DETAILED ACTION
Receipt of Applicant’s Amendment, filed October 28, 2025 is acknowledged.
Claims 1-10 were amended.
Claim 11 was newly added.
Claims 1-11 are pending in this office action.
Claim Objections
Claims 1-11 are objected to because of the following informalities. Appropriate correction is required.
With regard to claims 1, 9 and 10, claim 1 recites “transmitting the first provision information to the communication apparatus to cause the communication apparatus to display the first provision information”. Claims 9 and 10 recite substantially similar limitations and are subject to the same reasoning and rational. It is noted that the functionality recited is the transmitting. The claim does not actually require displaying the first provisional information. The intended purpose of the transmitting (e.g. to cause the display) is a recitation of intended use which does not impose a functional limitation on the claimed device.
It is further noted that Claim 3, which depends from claim 1 recites displaying functionality. Should the language in claim 1 be interpreted as reciting displaying functionality, this would cause an antecedent basis issue with claim 3, as it would be unclear how many times the information is displayed, or if claim 3 is intended to modify the displaying of claim 1.
It is suggested that claim 1 be amended to clearly recite the displaying functionality, and that claim 3 be amended for further define the displaying.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regard to claims 1, 9 and 10, claim 1 recites “expanding the first attribute information to include at least one additional attribute related to the one or more first user information attributes by inputting the first attribute information into a first generative model trained to output a predicted character string corresponding to a second attribute based on an input character string corresponding to a first attribute, wherein the expanded first attribute information is acquired from an output of the first generative model.” This claim limitation lacks antecedent basis. Claims 9 and 10 appear to recite substantially similar limitations and are rejected based upon the same rational.
The recitation of “first attribute” lacks antecedent basis as the claim has previously recited “first attribute information” and “first user information attributes”. It is unclear if applicant is attempting to define a new claim element or attempting to refer to the previously recited claim elements. For examination purposes this claim limitation has been understood as referring to --the first attribute information--.
It is unclear what the actual output of the first generative model is, or even how many outputs are being claimed. The claim recites ‘a generative model trained to output…” and “acquired from an output of the first generative model”. Is the claim referring to the same output step, yet using distinct labels for the output generated? Or is the claim reciting distinct outputting steps?
The claim recites ‘a predicted character string’, ‘a second attribute’, “the expanded first attribute information”, and ‘an output’ all as being output by the first generative model. This raises the question of what is the distinction between all of these elements. When read in light of the instant specification, there appears to only be a single output, which is the expanded fist attribute information (See Paragraph [0017], [0043]).
Furthermore, what the generative model is trained to output does not recite any structural or functional limitation for the claimed device.
For examination purposes this claim limitation has been construed to mean --expanding the first attribute information to include at least one additional attribute related to the one or more first user information attributes by inputting the first attribute information into a first generative model, wherein the expanded first attribute information is acquired from an output of the first generative model --.
With regard to claims 1, 9 and 10, claim 1 recites “acquiring first provision information by inputting the expanded first attribute information and the subject information into a second generative model trained to output predicted text related to (i) a first input character string corresponding to subject text and (ii) a second input character string corresponding to a plurality of user attributes, the first provision information being acquired from an output of the second generative model, and the first provision information comprising (i) supplementary information that supplements the subject information or (ii) revised information that revises the subject information;” This claim limitation lacks antecedent basis. Claims 9 and 10 appear to recite substantially similar language and are subject to the same reasoning and rational.
With regard to the ‘first provisional information”: the claim recites that the first provisional information is obtained from the second generative model. The claim also recites that the second generative model outputs predicted text. The distinction between the ‘fist provisional information’ and ‘the predicted text’ is unclear. Furthermore, based on the names used for the label, one of ordinary skill in the art would recognize that the output of a standard generative model as being predicted text, meaning that the first provisional information would be understood to be predicted text as it is output by the generative model. Each unique claim element is expected to have a single unique claim label. The use of two distinct labels to refer to the same claim element creates antecedent basis issues.
With regard to the ‘subject text’: The claim has previously recited subject information, it was recited that this subject information comprises at least one character string of text. It is unclear if applicant is referring to the subject information when reciting the ‘subject text’ or if applicant is attempting to recite a new claim element. It is suggested that the claim labels be used consistently to ensure that antecedent basis is clear.
With regard to ‘a plurality of user attributes’: this claim element is recited in a form that suggests it is a newly recited claim element. The claim has previously recited ‘first attribute information from a user account of the user’ and ‘the first attribute information comprising one or more fist user information attributes’. It is unclear if the ‘plurality of user attributes’ recited herein is intended to be a newly recited element or intended to refer to the previously recited first attribute information.
With regard to ‘a second generative model trained to output predicted text related to …, the first provision information being acquired from an output of the second generative model’. The recitation of “output’ is unclear. It is unclear how many outputs of the second generative model is claimed to have. It is unclear how the predicted text (the claimed output of the second generative model) related to first provisional information that is acquired from an output of the second generative model. On the surface, the claim appears to require the output to be related to itself. Furthermore, Reciting what the second generative model is trained to do does not impose a functional limitation the claimed device, and instead appears to be an intended use recitation. To be clear, it does not recite the actual outputting of the predicted text, merely that the model is trained with the intention to perform the outputting. It is suggested that the claim limitations be recited in a positive manner so that it is clear what the scope of the claim is.
For examination purposes this claim limitation has been construed to mean -- - acquiring first provision information by inputting the expanded first attribute information and the subject information into a second generative model, wherein the first provisional information is predicted text related to (i) a first input character string corresponding to the subject information and (ii) a second input character string corresponding to the first attribute information, wherein the first provision information comprising (i) supplementary information that supplements the subject information or (ii) revised information that revises the subject information;--.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-5, 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Hutchings [2021/0326536] in view of Hwang [10699062].
With regard to claim 1 Hutchins teaches An information output (Hutchins, ¶12 “The popup can be an HTML page that overlays a portion of the web page on which the article is displayed. The sentiment and the summary can be displayed in the popup on the user device in real time in context of the article within the web page.”) apparatus (Hutchins, ¶76 “apparatus”) comprising:
at least one memory (Hutchins, ¶76 “The computer readable medium can be, by way of example only but not by limitation… computer memory”) configured to store instructions (Hutchins, ¶76 “"computer-readable medium" may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, system, or device.”); and
at least one processor (Hutchins, ¶76 “one or more processors in a computing environment”) configured to execute the instruction to perform operations (Hutchins, ¶76 “As one skilled in the art can appreciate, a computer program product implementing an embodiment disclosed herein may comprise one or more non-transitory computer readable media storing computer instructions translatable by one or more processors in a computing environment”) comprising:
receiving first user input information transmitted from a communication apparatus of a user (Hutchings, ¶7 “In some embodiments, the summarizer is operable to run on a user device and can receive an indication or an instruction from a user to summarize content displayed on the user device.”; ¶22 “a summarizer on a user device may receive an indication or instruction from a user to summarize content displayed on the user device (101).”);
acquiring, in response to receiving the first user input information as the instruction to summarize the displayed content (Hutchings, ¶7, ¶22), subject information as extracted text (Hutchins, ¶22 “the summarizer is operable to extract texts from the main body of the content (115).”; ¶29 “For instance, a news article discusses that a president is going to visit a country. NLP text mining engine 235 is operable to programmatically examine the article, determine that this article concerns foreign affair and/or diplomacy, and add "foreign affair" and/or "diplomacy" as metadata (e.g., "category=foreign affair" or "topic=diplomacy") to the article, even if the article itself does not literally contain "foreign affair" or "diplomacy."”) related to a subject as the category/topic (Id);
acquiring first attribute information as metadata (Hutchins, ¶26 “An authority file refers to a controlled vocabulary of terms and cross-reference terms that assists entity extraction to return additional relevant items and related metadata ( e.g., geopolitical locations, person names, organization names, trademarks, events, etc.).”; ¶29 “NLP text mining engine 235 is operable to programmatically examine the article, determine that this article concerns foreign affair and/or diplomacy, and add "foreign affair" and/or "diplomacy" as metadata (e.g., "category=foreign affair" or "topic=diplomacy") to the article, even if the article itself does not literally contain "foreign affair" or "diplomacy." Downstream from text mining, these pieces of metadata can be used by AI platform 230 in different ways for various reasons”) from a user account of the user as the user configurations, e.g. summarization range and user-created taxonomy (Hutchings, ¶22 “The summarization range is configurable by the user”; ¶37 “a user can create a taxonomy and direct NLP text mining engine 235 to use the user-created taxonomy to perform the summarization.”), [[
expanding (Hutchins, ¶26 “For synonyms, acronyms, and variations thereof, an authority file may be used. An authority file refers to a controlled vocabulary of terms and cross-reference terms that assists entity extraction to return additional relevant items and related metadata ( e.g., geopolitical locations, person names, organization names, trademarks, events, etc.). There can be multiple authority files, each for a particular controlled vocabulary of terms and cross-reference terms.”) the first attribute information as metadata (Hutchins, ¶26, ¶29) to include at least one additional attribute related to the one or more first user information attributes as related metadata and additional relevant items (Id) by inputting the first attribute information into a first generative model trained as trained machine learning (Hutchins, ¶26 “Since text mining is performed at the document level, the extracted metadata (e.g., the company's name and the new product name in this example) can be used to enrich the document. NLP text mining engine 235 can learn how to determine an entity based on previous examples from which a model has been trained using machine learning.”) to output a predicted character string as the output list of extracted entities with attributes (Hutchings, ¶26 “Output from entity extraction can be a list of extracted entities with attributes and relevancy ranking”) corresponding to a second attribute as additional relevant items, related metadata and cross-reference terms (Hutchins, ¶26 “For synonyms, acronyms, and variations thereof, an authority file may be used. An authority file refers to a controlled vocabulary of terms and cross-reference terms that assists entity extraction to return additional relevant items and related metadata ( e.g., geopolitical locations, person names, organization names, trademarks, events, etc.). There can be multiple authority files, each for a particular controlled vocabulary of terms and cross-reference terms.”) based on an input character string corresponding to a first attribute as the extracted metadata (Hutchens, ¶26 “Based on linguistic rules and statistical patterns, NLP text mining engine 235 can extract the company's name, the new product name, etc. from the document. All occurrences of an entity type may be extracted.”), wherein the expanded first attribute information is acquired from an output as the output (Hutchins, ¶26) of the first generative model as trained machine learning (Hutchins, ¶26;
acquiring first provision information as the summary (Hutchings, ¶30 “generate a summary”) by inputting (Hutchings, ¶29 “Downstream from text mining, these pieces of metadata can be used by AI platform 230 in different ways for various reasons.”) the expanded first attribute information (Hutchings, ¶26 “Since text mining is performed at the document level, the extracted metadata (e.g., the company's name and the new product name in this example) can be used to enrich the document.”) and the subject information as category/topic (Hutchings, ¶29 “NLP text mining engine 235 is operable to programmatically examine the article, determine that this article concerns foreign affair and/or diplomacy, and add "foreign affair" and/or "diplomacy" as metadata (e.g., "category=foreign affair" or "topic=diplomacy") to the article, even if the article itself does not literally contain "foreign affair" or "diplomacy."”) into a second generative model trained as the AI platform (Hutchings, ¶22 “The AI platform can process the text block, generate a sentiment and a summary of the text block, and return them to the summarizer in response to the call.”) to output predicted text as the text of the summary (Hutchings, ¶30 “generate a summary”) related to (i) a first input character string corresponding to subject text as extracted text (Hutchins, ¶22 “the summarizer is operable to extract texts from the main body of the content (115).”; ¶29 “For instance, a news article discusses that a president is going to visit a country. NLP text mining engine 235 is operable to programmatically examine the article, determine that this article concerns foreign affair and/or diplomacy, and add "foreign affair" and/or "diplomacy" as metadata (e.g., "category=foreign affair" or "topic=diplomacy") to the article, even if the article itself does not literally contain "foreign affair" or "diplomacy."”) and (ii) a second input character string corresponding to a plurality of user attributes as the user configurations, e.g. summarization range and user-created taxonomy (Hutchings, ¶22 “The summarization range is configurable by the user”; ¶37 “a user can create a taxonomy and direct NLP text mining engine 235 to use the user-created taxonomy to perform the summarization.”), the first provision information being acquired from an output of the second generative model as the AI platform (Hutchings, ¶22 “The AI platform can process the text block, generate a sentiment and a summary of the text block, and return them to the summarizer in response to the call.”), and the first provision information comprising (i) supplementary information that supplements the subject information as providing analytics on sentiment, tonality, category, concept, or named entity (Hutchings, ¶30 “return the summary (with any desired AI-augmented analytics on sentiment, tonality, category, concept, named entity, etc.)”) or (ii) revised information that revises the subject information as shortening (Hutchings, ¶30 “summarization refers to the process of shortening a text document in order to create a summary with the major points of the original document.”); and
transmitting the first provision information to the communication apparatus to cause the communication apparatus to display the first provision information (Hutchings, ¶39 “The results returned by AI platform 630 can be presented in many ways. For example, a popup window or page (referred to herein as a "popup") can be generated to display the sentiment and the summary on client device 210 in real time in context of the article within the web page.”).
Hutchings does not explicitly teach the first attribute information comprising one or more first user information attributes from among: a gender, an age group, an occupation, a shopping preference, a dining preference, or a hobby.
Hwang teaches the first attribute information comprising one or more first user information attributes as preference category (Hwang, Column 11, lines 18-23 “At this time, the user profile information is information that is pre-registered to the electronic apparatus 100, including at least one of the user's name, gender, ID, preference category, biometric information (for example, key, weight, and medical history)”) from among:
a gender as gender (Id), an age group as user profile information including age (Hwan, Column 16, lines 6-9 “the document summary apparatus 200 can generate summary information by determining the degree of interest in a document based on user profile information (for example, age, gender, etc.).”), an occupation, a shopping preference as users preference category (Hwang, Column 11, lines 18-23 “At this time, the user profile information is information that is pre-registered to the electronic apparatus 100, including at least one of the user's name, gender, ID, preference category, biometric information (for example, key, weight, and medical history)”), a dining preference as usage history (Hwang, Column 17, lines 5-8 “acquire the summary setting information via the UI and obtain user history information including user profile information and user's usage history information.”), or a hobby;
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have customized the user-created taxonomy taught by Hutchins using the user profiles taught by Hwang as it yields the predictable results of enabling the AI model to generate summary information based on user history or demographic information (Hwang, Column 6, lines 44-49 “the AI model may generate summary information based on user history information or demographic. Specifically, the AI learning model can set the tone or length of summary information based on user history information (e.g., user's political orientation, knowledge level, etc.).”). One of ordinary skill in the art would recognize that the taxonomy may be customized to the specific user in question (Hutchings, ¶37) by incorporating information from the user’s profile (Hwang). This information may be used by the proposed combination to classify the terms being extracted by Hutchins and used to generate the summary. Which may be used to ensure that the summary would be of interest to the user (Hwang, Column 16, lines 5-31).
With regard to claim 2 the proposed combination further teaches wherein the first provision information comprises the supplementary information as providing analytics on sentiment, tonality, category, concept, or named entity (Hutchings, ¶30 “return the summary (with any desired AI-augmented analytics on sentiment, tonality, category, concept, named entity, etc.)”).
With regard to claim 3 the proposed combination further teaches wherein the supplementary information and at least a part of the subject information are displayed on a same screen of the communication apparatus (Hutchins, ¶12 “In some embodiments, the window or page generated by the browser application can comprise a popup. The popup can be an HTML page that overlays a portion of the web page on which the article is displayed. The sentiment and the summary can be displayed in the popup on the user device in real time in context of the article within the web page.”; ¶59 “Moreover, because the summarizer can be implemented as an extension of a browser application, the summary returned by the AI platform can be displayed right next to or otherwise in close proximity to the article within the same web page where the article is displayed. This allows the user to view the article and the summary at the same time.”; ¶60 “As described above, the summarizer is operable to examine the content a user is viewing on a user device, parse the textual information to get the relevant main body of the content, prepare a text block, get a summary and a sentiment of the text block from an AI platform, and instruct an application to display the summary and the sentiment all within the same page, in real time in context of the article or piece of content that the user is viewing.”).
With regard to claim 4 the proposed combination further teaches wherein the first provision information comprises the revised information as shortening (Hutchings, ¶30 “summarization refers to the process of shortening a text document in order to create a summary with the major points of the original document.”).
With regard to claim 5 the proposed combination further teaches wherein the operations further comprise after the first provision information is transmitted to the communication apparatus (Hutchings, Claim 1 “displaying, through the summarizer, the summary of the text block in the summarization range and the sentiment of the text block in the browser on the user device.”), receive second user input information transmitted from the communication apparatus as receiving an adjusted summarization range (Hutchings, Claim 4 “receiving, through the user interface element, an adjusted summarization range;”;
Updating at least a part of the first attribute information based on the second user input information to generate updated first attribute information as the adjusted summarization range (Hutchings, Claim 4 “receiving, through the user interface element, an adjusted summarization range;”);
in response to receiving third user input information transmitted from the communication apparatus:
expanding the updated first attribute information (Hutchigns, Claim 4 “making, by the summarizer, another call to the AI platform, wherein the call contains the adjusted summarization range, wherein the text block is processed by the summarization component of the AI platform according to the adjusted summarization range so as to produce a modified summary of the text block in the adjusted summarization range;”) by inputting the updated first attribute information as the adjusted summarization range (Id) into the first generative model as the text block is processed by the summarization component (Id) e.g. the trained machine learning (Hutchins, ¶26);
obtaining second provision information as the modified summary (Hutchings, Claim 4 “receiving, by the summarizer from the AI platform, the modified summary of the”) by inputting the expanded updated first attribute information (Hutchings, ¶26) into the second generative model as the AI platform (Hutchings, ¶22 “The AI platform can process the text block, generate a sentiment and a summary of the text block, and return them to the summarizer in response to the call.”); and
transmitting the second provision information to the communication apparatus to cause the communication apparatus to display the second provision information (Hutchings, Claim 4, “displaying, through the summarizer, the modified summary of the text block in the adjusted summarization range in the browser on the user device.”).
With regard to claim 9 Hutchings taches An information output (Hutchins, ¶12 “The popup can be an HTML page that overlays a portion of the web page on which the article is displayed. The sentiment and the summary can be displayed in the popup on the user device in real time in context of the article within the web page.”) method, performed by at least one computer (Hutchins, ¶76 “one or more processors in a computing environment”), the method comprising:
receiving first user input information transmitted from a communication apparatus of a user (Hutchings, ¶7 “In some embodiments, the summarizer is operable to run on a user device and can receive an indication or an instruction from a user to summarize content displayed on the user device.”; ¶22 “a summarizer on a user device may receive an indication or instruction from a user to summarize content displayed on the user device (101).”);
acquiring, in response to receiving the first user input information as the instruction to summarize the displayed content (Hutchings, ¶7, ¶22), subject information as extracted text (Hutchins, ¶22 “the summarizer is operable to extract texts from the main body of the content (115).”; ¶29 “For instance, a news article discusses that a president is going to visit a country. NLP text mining engine 235 is operable to programmatically examine the article, determine that this article concerns foreign affair and/or diplomacy, and add "foreign affair" and/or "diplomacy" as metadata (e.g., "category=foreign affair" or "topic=diplomacy") to the article, even if the article itself does not literally contain "foreign affair" or "diplomacy."”) related to a subject as the category/topic (Id);
acquiring first attribute information as metadata (Hutchins, ¶26 “An authority file refers to a controlled vocabulary of terms and cross-reference terms that assists entity extraction to return additional relevant items and related metadata ( e.g., geopolitical locations, person names, organization names, trademarks, events, etc.).”; ¶29 “NLP text mining engine 235 is operable to programmatically examine the article, determine that this article concerns foreign affair and/or diplomacy, and add "foreign affair" and/or "diplomacy" as metadata (e.g., "category=foreign affair" or "topic=diplomacy") to the article, even if the article itself does not literally contain "foreign affair" or "diplomacy." Downstream from text mining, these pieces of metadata can be used by AI platform 230 in different ways for various reasons”) from a user account of the user as the user configurations, e.g. summarization range and user-created taxonomy (Hutchings, ¶22 “The summarization range is configurable by the user”; ¶37 “a user can create a taxonomy and direct NLP text mining engine 235 to use the user-created taxonomy to perform the summarization.”), [[
expanding (Hutchins, ¶26 “For synonyms, acronyms, and variations thereof, an authority file may be used. An authority file refers to a controlled vocabulary of terms and cross-reference terms that assists entity extraction to return additional relevant items and related metadata ( e.g., geopolitical locations, person names, organization names, trademarks, events, etc.). There can be multiple authority files, each for a particular controlled vocabulary of terms and cross-reference terms.”) the first attribute information as metadata (Hutchins, ¶26, ¶29) to include at least one additional attribute related to the one or more first user information attributes as related metadata and additional relevant items (Id) by inputting the first attribute information into a first generative model trained as trained machine learning (Hutchins, ¶26 “Since text mining is performed at the document level, the extracted metadata (e.g., the company's name and the new product name in this example) can be used to enrich the document. NLP text mining engine 235 can learn how to determine an entity based on previous examples from which a model has been trained using machine learning.”) to output a predicted character string as the output list of extracted entities with attributes (Hutchings, ¶26 “Output from entity extraction can be a list of extracted entities with attributes and relevancy ranking”) corresponding to a second attribute as additional relevant items, related metadata and cross-reference terms (Hutchins, ¶26 “For synonyms, acronyms, and variations thereof, an authority file may be used. An authority file refers to a controlled vocabulary of terms and cross-reference terms that assists entity extraction to return additional relevant items and related metadata ( e.g., geopolitical locations, person names, organization names, trademarks, events, etc.). There can be multiple authority files, each for a particular controlled vocabulary of terms and cross-reference terms.”) based on an input character string corresponding to a first attribute as the extracted metadata (Hutchens, ¶26 “Based on linguistic rules and statistical patterns, NLP text mining engine 235 can extract the company's name, the new product name, etc. from the document. All occurrences of an entity type may be extracted.”), wherein the expanded first attribute information is acquired from an output as the output (Hutchins, ¶26) of the first generative model as trained machine learning (Hutchins, ¶26;
acquiring first provision information as the summary (Hutchings, ¶30 “generate a summary”) by inputting (Hutchings, ¶29 “Downstream from text mining, these pieces of metadata can be used by AI platform 230 in different ways for various reasons.”) the expanded first attribute information (Hutchings, ¶26 “Since text mining is performed at the document level, the extracted metadata (e.g., the company's name and the new product name in this example) can be used to enrich the document.”) and the subject information as category/topic (Hutchings, ¶29 “NLP text mining engine 235 is operable to programmatically examine the article, determine that this article concerns foreign affair and/or diplomacy, and add "foreign affair" and/or "diplomacy" as metadata (e.g., "category=foreign affair" or "topic=diplomacy") to the article, even if the article itself does not literally contain "foreign affair" or "diplomacy."”) into a second generative model trained as the AI platform (Hutchings, ¶22 “The AI platform can process the text block, generate a sentiment and a summary of the text block, and return them to the summarizer in response to the call.”) to output predicted text as the text of the summary (Hutchings, ¶30 “generate a summary”) related to (i) a first input character string corresponding to subject text as extracted text (Hutchins, ¶22 “the summarizer is operable to extract texts from the main body of the content (115).”; ¶29 “For instance, a news article discusses that a president is going to visit a country. NLP text mining engine 235 is operable to programmatically examine the article, determine that this article concerns foreign affair and/or diplomacy, and add "foreign affair" and/or "diplomacy" as metadata (e.g., "category=foreign affair" or "topic=diplomacy") to the article, even if the article itself does not literally contain "foreign affair" or "diplomacy."”) and (ii) a second input character string corresponding to a plurality of user attributes as the user configurations, e.g. summarization range and user-created taxonomy (Hutchings, ¶22 “The summarization range is configurable by the user”; ¶37 “a user can create a taxonomy and direct NLP text mining engine 235 to use the user-created taxonomy to perform the summarization.”), the first provision information being acquired from an output of the second generative model as the AI platform (Hutchings, ¶22 “The AI platform can process the text block, generate a sentiment and a summary of the text block, and return them to the summarizer in response to the call.”), and the first provision information comprising (i) supplementary information that supplements the subject information as providing analytics on sentiment, tonality, category, concept, or named entity (Hutchings, ¶30 “return the summary (with any desired AI-augmented analytics on sentiment, tonality, category, concept, named entity, etc.)”) or (ii) revised information that revises the subject information as shortening (Hutchings, ¶30 “summarization refers to the process of shortening a text document in order to create a summary with the major points of the original document.”); and
transmitting the first provision information to the communication apparatus to cause the communication apparatus to display the first provision information (Hutchings, ¶39 “The results returned by AI platform 630 can be presented in many ways. For example, a popup window or page (referred to herein as a "popup") can be generated to display the sentiment and the summary on client device 210 in real time in context of the article within the web page.”).
Hutchings does not explicitly teach the first attribute information comprising one or more first user information attributes from among: a gender, an age group, an occupation, a shopping preference, a dining preference, or a hobby.
Hwang teaches the first attribute information comprising one or more first user information attributes as preference category (Hwang, Column 11, lines 18-23 “At this time, the user profile information is information that is pre-registered to the electronic apparatus 100, including at least one of the user's name, gender, ID, preference category, biometric information (for example, key, weight, and medical history)”) from among:
a gender as gender (Id), an age group as user profile information including age (Hwan, Column 16, lines 6-9 “the document summary apparatus 200 can generate summary information by determining the degree of interest in a document based on user profile information (for example, age, gender, etc.).”), an occupation, a shopping preference as users preference category (Hwang, Column 11, lines 18-23 “At this time, the user profile information is information that is pre-registered to the electronic apparatus 100, including at least one of the user's name, gender, ID, preference category, biometric information (for example, key, weight, and medical history)”), a dining preference as usage history (Hwang, Column 17, lines 5-8 “acquire the summary setting information via the UI and obtain user history information including user profile information and user's usage history information.”), or a hobby;
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have customized the user-created taxonomy taught by Hutchins using the user profiles taught by Hwang as it yields the predictable results of enabling the AI model to generate summary information based on user history or demographic information (Hwang, Column 6, lines 44-49 “the AI model may generate summary information based on user history information or demographic. Specifically, the AI learning model can set the tone or length of summary information based on user history information (e.g., user's political orientation, knowledge level, etc.).”). One of ordinary skill in the art would recognize that the taxonomy may be customized to the specific user in question (Hutchings, ¶37) by incorporating information from the user’s profile (Hwang). This information may be used by the proposed combination to classify the terms being extracted by Hutchins and used to generate the summary. Which may be used to ensure that the summary would be of interest to the user (Hwang, Column 16, lines 5-31).
With regard to claim 10 Hutchings taches A non-transitory computer-readable medium storing a program causing at least one computer to perform operations (Hutchins, ¶76 “As one skilled in the art can appreciate, a computer program product implementing an embodiment disclosed herein may comprise one or more non-transitory computer readable media storing computer instructions translatable by one or more processors in a computing environment”) comprising:
receiving first user input information transmitted from a communication apparatus of a user (Hutchings, ¶7 “In some embodiments, the summarizer is operable to run on a user device and can receive an indication or an instruction from a user to summarize content displayed on the user device.”; ¶22 “a summarizer on a user device may receive an indication or instruction from a user to summarize content displayed on the user device (101).”);
acquiring, in response to receiving the first user input information as the instruction to summarize the displayed content (Hutchings, ¶7, ¶22), subject information as extracted text (Hutchins, ¶22 “the summarizer is operable to extract texts from the main body of the content (115).”; ¶29 “For instance, a news article discusses that a president is going to visit a country. NLP text mining engine 235 is operable to programmatically examine the article, determine that this article concerns foreign affair and/or diplomacy, and add "foreign affair" and/or "diplomacy" as metadata (e.g., "category=foreign affair" or "topic=diplomacy") to the article, even if the article itself does not literally contain "foreign affair" or "diplomacy."”) related to a subject as the category/topic (Id);
acquiring first attribute information as metadata (Hutchins, ¶26 “An authority file refers to a controlled vocabulary of terms and cross-reference terms that assists entity extraction to return additional relevant items and related metadata ( e.g., geopolitical locations, person names, organization names, trademarks, events, etc.).”; ¶29 “NLP text mining engine 235 is operable to programmatically examine the article, determine that this article concerns foreign affair and/or diplomacy, and add "foreign affair" and/or "diplomacy" as metadata (e.g., "category=foreign affair" or "topic=diplomacy") to the article, even if the article itself does not literally contain "foreign affair" or "diplomacy." Downstream from text mining, these pieces of metadata can be used by AI platform 230 in different ways for various reasons”) from a user account of the user as the user configurations, e.g. summarization range and user-created taxonomy (Hutchings, ¶22 “The summarization range is configurable by the user”; ¶37 “a user can create a taxonomy and direct NLP text mining engine 235 to use the user-created taxonomy to perform the summarization.”), [[
expanding (Hutchins, ¶26 “For synonyms, acronyms, and variations thereof, an authority file may be used. An authority file refers to a controlled vocabulary of terms and cross-reference terms that assists entity extraction to return additional relevant items and related metadata ( e.g., geopolitical locations, person names, organization names, trademarks, events, etc.). There can be multiple authority files, each for a particular controlled vocabulary of terms and cross-reference terms.”) the first attribute information as metadata (Hutchins, ¶26, ¶29) to include at least one additional attribute related to the one or more first user information attributes as related metadata and additional relevant items (Id) by inputting the first attribute information into a first generative model trained as trained machine learning (Hutchins, ¶26 “Since text mining is performed at the document level, the extracted metadata (e.g., the company's name and the new product name in this example) can be used to enrich the document. NLP text mining engine 235 can learn how to determine an entity based on previous examples from which a model has been trained using machine learning.”) to output a predicted character string as the output list of extracted entities with attributes (Hutchings, ¶26 “Output from entity extraction can be a list of extracted entities with attributes and relevancy ranking”) corresponding to a second attribute as additional relevant items, related metadata and cross-reference terms (Hutchins, ¶26 “For synonyms, acronyms, and variations thereof, an authority file may be used. An authority file refers to a controlled vocabulary of terms and cross-reference terms that assists entity extraction to return additional relevant items and related metadata ( e.g., geopolitical locations, person names, organization names, trademarks, events, etc.). There can be multiple authority files, each for a particular controlled vocabulary of terms and cross-reference terms.”) based on an input character string corresponding to a first attribute as the extracted metadata (Hutchens, ¶26 “Based on linguistic rules and statistical patterns, NLP text mining engine 235 can extract the company's name, the new product name, etc. from the document. All occurrences of an entity type may be extracted.”), wherein the expanded first attribute information is acquired from an output as the output (Hutchins, ¶26) of the first generative model as trained machine learning (Hutchins, ¶26;
acquiring first provision information as the summary (Hutchings, ¶30 “generate a summary”) by inputting (Hutchings, ¶29 “Downstream from text mining, these pieces of metadata can be used by AI platform 230 in different ways for various reasons.”) the expanded first attribute information (Hutchings, ¶26 “Since text mining is performed at the document level, the extracted metadata (e.g., the company's name and the new product name in this example) can be used to enrich the document.”) and the subject information as category/topic (Hutchings, ¶29 “NLP text mining engine 235 is operable to programmatically examine the article, determine that this article concerns foreign affair and/or diplomacy, and add "foreign affair" and/or "diplomacy" as metadata (e.g., "category=foreign affair" or "topic=diplomacy") to the article, even if the article itself does not literally contain "foreign affair" or "diplomacy."”) into a second generative model trained as the AI platform (Hutchings, ¶22 “The AI platform can process the text block, generate a sentiment and a summary of the text block, and return them to the summarizer in response to the call.”) to output predicted text as the text of the summary (Hutchings, ¶30 “generate a summary”) related to (i) a first input character string corresponding to subject text as extracted text (Hutchins, ¶22 “the summarizer is operable to extract texts from the main body of the content (115).”; ¶29 “For instance, a news article discusses that a president is going to visit a country. NLP text mining engine 235 is operable to programmatically examine the article, determine that this article concerns foreign affair and/or diplomacy, and add "foreign affair" and/or "diplomacy" as metadata (e.g., "category=foreign affair" or "topic=diplomacy") to the article, even if the article itself does not literally contain "foreign affair" or "diplomacy."”) and (ii) a second input character string corresponding to a plurality of user attributes as the user configurations, e.g. summarization range and user-created taxonomy (Hutchings, ¶22 “The summarization range is configurable by the user”; ¶37 “a user can create a taxonomy and direct NLP text mining engine 235 to use the user-created taxonomy to perform the summarization.”), the first provision information being acquired from an output of the second generative model as the AI platform (Hutchings, ¶22 “The AI platform can process the text block, generate a sentiment and a summary of the text block, and return them to the summarizer in response to the call.”), and the first provision information comprising (i) supplementary information that supplements the subject information as providing analytics on sentiment, tonality, category, concept, or named entity (Hutchings, ¶30 “return the summary (with any desired AI-augmented analytics on sentiment, tonality, category, concept, named entity, etc.)”) or (ii) revised information that revises the subject information as shortening (Hutchings, ¶30 “summarization refers to the process of shortening a text document in order to create a summary with the major points of the original document.”); and
transmitting the first provision information to the communication apparatus to cause the communication apparatus to display the first provision information (Hutchings, ¶39 “The results returned by AI platform 630 can be presented in many ways. For example, a popup window or page (referred to herein as a "popup") can be generated to display the sentiment and the summary on client device 210 in real time in context of the article within the web page.”).
Hutchings does not explicitly teach the first attribute information comprising one or more first user information attributes from among: a gender, an age group, an occupation, a shopping preference, a dining preference, or a hobby.
Hwang teaches the first attribute information comprising one or more first user information attributes as preference category (Hwang, Column 11, lines 18-23 “At this time, the user profile information is information that is pre-registered to the electronic apparatus 100, including at least one of the user's name, gender, ID, preference category, biometric information (for example, key, weight, and medical history)”) from among:
a gender as gender (Id), an age group as user profile information including age (Hwan, Column 16, lines 6-9 “the document summary apparatus 200 can generate summary information by determining the degree of interest in a document based on user profile information (for example, age, gender, etc.).”), an occupation, a shopping preference as users preference category (Hwang, Column 11, lines 18-23 “At this time, the user profile information is information that is pre-registered to the electronic apparatus 100, including at least one of the user's name, gender, ID, preference category, biometric information (for example, key, weight, and medical history)”), a dining preference as usage history (Hwang, Column 17, lines 5-8 “acquire the summary setting information via the UI and obtain user history information including user profile information and user's usage history information.”), or a hobby;
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have customized the user-created taxonomy taught by Hutchins using the user profiles taught by Hwang as it yields the predictable results of enabling the AI model to generate summary information based on user history or demographic information (Hwang, Column 6, lines 44-49 “the AI model may generate summary information based on user history information or demographic. Specifically, the AI learning model can set the tone or length of summary information based on user history information (e.g., user's political orientation, knowledge level, etc.).”). One of ordinary skill in the art would recognize that the taxonomy may be customized to the specific user in question (Hutchings, ¶37) by incorporating information from the user’s profile (Hwang). This information may be used by the proposed combination to classify the terms being extracted by Hutchins and used to generate the summary. Which may be used to ensure that the summary would be of interest to the user (Hwang, Column 16, lines 5-31).
With regard to claim 11 the proposed combination further teaches wherein the first user input information corresponds to an input of the user into an application comprising a browser (Hutchings, ¶41 “Non-limiting examples of application 222 can include a browser application”),
wherein the subject information is a news article (Hutchings, ¶29 “For instance, a news article discusses that a president is going to visit a country.”), and
wherein the first provision information as the summary (Hutchings, ¶30 “generate a summary”) is a revised as shortening (Hutchings, ¶30 “summarization refers to the process of shortening a text document in order to create a summary with the major points of the original document.”) news article (Hutchings, ¶29).
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Hutchings in view of Hwang and Jain [2014/0316930].
With regard to claim 6 the proposed combination further teaches wherein the one or more first user information attributes as metadata (Hutchings, ¶26) including user profile information (Hwang, Column 11, lines 18-23) further comprise at least one of:
[[as the user profile information (Hwang, Column 11, lines 18-23 “At this time, the user profile information is information that is pre-registered to the electronic apparatus 100, including at least one of the user's name, gender, ID, preference category, biometric information (for example, key, weight, and medical history)”; Please note this claim limitation has been interpreted in light of Paragraph [0034] of the original specification which recites “As another example, the second acquisition unit 3 0 120 acquires an SNS account being input to the communication apparatus 20 by a user, and acquires information associated with the account, for example, at least one of gender, age, and an occupation, as at least a part of the user information.”), a search log of the user as keyword to be searched by a user (Hwang, Column 11, lines 23-30 “The use history information is information collected from the user using the electronic apparatus 100, and may include …, a keyword to be searched by a user.”), or a [[ as usage history (Hwang, Column 11, lines 12-18 “The user history collection module 127 may collect user history information from the electronic apparatus 100. At this time, the user history collection module 127 may collect user profile information registered by the user and use history information collected while the user uses the electronic apparatus 100.”).
Hwang does not explicitly teach that the profile is social media information associated with a social networking service (SNS) account of the user, …or a transaction history of the user.
Jain teaches social media information associated with a social networking service (SNS) account of the user as social profile, e.g. social network information (Jain, ¶37 “The user information database 114 may also comprise user social profile information 214 (e.g., information on user's friends in a social network) and 216 (e.g., information on the user's friends' viewing/purchase history).”), a search log of the user as user search history (¶38 “The user information database 114 may further include user search history 220”), or a transaction history of the user as purchase history (Jain, ¶37).
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented the proposed combination incorporating the user information and user categories taught by Jain (Jain, ¶36, ¶38) into the user profile information (Hwang, Column 11, lines 18-23) used to select/create the user taxonomies (Hutchins, ¶37). This proposed combination would enable to device to make use of more information about the user that may be available, thereby expanding the customization to that particular user.
With regard to claim 7 the proposed combination further teaches
acquiring, for each of a plurality of users as the user associated with the user profile information (Jain, ¶37), e.g. the user of the device Hutchings, ¶7 “In some embodiments, the summarizer is operable to run on a user device and can receive an indication or an instruction from a user to summarize content displayed on the user device.”), one or more second user information attributes as information on the user’s friends (Jain, ¶37 “The user information database 114 may also comprise user social profile information 214 (e.g., information on user's friends in a social network)”; ¶43 “user contexts 126 may also comprise recommendations 248a associated with user reviews and/or reviews by user's friends, recommendations 248b associated with reviews/ratings by brands/entities the user connects with, and/or recommendations 248c associated with recommended (such as + 1 'd or Liked) by user's friends.”); and
including the one or more second user information attributes in the first attribute information as using the user context, including the user’s friends reviews (Jain, ¶43) as metadata (Hutchins, ¶26) and to specify user configurations (Hutchins, ¶22; ¶37).
With regard to claim 8 the proposed combination further teaches wherein the one or more first user information attributes comprise a character string of text as extracted text (Hutchins, ¶22, ¶29), and
the operations further comprise:
inputting the character string (Hutchins, ¶26 “For synonyms, acronyms, and variations thereof, an authority file may be used. An authority file refers to a controlled vocabulary of terms and cross-reference terms that assists entity extraction to return additional relevant items and related metadata ( e.g., geopolitical locations, person names, organization names, trademarks, events, etc.). There can be multiple authority files, each for a particular controlled vocabulary of terms and cross-reference terms.”) into the first generative model as trained machine learning (Hutchins, ¶26 “Since text mining is performed at the document level, the extracted metadata (e.g., the company's name and the new product name in this example) can be used to enrich the document. NLP text mining engine 235 can learn how to determine an entity based on previous examples from which a model has been trained using machine learning.”) to generate second attribute information as the additional relevant items, related metadata and cross-reference terms (Hutchings, ¶26); and
including the second attribute information in the first attribute information (Hutchings, ¶26 “Since text mining is performed at the document level, the extracted metadata (e.g., the company's name and the new product name in this example) can be used to enrich the document.”).
Response to Arguments
With regard to the 101, the rejection is withdrawn in view of the claim amendments.
Applicant's arguments filed October 28, 2025 have been fully considered but they are not persuasive.
With regard to the prior art rejection, applicants arguments appear to argue a different mapping than what is put forth by the office, and as such the arguments do not apply to the rejection put forth.
Within the proposed combination put forth above, the first attribute information has been mapped to the metadata (Hutchings, ¶26, ¶29), which may include the user configurations, summarization range, and user created taxonomy (Hutchings, ¶22, ¶37). Within the proposed combination metadata also includes the user profile information as taught by Hwang (Column 11, lines 18-23) and in the particular combination addressing claims 6-8, may also include the social media profile information (Jain). Within the proposed device, the system cross-references these terms to identify “additional relevant items” and “related metadata” (Hutchings, ¶26). One of ordinary skill in the art would recognize the generation of the additional relevant items and related metadata as second attributes that are based on the first attribute, which expands the attributes being processed.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AMANDA L WILLIS/ Primary Examiner, Art Unit 2156
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