DETAILED ACTION
Claims 1-6, 8, 9, 14, 16, and 21-30 are presented for examination.
This office action is in response to submission of application on 07/05/2024.
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 .
Claim Objections
Claim 3 is objected to because of the following informalities: “computer-implemented method claim 1” should be “computer-implemented method of claim 1”. Appropriate correction is required.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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.
The claims 1-6, 8, 9, 14, 16, and 21-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 1 includes the steps of:
receiving request data indicating a request for content;
determining a request context associated with the request data, wherein the request context is based on account data for a user device associated with the request;
determining, based on the request and the request context, a data object from a knowledge graph, wherein the data object comprises a subject and one or more attributes for the subject; and
generating, using a machine-learned content generation model, content descriptive of the subject, the content generated based on the request, the request context, and the data object.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind through the use of a physical aid, like a pen and paper. A human can:
determine a request context associated with request data, wherein the request context is based on account data for a user device associated with the request,
determine, based on the request and the request context, a data object from a knowledge graph, wherein the data object comprises a subject and one or more attributes for the subject,
generate content descriptive of the subject, the content generated based on the request, the request context, and the data object. For example, a human can generate content descriptive of a subject by writing down a description of a subject.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
As drafted and under their broadest reasonable interpretation, the following limitations recite additional elements which amount to generic computer components recited at a high level of generality, with merely the words “apply it” or an equivalent with the judicial exception, merely including instructions to implement an abstract idea on the additional elements, or merely using the additional elements as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
using a machine-learned content generation model.
As drafted and under their broadest reasonable interpretation, the following limitations recite additional elements which amount to mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
receiving request data indicating a request for content.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application. Therefore, no meaningful claim limits are imposed practicing the abstract idea. Accordingly, at Step 2A, prong two, the additional elements do not integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim limitation(s) reciting generic computer elements amounts to no more than mere instructions to apply the exception using a generic computer.
The claim reciting the additional element(s) of “obtaining”, “receiving” and/or “transmitting” amount to necessary data gathering and output.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. Thus, the claim does not provide an inventive concept.
The claim is ineligible.
Claims 2-6, 8, 9, 14, 16, and 21-27 further recite limitations that encompass mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components (such as “machine learning model” and “user interface”). Claims reciting additional element(s) of “transmitting”, “providing”, “receiving” and “obtaining” amount to necessary data gathering and output. Claim 9 reciting “optical character recognition system to extract text… a speech recognition system to extract text” merely indicate a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment (optical character recognition and speech recognition) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). The claims do not integrate the judicial exception into practical application. The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 2-6, 8, 9, 14, 16, and 21-27 are ineligible.
Claims 28 and 29 are substantially similar to claim 1, and are rejected on the same basis as claim 1. These claims recite additional elements that amount to generic computer components recited at a high level of generality, with merely the words “apply it” or an equivalent with the judicial exception, merely including instructions to implement an abstract idea on the additional elements, or merely using the additional elements as a tool to perform an abstract idea.
Claim 30 is substantially similar to claims 24, 5, and 9, and are rejected on the same basis as claims 24, 5, and 9.
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 1-6, 21-23, 25-26, and 28-29 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning”), hereafter Wang, in view of Zarpas et al. (US 2025/0036405 A1), hereafter Zarpas.
Regarding claim 1, Wang discloses:
A computer-implemented method, comprising: receiving request data indicating a request for content (Figure 1 teaches receiving request data indicating a request for content in element “Dialogue history”),
determining a request context associated with the request data (Figure 1 and equation (1) teaches determining request context, entities in dialogue history, associated with the request data),
determining, based on the request and the request context, a data object from a knowledge graph (Figure 1 and page 4, left column, final paragraph, lines 1-3 “Given a dialogue history C, we first separately encode the dialogue words and KG entities that appear in C into word embeddings and entity embeddings.” teaches determining data objects, i.e. entity embeddings, from a knowledge graph based on the dialogue and the dialogue entities),
wherein the data object comprises a subject and one or more attributes for the subject (Figure 1 and page 4, paragraph “Encoding Word Tokens and KG Entities”, lines 9-14 “For entity embeddings, following previous works [4, 35], we first perform entity linking based on an external KG DBpedia [1], and then obtain the corresponding entity embeddings via a relational graph neural networks (RGCN) [25], which can model the relational semantics through information propagation and aggregation over the KG” teaches one or more attributes for each entity through aggregation over the KG).
generating, using a machine-learned content generation model, content descriptive of the subject, the content generated based on the request, the request context, and the data object (Figure 1 teaches generating a response as content descriptive of the subject, where the content generation model DialoGPT generates the response based on the request, the request context, and the data object).
While Wang discloses receiving request data indicating a request for content and determining a request context associated with the request data, they do not teach wherein the request context is based on account data for a user device associated with the request.
Zarpas discloses:
wherein the request context is based on account data for a user device associated with the request (Fig. 1A element 108, and ¶[0023] teaches the request context for chatbot to be based on customer master data as account data for a user device associated with the request)
Wang and Zarpas are analogous art because they are from the same field of endeavor, content generation machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wang to include wherein the request context is based on account data for a user device associated with the request, based on the teachings of Zarpas. One of ordinary skill in the art would have been motivated to make this modification in order to enhance models with customer specific master data, as suggested by Zarpas (¶[0006]).
Regarding claim 2, Wang, in view of Zarpas, discloses the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Zarpas further discloses:
transmitting the content to the user device (Fig. 1A element 111-112 and ¶[0044) teaches transmitting the generated content to an end user).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wang to include wherein the request context is based on account data for a user device associated with the request, based on the teachings of Zarpas. One of ordinary skill in the art would have been motivated to make this modification in order to create scalable conversational experiences and saving resources, as suggested by Zarpas (¶[0018]).
Regarding claim 3, Wang, in view of Zarpas, discloses the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Wang further discloses:
generating, based on the request and the request context, input data for input to the machine-learned content generation model (Figure 1 teaches generating input data, i.e. dialogue history, prompts, and fused representations based on the request and the request context, for input to the DialoGPT model),
wherein the input data is configured to cause the content generated by the machine-learned content generation model to describe the subject using the one or more attributes for the subject (Figure 1 elements “Response” and “knowledge graph” and page 5, right column, second paragraph “Prompt for Item Recommendation. The subtask of recommendation aims to predict items that a user might be interested in. The prompting design mainly enhances the user preference semantics, in order to predict more satisfactory recommendations.” Teaches causing the content generated by the machine-learned content generation model to describe the subject using the one or more attributes for the subject).
Regarding claim 4, Wang, in view of Zarpas, discloses the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Wang further discloses:
generating, using a machine-learned model and based on the request data and the request context, a query for querying the knowledge graph to obtain the data object (page 4, paragraph “Encoding Word Tokens and KG Entities”, lines 9-14 “For entity embeddings, following previous works [4, 35], we first perform entity linking based on an external KG DBpedia [1], and then obtain the corresponding entity embeddings via a relational graph neural networks (RGCN) [25], which can model the relational semantics through information propagation and aggregation over the KG” teaches querying an external knowledge graph to obtain the data object).
Regarding claim 5, Wang, in view of Zarpas, discloses the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Wang further discloses:
obtaining preferences associated with an entity corresponding to the content; and generating the content according to the preferences (Figure 1 and page 5, left column, paragraph 4, lines 1-3 “The subtask of response generation aims to generate informative utterances in order to clarify user preferences” teaches generating content according to user preferences associated with an entity).
Regarding claim 6, Wang, in view of Zarpas, discloses the computer-implemented method of claim 5 (and thus the rejection of claim 5 is incorporated). Wang further discloses:
providing a user interface that facilitates entry of the preferences (Table 2 and page 6, right column, final paragraph, last 3 lines “We implement all baseline models using the open-source toolkit CRSLab” teaches facilitating entry of preferences through a user interface).
Regarding claim 21, Wang, in view of Zarpas, discloses the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Wang further discloses:
parsing data comprising at least one of: text of the generated content, image data of the generated content, or audio data of the generated content; comparing the parsed data with at least one of: input data for the machine-learned content generation model, the request data, the request context, or a portion of the data object (Figure 2 and its caption, and page 6, right column, penultimate paragraph, lines 1-6 “we adopt different metrics to evaluate the recommendation and conversation task separately. For the recommendation task, following [4, 35], we use Recall@𝑘 (𝑘=1,10,50) for evaluation. For the conversation task, following [4, 35], we adopt Distinct-𝑛 (𝑛=2,3,4) at the word level to evaluate the diversity of the generated responses.” Teaches parsing the text of the generated content and comparing the parsed data with input data for the model).
Regarding claim 22, Wang, in view of Zarpas, discloses the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Wang further discloses:
parsing the generated content to identify the subject and the one or more attributes (Figure 1 teaches parsing the generated content to identify the subject and the one or more attributes for the template and response).
Regarding claim 23, Wang, in view of Zarpas, discloses the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Zarpas further discloses:
the request context comprises data indicative of an environment in which the user device is located (Fig 1A and ¶[0021] teaches the request context comprises data indicative of an environment in which the user device is located).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wang to include the request context comprises data indicative of an environment in which the user device is located, based on the teachings of Zarpas. One of ordinary skill in the art would have been motivated to make this modification in order to create scalable conversational experiences and saving resources, as suggested by Zarpas (¶[0018]).
Regarding claim 25, Wang, in view of Zarpas, discloses the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Wang further discloses:
obtaining the subject and the one or more attributes for the subject is performed by processing webpage content with a machine-learned model to output the subject and the one or more attributes (Figure 1 and page 4, right column, lines 1-3 “perform entity linking based on an external KG DBpedia [1], and then obtain the corresponding entity embeddings via a relational graph neural networks (RGCN) [25], ” teaches obtaining the subject and the one or more attributes for the subject by processing webpage content, i.e. Wikipedia content in DBpedia, with a machine-learned model to output the subject and the one or more attributes).
Regarding claim 26, Wang, in view of Zarpas, discloses the computer-implemented method of claim 5 (and thus the rejection of claim 5 is incorporated). Wang further discloses:
obtaining the subject and the one or more attributes for the subject comprises: determining, based on one or more of the preferences associated with the entity, an authorization to obtain the subject and the one or more attributes (Examiner notes: for prior art purposes, the examiner interprets the BRI “authorization” to include any indication from a user) (Figure 1 teaches, based on user preferences indicated in the dialogue, an authorization to obtain the subject and the one or more attributes).
Claims 28 and 29 are substantially similar to claim 1, and are rejected on the same basis as claim 1.
Claims 8, 9, 14, 16, 24, 27, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning”), hereafter Wang, in view of Zarpas et al. (US 2025/0036405 A1), hereafter Zarpas, in further view of Adada et al. (US 2024/0256618 A1), hereafter Adada.
Regarding claim 8, Wang, in view of Zarpas, discloses the computer-implemented method of claim 5 (and thus the rejection of claim 5 is incorporated).
Wang, in view of Zarpas, does not teach wherein the content is packaged with a hyperlink to a web resource associated with the entity.
Adada discloses:
wherein the content is packaged with a hyperlink to a web resource associated with the entity (Fig. 5 and ¶[0049] teaches the presented content to be packaged with a hyperlink to a web resource associated with the entity .
Wang, Zarpas, Adada are analogous art because they are from the same field of endeavor, content generation machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wang, in view of Zarpas, to include wherein the content is packaged with a hyperlink to a web resource associated with the entity, based on the teachings of Adada. One of ordinary skill in the art would have been motivated to make this modification in order to provide increasingly relevant results to users, as suggested by Adada (¶[0003]).
Regarding claim 9, Wang, in view of Zarpas, discloses the computer-implemented method of claim 5 (and thus the rejection of claim 5 is incorporated). Wang further discloses:
processing the generated content to verify that it is consistent with the preferences (page 6, right column, penultimate paragraph, lines 7-9 “three annotators to score the generated responses of our model and baselines from two aspects” teaches verifying the generated content is consistent with preferences),
wherein processing the generated content to verify that it is consistent with the preferences comprises: …at least one of: parsing text of the generated content to compare against the preferences; or parsing text of the generated content to identify any excluded terms (page 5, left column, paragraph 4, lines 1-3 “The subtask of response generation aims to generate informative utterances in order to clarify user preferences” and page 6, right column, penultimate paragraph, lines 7-9 “three annotators to score the generated responses of our model and baselines from two aspects, namely Fluency and Informativeness” teaches parsing text of the generated content to compare against the preferences for informativeness).
While Wang discloses processing the generated content to verify that it is consistent with the preferences, Wang, in view of Zarpas, do not teach at least one of: processing image data with an optical character recognition system to extract text; or processing audio data with a speech recognition system to extract text.
Adada discloses:
at least one of: processing image data with an optical character recognition system to extract text; or processing audio data with a speech recognition system to extract text (¶[0110] and ¶[0142] teaches processing audio data with a speech recognition system to extract text),
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wang, in view of Zarpas, to include at least one of: processing image data with an optical character recognition system to extract text; or processing audio data with a speech recognition system to extract text, based on the teachings of Adada. One of ordinary skill in the art would have been motivated to make this modification in order to provide increasingly relevant results to users, as suggested by Adada (¶[0003]).
Regarding claim 14, Wang, in view of Zarpas, in further view of Adada, discloses the computer-implemented method of claim 9 (and thus the rejection of claim 9 is incorporated). Adada further discloses:
based on detecting a deviation from the preferences, initiating regeneration of the content (¶[0075] discloses initiating regeneration of the content based on detecting a deviation from the preferences).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wang, in view of Zarpas, to include based on detecting a deviation from the preferences, initiating regeneration of the content, based on the teachings of Adada. One of ordinary skill in the art would have been motivated to make this modification in order to provide increasingly relevant results to users, as suggested by Adada (¶[0003]).
Regarding claim 16, Wang, in view of Zarpas, in further view of Adada, discloses the computer-implemented method of claim 14 (and thus the rejection of claim 14 is incorporated). Adada further discloses:
wherein initiating regeneration comprises editing input data for the machine-learned content generation model, wherein editing the input data for the machine-learned content generation model comprises: updating a prompt to include an instruction regarding the detected deviation; updating the prompt to include an instruction to correct the detected deviation; or updating the prompt to include an instruction providing the detected deviation as a negative example (¶[0075] and ¶[0081] teaches updating the prompt to include an instruction to correct the detected deviation when editing input data for the machine-learned content generation model).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wang, in view of Zarpas, to include wherein initiating regeneration comprises editing input data for the machine-learned content generation model, wherein editing the input data for the machine-learned content generation model comprises: updating a prompt to include an instruction regarding the detected deviation; updating the prompt to include an instruction to correct the detected deviation; or updating the prompt to include an instruction providing the detected deviation as a negative example, based on the teachings of Adada. One of ordinary skill in the art would have been motivated to make this modification in order to provide increasingly relevant results to users, as suggested by Adada (¶[0003]).
Regarding claim 24, Wang, in view of Zarpas, discloses the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Wang further discloses:
generating the knowledge graph, wherein generating the knowledge graph comprises: … generating the data object comprising the subject and one or more attributes for the subject (Figure 1 and page 4, right column, line 1 “perform entity linking based on an external KG DBpedia” teaches entity linking to generate a knowledge graph, and generating data objects comprising the subject and one or more attributes for the subject),
storing the data object in the knowledge graph based on the one of the one or more attributes that is the entity (Figure 1 and page 4, right column, lines 1-2 “perform entity linking based on an external KG DBpedia and then obtain the corresponding entity embeddings” teaches storing the data object based on the entity).
Wang does not disclose:
receiving a starting page indicative of an entity’s webpage,
traversing a plurality of pages associated with the starting page, and
obtaining the subject and the one or more attributes for the subject based on at least a portion of the plurality of pages, wherein at least one of the one or more attributes is the entity.
Adada discloses:
receiving a starting page indicative of an entity’s webpage (¶[0034-0035] teaches a knowledge module that receives a starting page indicative of an entity’s webpage),
traversing a plurality of pages associated with the starting page (¶[0031-0032] teaches traversing a plurality of pages associated with the starting page),
obtaining the subject and the one or more attributes for the subject based on at least a portion of the plurality of pages, wherein at least one of the one or more attributes is the entity (¶[0031-0032] teaches obtaining the subject and entities for the subject based on at least a portion of the plurality of pages).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wang, in view of Zarpas, to include receiving a starting page indicative of an entity’s webpage, traversing a plurality of pages associated with the starting page, and obtaining the subject and the one or more attributes for the subject based on at least a portion of the plurality of pages, wherein at least one of the one or more attributes is the entity, based on the teachings of Adada. One of ordinary skill in the art would have been motivated to make this modification in order to provide increasingly relevant results to users, as suggested by Adada (¶[0003]).
Regarding claim 27, Wang, in view of Zarpas, discloses the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated).
Wang, in view of Zarpas, does not disclose caching the generated content; and serving the cached generated content to another user device based on determining a relevance between request data for the other user device and the cached generated content.
Adada discloses:
caching the generated content; and serving the cached generated content to another user device based on determining a relevance between request data for the other user device and the cached generated content (¶[0040] teaches caching generated responses and serving the cached generated content to another user device based on determining a relevance between request data for the other user device and the cached generated content).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wang, in view of Zarpas, to include caching the generated content; and serving the cached generated content to another user device based on determining a relevance between request data for the other user device and the cached generated content, based on the teachings of Adada. One of ordinary skill in the art would have been motivated to make this modification in order to provide increasingly relevant results to users, as suggested by Adada (¶[0003]).
Regarding claim 30, Wang, in view of Zarpas, discloses the system of claim 29 (and thus the rejection of claim 29 is incorporated). Wang further discloses:
generating the knowledge graph, wherein generating the knowledge graph comprises: … generating the data object comprising the subject and one or more attributes for the subject (Figure 1 and page 4, right column, line 1 “perform entity linking based on an external KG DBpedia” teaches entity linking to generate a knowledge graph, and generating data objects comprising the subject and one or more attributes for the subject),
storing the data object in the knowledge graph based on the one of the one or more attributes that is the entity (Figure 1 and page 4, right column, lines 1-2 “perform entity linking based on an external KG DBpedia and then obtain the corresponding entity embeddings” teaches storing the data object based on the entity),
obtaining preferences associated with an entity corresponding to the content; and generating the content according to the preferences (Figure 1 and page 5, left column, paragraph 4, lines 1-3 “The subtask of response generation aims to generate informative utterances in order to clarify user preferences” teaches generating content according to user preferences associated with an entity),
processing the generated content to verify that it is consistent with the preferences (page 6, right column, penultimate paragraph, lines 7-9 “three annotators to score the generated responses of our model and baselines from two aspects” teaches verifying the generated content is consistent with preferences),
wherein processing the generated content to verify that it is consistent with the preferences comprises: …at least one of: parsing text of the generated content to compare against the preferences; or parsing text of the generated content to identify any excluded terms (page 5, left column, paragraph 4, lines 1-3 “The subtask of response generation aims to generate informative utterances in order to clarify user preferences” and page 6, right column, penultimate paragraph, lines 7-9 “three annotators to score the generated responses of our model and baselines from two aspects, namely Fluency and Informativeness” teaches parsing text of the generated content to compare against the preferences for informativeness).
Wang does not disclose:
receiving a starting page indicative of an entity’s webpage;
traversing a plurality of pages associated with the starting page;
obtaining the subject and the one or more attributes for the subject based on at least a portion of the plurality of pages, wherein at least one of the one or more attributes is the entity;
Adada discloses:
receiving a starting page indicative of an entity’s webpage (¶[0034-0035] teaches a knowledge module that receives a starting page indicative of an entity’s webpage),
traversing a plurality of pages associated with the starting page (¶[0031-0032] teaches traversing a plurality of pages associated with the starting page),
obtaining the subject and the one or more attributes for the subject based on at least a portion of the plurality of pages, wherein at least one of the one or more attributes is the entity (¶[0031-0032] teaches obtaining the subject and entities for the subject based on at least a portion of the plurality of pages).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wang, in view of Zarpas, to include receiving a starting page indicative of an entity’s webpage, traversing a plurality of pages associated with the starting page, and obtaining the subject and the one or more attributes for the subject based on at least a portion of the plurality of pages, wherein at least one of the one or more attributes is the entity, based on the teachings of Adada. One of ordinary skill in the art would have been motivated to make this modification in order to provide increasingly relevant results to users, as suggested by Adada (¶[0003]).
While Wang discloses processing the generated content to verify that it is consistent with the preferences, Wang, in view of Zarpas, do not teach at least one of: processing image data with an optical character recognition system to extract text; or processing audio data with a speech recognition system to extract text.
Adada discloses:
at least one of: processing image data with an optical character recognition system to extract text; or processing audio data with a speech recognition system to extract text (¶[0110] and ¶[0142] teaches processing audio data with a speech recognition system to extract text),
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Wang, in view of Zarpas, to include at least one of: processing image data with an optical character recognition system to extract text; or processing audio data with a speech recognition system to extract text, based on the teachings of Adada. One of ordinary skill in the art would have been motivated to make this modification in order to provide increasingly relevant results to users, as suggested by Adada (¶[0003]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
U.S. Pub No. 20210064932 A1: Wang et al. teaches knowledge graph and content generation.
Brate et al. (“Improving Language Model Predictions via Prompts Enriched with Knowledge Graphs”) teaches knowledge graphs and content generation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUMAIRA ZAHIN MAUNI whose telephone number is (703)756-5654. The examiner can normally be reached Monday - Friday, 9 am - 5 pm (ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MATT ELL can be reached at (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/H.Z.M./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
