DETAILED ACTION
This communication is in response to the Application filed on 12/11/2024. Claims 1-18 are pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 13, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/11/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP 2023213335, filed on 12/18/2023.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites An information processing system, comprising circuitry configured to: control reading of image data from a document according to an instruction from a user; acquire characteristic information of the user; receive, from the user, information indicating conversion processing to be performed on text included in the image data; extract the text from the image data; input, to a large language model, information including an instruction instructing that the conversion processing is to be performed on the text and that a result of the conversion processing is to be suitable for a person corresponding to the characteristic information; acquire a conversion result that is output by the large language model; and output the conversion result.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of performing this process. Someone can read text found in an image such as reading a sign in a picture. They can learn information about the person who took the picture. The person who took the picture could then ask for someone to summarize the text found in it. In this instance, the person giving the photo, information about themselves, and requesting a summary/translation from someone else is the transmission. The person they asked can use this information to provide a summary/translation of the text in the image. They could output this summary/translation by writing it on a paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims recite the additional components of a large language model. The large language model is merely being used to apply the method via a generic computing device. The large language model is detailed on Page 4, Lines 19-32 and is described as being any general purpose LLM such as GPT or BERT. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 9, 17, and 18 recite control reading of image data from a document according to an instruction from a user; acquire characteristic information of the user; receive, from the user, information indicating conversion processing to be performed on text included in the image data; transmit the image data, the characteristic information, and information indicating the conversion processing to an information processing apparatus through a network; receive, from the information processing apparatus, a conversion result obtained by a large language model to which information including an instruction is input, the instruction instructing that the conversion processing is to be performed on the text extracted from the image data and that a result of the conversion processing is to be suitable for a person corresponding to the characteristic information; and output the conversion result.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of performing this process. Someone can read text found in an image such as reading a sign in a picture. They can learn information about the person who took the picture. The person who took the picture could then ask for someone to summarize the text found in it. In this instance, the person giving the photo, information about themselves, and requesting a summary/translation from someone else is the transmission. The person they asked can use this information to provide a summary/translation of the text in the image. They could output this summary/translation by writing it on a paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims recite the additional components of a large language model. The large language model is merely being used to apply the method via a generic computing device. The large language model is detailed on Page 4, Lines 19-32 and is described as being any general purpose LLM such as GPT or BERT. Claim 18 specifically lists the additional components of a non-transitory recording medium and processor. Both the recording medium and processor are generic components merely used to apply the mental process via a computing device. The non-transitory recording medium is detailed on Page 6, Lines 6-12 and is described using generic computing components. The processor is detailed on Page 6, Lines 13-18 and is described using generic computing components. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 2 and 10 recite wherein the conversion processing is summarization of the text or translation of the text.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind can summarize and translate text. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional elements that were not present in the independent claim. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 3 and 11 recite wherein the circuitry acquires setting information relating to a display language of the information processing system as a part of the characteristic information.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind can specify a text for translation. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional elements that were not present in the independent claim. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 4 and 12 recite wherein the circuitry outputs, with the conversion result, information indicating a storage location where the image data or the text extracted from the image data is stored.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind can signify a storage location such as a folder or filing cabinet location where the text/picture is stored. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional elements that were not present in the independent claim. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 5 and 13 recite wherein the circuitry applies text styling to a word that appears more frequently than other words in text indicated by the conversion result.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind can stylize words based on their frequency, for example, highlighting any words that appear more than 5 times. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional elements that were not present in the independent claim. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 6 and 14 recite wherein the characteristic information includes information indicating an organization to which the user belongs.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind can indicate what organization they belong to. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional elements that were not present in the independent claim. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 7 and 15 recite wherein the circuitry acquires a part of the characteristic information from image data including the user as a subject.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind can find characteristic information in an image such as if the image shows certain people or a certain company within it. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional elements that were not present in the independent claim. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 8 and 16 recite wherein the circuitry further receives an evaluation by the user for the conversion result.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind can receive feedback on a summary/translation they created. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional elements that were not present in the independent claim. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 6-11, and 14-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US Patent Publication US 12008332 B1 (Gardner et al.).
Regarding Claim 1, Gardner et al. teaches An information processing system, comprising circuitry configured to:
(Example embodiments may be implemented using a computer program product, e.g., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable medium for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers.) (Col. 64, Line 65 to Col. 65 to Line 4).
control reading of image data from a document according to an instruction from a user;
(The disclosed system enables users to precisely define a target level of abstraction (e.g., by specifying a percentage of length reduction) for a representation of a summary of a content item.) (Col. 2, Lines 7-10).
(For image and video inputs, multimedia analysis techniques may be applied to extract salient objects, people, scenes, and text segments from the visual content.) (Col. 12, Lines 31-33).
The user provides an input and a request for a summary. That input can take the form of an image and text is extracted from the image.
acquire characteristic information of the user;
(Within the system itself, aggregated behavioral data from user interactions can also be leveraged as a feedback signal for ongoing training. This can include data on which summaries users find most helpful, edits they make, zoom levels used, and other actions reflecting subjective preferences. …The abstraction parameter module 210 may be configured to allow users to precisely define a target abstraction level, such as by selecting a specific compression percentage between 0-100%. The module 210 provides interfaces allowing users to numerically set the percentage of length reduction or manually choose from pre-defined abstraction levels (e.g., high, medium, low). ) (Col. 8, Lines 30-61).
(In example embodiments, summaries can be tailored to different use cases through customized vocabulary, style, length, and formatting; for example: Executives get bulleted overviews with simple vocabulary; Legal professionals receive legalese summaries with key citations preserved; Doctors get medically accurate summaries of health records with jargon explained; Engineers get technically precise summaries with math/code formatted properly; Children get summaries written at lower reading levels using simpler words; Blind users get text-to-speech converted summaries; Foreign users get summaries translated to their native language; Analysis use cases optimize vocabulary and length for easy skimming; Reporting use cases format nicely with fonts, colors, and data visualizations; Compliance use cases redact or mask sensitive information; and/or Profiles with user role, reading level, language, accessibility needs, and purpose guide customization. In example embodiments, summaries are adapted to improve understanding and utility for target audiences.) (Col. 11, Lines 14-39).
Users can directly input a level of abstraction for the summary or the LLM can take a user profile into account when generating the summary such as the users job changing the output.
receive, from the user, information indicating conversion processing to be performed on text included in the image data;
(The disclosed system enables users to precisely define a target level of abstraction (e.g., by specifying a percentage of length reduction) for a representation of a summary of a content item. For example, the system gives granular control over depth and brevity. The system may also be configured to engineer iterative prompts to have the LLM summarize its own prior outputs at increasing levels of abstraction. This allows for gradual refinement while preserving substantive information.) (Col. 7-15).
Users can submit a level of abstraction for the summary they requested. This represents information indicating conversion processing.
input, to a large language model, information including an instruction instructing that the conversion processing is to be performed on the text and that a result of the conversion processing is to be suitable for a person corresponding to the characteristic information;
(FIG. 1 is a network diagram depicting a system 100 within which various example embodiments may be deployed. A networked system 102, in the example form of a cloud computing service, such as Microsoft Azure or other cloud service, provides server-side functionality, via a network 104 (e.g., the Internet or Wide Area Network (WAN)) to one or more endpoints (e.g., client machines 110). FIG. 1 illustrates client application(s) 112 on the client machines 110.) (Col. 4, Lines 58-66).
(The content summarization application(s) 120 may be connected to one or more LLM or other artificial intelligence machine(s) 111.) (Col. 6, Lines 7-9).
(The prompts can include the original text to summarize along with instructions tailored to elicit the target summary characteristics from the LLM. The system sends the prompts through the API and ingests the LLM-generated summaries to present to the user.) (Col. 6, Lines 45-49).
Fig. 1 shows the network connected system where the LLM that does the processing is connected through the network. The content for the summary and associated characteristics are provided to the LLM via a prompt.
acquire a conversion result that is output by the large language model;
(The prompts can include the original text to summarize along with instructions tailored to elicit the target summary characteristics from the LLM. The system sends the prompts through the API and ingests the LLM-generated summaries to present to the user.) (Col. 6, Lines 45-49).
(At operation 310, a response is received from the LLM. The response contains a second content item representing the first content item. The representation omits or simplifies sub-content items included in the first content item based on the abstraction level.) (Col. 13, Lines 43-47).
The LLM takes the information and creates a summary.
and output the conversion result.
(Operation 312, the representation is applied (e.g., used to control output communicated to a target device. Example target devices may include screens, speakers, haptic interfaces, augmented reality displays, etc. The representation may be converted to speech, displayed text, graphics, video, animations, or other formats.) (Col. 13, Lines 62-67).
The representation being applied means that the generated summary is output to the user via various different target devices.
Regarding Claim 9, 17, and 18, Gardner et al. teaches An image processing apparatus, comprising circuitry configured to:
(Example embodiments may be implemented using a computer program product, e.g., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable medium for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers.) (Col. 64, Line 65 to Col. 65 to Line 4).
Claim 17 states An information processing method, comprising:
(A method of generating abstractive summaries of content items using one or more large language models (LLMs) is disclosed.) (Col. 3, ).
Claim 18 states A non-transitory recording medium storing a plurality of program codes which, when executed by one or more processors, causes the one or more processors to perform the method according to claim 17.
(Example embodiments may be implemented using a computer program product, e.g., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable medium for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers.) (Col. 64, Line 65 to Col. 65 to Line 4).
control reading of image data from a document according to an instruction from a user;
(The disclosed system enables users to precisely define a target level of abstraction (e.g., by specifying a percentage of length reduction) for a representation of a summary of a content item.) (Col. 2, Lines 7-10).
(For image and video inputs, multimedia analysis techniques may be applied to extract salient objects, people, scenes, and text segments from the visual content.) (Col. 12, Lines 31-33).
The user provides an input and a request for a summary. That input can take the form of an image and text is extracted from the image.
acquire characteristic information of the user;
(Within the system itself, aggregated behavioral data from user interactions can also be leveraged as a feedback signal for ongoing training. This can include data on which summaries users find most helpful, edits they make, zoom levels used, and other actions reflecting subjective preferences. …The abstraction parameter module 210 may be configured to allow users to precisely define a target abstraction level, such as by selecting a specific compression percentage between 0-100%. The module 210 provides interfaces allowing users to numerically set the percentage of length reduction or manually choose from pre-defined abstraction levels (e.g., high, medium, low). ) (Col. 8, Lines 30-61).
(In example embodiments, summaries can be tailored to different use cases through customized vocabulary, style, length, and formatting; for example: Executives get bulleted overviews with simple vocabulary; Legal professionals receive legalese summaries with key citations preserved; Doctors get medically accurate summaries of health records with jargon explained; Engineers get technically precise summaries with math/code formatted properly; Children get summaries written at lower reading levels using simpler words; Blind users get text-to-speech converted summaries; Foreign users get summaries translated to their native language; Analysis use cases optimize vocabulary and length for easy skimming; Reporting use cases format nicely with fonts, colors, and data visualizations; Compliance use cases redact or mask sensitive information; and/or Profiles with user role, reading level, language, accessibility needs, and purpose guide customization. In example embodiments, summaries are adapted to improve understanding and utility for target audiences.) (Col. 11, Lines 14-39).
Users can directly input a level of abstraction for the summary or the LLM can take a user profile into account when generating the summary such as the users job changing the output.
receive, from the user, information indicating conversion processing to be performed on text included in the image data;
(The disclosed system enables users to precisely define a target level of abstraction (e.g., by specifying a percentage of length reduction) for a representation of a summary of a content item. For example, the system gives granular control over depth and brevity. The system may also be configured to engineer iterative prompts to have the LLM summarize its own prior outputs at increasing levels of abstraction. This allows for gradual refinement while preserving substantive information.) (Col. 7-15).
User’s can submit a level of abstraction for the summary they requested. This represents information indicating conversion processing.
transmit the image data, the characteristic information, and information indicating the conversion processing to an information processing apparatus through a network;
(FIG. 1 is a network diagram depicting a system 100 within which various example embodiments may be deployed. A networked system 102, in the example form of a cloud computing service, such as Microsoft Azure or other cloud service, provides server-side functionality, via a network 104 (e.g., the Internet or Wide Area Network (WAN)) to one or more endpoints (e.g., client machines 110). FIG. 1 illustrates client application(s) 112 on the client machines 110.) (Col. 4, Lines 58-66).
(The content summarization application(s) 120 may be connected to one or more LLM or other artificial intelligence machine(s) 111.) (Col. 6, Lines 7-9).
(The prompts can include the original text to summarize along with instructions tailored to elicit the target summary characteristics from the LLM. The system sends the prompts through the API and ingests the LLM-generated summaries to present to the user.) (Col. 6, Lines 45-49).
Fig. 1 shows the network connected system where the LLM that does the processing is connected through the network. The content for the summary and associated characteristics are provided to the LLM via a prompt.
receive, from the information processing apparatus, a conversion result obtained by a large language model to which information including an instruction is input, the instruction instructing that the conversion processing is to be performed on the text extracted from the image data and that a result of the conversion processing is to be suitable for a person corresponding to the characteristic information;
(The prompts can include the original text to summarize along with instructions tailored to elicit the target summary characteristics from the LLM. The system sends the prompts through the API and ingests the LLM-generated summaries to present to the user.) (Col. 6, Lines 45-49).
(At operation 310, a response is received from the LLM. The response contains a second content item representing the first content item. The representation omits or simplifies sub-content items included in the first content item based on the abstraction level.) (Col. 13, Lines 43-47).
The LLM takes the information and creates a summary.
and output the conversion result.
(Operation 312, the representation is applied (e.g., used to control output communicated to a target device. Example target devices may include screens, speakers, haptic interfaces, augmented reality displays, etc. The representation may be converted to speech, displayed text, graphics, video, animations, or other formats.) (Col. 13, Lines 62-67).
The representation being applied means that the generated summary is output to the user via various different target devices.
Regarding Claims 2 and 10, Gardner et al. teaches the system of claims 1 and 9.
Furthermore, Gardner et al. teaches wherein the conversion processing is summarization of the text or translation of the text.
(A method of generating abstractive summaries of content items using one or more large language models (LLMs) is disclosed.) (Col. 3, Lines 38-40).
(Foreign users get summaries translated to their native language) (Col. 11, Lines 28-29).
Both summarization and translation can occur on the input.
Regarding Claims 3 and 11, Gardner et al. teaches the system of claims 1 and 9.
Furthermore, Gardner et al. teaches wherein the circuitry acquires setting information relating to a display language of the image processing apparatus as a part of the characteristic information.
(In example embodiments, summaries can be tailored to different use cases through customized vocabulary, style, length, and formatting; for example: Executives get bulleted overviews with simple vocabulary; Legal professionals receive legalese summaries with key citations preserved; Doctors get medically accurate summaries of health records with jargon explained; Engineers get technically precise summaries with math/code formatted properly; Children get summaries written at lower reading levels using simpler words; Blind users get text-to-speech converted summaries; Foreign users get summaries translated to their native language; Analysis use cases optimize vocabulary and length for easy skimming; Reporting use cases format nicely with fonts, colors, and data visualizations; Compliance use cases redact or mask sensitive information; and/or Profiles with user role, reading level, language, accessibility needs, and purpose guide customization. In example embodiments, summaries are adapted to improve understanding and utility for target audiences.) (Col. 11, Lines 14-39).
Summaries can be adapted to the user information including translating it to their native language
Regarding Claims 6 and 14, Gardner et al. teaches the system of claims 1 and 9.
Furthermore, Gardner et al. teaches wherein the characteristic information includes information indicating an organization to which the user belongs.
(In example embodiments, summaries can be tailored to different use cases through customized vocabulary, style, length, and formatting; for example: Executives get bulleted overviews with simple vocabulary; Legal professionals receive legalese summaries with key citations preserved; Doctors get medically accurate summaries of health records with jargon explained; Engineers get technically precise summaries with math/code formatted properly; Children get summaries written at lower reading levels using simpler words; Blind users get text-to-speech converted summaries; Foreign users get summaries translated to their native language; Analysis use cases optimize vocabulary and length for easy skimming; Reporting use cases format nicely with fonts, colors, and data visualizations; Compliance use cases redact or mask sensitive information; and/or Profiles with user role, reading level, language, accessibility needs, and purpose guide customization. In example embodiments, summaries are adapted to improve understanding and utility for target audiences.) (Col. 11, Lines 14-39).
Summaries can be adapted to a user’s organization by providing different outputs depending on the field the user operates in.
Regarding Claims 7 and 15, Gardner et al. teaches the system of claims 1 and 9.
Furthermore, Gardner et al. teaches wherein the circuitry acquires a part of the characteristic information from image data including the user as a subject.
(For image and video inputs, multimedia analysis techniques may be applied to extract salient objects, people, scenes, and text segments from the visual content. This extracted visual metadata provides signals for determining importance and relevance when generating a summary. For example, key frames containing presenters, slides, or illustrations could be prioritized from video. The system's summarization models may be trained to incorporate both textual and visual relevance cues.) (Col. 12, Lines 31-39).
Images have metadata extracted from them such as important frames containing the presenter.
Regarding Claims 8 and 16, Gardner et al. teaches the system of claims 1 and 9.
Furthermore, Gardner et al. teaches wherein the circuitry further receives an evaluation by the user for the conversion result.
(Human evaluation studies may be conducted where subject matter experts like lawyers and scientists rate summaries for accuracy, fluency, and completeness.) (Col. 12, Lines 13-15).
(In addition to pre-training, online learning during inference continuously improves prompt engineering. User feedback like summary ratings and edits may provide a personalized training signal. User interactions may be logged as {content, prompt, summary, feedback} samples.) (Col. 14, Lines 66-67).
User feedback of summaries is used to improve the model.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication US 12008332 B1 (Gardner et al.) in view of US Patent Publication US 10783192 B1 (Soubbotin)
Regarding Claims 4 and 12, Gardner et al. teaches the system of claims 1 and 9.
Gardner et al. does not explicitly teach: wherein the circuitry outputs, with the conversion result, information indicating a storage location where the image data or the text extracted from the image data is stored.
However, Soubbotin teaches wherein the circuitry outputs, with the conversion result, information indicating a storage location where the image data or the text extracted from the image data is stored.
(FIG. 2 illustrates an exemplary summary 200 on a user's query, produced by a search system, in accordance with an embodiment of the present invention. In the present embodiment, summary 200 is presented as a list of sentences; however, in alternate embodiments, the summary may be displayed in different formats such as, but not limited to, a list of semantic concepts, a cluster hierarchy, a tag cloud, etc. The sentences or text fragments within summary 200 originated from various sources, which are listed below the summary in a source list 205.) (Col. 20, Lines 7-16).
Soubbotin teaches a system which summarizes online documents and provides a URL link to the document within the output. Fig. 2 shows summaries and associated storage locations provided in one output user interface.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the summarization apparatus as taught by Gardner et al. to include source locations as taught by Soubbotin. This would have been an obvious improvement to allow the user to navigate the source for themselves after receiving the summary (Soubbotin, Col. 17, Lines 22-45).
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication US 12008332 B1 (Gardner et al.) in view of Korea Patent Publication KR 20230014477 A (Young).
Regarding Claims 5 and 13, Gardner et al. teaches the system of claims 1 and 9.
Gardner et al. does not explicitly teach: wherein the circuitry applies text styling to a word that appears relatively frequently in text indicated by the conversion result.
However, Young teaches wherein the circuitry applies text styling to a word that appears relatively frequently in text indicated by the conversion result.
(For example, the AI module 120 inserts the contents of the content summary into the original file (eg, in the case of a word file, inserts the content summary on the first page of the document), or highlights keywords, key sentences, most frequent words, etc. A new file (for example, making the shape of a character bold or changing the color or background color of a character) may be newly created and stored in the database 110.) (Page 3, Paragraph 11).
Young highlights the terms in a summary that appear most frequently.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the summarization apparatus as taught by Gardner et al. to modify the style of words that appear frequently as taught by Young. This would have been an obvious improvement as it is supplementary to the summarization process already happening and adds additional content that helps quickly identify important terms (Young, Page 4, Paragraph 8).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS DANIEL LOWEN whose telephone number is (571)272-5828. The examiner can normally be reached Mon-Fri 8:00am - 4:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Paras D Shah can be reached at (571) 270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NICHOLAS D LOWEN/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
07/22/2026
Prosecution Insights