DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant's claim for priority to foreign application JP2023-063069 filed on 04/07/2023.
Response to Arguments
Applicant argument/remarks made in the Amendment filed on 03/03/2025 have been fully considered but were not found to be persuasive. Applicant has amended Claims 1-4, 9-10. Accordingly, claims 1-10 has been considered and this office action is made final.
With respect to Applicant’s remarks on page 7 it is stated that: “the process of deriving properties of each pixel of an image by using a trained model, where the trained model has been trained to discriminate which property each pixel represents in the image, and extracting at least one related keyword related to an image from the image based on the derived properties cannot be performed manually or mentally.”
In response, the Examiner agrees with Applicant and he withdraws 35 USC § 101 claim rejection to 1-10.
With respect to Applicant’s remarks on page 8, it is stated that: “The Office has rejected claims 1-10, under 35 U.S.C. §112(b). Applicant has amended claims 1, 2, 3, 9 and 10, to replace ‘similar to’ with ‘synonymous with’. Applicant has amended claim 4 to recite that the processor preferentially provides notifications based on a degree of relevance to the related keyword, to eliminate the relative phrase "high relevance".”
In light of amendment made, the Examiner withdraws 35 USC § 112 (b) claim rejections to 1-10.
With respect to Applicant’s argument on page 8-9, it is stated that: “Claim 1 recites extracting at least one related keyword related to an image from the image based on the derived properties. In contrast, paragraph [0009] of Nakazawa simply discloses retrieving similar image data ... Claim 1 also recites referring to correspondence information in which a plurality of second documents of a different type from a first document related to the image are associated with at least one keyword included in each of the second documents, and specifying at least one second document associated with a keyword that matches or is similar to the related keyword ... Additionally, Applicant submits that Nakazawa does not disclose deriving properties of each pixel of an image by using a trained model, wherein the trained model has been trained to discriminate which property each pixel represents”
In response to the amendments made to the claims, the Examiner has issued a new 35 USC 103 rejection, utilizing the entirely new combination of references to address the revised claim scope.
Accordingly, the Applicant is advised to review the updated mapping of claim limitations to the pertinent sections of the claim rejections under 35 USC § 103
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
Claims 1-20, are rejected under 35 U.S.C. 103 as being unpatentable over Kennberg (US 10,091,202 B2), in view of Hull (US 7,885,955 B2)
1. (Currently amended) (With respect to claim 1, Kennberg discloses) An information processing apparatus comprising at least one processor, wherein the processor is configured to: (Kennberg [0026] “a processor will receive instructions and data from a read only memory or a random-access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data.”)
extract at least one related keyword related to an image from the image based on the derived properties (Kennberg teaches extracting image features and generating keywords suggested to the user: col.5 lines 39-43: “Any appropriate feature extraction engine can be used to extract features from the image ... col.5 lines 58-60: the server system can generate keywords related to the features extracted from the image”)
refer to correspondence information in which a plurality of second documents of a different type from a first document related to the image are associated with at least one keyword included in each of the second documents, and specify at least one second document associated with a keyword that matches or is synonymous with the related keyword; (Kennberg in col.12 lines 4-14 discloses conducting search queries based on image-derived features/keywords to retrieve web pages and then extracting keywords included in those pages, thereby providing correspondence information that associates each retrieved page with at least one keyword contained in that page. The retrieved web pages constitute a plurality of second documents (Kennberg describes retrieving “one or more” pages and processing multiple results), and they are a different type than the first document, which in Kennberg is the image from which the related keyword(s) were generated. Kennberg further teaches specifying document(s) by matching the image-derived keyword(s) to the page-extracted keyword(s) (and their synonyms), thus meeting the requirement to select at least one second document whose associated keyword matches or is synonymous with the related keyword from the image. Accordingly, Kennberg alone maps to (1) “a plurality of second documents” and (2) “first document” to “correspondence information” via “specify second document(s)” as claimed: col.12 lines 4-14: “conducting one or more search queries based on the identification of the two or more select features, to retrieve one or more web pages associated with the two or more select features extracting, from information provided on the retrieved one or more web pages, two or more keywords associated with each of the two or more select features, wherein at least one of the two or more keywords includes a fact associated with at least one of the two or more select features;
(With respect to claim 1, Kennberg does not explicitly teach) derive properties of each pixel of an image by using a trained model, wherein the trained model has been trained to discriminate which property each pixel represents in the image
However, Hull teaches trained classifiers and decision-making at x-y image positions with pixel-level comparison metrics. Together, these passages show per-location (x,y) classification using trained models and pixel-granularity operations on the input image patch. (Hull col.38 lines 20-25: “an automatic classifier design method 2720 that includes, for example, a neural network, Support vector machine, and/or nearest neighbor classifier that are designed to classify an unknown sample as one of the pristine patches ... col.29 lines 37-42: “A classification module 720 converts a feature description from the feature extraction module 718 into an identification of one or more pages within a document and the x,y positions [i.e., ‘derive properties of each pixel of an image’ as claimed] within those pages where an input image patch occurs. The identification is made dependent on feedback from a database 3400 as described in turn.”)
(Kennberg does not explicitly teach) and provide a notification of the specified document.
However, Hull teaches a method of providing a real-time notification device for a document being read (Hull col.19 lines 21-23: “The real-time notification component 424 is a software application that performs a real-time notification of a document being read.”)
Therefore, it would have been obvious for ordinary skill in the art before the filing data of claimed invention to incorporate Hull’s real-time notification and in-context overlay list UI into Kennberg’s feature-to-keyword-to-document retrieval pipeline to present the specified second documents in place of the user’s current view.
Kennberg already computes relevance/certainty scores for keywords and returns multiple candidate documents; Hull contributes a well-known UI pattern (notification + overlay) that can prioritize and surface those results without forcing a context switch. The combination is a predictable use of prior-art elements using an established notification/overlay mechanism to display items that Kennberg already identifies and ranks—yielding expected benefits: faster attention routing, reduced cognitive load, and quicker access to relevant documents. Since the both disclosures run on general-purpose processors executing software modules Kennberg’s scores naturally drive Hull’s notification priority and list ordering. Under KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007), this is “The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.” to achieve a predictable improvement, and supported by the rationale in KSR and the articulated reasoning standard of MPEP § 2143 [R-01.2024] (I)(A)-(G).
2. (Currently amended) The information processing apparatus according to The information processing apparatus according to wherein the processor is configured to: (Kennberg teaches) extract the related keyword from both the image and the sentence described in the first document; and specify, in a case in which a related keyword common to the image and the sentence described in the first document is extracted, the second document associated with a keyword that matches or is synonymous with the common related keyword. (Kennberg teaches image-derived keywords and document/keyword association (Kennberg Col. 1 lines 37-42: “processing the image data to identify one or more features within the image, generating one or more keywords based on each of the one or more features, transmitting the one or more keywords to a computing device for displaying a list” and performing searches that retrieve web pages and extract keywords from those pages ... Col.4 lines 26-30: The server system 104 can analyze image data of the image file to extract features that may be viewable in the image. The features can be used to provide suggested keywords that can be used as tags and/or a caption for the image.)
3. (Currently amended) The information processing apparatus according to The information processing apparatus according to wherein the processor is configured to: (Kennberg teaches processing image data to identify features and generate one or more keywords from those features: Col.5 lines 58-62: “Continuing with the example above, the server system can generate keywords related to the features extracted from the image. For example, keywords can include “Statue of Liberty,” “Lady Liberty,” and “New Colossus,” generated from the recognized landmark in the photo 204.”
extract the related keyword from both the image and the sentence described in the first document; (Kennberg col.9 lines 25-30: Keywords are generated based on the features (414). Each feature can be the basis for one or more keywords. The number of keywords generated for each feature can vary, for example, depending on how many features were extracted from the image. As discussed herein, each keyword can include an associated score, which can also vary.)
and provide, in a case in which a related keyword common to the image and the sentence described in the first document is extracted, (Kennberg’s pipeline yields sets of keywords from different sources (e.g., image features; text of retrieved pages). Col.3 lines 38-42: “The selected keywords can be included in a textbox, so that the user can add additional text or edit the suggested keywords to tag the image and/or add a caption to the image. In some implementations, suggested facts can be selected by the user to add to the post.”)
(Kennberg does not explicitly teach) a notification of the second document associated with a keyword that matches or is synonymous with the common related keyword with priority over the second document associated with a keyword that matches or is synonymous with a related keyword other than the common related keyword.
However, Hull teaches a method of providing a real-time notification device for a document being read (Hull col.19 lines 21-23: “The real-time notification component 424 is a software application that performs a real-time notification of a document being read.”)
4. (Currently amended) The information processing apparatus according to claim 1, wherein the processor is configured to preferentially provide a notification of the second document associated with a keyword based on a degree of relevance to the related keyword. (Kennberg explicitly teaches computing certainty scores, ordering features based on certainty, selecting top X, and generating keywords with associated scores — standard relevance signals for prioritization. Col.9 lines 19-24: “The features are ordered based on certainty scores (410). In some implementations, only features with a minimum threshold certainty score are included in the ordering. The top X features are selected (412). X can be any appropriate number of features. For example, the user can set how many features are selected”)
5. (Original) The information processing apparatus according to claim 1, wherein the keywords are classified for each of properties of the keywords. (Kennberg col.7 lines 52-58: “each selected feature, one or more keywords can be identified. The number of keywords provided for each feature can be limited to a threshold number of keywords. In some implementations, each identified keyword can include an associated popularity score that can reflect how common and/or recognizable a keyword may be”)
6. (Original) The information processing apparatus according to claim 1, wherein the processor is configured to: extract a keyword from the second document; and derive the correspondence information by associating the extracted keyword with the second document. (Kennberg teaches performing a search, retrieving web pages, that is extracting a keyword from the second document (web page) and associating it with that page. Directly matches extracting keywords from documents and building correspondence (doc <-> keyword): col.6 lines 19-25: “the extracted feature can include “Statue of Liberty.” “Statue of Liberty” can be provided as a search query and can be input to a search engine. One or more search results can be generated and can include information (e.g., content provided in one or more web pages) corresponding to the Statue of Liberty. The information can be processed to extract one or more keywords.”)
7. (Original) The information processing apparatus according to claim 1, wherein the processor is configured to provide the notification of the specified document by displaying a list of the specified second documents. (Kennberg’s search flow returns “one or more search results” corresponding to web pages (second documents), which are then processed and surfaced; the system also displays related items to the user (keywords/UI). It teaches returning/handling multiple documents (search results) suitable for display in a list.)
8. (Original) The information processing apparatus according to claim 7, wherein the processor is configured to (Kennberg does not explicitly teach) display the list such that at least a part of the list overlaps a region in which the first document is displayed.
However, Hull describes overlaying annotations on top of a document thumbnail while the document is displayed: “the annotations may be displayed… as overlays on top of a document thumbnail.” -- Direct overlap/overlay UI on a displayed document region. (Hull Col.19, lines 11-21: “The collaborative document review component 422 is a software application that allows more than one reader of different versions of the same paper document to review comments applied by other readers by pointing his/her capture device 106 at any section of the document. For example, the annotations may be displayed on capture device 106 as overlays on top of a document thumbnail. The collaborative document review component 422 may be implemented with or otherwise cooperate with any type of existing collaboration software.”)
9. (Currently amended) An information processing method executed by a computer, the information processing method comprising: deriving properties of each pixel of an image by using a trained model, wherein the trained model has been trained to discriminate which property each pixel represents in the image; extracting at least one related keyword related to an image from the image based on the derived properties to correspondence information in which a plurality of second documents of a different type from a first document related to the image are associated with at least one keyword included in each of the second documents, and specifying at least one second document associated with a keyword that matches or is similar-to synonymous with the related keyword; and providing a notification of the specified document.
Claims 9 is analogous to claim 1 except that it is directed to a method and is rejected under the same rationale as indicated above.
10. (Currently amended) A non-transitory computer-readable storage medium that stores an information processing program causing a computer to execute: deriving properties of each pixel of an image by using a trained model, wherein the trained model has been trained to discriminate which property each pixel represents in the image; extracting at least one related keyword related to an image from the image based on the derived properties referring to correspondence information in which a plurality of second documents of a different type from [[the]] a first document related to the image are associated with at least one keyword included in each of the second documents, and specifying at least one second document associated with a keyword that matches or is synonymous with the related keyword; and providing a notification of the specified document.
Claims 10 is analogous to claim 1 except that it is directed to a non-transitory computer-readable storage medium and is rejected under the same rationale as indicated above.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chongsuh (John) Park whose telephone number is (408)918-7574. The examiner can normally be reached on Monday - Friday 9:00-6:00 PST.
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/CHONGSUH PARK/Examiner, Art Unit 2156
/AJAY M BHATIA/Supervisory Patent Examiner, Art Unit 2156
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