DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on September 26, 2025 has been entered.
Response to Arguments
Applicants argue that the prior art cited fails to teach the claims as amended. Applicants’ arguments are persuasive, but are moot in view of new grounds of rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4, 6-10 and 12-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mokhnatkina (PGPUB 2020/0041289) in view of Papp et al. (PGPUB 2024/0397279), hereinafter referenced as Papp and in further view of Klappert et al. (PGPUB 2021/0097893), hereinafter referenced Klappert.
Regarding claim 1, Mokhnatkina discloses a computer-implemented method, comprising:
receiving location information associated with a user (location of user; abstract; p. 0218);
identifying one or more points of interest (POIs) based at least in part on the location information (POI based on location; p. 0188-0197, 0218-0222, 0233);
dynamically generating story content based at least in part on the identified one or more POIs (dynamic; abstract with p. 0198, 0219, 0229, 0233, 0252-0253);
generating a story transcript based at least in part on the identified one or more POIs, generating the story transcript (speech-to-text; p. 0056-0059);
providing the story content for presentation to the user (present tour data; abstract with p. 0198, 0219, 0229, 0233, 0252-0253);
preparing a custom prompt based at least in part on the identified one or more POIs (customized guide; p. 0017, 0219); and
providing the story content for presentation to the user, wherein providing the story content to the user includes initiating playback of the generated story audio content (p. 0198), but does not specifically teach passing the custom prompt to a large language model (LLM) generative artificial intelligence and generating story audio content based at least in part on the story and the story host selection, the story host selection being indicative of a selected voice out of a plurality of available voices for generation of audio content, the story audio content being generated using the selected voice and receiving, from the user device, a story host selection indicative of a story host, the story host including parameters indicative of a story theme, story genre, and a story voice selection indicative of a selected voice out of a plurality of available voices for generation of audio content from text.
Papp discloses a method comprising:
receiving location information associated with a user (location information of a user in a vehicle; p. 0016);
identifying one or more points of interest (POIs) based at least in part on the location information (gather and aggregate information about selected points of interest; p. 0048);
receiving a story host selection (story about a selected POI; p. 0036-0038, 0054-0056);
dynamically generating story content based at least in part on the identified one or more POIs and the story host selection, wherein dynamically generating the story content (dynamically gather and aggregate information about POI; p. 0032-0035, 0048, 0063);
generating a custom prompt based at least in part on the identified one or more POIs and the story host selection (p. 0061);
generating receiving an Al response from the LLM generative Al, the story being based at least in part on the Al response (p. 0032-0037);
generating story audio content based at least in part on the story wherein the story audio content being generated using the selected voice (story presented in various voice tones; p. 0037); and
providing the story content for presentation to the user, wherein providing the story content to the user includes initiating playback of the generated story audio content (present information; p. 0036-0039), to enhance users experience.
Therefore, it would have been obvious to one of ordinary skill of the art, before the effective filing date of the claimed invention, to modify the method as described above, to provide customized information about POI’s.
Klappert discloses a method comprising:
receiving, from the user device, a story host selection indicative of a story host, the story host including parameters indicative of a story theme (topics), story genre (genre; p. 0097-0102), and a story voice selection indicative of a selected voice out of a plurality of available voices for generation of audio content from text (character simulation; p. 0051); and
generating, based on an application of a trained large language model (LLM) generative artificial intelligence (AI) process to the custom prompt (trained machine learning algorithm; p. 0016), to customize the tour.
Therefore, it would have been obvious to one of ordinary skill of the art, before the effective filing date of the claimed invention, to modify the method as described above, to deliver relevant data accordingly.
Regarding claim 4, it is interpreted and rejected for similar reasons as set forth above. In addition, Papp discloses a method wherein generating the story transcript includes:
wherein generating the story audio content includes:
passing the story host and the story transcript to an artificial intelligence (AI) audio synthesis engine (p. 0036-0037, 0054-0055, 0060); and
receiving synthesized audio based on the selected voice of the story host selection in response to the passing of the story host selection and the story transcript, the story audio content being based at least in part on the synthesized audio (p. 0036-0037, 0054-0055, 0060).
Regarding claim 6, Mokhnatkina discloses a method wherein preparing the custom prompt is further based at least in part on one or more preferences associated with the user (user preference impact guide; p. 0210, 0017, 0099, 0143, 0193, 0177-0181, 0221).
Regarding claim 7, Mokhnatkina discloses a method wherein preparing the custom prompt includes selecting a prompt template from a plurality of prompt templates, and wherein the custom prompt is based at least in part on the selected prompt template (p. 0095-0102, 0230).
Regarding claim 8, Mokhnatkina discloses a method wherein the location information includes a location name, and wherein preparing the custom prompt is further based at least in part on the location name (p. 0039-0041, 0210-0216).
Regarding claim 9, Mokhnatkina discloses a method further comprising identifying one or more advertisement vendors associated with the location information or the one or more POIs, wherein preparing the custom prompt is based at least in part on the one or more advertisement vendors such that the AI response includes content associated with the one or more advertisement vendors (business owners; p. 0021, 0039-0041, 0210-0216).
Regarding claim 10, Mokhnatkina discloses a method further comprising identifying one or more advertisements associated with the location information or the one or more POIs, wherein generating the story transcript includes combining the one or more advertisements with at least a portion of the AI response to create the story transcript (promote; p. 0021-0029, 0190).
Regarding claim 12, Mokhnatkina discloses a method further comprising identifying one or more audio advertisements associated with the location information or the one or more POIs, wherein generating the story audio content includes combining the one or more audio advertisements with at least a portion of the synthesized audio to create the story audio content (promote; p. 0021-0029, 0190).
Regarding claim 13, Mokhnatkina discloses a method wherein receiving the location information includes:
receiving GPS coordinates associated with a user device of the user (p. 0234); and
determining the location information based at least in part on the GPS coordinates (p. 0232-0237).
Regarding claim 14, Mokhnatkina discloses a method wherein identifying the one or more POIs includes receiving a user POI selection indicative of at least one POI of the one or more POIs (POI; p. 0189-0194).
Regarding claim 15, Mokhnatkina discloses a method further comprising receiving one or more camera images associated with a user device of the user, wherein identifying the one or more POIs is based at least in part on the one or more camera images (picture; p. 0024, 0044-0047, 0231, 0252).
Regarding claim 16, Mokhnatkina discloses a method further comprising storing the dynamically generated story content for later use (data stored; p. 0137).
Regarding claim 17, Mokhnatkina discloses a method further comprising generating a shareable URL associated with the stored story content, wherein the URL, when accessed by an additional user, causes the stored story content to be provided to the additional user (website; p. 0032-0035, 0217, 0242).
Regarding claim 18, Mokhnatkina discloses a method wherein providing the story content for presentation to the user includes providing one or more follow-up prompts for presentation to the user following presentation of the story content (suggest; p. 0144, 0150, 0162-0188, 0193).
Regarding claim 19, Mokhnatkina discloses a system comprising:
a control system including one or more processors (p; 0218, 0243); and
a memory having stored thereon machine readable instructions (p; 0243);
wherein the control system is coupled to the memory, and the method of claim 1 is implemented when the machine executable instructions in the memory are executed by at least one of the one or more processors of the control system (p. 0243).
Regarding claim 20, Mokhnatkina discloses a computer program product embodied on a non-transitory computer-readable medium and comprising instructions which, when executed by a computer, cause the computer to carry out the method of claim 1 (p. 0243).
Regarding claim 21, it is interpreted and rejected for similar reasons as set forth above. In addition, Mokhnatkina discloses a method wherein selecting the prompt template from the plurality of prompt templates is based at least in part on the story host selection, the story host selection being indicative of the selected prompt template (p. 0095).
Regarding claim 22, it is interpreted and rejected for similar reasons as set forth above. In addition, Papp discloses a method wherein passing the custom prompt to the LLM generative AI includes passing the custom prompt via an application programming interface (API) associated with the LLM generative AI (p. 0033-0034).
Regarding claim 23, it is interpreted and rejected for similar reasons as set forth above. In addition, Papp discloses a method wherein generating the story audio content includes passing the story transcript to an audio synthesis engine, wherein the audio synthesis engine is an AI-based text-to-speech engine, the AI-based text-to-speech engine being trained on human voices such that, when provided with input text, the AI-based text-to-speech engine outputs audio data representative of human-like speech (p. 0036-0037, 0054, 0060).
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mokhnatkin in view of Papp and Klappert and in further view of Snibbe et al. (PGPUB 2018/0300100), hereinafter referenced as Snibbe.
Regarding claim 24, it is interpreted and rejected for similar reasons as set forth above, however, the prior art cited fails to teach a method wherein generating the story audio content includes passing the story transcript to a music synthesis engine, wherein the music synthesis engine is configured to (i) generate synthesized music based on the story transcript and the identified POIs, (11) select recorded music based on the story transcript and the identified POIs, or (iii) any combination of (i)-(ii).
Snibbe discloses a method comprising:
generating the story audio content includes passing the story transcript (social media networking data/crowdsourcing; p. 0038, 0079, 0104) to a music synthesis engine (synthesize music; p. 0034), wherein the music synthesis engine is configured to (i) generate synthesized music based on the story transcript and the identified POIs (spatialized effects and locations in the real world; p. 0005, 0013, 0034), (ii) select recorded music based on the story transcript and the identified POIs (recorded tracks; p. 0034), or (iii) any combination of (i)-(ii), to apply audiovisual content.
Therefore, it would have been obvious to one of ordinary skill of the art, before the effective filing date of the claimed invention, to modify the method as described above, to enhance a user’s experience.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. This information has been detailed in the PTO 892 attached (Notice of References Cited).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAKIEDA R JACKSON whose telephone number is (571)272-7619. The examiner can normally be reached Mon - Fri 6:30a-2:30p.
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/JAKIEDA R JACKSON/Primary Examiner, Art Unit 2657