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
Claims 1-20 are pending. Claims 1 and 12 are independent.
Claims 2-11 depend from Claim 1.
Claims 13-20 depend from Claim 12.
This Application was published as U.S. 2025/0028745.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 112(a) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/514438, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
Accordingly, claims 1-20 are not entitled to the benefit of prior application 63/514438. Claims 1 and 12 refer to the following limitations not supported by the provisional:
Claim 1:
generating a ranking of the plurality of contents, wherein the ranking indicates a relevancy of each content in the plurality of contents to the query;
selecting, from the plurality of contents, one or more contents based on the ranking;
Claim 12:
generating, by the computer system, a ranking of the plurality of contents, wherein the ranking indicates a relevancy of each content in the plurality of contents to the query;
selecting, from the plurality of contents, one or more contents based on the ranking;
The provisional does not mention generating ranking of contents, selecting contents based on rankings, or anything similar because the provisional application does not mention ranking. Due to not finding support in the provisional for the independent claims, the dependent claims will not be given the benefit of the prior application 63/514438.
On the other hand, provisional application 63/652384 does mention generating ranking of contents and selecting contents based on rankings. Therefore, claims 1-20 will have the benefit of this application and its priority date of 28 May 2024.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters "324" and "200" have both been used to designate “Beats” in Fig 3 and Fig 4, respectively. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Fig 7 refers to refence item “722”, “724”, and “726” that are not mentioned in the specification.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claim 12 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 12 refers to generating, by the computer system, a prompt for an artificial intelligence (AI) model by integrating the one or more contents into the query; and
providing, by the computer system, the enriched prompt to the AI model.
It is not clear if a prompt that is generated is the enriched prompt because the claim does not mention generating an enriched prompt. Thus, it is not clear if prompt and the enriched prompt are referring to the same prompt.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claims 1 and 12 recite various limitations that, but for generic computer components (i.e. one or more processors, memory, AI model, or computer system) can be performed in the human mind or with pen and paper, and are considered abstract ideas. The claims under the broadest reasonable interpretation cover the concept of receiving a query associated with a domain, obtain contents associated with the domain, generate ranking of the contents indicating relevancy of the content to the query, select contents based on the ranking, generate a prompt by integrating the contents into the query, and provide the prompt to the AI model. (See MPEP 2106.04(a)(2) III)
This judicial exception is not integrated into a practical application because the claims only recite elements in the form of “memory,” “processor,” “AI model,” or “computer system.” These elements are used to perform the claimed methods and steps, and are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components and provided to an AI model. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not include subject matter that could not be performed by a human, as discussed above with respect to integration of the abstract idea into a practical application. The additional elements of using the generic computing elements to perform the claimed elements amount to no more than mere instructions to apply the exception using a generic computer component or can be considered insignificant extra solution activity. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, and mere data gathering in conjunction with an abstract idea cannot provide an inventive concept. For all the reasons stated above, the claims are not patent eligible.
With regards to claim 2, the claim further limits the elements of claim 1; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because whether domain is associated with a network address or the contents are based on a network address are neither a mathematical concept nor a mental process, nor a method of organizing human activity because it does not fall within the enumerated sub-groupings. Similar to claim 1, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception.
With regards to claim 3, the claim further limits the elements of claim 1; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because the network address being associated with a website is neither a mathematical concept nor a mental process, nor a method of organizing human activity because it does not fall within the enumerated sub-groupings, and crawling a website is a data gathering step. Similar to claim 1, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception.
With regards to claim 4, the claim further limits the elements of claim 1; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because receiving a query and retrieving contents from a user device are data gathering steps. Similar to claim 1, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception.
With regards to claim 5, the claim further limits the elements of claim 1; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because generating a semantic or termed -based ranking are data gathering step. Similar to claim 1, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception.
With regards to claim 6, the claim further limits the elements of claim 5; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because generating embeddings are data gathering steps and comparing embeddings can be done in one’s one mind as an abstract idea. Similar to claim 5, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception.
With regards to claim 7, the claim further limits the elements of claim 5; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because generating attributes are data gathering steps and comparing attributes can be done in one’s one mind as an abstract idea. Similar to claim 5, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception.
With regards to claim 8, the claim further limits the elements of claim 5; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because determining a weight can be done in one’s own mind as an abstract idea. Similar to claim 5, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception.
With regards to claim 9, the claim further limits the elements of claim 1; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because providing a prompt to AI model is a data gathering step. Similar to claim 1, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception.
With regards to claim 10, the claim further limits the elements of claim 9; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because obtaining an output from an AI model and generating a response are data gathering steps. Similar to claim 9, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception.
With regards to claim 11, the claim further limits the elements of claim 10; however, these limitations do not preclude the limitations from being performed by mental observations or evaluation, or as a data gathering step that a person does in one’s one head as a mental process because identifying
obtaining an output from an AI model and generating a response are data gathering steps. Similar to claim 10, no additional elements beyond the use of generic computing elements that are well-understood, routine, conventional activities previously known to the industry. Therefore, the judicial exception is not integrated into a practical application nor are the elements sufficient to amount to significantly more than the judicial exception.
Claim 13 is a method claim with limitations corresponding to the limitations of method Claim 2 and is rejected under similar rationale.
Claim 14 is a method claim with limitations corresponding to the limitations of method Claim 3 and is rejected under similar rationale.
Claim 15 is a method claim with limitations corresponding to the limitations of method Claim 4 and is rejected under similar rationale.
Claim 16 is a method claim with limitations corresponding to the limitations of method Claim 5 and is rejected under similar rationale.
Claim 17 is a method claim with limitations corresponding to the limitations of method Claim 6 and is rejected under similar rationale.
Claim 18 is a method claim with limitations corresponding to the limitations of method Claim 7 and is rejected under similar rationale.
Claim 19 is a method claim with limitations corresponding to the limitations of method Claim 8 and is rejected under similar rationale.
Claim 20 is a method claim with limitations corresponding to the limitations of method Claim 10 and is rejected under similar rationale.
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.
Claims 1-7, 9-10, and 12-18 are rejected under 35 U.S.C. 103 as being unpatentable over Marey (US2022/0012296 hereinafter Marey) in view of Howard (US2021/0232632 hereinafter Howard)
With regards to claim 1, Marey teaches:
A system, comprising: a non-transitory memory; [Marey Fig 5 item 508, Par [0049]] and one or more hardware processors coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising: [Marey Fig 5 item 504, Par [0048]]
receiving a query associated with a first domain; [Marey Fig1 teaches receiving a query or request to generate comments (116, 118) associated with a social media post (106) which is a first domain]
obtaining a plurality of contents from one or more data sources that are associated with the first domain; [Marey Fig 4 teaches obtaining social media content from a database (414) associated with the original post (Par [0042]) that is associated with the first domain]
generating a ranking of the plurality of contents, wherein the ranking indicates a relevancy of each content in the plurality of contents to the query; [Marey Fig 4 teaches using a database (414) to generate contents “ranked by frequency-weighted term overlap … based on a closest match (e.g., based on sentence structure and/or overlap of content)” (Par [0042]) which indicates a relevancy of the contents to the query]
selecting, from the plurality of contents, one or more contents based on the ranking; [Marey Fig 1 teaches “rank candidate posts to be recommended” (Par [0034]) and example is shown in Fig 1 item 116 and 118]
With regards to claim 1, Marey fails to teach:
generating an enriched prompt for an artificial intelligence (AI) model by integrating the one or more contents into the query; and
providing the enriched prompt to the AI model.
With regards to claim 1, Howard teaches:
generating an enriched prompt for an artificial intelligence (AI) model by integrating the one or more contents into the query; and [Howard Fig 1 teaches subject matter prompt (112) that uses context analysis (121) to generate an enriched prompt that integrates the contents into the query by using “subject matter context in which the target identities act” (Par [0034]) ]
providing the enriched prompt to the AI model. [Howard Fig 4A-4B teaches subject matter enriched prompt is provided to step 402, that is implemented by neural network (Par [0164-165]) where a neural network is an AI model.
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the social media comment recommendation system as taught by Marey with the multi-modal virtual experience system that uses prompts and contexts as taught by Howard. The motivation to combine the teachings of Marey with Howard is because Howard teaches “multi-faceted and flexibly-dimensional virtual experience of the selected target identities in a subject matter context, matched to the capabilities of the user experience device 100” (Par [0020]) which increases the capabilities of the invention of Marey to provide better user experiences for the user device]
With regards to claim 2, Marey in view of Howard teaches:
All the limitations of claim 1
wherein the first domain is associated with a network address, and wherein the plurality of contents is obtained based on the network address. [Marey Fig 4 teaches database (414) includes social media posts and associated entity information such as “an identifier for an entity, details describing an entity, a title referring to the entity, phrases associated with the entity, links (e.g., IP addresses, URLs, hardware addresses) associated with the entity, keywords associated with the entity (e.g., tags or other keywords), any other suitable information associated with an entity, or any combination thereof” (Par [0032]) where social media entities are domains associated with a network address]
With regards to claim 3, Marey in view of Howard teaches:
All the limitations of claim 1
wherein the network address is associated with a website, and wherein the obtaining the plurality of contents comprises crawling the website for the plurality of contents. [Marey Fig 4 teaches network address is associated with a URL (Par [0032]) or website and “performing a web crawl (e.g., for relevant text or images)” (Par [0033])]
With regards to claim 4, Marey in view of Howard teaches:
All the limitations of claim 1
wherein the query is received from a user device, and wherein the obtaining the plurality of contents comprises retrieving the plurality of contents from the user device. [Marey Fig 1 teaches query is received from a user device and obtaining contents from the user device (Fig 4-5, Par [0024])]
With regards to claim 5, Marey in view of Howard teaches:
All the limitations of claim 1
wherein the operations further comprise: generating a semantic-based ranking of the plurality of contents, the semantic-based ranking indicating a relevancy of each content in the plurality of contents to the query based on a semantic-based algorithm; and [Howard teaches various semantic analysis methods of ranking content such as “calculating a weighted average of the score or rank assigned by each semantic analysis method to a concept.” (Par [0111])]
generating a term-based ranking of the plurality of contents, the term-based ranking indicating a relevancy of each content in the plurality of contents to the query based on the term-based algorithm, wherein the ranking is generated based on the semantic-based ranking and the term-based ranking. [Marey teaches using vector semantics to “facilitate the recommendation of social media posts …[using] For example, models driven by statistical methods such as term frequency-inverse document frequency (TF-IDF)” (Par [0039]) where the social media posts are “ranked by frequency-weighted term overlap” (Par [0042]).
While Marey does not state using semantic-based rankings, it would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the semantic rankings as taught by Howard with the vector semantics and term-based rankings as taught by Marey. The motivation to combine the teachings of Howard and Marey is because Howard teaches that “Different semantic analysis methods may yield a different selection of key concepts in the search results, as some methods may be more effective with certain kinds of textual material than others. Hence, more than one kind of semantic analysis method may be used to determine key concepts” (Par [0111]) which increases the capabilities of Howard in view of Marey provide better user experiences for the user device]
With regards to claim 6, Marey in view of Howard teaches:
All the limitations of claim 5
wherein the operations further comprise: generating first embeddings for the query;
generating second embeddings for each content in the plurality of contents; and comparing the first embeddings against the second embeddings, wherein the generating the semantic-based ranking is based on the comparing. [Marey Fig 4 teaches database (414) “finding semantically similar posts (e.g., based on word and/or sentence embeddings)” (Par [0033]) which is generating embeddings for the social media post and the semantically similar post.
While Marey does not state using semantic-based rankings, it would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the semantic rankings as taught by Howard with the vector semantics and term-based rankings as taught by Marey. The motivation to combine the teachings of Howard and Marey is because Howard teaches that “Different semantic analysis methods may yield a different selection of key concepts in the search results, as some methods may be more effective with certain kinds of textual material than others. Hence, more than one kind of semantic analysis method may be used to determine key concepts” (Par [0111]) which increases the capabilities of Howard in view of Marey provide better user experiences for the user device]
With regards to claim 7, Marey in view of Howard teaches:
All the limitations of claim 5
wherein the operations further comprise: generating first word attributes based on the query; generating second word attributes based on each content in the plurality of contents; and comparing the first word attributes against the second word attributes, wherein the generating the term-based ranking is based on the comparing. [Marey Fig 4 teaches using term frequency-inverse document frequency (TF-IDF) as a way for database (414) to compare ”content for similar posts to be recommended (e.g., retrieving historical posts with similar statistical features to posts 402, 404)” (Par [0039]) which is generating word attributes for the social media post and similar posts where “ database 414 candidate social media posts (e.g., ranked by frequency-weighted term overlap), and a post may be recommended based on a closest match (e.g., based on sentence structure and/or overlap of content).” (Par [0042])]
With regards to claim 9, Marey in view of Howard teaches:
All the limitations of claim 1
wherein the enriched prompt is provided to a plurality of AI models. [Howard teaches a plurality of models such as “RNNs, Long Short Term Memory models (LSTMs), Generational Adversarial Networks (GANs), and Convolutional Nets (CNNs), or other types of neural network model or combination thereof.” (Par [0168])]
With regards to claim 10, Marey in view of Howard teaches:
All the limitations of claim 9
wherein the operations further comprise: obtaining a first output from a first AI model from the plurality of AI models; obtaining a second output from a second AI model from the plurality of AI models; generating a response for the query based on the first output and the second output. [Howard teaches a first output from “A neural network, such as a recurrent neural network (RNN), can be trained to associate existing content patterns with various subject matter prompt ” (Par [0164]) and “Each subject matter prompt may have its own trained neural network, or more than one subject matter prompt may share a trained neural network” (Par [0168]) where each AI model creates an output to generate the response]
With regards to claim 12, Marey teaches:
A method comprising: receiving, by a computer system, a query associated with a first domain; [Marey Fig1 teaches receiving a query or request to generate comments (116, 118) associated with a social media post (106) which is a first domain]
obtaining, by the computer system, a plurality of contents from one or more data sources that are associated with the first domain; [Marey Fig 4 teaches obtaining social media content from a database (414) associated with the original post (Par [0042]) that is associated with the first domain]
generating, by the computer system, a ranking of the plurality of contents, wherein the ranking indicates a relevancy of each content in the plurality of contents to the query; [Marey Fig 4 teaches using a database (414) to generate contents “ranked by frequency-weighted term overlap … based on a closest match (e.g., based on sentence structure and/or overlap of content)” (Par [0042]) which indicates a relevancy of the contents to the query]
selecting, from the plurality of contents, one or more contents based on the ranking;
[Marey Fig 1 teaches “rank candidate posts to be recommended” (Par [0034]) and example is shown in Fig 1 item 116 and 118]
With regards to claim 12, Marey fails to teach:
generating, by the computer system, a prompt for an artificial intelligence (AI) model by integrating the one or more contents into the query; and
providing, by the computer system, the enriched prompt to the AI model.
With regards to claim 12, Howard teaches:
generating, by the computer system, a prompt for an artificial intelligence (AI) model by integrating the one or more contents into the query; and [Howard Fig 1 teaches subject matter prompt (112) that uses context analysis (121) to generate an enriched prompt that integrates the contents into the query by using “subject matter context in which the target identities act” (Par [0034]) ]
providing, by the computer system, the enriched prompt to the AI model. [Howard Fig 4A-4B teaches subject matter enriched prompt is provided to step 402, that is implemented by neural network (Par [0164-165]) where a neural network is an AI model.
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the social media comment recommendation system as taught by Marey with the multi-modal virtual experience system that uses prompts and contexts as taught by Howard. The motivation to combine the teachings of Marey with Howard is because Howard teaches “multi-faceted and flexibly-dimensional virtual experience of the selected target identities in a subject matter context, matched to the capabilities of the user experience device 100” (Par [0020]) which increases the capabilities of the invention of Marey to provide better user experiences for the user device]
Claim 13 is a method claim with limitations corresponding to the limitations of method Claim 2 and is rejected under similar rationale.
Claim 14 is a method claim with limitations corresponding to the limitations of method Claim 3 and is rejected under similar rationale.
Claim 15 is a method claim with limitations corresponding to the limitations of method Claim 4 and is rejected under similar rationale.
Claim 16 is a method claim with limitations corresponding to the limitations of method Claim 5 and is rejected under similar rationale.
Claim 17 is a method claim with limitations corresponding to the limitations of method Claim 6 and is rejected under similar rationale.
Claim 18 is a method claim with limitations corresponding to the limitations of method Claim 7 and is rejected under similar rationale.
Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Marey (US2022/0012296) in view of Howard (US2021/0232632) in further view of Prasad Tanniru et al. (US2021/0286832 hereinafter Prasad Tanniru)
With regards to claim 8, Marey in view of Howard teaches:
All the limitations of claim 5
With regards to claim 8, Marey in view of Howard fails to teach:
wherein the operations further comprise: determining a first weight associated with the semantic-based ranking and a second weight associated with the term-based ranking, wherein the ranking is generated further based on the first weight and the second weight.
With regards to claim 8, Prasad Tanniru teaches:
wherein the operations further comprise: determining a first weight associated with the semantic-based ranking and a second weight associated with the term-based ranking, wherein the ranking is generated further based on the first weight and the second weight. [Prasad Tanniru teaches “TF-IDF score may include a weight used in information retrieval and text mining, and used by search engines in scoring and ranking a document's relevance given a query” (Par [0039])
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the social media comment recommendation system as taught using TF-IDF as taught by Marey in view of Howard with the TF-IDF system using weights as taught by Prasad Tanniru.
The motivation to combine the teachings of Marey and Howard with Prasad Tanniru is because Prasad Tanniru teaches “importance increases proportionally to a quantity of times a word appears in the document but may be offset by a frequency of the word in the corpus. In some implementations, the knowledge platform may scale up a second document score caused by rare words and/or may scale down a second document score caused by frequently appearing words” (Par [0039]) which increases the capabilities of the invention of Marey in view of Howard to provide better recommendations for the user and increase user experiences for the user device]
Claim 19 is a method claim with limitations corresponding to the limitations of method Claim 8 and is rejected under similar rationale.
Claims 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Marey (US2022/0012296) in view of Howard (US2021/0232632) in further view of Horesh et al. (US11972333 hereinafter Horesh)
With regards to claim 11, Marey in view of Howard teaches:
All the limitations of claim 10
With regards to claim 11, Marey in view of Howard fails to teach:
wherein the operations further comprise: identifying one or more portions that are common in the first output and the second output, wherein the response is generated based on the one or more portions.
With regards to claim 11, Horesh teaches:
wherein the operations further comprise: identifying one or more portions that are common in the first output and the second output, wherein the response is generated based on the one or more portions. [Horesh Fig 2 teaches classification model (230) compares the outputs from the first AI model (210) with the outputs of the second AI model (220), and outputs the response based on the common portions. (Col 13 lines 32-47)
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the social media comment recommendation system as taught using TF-IDF as taught by Marey in view of Howard with the generative AI model as taught by Horesh. The motivation to combine the teachings of Marey and Howard with Horesh is because Horesh teaches two generative AI modes to “ensure that the outputs generated and provided by a generative AI model are relevant … [and] second generative AI model is trained using fresher data than the managed generative AI model so that outputs from the second generative AI model may be more relevant for, e.g., evolving topics.” (Col 56-66) which increases the capabilities of the invention of Marey in view of Howard to provide better recommendations for the user and increase user experiences for the user device]
Claim 20 is a method claim with limitations corresponding to the limitations of method Claim 10 and is rejected under similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph J Yamamoto whose telephone number is (571)272-4020. The examiner can normally be reached M-F 1000-1800 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached at 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOSEPH J. YAMAMOTO
Examiner
Art Unit 2656
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656
Prosecution Insights