DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/26/2026 has been entered.
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.
Claim 1 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Chiguchi, Satoshi et al (Japanese Patent Document No. JP 2025051488 ), hereafter referred as to “Chiguchi”, in view of Zhang, Han et al (Chinese Patent Document No. CN 111611488), hereafter, referred to as “Zhang”, in view of Liu, Yudan et al (PGPUB Document No. 20210326674), hereafter, referred to as “Liu”, in further view of Turner, Badiei et al (PGPUB Document No. 20250238607), hereafter, referred to as “Turner”.
Regarding Claim 1 (Currently Amended), Chiguchi teaches An apparatus for personalizing document search and creating a new document within an institution using artificial intelligence, comprising(Chiguchi, page 5 para 5 discloses performing search using AI “The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network”): an input module configured to acquire data; a communication module configured to transmit and receive the data with an external device; a memory configured to store at least one process for performing an operation and storing user input and data; a display configured to display a graphic image; and a processor configured to perform a control method according to the process, wherein the processor is configured to(Chiguchi, Fig. 1 and page 2 discloses an apparatus with receiving, communication, processing, outputting etc. units):
obtain first data including a search history of a user, a document viewing history, department information, and an electronic document through the input module(Chiguchi, page 5 para 5 discloses obtaining/determining department information and for querying “This system identifies the appropriate subject or department based on the content of the inquiry and presents that information to the employee”; further on page 5 para 8 disclose inputting document “A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input”; where “search history of a user, a document viewing history” as input to query is taught the by prior art Zhang to be discussed later), preprocess the first data(Chiguchi, page 15 para 3 discloses preprocessing input data by analysis, classification, summarization etc. “The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization”), learn the preprocessed first data, generate a document search personalization and new document creation model using the learning result (Chiguchi, page 17 para 3 discloses learning/pre-training the model/neural network using user input information “This neural network is pre-trained based on multiple learning data that are combinations of user input and emotion values indicating each emotion shown in the emotion map 400”), obtain a request message through the input module, generate at least one of a document search result or a new document corresponding to the request message using the document search personalization and new document creation model, and control the display to display the generated result (Chiguchi, page 5 para 5 discloses generating result based on the obtained input “The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data”).
But Chiguchi does not explicitly teach first data including a search history of a user, a document viewing history; wherein the processor is further configured to: learn a user feedback, which include a click rate and a document opening time, by applying a machine learning model, to generate the learning result, wherein the processor is further configured to: based on the new document creation model, search for a similar document of which content and similarity to the new document is within a predetermined range or higher;
analyze a structure of the searched similar document; and control the display to sequentially display a plurality of templates, which reflect the analysis result, wherein the processor is further configured to:
search for real-time news and select a document of which similarity to a subject of the document currently being written is greater than a threshold, and control the display to display the selected document, and wherein the processor is further configured to search for legal information with a similarity greater than a threshold value in relation to the subject of the document currently being written, and control the display to display the searched legal information.
However in the same field of endeavor of training models with user feedback Zhang teaches first data including a search history of a user, a document viewing history (Zhang, page 15 para 5 discloses obtaining user search/query history and viewing/display history “obtaining the click history information of the user account and the display history information; in the object image database query corresponding to the click history information and the object image of the display history information”);
wherein the processor is further configured to: learn a user feedback, which include a click rate and a document opening time, by applying a machine learning model, to generate the learning result (Zhang, page 21 para 5 discloses training/learning with user feedback such as click rate and viewing/opening time and producing learning result as user vector “object image calculation and click rate (CTR, Click-Through Rate) prediction model training to calculate three parts, the object action collection mainly comprises clicking, display exposure, point praise, viewing time and so on”):
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of training model with of Zhang into document or result generation in response to user query of Chiguchi to produce an expected result of making the query result user specific. The modification would be obvious because one of ordinary skill in the art would be motivated to train learning models to predict based on behavior(Zhang, page 21 para 5).
But Chiguchi and Zhang don’t explicitly teach based on the new document creation model, search for a similar document of which content and similarity to the new document is within a predetermined range or higher;
analyze a structure of the searched similar document; and control the display to sequentially display a plurality of templates, which reflect the analysis result, and wherein the processor is further configured to:
search for real-time news and select a document of which similarity to a subject of the document currently being written is greater than a threshold, and control the display to display the selected document, and wherein the processor is further configured to search for legal information with a similarity greater than a threshold value in relation to the subject of the document currently being written, and control the display to display the searched legal information.
However, in the same field of endeavor of document recommendation Liu teaches based on the new document creation model, search for a similar document of which content and similarity to the new document is within a predetermined range or higher; search for real-time news and select a document of which similarity to a subject of the document currently being written is greater than a threshold, and control the display to display the selected document(Liu, para 0115 discloses recommending documents which are having contents/subject similarity above a predetermined threshold “candidate recommendation content may be ranked by similarity, and the top m ranked pieces of candidate recommendation content are determined as target content to be recommended to the target user, ….. candidate recommendation content whose similarity is higher than a preset threshold may also be determined as target content to be recommended to the target user”; where para 0229 further teaches documents are being recommended for users “The content recommendation method in the foregoing implementation may be applied to a document recommendation application, such as news recommendation or article recommendation” ), and wherein the processor is further configured to search for legal information with a similarity greater than a threshold value in relation to the subject of the document currently being written, and control the display to display the searched legal information(Liu, para 0115 discloses recommending documents which are having contents/subject similarity above a predetermined threshold and this feature can similarly be applied for legal/any documents “candidate recommendation content may be ranked by similarity, and the top m ranked pieces of candidate recommendation content are determined as target content to be recommended to the target user, ….. candidate recommendation content whose similarity is higher than a preset threshold may also be determined as target content to be recommended to the target user”; where prior art Turner discussed later teaches generation legal document in para 0048 ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of training machine learning model with user information of Zhang into document or result generation in response to user query of Chiguchi and Zhang to produce an expected result of making the query result user specific. The modification would be obvious because one of ordinary skill in the art would be motivated to recommend contents which are in-line with user interest by using similarity score calculation to resolve the problem of low accuracy recommendation method(Liu, abstract).
But Chiguchi, Zhang and Liu don’t explicitly teach analyze a structure of the searched similar document; and control the display to sequentially display a plurality of templates, which reflect the analysis result, and wherein the processor is further configured to:
However, in the same field of endeavor of document generation Turner teaches analyze a structure of the searched similar document; and control the display to sequentially display a plurality of templates, which reflect the analysis result, and wherein the processor is further configured to(Turner, para 0085 discloses determining document type and displaying generated template “the engine 150 may be configured to identify a type of an electronic document and generate, using a machine learning model, one or more templates defining a structural arrangement of one or more portions of the electronic document for each type of electronic document (as for example, is shown in FIG. 13). …… The generated template may be presented on a graphical user interface of a user computing device.”):
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of presenting document template of Turner into document or result generation in response to user query of Chiguchi, Zhang and Liu to produce an expected result of generating documents based on provided document templates. The modification would be obvious because one of ordinary skill in the art would be motivated to generated document using document generation rules for reducing resource consumption (Turner, para0049).
Regarding Claim 10(Currently Amended), Chiguchi teaches A method for personalizing document search and creating a new document using artificial intelligence performed by a processor of an apparatus, comprising(Chiguchi, page 5 para 5 discloses performing search using AI “The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network”):
obtaining first data including a search history of a user, a document viewing history, department information, and an electronic document through an input module (Chiguchi, page 5 para 5 discloses obtaining/determining department information and for querying “This system identifies the appropriate subject or department based on the content of the inquiry and presents that information to the employee”; further on page 5 para 8 disclose inputting document “A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input”; where “search history of a user, a document viewing history” as input to query is taught the by prior art Zhang to be discussed later);
preprocessing the first data(Chiguchi, page 15 para 3 discloses preprocessing input data by analysis, classification, summarization etc. “The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization”); learning the preprocessed first data; generating a document search personalization and new document creation model using the learning result(Chiguchi, page 17 para 3 discloses learning/pre-training the model/neural network using user input information “This neural network is pre-trained based on multiple learning data that are combinations of user input and emotion values indicating each emotion shown in the emotion map 400”);
obtaining a request message through the input module; generating at least one of a document search result and a new document corresponding to the request message using the document search personalization and new document creation model; and controlling a display to display the generated result(Chiguchi, page 5 para 5 discloses generating result based on the obtained input “The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data”).
But Chiguchi does not explicitly teach first data including a search history of a user, a document viewing history, wherein the learning comprises: learning a user feedback, which include a click rate and a document opening time, by applying a machine learning model, to generate the learning result, wherein the controlling comprises: based on the new document creation model, searching for a similar document of which content and similarity to the new document is within a predetermined range or higher; analyzing a structure of the searched similar document; and controlling the display to sequentially display a plurality of templates, which reflect the analysis result, wherein the controlling further comprises: searching for real-time news and select a document of which similarity to a subject of the document currently being written is greater than a threshold, and controlling the display to display the selected document, and searching for legal information with a similarity greater than a threshold value in relation to the subject of the document currently being written, and controlling the display to display the searched legal information.
However in the same field of endeavor of training models with user feedback Zhang teaches first data including a search history of a user, a document viewing history (Zhang, page 15 para 5 obtaining user search/query history and viewing/display history “obtaining the click history information of the user account and the display history information; in the object image database query corresponding to the click history information and the object image of the display history information”);
wherein the learning comprises: learning a user feedback, which include a click rate and a document opening time, by applying a machine learning model, to generate the learning result, wherein the controlling comprises (Zhang, page 21 para 5 discloses training/learning with user feedback such as click rate and viewing/opening time and producing learning result as user vector “object image calculation and click rate (CTR, Click-Through Rate) prediction model training to calculate three parts, the object action collection mainly comprises clicking, display exposure, point praise, viewing time and so on”):
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of training model with user feedback of Zhang into document or result generation in response to user query of Chiguchi to produce an expected result of making the query result user specific. The modification would be obvious because one of ordinary skill in the art would be motivated to train learning models to predict based on behavior(Zhang, page 21 para 5).
But Chiguchi and Zhang don’t explicitly teach wherein the learning comprises: learning a user feedback, which include a click rate and a document opening time, by applying a machine learning model, to generate the learning result, wherein the controlling comprises: based on the new document creation model, searching for a similar document of which content and similarity to the new document is within a predetermined range or higher; analyzing a structure of the searched similar document; and controlling the display to sequentially display a plurality of templates, which reflect the analysis result, wherein the controlling further comprises: searching for real-time news and select a document of which similarity to a subject of the document currently being written is greater than a threshold, and controlling the display to display the selected document and searching for legal information with a similarity greater than a threshold value in relation to the subject of the document currently being written, and controlling the display to display the searched legal information.
However, in the same field of endeavor of document recommendation Liu teaches based on the new document creation model, searching for a similar document of which content and similarity to the new document is within a predetermined range or higher; searching for real-time news and select a document of which similarity to a subject of the document currently being written is greater than a threshold, and controlling the display to display the selected document (Liu, para 0115 discloses recommending documents which are having contents/subject similarity above a predetermined threshold “candidate recommendation content may be ranked by similarity, and the top m ranked pieces of candidate recommendation content are determined as target content to be recommended to the target user, ….. candidate recommendation content whose similarity is higher than a preset threshold may also be determined as target content to be recommended to the target user”; where para 0229 further teaches documents are being recommended for users “The content recommendation method in the foregoing implementation may be applied to a document recommendation application, such as news recommendation or article recommendation”) , and searching for legal information with a similarity greater than a threshold value in relation to the subject of the document currently being written, and controlling the display to display the searched legal information (Liu, para 0115 discloses recommending documents which are having contents/subject similarity above a predetermined threshold and this feature can similarly be applied for legal/any documents “candidate recommendation content may be ranked by similarity, and the top m ranked pieces of candidate recommendation content are determined as target content to be recommended to the target user, ….. candidate recommendation content whose similarity is higher than a preset threshold may also be determined as target content to be recommended to the target user”; where prior art Turner discussed later teaches generation legal document in para 0048 )
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of training machine learning model with user information of Liu into document or result generation in response to user query of Chiguchi and Zhang to produce an expected result of making the query result user specific. The modification would be obvious because one of ordinary skill in the art would be motivated to recommend contents which are in-line with user interest by using similarity score calculation to resolve the problem of low accuracy recommendation method(Liu, abstract).
But Chiguchi, Zhang and Liu don’t explicitly teach analyzing a structure of the searched similar document; and controlling the display to sequentially display a plurality of templates, which reflect the analysis result, wherein the controlling further comprises:
However, in the same field of endeavor of document generation Turner teaches analyzing a structure of the searched similar document; and controlling the display to sequentially display a plurality of templates, which reflect the analysis result, wherein the controlling further comprises (Turner, para 0085 discloses determining document type and displaying generated template “the engine 150 may be configured to identify a type of an electronic document and generate, using a machine learning model, one or more templates defining a structural arrangement of one or more portions of the electronic document for each type of electronic document (as for example, is shown in FIG. 13). …… The generated template may be presented on a graphical user interface of a user computing device.”):
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of presenting document template of Turner into document or result generation in response to user query of Chiguchi and Zhang and Liu to produce an expected result of generating documents based on provided document templates. The modification would be obvious because one of ordinary skill in the art would be motivated to generated document using document generation rules for reducing resource consumption (Turner, para 0049).
Claim 11-12, cancelled.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Chiguchi, Satoshi et al (Japanese Patent Document No. JP 2025051488 ), hereafter referred as to “Chiguchi”, in view of Zhang, Han et al (Chinese Patent Document No. CN 111611488), hereafter, referred to as “Zhang”, in view of Liu, Yudan et al (PGPUB Document No. 20210326674), hereafter, referred to as “Liu”, in view of Turner, Badiei et al (PGPUB Document No. 20250238607), hereafter, referred to as “Turner”, in further view of Norman; Morgan et al (PGPUB Document No. 20150347595 ), hereafter, referred to as “Norman”.
Regarding claim 2 (Previously Presented), Chiguchi, Zhang, Liu and Turner teach all the limitations of claim 1 but don’t explicitly tech wherein the processor is configured to: generate a personal profile based on at least one of the search history of the user, the document viewing history, or the department information, and control the display to display the generated personal profile.
However in the same field of endeavor of user profile generation Norman teaches wherein the processor is configured to: generate a personal profile based on at least one of the search history of the user, the document viewing history, or the department information (Norman, para 0028 discloses generation of user profile based on user’s search history “Personal intelligence profile generator system 132 allows user 128 to generate a dynamic social profile (or have it automatically generated) that incorporates a user's search history”), and control the display to display the generated personal profile(Norman, para 0040 discloses displaying of the generated user profile “Once the personal intelligence profile 134 is generated, it can illustratively be provided to user 128 (such as through a display or other user interface) for correction or validation”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of user profile generation based on user’s search history of Norman into document or result generation in response to user query of Chiguchi, Zhang, Liu and Turner to produce an expected result of making the query result user specific. The modification would be obvious because one of ordinary skill in the art would be motivated to have a user profile that considers variety of user information and suggest activities to users accordingly(Norman, para 0064-0065).
Claim 3 & 5 are rejected under 35 U.S.C. 103 as being unpatentable over Chiguchi, Satoshi et al (Japanese Patent Document No. JP 2025051488 ), hereafter referred as to “Chiguchi”, in view of Zhang, Han et al (Chinese Patent Document No. CN 111611488), hereafter, referred to as “Zhang”, in view of Liu, Yudan et al (PGPUB Document No. 20210326674), hereafter, referred to as “Liu”, in view of Turner, Badiei et al (PGPUB Document No. 20250238607), hereafter, referred to as “Turner”, in further view of Freitag, Dayne et al (PGPUB Document No. 20240281610), hereafter, referred to as “Freitag”.
Regarding claim 3(Previously Presented), Chiguchi, Zhang, Liu and Turner teach all the limitations of claim 1 and Chiguchi further teaches and control the display to display the generated recommended document search result(Chiguchi, page 5 para 5 discloses generating result based on the obtained input “The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data”).
But Chiguchi, Zhang, Liu and Turner don’t explicitly teach wherein the processor is configured to: generate a recommended document search result by analyzing search patterns of users performing similar tasks,
However in the same field of endeavor of search result generation Freitag teaches wherein the processor is configured to: generate a recommended document search result by analyzing search patterns of users performing similar tasks (Freitag, para 0057 discloses generation of content recommendation based on other users having similar task context “the system supports automated proactivity, via generalized knowledge routing, as it recommends content that other users typically interact with in similar task contexts”),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of recommending contents based on other users performing similar tasks of Freitag into document or result generation in response to user query of Chiguchi, Zhang, Liu and Turner to produce an expected result of recommending user task and role related contents. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the effectiveness of employees in an organization by providing task-aware, user role aware, and/or subject matter aware contents to employee (Freitag, para 0077).
Regarding claim 5(Previously Presented), Chiguchi, Zhang, Liu and Turner teach all the limitations of claim 1 but don’t explicitly teach wherein the processor is configured to: control the display to display the document with a relevance exceeding a threshold by reflecting recent search history and information on current proceeding project.
However in the same field of endeavor of search result generation Freitag teaches wherein the processor is configured to: control the display to display the document with a relevance exceeding a threshold by reflecting recent search history and information on current proceeding project (Freitag, para 0018 discloses generation of content recommendation based on other users having similar task/project with considering a relatedness threshold “the delivery module 10 is further configured to proactively push the notice regarding the potentially related embedding out to the second user on the second computing device based on the threshold amount of relatedness between all three factors selected from the group consisting of i) the first task undertaken by the first user and the second task undertaken by the second user, ii) the role of the first user and the role of the second user, and iii) the subject matter of the embedding to the subject matter of task undertaken by the second user”; where prior art Zhang, page 15 para 5 discloses consideration of user search history for recommendation contents),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of recommending contents based on other users performing similar tasks of Freitag into document or result generation in response to user query of Chiguchi, Zhang, Liu and Turner to produce an expected result of recommending user task and role related contents. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the effectiveness of employees in an organization by providing task-aware, user role aware, and/or subject matter aware contents to employee (Freitag, para 0077).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Chiguchi, Satoshi et al (Japanese Patent Document No. JP 2025051488 ), hereafter referred as to “Chiguchi”, in view of Zhang, Han et al (Chinese Patent Document No. CN 111611488), hereafter, referred to as “Zhang”, in view of Liu, Yudan et al (PGPUB Document No. 20210326674), hereafter, referred to as “Liu”, in view of Turner, Badiei et al (PGPUB Document No. 20250238607), hereafter, referred to as “Turner”, in further view of Tsai, Tsung-Lin et al (PGPUB Document No. 20170169096), hereafter, referred to as “Tsai”.
Regarding claim 4(Previously Presented), Chiguchi, Zhang, Liu and Turner teach all the limitations of claim 1 and Liu further teaches and control the display to display the document with a similarity exceeding a threshold based on the analysis result (Liu, para 0115 discloses recommending documents which are having contents/subject similarity above a predetermined threshold “candidate recommendation content may be ranked by similarity, and the top m ranked pieces of candidate recommendation content are determined as target content to be recommended to the target user, ….. candidate recommendation content whose similarity is higher than a preset threshold may also be determined as target content to be recommended to the target user”).
But Chiguchi, Zhang, Liu and Turner don’t explicitly teach wherein the processor is configured to: analyze a keyword and a topic of the document viewed by the user within a predetermined period,
However, in the same field of endeavor of keyword and topic analysis in documents Tsai teaches wherein the processor is configured to: analyze a keyword and a topic of the document viewed by the user within a predetermined period (Tsai, para 0007 discloses analyzing keywords and topics of viewed/already read document for a pre-determined period of time “acquiring a reading log and documents corresponding thereto, wherein the reading log at least includes reading-related information about the documents within a predetermined period of time………. pre-processing on the interesting document sets to determine keyword sets corresponding to the interesting document sets; performing a cluster calculation on the keyword sets to obtain topics”),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of analyzing keywords and topics in users’ viewed documents of Tsai into document or result generation in response to user query of Chiguchi, Zhang, Liu and Turner to produce an expected result of recommending contents based on user interest. The modification would be obvious because one of ordinary skill in the art would be motivated to determine user reading trend by finding user’s degree of interest for a topic using keyword and topic analysis(Tsai, para 0020).
Claim 6, cancelled.
Claim 7 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Chiguchi, Satoshi et al (Japanese Patent Document No. JP 2025051488 ), hereafter referred as to “Chiguchi”, in view of Zhang, Han et al (Chinese Patent Document No. CN 111611488), hereafter, referred to as “Zhang”, in view of Liu, Yudan et al (PGPUB Document No. 20210326674), hereafter, referred to as “Liu”, in view of Turner, Badiei et al (PGPUB Document No. 20250238607), hereafter, referred to as “Turner”, in further view of Strope, Brian et al (PGPUB Document No. 20180240013), hereafter, referred to as “Strope”.
Regarding claim 7(Previously Presented), Chiguchi, Zhang, Liu and Turner teach all the limitations of claim 1 but don’t explicitly teach wherein the processor is configured to: search for a related document of which similarity level with the new document is a predetermined range or higher, automatically summarize and cite a core content of the related document, and control the display to display the summary and citation result.
However, in the same field of endeavor of content similarity analysis in documents Strope teaches wherein the processor is configured to: search for a related document of which similarity level with the new document is a predetermined range or higher(Strope, para 0050 discloses analyzing similarity between input document to documents to be retrieved to threshold (any similarity value above the threshold falls into the acceptance range) “to determine the relevance of a given content item to the input, a relevance measure module 122 of the engine 121 can determine a relevance value ………. the retrieval relevance engine 121 may determine, as responsive to an input, only content items whose corresponding relevance values satisfy a threshold”), automatically summarize and cite a core content of the related document, and control the display to display the summary and citation result(Strope, Fig. 9 A-B and para 0113 discloses displaying summary along with the cited results “Each of the results may include, for example, the corresponding content item and a summary or other additional content that is from (or based on) the same resource as the corresponding content item”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of presenting documents based on a similarity threshold of Strope into document or result generation in response to user query of Chiguchi, Zhang, Liu and Turner to produce an expected result of recommending contents with higher similarities. The modification would be obvious because one of ordinary skill in the art would be motivated to provide documents/ contents that are high in relatedness to user interest(Strope, para 0050).
Regarding claim 9(Previously Presented), Chiguchi, Zhang, Liu and Turner teach all the limitations of claim 1 but don’t explicitly teach wherein the processor is configured to: analyze a context of the document being written, and control the display to display a text message containing a phrase corresponding to the analyzed context.
However, in the same field of endeavor of content similarity analysis in documents Strope teaches wherein the processor is configured to: analyze a context of the document being written (Strope, para 0041 discloses context analysis of document “The summarization engine 160 uses a summarization model 165 to generate a summary that provides an indication of the local and/or global context of the text segment”), and control the display to display a text message containing a phrase corresponding to the analyzed context (Strope, Fig. 9B discloses displaying context of the analyzed document “What do you watch…with your kids?”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of presenting documents based on a similarity threshold of Strope into document or result generation in response to user query of Chiguchi, Zhang, Liu and Turner to produce an expected result of recommending contents with higher similarities. The modification would be obvious because one of ordinary skill in the art would be motivated to provide documents/ contents that are high in relatedness to user interest(Strope, para 0050).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Chiguchi, Satoshi et al (Japanese Patent Document No. JP 2025051488 ), hereafter referred as to “Chiguchi”, in view of Zhang, Han et al (Chinese Patent Document No. CN 111611488), hereafter, referred to as “Zhang”, in view of Liu, Yudan et al (PGPUB Document No. 20210326674), hereafter, referred to as “Liu”, in view of Turner, Badiei et al (PGPUB Document No. 20250238607), hereafter, referred to as “Turner”, in further of Raimondo, Stefania et al (PGPUB Document No. 20220414128), hereafter, referred to as “Raimondo”.
Regarding claim 8 (Previously Presented), Chiguchi, Zhang, Liu and Turner teach all the limitations of claim 1 and Liu further teaches wherein the processor is configured to: search for a related document of which similarity level with the new document is a predetermined range or higher (Liu, para 0115 discloses recommending documents which are having contents/subject similarity above a predetermined threshold “candidate recommendation content may be ranked by similarity, and the top m ranked pieces of candidate recommendation content are determined as target content to be recommended to the target user, ….. candidate recommendation content whose similarity is higher than a preset threshold may also be determined as target content to be recommended to the target user”),
But Chiguchi, Zhang, Liu and Turner don’t explicitly teach extract a main keyword of the related document, and control the display to display the extracted main keyword.
However, in the same field of endeavor of document similarity determination Raimondo teaches extract a main keyword of the related document, and control the display to display the extracted main keyword (Raimondo, Fig. 6 and para 0228 disclose displaying extracted related keywords from other document “a first set of semantically similar documents 620, where a first document 622 has been selected by the user 215 to display a first set semantically similar sentences 630 extracted from the first document 622 and having been used to determine a semantic similarity between the input document 610 and the first document 622, as well as a first document set of semantically similar keywords 640 extracted from the first document 622….”),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of presenting extracted similar keywords of Raimondo into document or result generation in response to user query of Chiguchi, Zhang, Liu and Turner to produce an expected result of recommending contents based on various level of similarity analysis. The modification would be obvious because one of ordinary skill in the art would be motivated to provide documents/ contents that are high in relatedness to user interest by implementing similarity analysis at different level of the document(Raimondo, para 0006).
Response to Arguments
I. 35 U.S.C §103
Regarding feature related to opening time, the applicant on page 7 paragraph 4 argued that “Applicant respectfully disagrees with the above assertion. Neither Liu nor Toru even mentions the "document opening time.".
Applicant’s above mentioned argument has been fully considered and accordingly a new prior art Zhang is used for teaching the argued limitations.
The applicant further on last paragraph of page 7 stated that “Independent claim 1 explicitly recites, among other things……. The cited references fail to disclose the above feature. According to the above feature, the claimed subject matter provides a specific, time-ordered, sequential presentation of templates based on the analysis result. The Office Action fails to identify any portion of the cited references, which discloses or suggests a sequential display mechanism”.
Applicant’s above mentioned arguments regarding sequential display of templates have been fully considered but not found persuasive, the claimed sequential display of templates does not specify any particular way or type of the sequential arrangements as argued.
Regarding “Feature related to Displaying Legal Information” the applicant on page 8 paragraph 3 stated that “Applicant respectfully disagrees with the above assertion. Neither Liu nor Chiguchi even mentions the "legal information." Applicant submits that the Examiner cannot impermissibly using Applicant's claims as a blueprint to piece together disparate, generalized data-retrieval concepts from the cited references”.
The examiner respectfully disagrees as Prior art Liu in paragraph 0229 explicitly teaches document recommendation to target users as following “The content recommendation method in the foregoing implementation may be applied to a document recommendation application, such as news recommendation or article recommendation”, where Turner teaches generation legal document such as lease agreement in para 0048 as following “the current subject matter may be configured to create and/or generate rules and/or playbooks for generation of electronic documents. The documents may be of certain type (e.g., legal (e.g., agreements, etc.), non-legal (e.g., articles, books, etc.), and/or any other types)”. Therefore, Turner in view of Liu teaches the argued limitations.
Conclusion
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/ABDULLAH A DAUD/Examiner, Art Unit 2164 /AMY NG/Supervisory Patent Examiner, Art Unit 2164
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