:51-24:15; alters the “persona” of interactions with the personal AI agent system 600 by interacting with a personal AI configuration component 634 and specifically requesting that the personal AI agent system 600 respond or act in a specific way (e.g., “be funnier”, “answer in riddles” etc.) or provide certain type of content more often than others. In addition to modifying the personality or tone of the responses, the visual interface presented by the personal AI agent system 600 may also be altered to reflect a specific “persona” of the personal AI agent system 600. In some examples, a slide toggle may be presented to enable the user 638 to select between a menu of persona traits (e.g., “cheeky”, “wry”, “cute”, etc.)).
As per Claim 14. The method of claim 1, wherein the content is a predictive narrative corresponding to the one or more files based on a perspective of the persona (at least col. 30:47-31:40; 23:51-24:15; alters the “persona” of interactions with the personal AI agent system 600 by interacting with a personal AI configuration component 634 and specifically requesting that the personal AI agent system 600 respond or act in a specific way (e.g., “be funnier”, “answer in riddles” etc.) or provide certain type of content more often than others. In addition to modifying the personality or tone of the responses, the visual interface presented by the personal AI agent system 600 may also be altered to reflect a specific “persona” of the personal AI agent system 600. In some examples, a slide toggle may be presented to enable the user 638 to select between a menu of persona traits (e.g., “cheeky”, “wry”, “cute”, etc.); col. 15:19-25; generative AI examples, the prediction/inference data that is output include trend assessment and predictions, translations, summaries, image or video recognition and categorization, natural language processing, face recognition, user sentiment assessments, advertisement targeting and optimization, voice recognition, or media content generation, recommendation, and personalization).
As per Claim 15. The method of claim 1, wherein the content is media training support corresponding to the one or more files based on a perspective of the persona (at least col. 25:38-61; 13:26-52; training phases, the machine-learning pipeline uses the training data to find correlations among the features that affect a predicted outcome or prediction/inference data. With the training data and the identified features, the trained machine-learning program is trained during the training phase during machine-learning program training).
As per Claim 16, Ahafonov discloses one or more non-transitory computer storage media storing computer-readable instructions that when executed by a processor, cause the processor to perform operations, the operations comprising:
receiving context from a user, the context comprising a persona and a prompt (at least col. 30:47-31:40; 23:51-24:15; receive a prompt as input, as well as contextual data from user conversation and user selecting persona), wherein the persona corresponds to a target demographic (at least col. 29:3-23; 13:18-20; 30:47-31:40; an overall intent of the user is built using various signals or data such as user demographics; the personal AI agent system 600 stores a conversation or interaction state for the user 638 as part of the user's profile stored in the user profile 616. The personal AI agent system 600 uses the conversation or interaction state and demographic or other information about the user 638 to determine a personality or tone for the user 638, such as by adopting a formal tone for an older user, and a more informal tone for a younger user);
providing the context to a generative AI model (at least col. 30:47-31:40; 23:51-24:15; generative machine learning models are trained to receive a prompt as input), wherein the context instructs the generative AI model to:
identify themes and topics of one or more files in a dataset (at least col. 30:47-31:40; 23:51-24:15; 27:12-43; content response component 612 uses the intent 622 received from the intent component 614 and any extracted information from the intent component 614 to determine the appropriate content item 610; user-specific models 510 generate hashtags to categorize and organize user posts based on a particular theme or topic, which allow users to connect with other users who share similar interests or to participate in trending conversations); distill and summarize the one or more files into an executive summary; pinpoint a specific file of the one or more files; or generate a predictive narrative corresponding to the one or more files;
based on the context, receiving, from the generative AI model, content (at least col. 30:47-31:40; 23:51-24:15; the generative machine learning models are trained to receive a prompt as input (which can include any combination of text, images, audio, and/or videos) and to generate an output that responds to the prompt); and
providing the content corresponding to the context to the user (at least col. 30:47-31:40; 23:51-24:15; the user 638 specifically alters the “persona” of interactions with the personal AI agent system 600 by interacting with a personal AI configuration component 634 and specifically requesting that the personal AI agent system 600 respond or act in a specific way (e.g., “be funnier”, “answer in riddles” etc.) or provide certain type of content).
As per Claim 17. The one or more non-transitory computer storage media of claim 16, further comprising receiving a selection of the one or more files in the data set (at least col. 30:47-31:40; 23:51-24:15; information gathered from the UI components 520 (representing a current real-world environment) and user database 504. Namely, the personal AI agent 502 generates a prompt that includes an image captured by the user system 102 and one or more vectors derived from the multimodal memory 508. This prompt can be provided as input to the neural network engine 514. The neural network engine 514 then accesses additional sources of data, such as external data sources 516 and/or feature APIs 518, to generate content that matches the inputs of the prompt).
As per Claim 18. The one or more non-transitory computer storage media of claim 17, further comprising further comprising: extracting text from the one or more files (at least col. 30:47-31:40; 23:51-24:15; 27:39-57; response content is text, the content response component 612 uses the neural network 636 to identify and extract important information from unstructured text, such as a question of the user 638 or a request. This can be done using methodologies such as named entity recognition, part-of-speech tagging, sentiment analysis, and the like; the generative machine learning models are trained to receive a prompt as input (which can include any combination of text, images, audio, and/or videos) and to generate an output that responds to the prompt. In some cases, the generative machine learning models generate an artificial image/video and/or text that is responsive to the prompt); and converting the extracted text into vector representations describing content corresponding to the one or more files (at least col. 22:4-47; 23:51-24:15; personal AI agent 502 uses embeddings for the multimodal memory 508, which refers to a technique used in machine learning to represent and store data from multiple modalities (such as images, text, and audio) in a common vector space; if a user has stored an entity (e.g., a memory object) that includes an image, text description, and audio recording, embeddings can be used to represent each of these modalities in a common vector space. This allows for the efficient retrieval and integration of information from multiple modalities when accessing the memory object; the personal AI agent 502 generates a prompt that includes an image captured by the user system 102 and one or more vectors derived from the multimodal memory 508).
As per Claim 19. The one or more non-transitory computer storage media of claim 18, further comprising further comprising generating a database comprising vector representations corresponding to extracted text of the one or more files in the dataset (at least col. 22:4-47; 23:51-24:15; personal AI agent 502 uses embeddings for the multimodal memory 508, which refers to a technique used in machine learning to represent and store data from multiple modalities (such as images, text, and audio) in a common vector space; if a user has stored an entity (e.g., a memory object) that includes an image, text description, and audio recording, embeddings can be used to represent each of these modalities in a common vector space. This allows for the efficient retrieval and integration of information from multiple modalities when accessing the memory object; the personal AI agent 502 generates a prompt that includes an image captured by the user system 102 and one or more vectors derived from the multimodal memory 508).
As per Claim 20, Ahafonov discloses a system for leveraging generative artificial intelligence (AI) to identify products for completing a task, the system comprising:
at least one processor; and one or more computer storage media storing computer-readable instructions that when executed by the at least one processor (at least col. 40:44-64), cause the at least one processor to perform operations comprising:
receiving context from a user, the context comprising a persona and a prompt (at least col. 30:47-31:40; 23:51-24:15; receive a prompt as input, as well as contextual data from user conversation and user selecting persona);
providing the context to a generative AI model (at least col. 30:47-31:40; 23:51-24:15; generative machine learning models are trained to receive a prompt as input);
based on the context, receiving, from the generative AI model, content at least partially derived from one or more files in a dataset (at least col. 30:47-31:40; 23:51-24:15; the generative machine learning models are trained to receive a prompt as input (which can include any combination of text, images, audio, and/or videos) and to generate an output that responds to the prompt); and
providing the content corresponding to the context to the user (at least col. 30:47-31:40; 23:51-24:15; the user 638 specifically alters the “persona” of interactions with the personal AI agent system 600 by interacting with a personal AI configuration component 634 and specifically requesting that the personal AI agent system 600 respond or act in a specific way (e.g., “be funnier”, “answer in riddles” etc.) or provide certain type of content), wherein the content is an advertisement, a portion of a website, a reaction to information, a predictive narrative, or media training support corresponding to the one or more files based on a perspective of the persona (at least col. 33:32-53; the personal AI agent system 600 assists advertisers by simplifying the process of creating advertising creatives and/or generating advertising creatives for the advertisers. By providing inputs such as website, target application, additional assets, and target keywords, the personal AI agent system 600 can automatically generate advertisement creatives).
Conclusion
The prior art made of record and not relied upon considered pertinent to applicant's disclosure is indicated in PTO form 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY G TODD whose telephone number is (303)297-4763. The examiner can normally be reached 8:30-5 MST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Nicholas Taylor can be reached on (571)272-3889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GREGORY TODD/ Primary Examiner, Art Unit 2443
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