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 .
This Office Action has been issued in response to Applicant’s Communication of application S/N 18/861,275 filed on October 29, 2024. Claims 1 to 12 and 15 to 22 are currently pending with the application.
Priority
This application is a National Stage entry of PCT/US2023/081801, with international filing date of November 30, 2023. Applicant’s claim for the benefit of the prior-filed application under 35 U.S.C. 119(e), 120, 121, or 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/24/2025 and 07/23/2026 were filed before the mailing date of the first action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Examiner’s Note
Examiner respectfully points out that claims 5 and 18 recite the contingent limitations “adding the one or more new digital components and the first input prompt to the data structure for distribution to devices in response to new input prompts being classified as similar to the first input prompt”. The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the conditions precedent are not met (See MPEP 2111.04(II)). Therefore, since claim 5 is a method claim, the contingent limitations recited above are not required in claim 5. Nonetheless, and in the interest of compact prosecution, all the limitations have been considered as if they are positively recited.
Claim Objections
Claims 1, 12, and 20 objected to because of the following informalities:
Claim 1 recites the limitation “a stored embedding the represents a prompt” in line 3, which appears to contain a typographical error, and that should read “a stored embedding that represents a prompt”. Same rationale applies to claims 12 and 20, since they recite similar limitations.
Claim 1 recites the limitation “the input prompt” in line 10, which appears to refer to the “first input prompt” element recited in lines 5, 7 and 8. For purposes of clarity, and to maintain consistency in the terminology throughout the claims, the element in line 10 should read “the first input prompt”. Same rationale applies to claims 12 and 20, since they recite similar limitations.
Claim 1 recites the limitation “the stored embedding used to generate” in line 12, which appears to refer to the “stored embedding that represents a prompt used to generate” element recited in line 3. For purposes of clarity, and to maintain consistency in the terminology throughout the claims, the element in line 12 should read “the stored embedding that represents a prompt used to generate”. Same rationale applies to claims 12 and 20, since they recite similar limitations.
Appropriate corrections are required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 to 12 and 15 to 22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the multi-word text” in line 10. There is insufficient antecedent basis for this limitation in the claim. Same rationale applies to claims 12 and 20, since they recite similar limitations, and to claims 2 to 11, 15 to 19, 21, and 22, since they inherit the same deficiencies, by virtue of their dependency.
Claim 1 recites the limitation “the trained machine learning model” in line 17. It is not clear to which of the “one or more trained machine learning models” recited in line 6 this limitation is referring to, therefore, rendering the claim indefinite. . Same rationale applies to claims 2 to 4, 6, 12, 15 to 17, and 19 to 22, since they recite similar limitations, and to claims 5, 7 to 11, and 18, since they inherit the same deficiencies, by virtue of their dependency.
Claim 1 further recites the limitation “one or more new digital components pre-generated by the trained machine learning model using the first input prompt” in line 17. This limitation is not clear, and more specifically, it is not clear what the intention of the limitation is. That is, is not clear how a “new digital component” can also be “pre-generated” by the model using the first input prompt. This deficiency renders the claim indefinite. The same rationale applies to claims 12 and 20, since they recite similar limitations, and to claims 2 to 11, 15 to 19, 21, and 22, since they inherit the same deficiencies, by virtue of their dependency. For purposes of examination, Examiner will interpret the limitation as “one or more new digital components generated by the trained machine learning model using the first input prompt”.
Claim 8 recites the limitation “the cache” in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claims 1 to 7, 12, and 15 to 22 are rejected under 35 U.S.C. 103 as being unpatentable over ZHELEZNIAK et al. (U.S. Publication No. 2019/0354588) hereinafter Zhelezniak, and further in view of Hillborg et al (U.S. Publication No. 2025/0036356) hereinafter Hillborg.
As to claim 1:
Zhelezniak discloses:
A method, comprising:
maintaining a data structure that stores a set of pre-generated digital components and, for each pre-generated digital component, a stored embedding the represents a prompt used to generate the pre-generated digital component [Paragraph 0026 teaches embedded sentences from queries saved in a database; Paragraph 0108 teaches vector representations of previous queries are stored in the content database; Paragraph 0113 teaches content database stores predetermined queries that have predetermined responses; Paragraph 0151 teaches storing predefined phrases associated with predefined responses, where the predefined phrases may be stored as sets of embedding vectors, where the predefined responses represent the set of pre-generated digital components];
receiving, from a device, a first input prompt for generating one or more first target digital components using one or more trained machine learning models, the first input prompt comprising a multi-word phrase describing characteristics of the first target digital component to be generated using the first input prompt [Paragraph 0026 teaches receiving a natural language query submitted by a user, in other words, receiving a first input prompt for generating first target digital components (i.e., text); Paragraph 0105 teaches utilizing similarity models for human to machine interfaces, i.e., chatbots; Paragraph 0107 teaches receiving a query input by a user];
generating, by the system, a first input embedding that represents the multi-word text of the input prompt [Paragraph 0026 teaches generating an embedded sentence derived from the received natural language query; Paragraph 0107 teaches generating a vector representation of the input query based on machine learning models];
determining and for each pre-generated digital component, a similarity metric that represents a measure of similarity between the stored embedding used to generate the pre-generated digital component and the first input embedding [Paragraph 0026 teaches determining a similarity between the embedded sentence derived from the received natural language query and embedded sentences from queries saved in a database; Paragraph 0108 teaches comparing the vector representation of the input query to other vector representations in the database, to determine whether the input vector representation is similar to other vector representations within the content database]; and
sending to the device based on each similarity metric, either (i) one or more of the pre-generated digital components stored in the data structure or (ii) one or more new digital components pre-generated by the trained machine learning model using the first input prompt [Paragraph 0026 teaches retrieving a response for an embedded sentence determined to be similar to one of the saved queries and providing the response to the user via the user interface; Paragraph 0109 teaches sending to the user device, content associated with the vector representation that has been determined to be similar to the input vector representation].
Zhelezniak does not appear to expressly disclose an artificial intelligence system.
Hillborg discloses:
an artificial intelligence system [Paragraph 0023 teaches implementing a specific AI mechanism with natural language processing and text-to-speech mechanisms for automatic generation of content].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Zhelezniak, by incorporating an artificial intelligence system, as taught by Hillborg [Paragraph 0023], because both applications are directed to providing content to users in response to users’ requests; utilizing artificial intelligence enables to provide an improved tool with functionality to automatically generate content (See Hillborg Para [0015]).
As to claim 2:
Zhelezniak as modified by Hillborg discloses:
determining that the similarity metric for the one or more pre-generated digital components satisfies a similarity threshold [Paragraph 0073 teaches determining that the vector similarity is sufficiently high, by comparing the similarity measure to a predetermined threshold]; and
sending the one or more pre-generated digital components to the device in response to determining that the similarity metric for the one or more pre-generated digital components satisfies a similarity threshold [Paragraph 0074 teaches if the similarity measure is equal or higher than the predetermined threshold, then the previously generated and stored audio descriptions are retrieved and provided to the user device].
As to claim 3:
Zhelezniak discloses:
determining that the similarity metric for each of a plurality of the pre-generated digital components satisfies a similarity threshold [Paragraph 0073 teaches comparing similarity measure to a predetermined threshold, and determining if the similarity measure is sufficiently high]; and in response to determining that the similarity metric for each of the plurality of the pre-generated digital components satisfies the similarity threshold: selecting, from among the plurality of pre-generated digital components, the one or more pre-generated digital components based on the similarity metric for each of the plurality of pre-generated digital components [Paragraph 0073 teaches selecting the pairing associated with the highest similarity threshold]; and sending the one or more pre-generated digital components to the device [Paragraph 0074 teaches if the similarity measure is equal or higher than the predetermined threshold, then the previously generated and stored audio descriptions are retrieved and provided to the user device].
As to claim 4:
Zhelezniak as modified by Hillborg discloses:
determining that the similarity metric for each pre-generated digital components stored in the data structure fails to satisfy a similarity threshold [Paragraph 0073 teaches determining that the similarity measure is not equal to or higher than the predetermined threshold, therefore, that no existing content is appropriate for the request]; and
in response to determining that the similarity metric for each pre-generated digital component stored in the data structure fails to satisfy the similarity threshold: generating the one or more new digital components by providing the first input prompt to the one or more trained machine learning models [Paragraph 0073 teaches invoking automatic audio description generation when similarity measure is not equal to or higher than the predetermined threshold; Paragraph 0075 teaches when no suitable content exists, the automatic audio description generator initiates operations to automatically generate audio descriptions for the content]; and
sending the one or more new digital components to the device [Paragraph 0048 teaches automatically generated audio descriptions are presented].
As to claim 5:
Zhelezniak discloses:
adding the one or more new digital components and the first input prompt to the data structure for distribution to devices in response to new input prompts being classified as similar to the first input prompt [Paragraph 0110 teaches the new content is added to the content database].
As to claim 6:
Zhelezniak as modified by Hillborg discloses:
determining that the similarity metric for the one or more pre-generated digital components satisfies a similarity threshold [Paragraph 0073 teaches determining that the vector similarity is sufficiently high, by comparing the similarity measure to a predetermined threshold]; in response to determining that the similarity metric for the one or more pre-generated digital components satisfies the similarity threshold, determining, based on a function, whether to send the one or more pre-generated digital components [Paragraph 0072 teaches retrieving all previously generated content for the specified video, and then executing logic on user’s characteristics and preferences associated with the descriptions to determine which are most appropriate; Paragraph 0074 teaches if the similarity measure is equal or higher than the predetermined threshold, then the previously generated and stored audio descriptions are retrieved and provided to the user device] or to generate the one or more new digital components using customization information for a sender of the first input prompt; whenever a determination is made to send the one or more pre-generated digital components, sending the one or more pre-generated digital components to the device [Paragraph 0074 teaches if the similarity measure is equal or higher than the predetermined threshold, then the previously generated and stored audio descriptions are retrieved and provided to the user device]; and whenever a determination is made to generate the one or more new digital components using customization information for a sender of the first input prompt: generating an updated prompt based on the first input prompt and the customization information for the sender of the first input prompt; generating the one or more new digital components by providing the updated prompt to the one or more trained machine learning models; and sending the one or more new digital components to the device [Paragraph 0073 teaches invoking automatic audio description generation].
As to claim 7:
Zhelezniak as modified by Hillborg discloses:
the data structure comprises a cache of the artificial intelligence system [Paragraph 0049 teaches computing environment includes a processor, cache, processing circuitry, etc.].
Same rationale applies to claims 12, and 15 to 22, since they recite similar limitations as those recited in claims 1 to 7, and are therefore, similarly rejected.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over ZHELEZNIAK et al. (U.S. Publication No. 2019/0354588) hereinafter Zhelezniak, in view of Hillborg et al. (U.S. Publication No. 2025/0036356) hereinafter Hillborg, and further in view of TUNSTALL-PEDOE et al. (U.S. Publication No. 2024/0095468) hereinafter Tunstall.
As to claim 8:
Zhelezniak as modified by Hillborg discloses all the limitations as set forth in the rejections of claim 6 above, but does not appear to expressly disclose updating the cache, the updating comprising: determining, for each pre-generated digital component stored in the data structure, a duration of time since a last input prompt for which the similarity metric for the stored embedding used to generate the pre-generated digital component satisfies a similarity threshold was received; and removing each pre-generated digital component for which the duration of time exceeds a duration threshold.
Tunstall discloses:
updating the cache, the updating comprising: determining, for each pre-generated digital component stored in the data structure, a duration of time since a last input prompt for which the similarity metric for the stored embedding used to generate the pre-generated digital component satisfies a similarity threshold was received; and removing each pre-generated digital component for which the duration of time exceeds a duration threshold [Paragraph 0391 teaches questions with low hit counts that were processed some time ago are removed from the cache].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Zhelezniak, by updating the cache, the updating comprising: determining, for each pre-generated digital component stored in the data structure, a duration of time since a last input prompt for which the similarity metric for the stored embedding used to generate the pre-generated digital component satisfies a similarity threshold was received; and removing each pre-generated digital component for which the duration of time exceeds a duration threshold, as taught by Tunstall [Paragraph 0391], because both applications are directed to providing content to users in response to users’ requests; periodically cleaning up the cache improves resource utilization and efficiency.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over ZHELEZNIAK et al. (U.S. Publication No. 2019/0354588) hereinafter Zhelezniak, in view of Hillborg et al. (U.S. Publication No. 2025/0036356) hereinafter Hillborg, and further in view of Mitchell et al. (U.S. Publication No. 2025/0022185) hereinafter Mitchell.
As to claim 9:
Zhelezniak as modified by Hillborg discloses all the limitations as set forth in the rejections of claim 1 above, but does not appear to expressly disclose pre-populating the data structure with one or more digital components generated using human-created prompts that were not previously received from devices of senders.
Mitchell discloses:
pre-populating the data structure with one or more digital components generated using human-created prompts that were not previously received from devices of senders [Paragraph 0030 teaches generating training images and descriptions through the user’s prompts].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Zhelezniak, by pre-populating the data structure with one or more digital components generated using human-created prompts that were not previously received from devices of senders, as taught by Mitchell [Paragraph 0030], because both applications are directed to providing content to users in response to users’ requests; generating the training images with user’s prompts enables further improvement of training of the model (See Mitchell Para [0030]).
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over ZHELEZNIAK et al. (U.S. Publication No. 2019/0354588) hereinafter Zhelezniak, in view of Hillborg et al. (U.S. Publication No. 2025/0036356) hereinafter Hillborg, and further in view of BHUPATI et al. (U.S. Publication No. 2024/0378396) hereinafter Bhupati.
As to claim 10:
Zhelezniak as modified by Hillborg discloses all the limitations as set forth in the rejections of claim 1 above, but does not appear to expressly disclose pre-populating the data structure with
one or more digital components generated using prompts generated by a language model.
Bhupati discloses:
pre-populating the data structure with one or more digital components generated using prompts generated by a language model [Paragraph 0004 teaches the application generates a prompt for an LLM service which includes the content and the context].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Zhelezniak, by pre-populating the data structure with one or more digital components generated using prompts generated by a language model, as taught by Bhupati [Paragraph 0004], because both applications are directed to providing content to users in response to users’ requests; generating the digital components using prompts generated by a language model as opposed to prompts generated by a human is a simple substitution of one known element for another to obtain predictable results.
As to claim 11:
Zhelezniak as modified by Hillborg discloses all the limitations as set forth in the rejections of claim 1 above, but does not appear to expressly disclose generating, for a digital component provider, a new prompt based on information obtained from one or more resources of the content provider; generating one or more new digital components using the new prompt; and sending, as recommended digital components, the one or more new digital components to the content provider.
Bhupati discloses:
generating, for a digital component provider, a new prompt based on information obtained from one or more resources of the content provider; generating one or more new digital components using the new prompt; and sending, as recommended digital components, the one or more new digital components to the content provider [Paragraph 0059 teaches the LLM prompts the user to submit a job description to which the resume is being tailored, parses the content to identify related sources of information for generating suggestions, where the LLM determines from the content that the user has a LinkedIn profile and may retrieve from the user’s LinkedIn profile other indicators of the user’s subjective knowledge, such as the user’s education, work experience, training, and skill set, where the LLM further includes the additional information in the prompt to LLM, to further display two suggestions based on an evaluation of the content by LLM based on the content, context, and additional information provided in the prompt].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Zhelezniak, by generating, for a digital component provider, a new prompt based on information obtained from one or more resources of the content provider; generating one or more new digital components using the new prompt; and sending, as recommended digital components, the one or more new digital components to the content provider, as taught by Bhupati [Paragraph 0059], because both applications are directed to providing content to users in response to users’ requests; generating additional prompts based on identified content to further generate and provide recommendations, improves the likelihood of successful engagement with the content from the user (See Para [0017]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAQUEL PEREZ-ARROYO whose telephone number is (571)272-8969. The examiner can normally be reached Monday - Friday, 8:00am - 5:30pm, Alt Friday, EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAQUEL PEREZ-ARROYO/Primary Examiner, Art Unit 2169
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