DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 17 January 2025 is being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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.
Claim 15 is 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.
Claim 15 recites “…not apply the analysis task to the input image.” However, claim 13, from which claim 15 depends, recites “An information processing apparatus for applying an analysis task…apply the analysis task to the input image.” Thus, it is unclear how, if the analysis task is applied to the input image, as recited in claim 13, the analysis task can then not be applied to the input image. The analysis task cannot both be applied and not applied to the input image, therefore making the claim indefinite.
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 1-2, 11-14 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi (“Group Softmax Loss with Discriminative Feature Grouping”) in view of Tanaka (US 2018/0260737).
Regarding claim 1, Kobayashi discloses training a model that identifies a category of an object included in an image (See the Abstract and Introduction: DNNs, CNNS, SVMs [models]), comprising instructions to:
acquire an attribute from the image (See sections 2.2 and 2.3, the feature component is an attribute from the image.);
acquire information about a group of categories easily misidentified with each other under a specific attribute condition (Figure 1 and sections 2.2 and 2.3, discriminative vs non-discriminative is information about a group of categories that are easily misidentified with each other under a specific attribute condition [discriminatively score λ].);
generate a group including a plurality of categories when the model is trained based on the attribute and the information about the group (See section 2.3, second paragraph: “…we can group the feature index set into G groups.”); and
train the model based on an identification result generated by identifying the category of the object included in the image using the model, and the group of the categories (See section 2.3, starting in the second to last paragraph “…throughout training, the less-discriminative components are exposed to the loss of the group…As the training proceeds, the number of non-discriminative components are reduced and accordingly the optimal cardinality of group to capture them could be smaller…”).
Kobayashi fails to explicitly teach an information processing apparatus for training the model, the information processing apparatus comprising:
at least one processor; and
at least one memory that is in communication with the at least one processor, wherein the at least one memory stores instructions for causing the at least one processor and the at least one memory to perform the steps.
However, it was well known in the art, before the effective filing date of the claimed invention, that an information processing apparatus comprising a processor and memory is used for training a model.
See, for example, Tanaka that discloses an information processing apparatus for training a model (Figure 1), the information processing apparatus comprising:
at least one processor (Figure 1, 20); and
at least one memory that is in communication with the at least one processor (Figure 1, 22), wherein the at least one memory stores instructions (See paragraph [0207], for example.).
Hence the prior art includes each element claimed although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of the actual combination of the elements in a single prior art reference. In combination Kobayashi performs the same function as it does separately of training a model that identifies a category of an object, and Tanaka performs the same function as it does separately of providing an information processing apparatus comprising a processor and memory that is used for training a model.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the elements as claimed by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable and resulted in an information processing apparatus for training a model that identifies a category of an object included in an image.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 2, Kobayashi and Tanaka disclose the information processing apparatus according to claim 1, wherein in a case where the attribute satisfies the specific attribute condition, the group includes a correct category of the object and one or more categories to be easily misidentified as the correct category under the specific attribute condition (Kobayashi: Figure 1 and section 2.3).
Regarding claim 11, Kobayashi and Tanaka disclose the information processing apparatus according to claim 1, wherein the at least one memory further stores instructions for causing the at least one processor and the at least one memory to reduce a loss by applying a loss function to the identification result of a category belonging to the group (Kobayashi: See generally section 2.2 and the last 2 paragraphs of section 2.3).
Regarding claim 12, Kobayashi and Tanaka disclose the information processing apparatus according to claim 1, wherein the at least one memory further stores instructions for causing the at least one processor and the at least one memory to:
control whether to generate the group based on the attribute (Kobayashi: See section 2.3, the groups are generated based on the feature component [attribute] and that it is controlled whether to generate the group(s).),
in a case where the group is generated, apply a loss function to a category belonging to the group (Kobayashi: See section 2.3, when G=1 a loss function is applied [standard softmax loss].), and
in a case where the group is not generated, apply a loss function to all the categories (Kobayashi: See section 2.3, when “the” group is not generated then multiple group softmax losses (12) and (13) are used.).
Regarding claim 13, Kobayashi discloses applying an analysis task for each category to an input image (Training the model is an analysis task for each category to an input image), comprising instructions to:
acquire an attribute from the input image (See sections 2.2 and 2.3, the feature component is an attribute from the image.);
identify a category of an object included in the input image (Figure 1 and section 2.3, the categories for the feature components shown in Figure 1.);
acquire information about a group of categories easily misidentified with each other under a specific attribute condition (Figure 1 and sections 2.2 and 2.3, discriminative vs non-discriminative is information about a group of categories that are easily misidentified with each other under a specific attribute condition [discriminatively score λ].); and
apply the analysis task to the input image based on the attribute, the identified category, and the information of the group (See section 2.3, starting in the second to last paragraph “…throughout training, the less-discriminative components are exposed to the loss of the group…As the training proceeds, the number of non-discriminative components are reduced and accordingly the optimal cardinality of group to capture them could be smaller…”.).
Kobayashi fails to explicitly teach an information processing apparatus for applying the analysis task, the information processing apparatus comprising:
at least one processor; and
at least one memory that is in communication with the at least one processor, wherein the at least one memory stores instructions for causing the at least one processor and the at least one memory to perform the steps.
However, it was well known in the art, before the effective filing date of the claimed invention, that an information processing apparatus comprising a processor and memory is used for training a model [applying an analysis task].
See, for example, Tanaka that discloses an information processing apparatus for training a model (Figure 1), the information processing apparatus comprising:
at least one processor (Figure 1, 20); and
at least one memory that is in communication with the at least one processor (Figure 1, 22), wherein the at least one memory stores instructions (See paragraph [0207], for example.).
Hence the prior art includes each element claimed although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of the actual combination of the elements in a single prior art reference. In combination Kobayashi performs the same function as it does separately of applying an analysis task for each category to an input image, and Tanaka performs the same function as it does separately of providing an information processing apparatus comprising a processor and memory that is used for applying an analysis task.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the elements as claimed by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable and resulted in an information processing apparatus for applying an analysis task for each category to an input image.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 14, Kobayashi and Tanaka disclose the information processing apparatus according to claim 13, wherein the at least one memory further stores instructions for causing the at least one processor and the at least one memory to,
in a case where the attribute satisfies the specific attribute condition and a first category, which is the identified category, belongs to the group, apply, to the input image, an analysis task for the first category and an analysis task for a category that is other than the first category and that belongs to the group (Kobayashi: See section 2.3, the training is applied for all categories and groups and thus, an “analysis task” is applied for the first category and a category other than the first category.).
Regarding claim 16, this claim is rejected under the same rationale as claim 1.
Regarding claim 17, this claim is rejected under the same rationale as claim 1 [the memory is a “non-transitory computer-readable storage medium”].
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi (“Group Softmax Loss with Discriminative Feature Grouping”) in view of Tanaka (US 2018/0260737) and further in view of Li (WO 2021/135566 A1).
Regarding claim 3, Kobayashi and Tanaka disclose the information processing apparatus according to claim 1.
Kobayashi and Tanaka fail to teach wherein the at least one memory further stores instructions for causing the at least one processor and the at least one memory to aggregate misidentification rates of the categories for attributes for a data set of each of the categories to acquire the information about the group.
Li discloses aggregating misidentification rates of categories for attributes for a data set of each of the categories to acquire information about the group (See page 31 of the provided document, lines 6-14: “In some possible implementations, the probability of an already-identified category can be adjusted based on the misrecognition rate of a deep learning algorithm that recognizes one category of targets as another category of targets. For example, in the recognition result of the deep learning algorithm, the probability of the target belonging to the first category is the first probability, the probability of the target is the second probability, and the first probability is the maximum probability, the second probability can be compared with the misidentification The sum of the rates is regarded as the probability that the target belongs to the second category. The misrecognition rate mentioned here refers to the probability that the deep learning algorithm misidentifies the target of the second category as the first category.”).
Hence the prior art includes each element claimed although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of the actual combination of the elements in a single prior art reference. In combination, the combination of Kobayashi and Tanaka performs the same function as it does separately of a model that identifies a category of an object, and Li performs the same function as it does separately of aggregating misidentification rates.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the elements as claimed by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable and resulted in aggregating misidentification rates of the categories for attributes for a data set of each of the categories to acquire the information about the group.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi (“Group Softmax Loss with Discriminative Feature Grouping”) in view of Tanaka (US 2018/0260737) and further in view of Jain et al. (US 10,949,907 B1).
Regarding claim 7, Kobayashi and Tanaka disclose the information processing apparatus according to claim 1.
Kobayashi and Tanaka fail to teach wherein the attribute is a size of an object region in the image.
Jain et al. disclose wherein an attribute is a size of an object region in an image (Column 18, lines 56-58.).
Hence the prior art includes each element claimed although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of the actual combination of the elements in a single prior art reference. In combination, the combination of Kobayashi and Tanaka performs the same function as it does separately of providing attributes for an image, and Jain et al. performs the same function as it does separately of an attribute being a size of an object region in an image.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the elements as claimed by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable and resulted in the attribute being a size of an object region in the image.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi (“Group Softmax Loss with Discriminative Feature Grouping”) in view of Tanaka (US 2018/0260737) and further in view of Singhal et al. (US 2024/0193851).
Regarding claim 8, Kobayashi and Tanaka disclose the information processing apparatus according to claim 1.
Kobayashi and Tanaka fail to teach wherein the attribute is brightness of the image.
Singhal et al. disclose wherein an attribute is brightness of the image (See paragraph [0044] and claim 8.).
Hence the prior art includes each element claimed although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of the actual combination of the elements in a single prior art reference. In combination, the combination of Kobayashi and Tanaka performs the same function as it does separately of providing attributes for an image, and Singhal et al. performs the same function as it does separately of an attribute being brightness of the image.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the elements as claimed by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable and resulted in the attribute being brightness of the image.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 9, Kobayashi and Tanaka disclose the information processing apparatus according to claim 1.
Kobayashi and Tanaka fail to teach wherein the attribute is a motion blur in the image.
Singhal et al. disclose wherein an attribute is a motion blur in the image (Paragraph [0044].).
Hence the prior art includes each element claimed although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of the actual combination of the elements in a single prior art reference. In combination, the combination of Kobayashi and Tanaka performs the same function as it does separately of providing attributes for an image, and Singhal et al. performs the same function as it does separately of an attribute being a motion blur in the image.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the elements as claimed by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable and resulted in the attribute being a motion blur in the image.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi (“Group Softmax Loss with Discriminative Feature Grouping”) in view of Tanaka (US 2018/0260737) and further in view of Bustelo et al. (US 2020/0167381).
Regarding claim 10, Kobayashi and Tanaka disclose the information processing apparatus according to claim 1.
Kobayashi and Tanaka fail to teach wherein the attribute is a defocus in the image.
Bustelo et al. disclose wherein an attribute is a defocus in the image (Paragraph [0089]).
Hence the prior art includes each element claimed although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of the actual combination of the elements in a single prior art reference. In combination, the combination of Kobayashi and Tanaka performs the same function as it does separately of providing attributes for an image, and Bustelo et al. performs the same function as it does separately of an attribute being a defocus in the image.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined the elements as claimed by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable and resulted in the attribute being a defocus in the image.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Allowable Subject Matter
Claims 4-6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 15 may be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The primary reasons for indicating allowable subject matter in claim 4 is the inclusion of the limitations reciting “convert the image so as to correct the attribute acquired from the image, identify the category of the object included in the converted image, and generate the group based on the corrected attribute” which, in combination with the other recited features, is not taught and/or suggested either singularly or in combination within the prior art.
Claims 5-6 are objected to due to their dependency from claim 4.
The primary reasons for indicating allowable subject matter in claim 15 is the inclusion of the limitations reciting “in a case where the attribute satisfies the specific attribute condition, a first category, which is the identified category, belongs to the group, and a category other than the first category belongs to the group, not apply the analysis task to the input image” which, in combination with the other recited features, is not taught and/or suggested either singularly or in combination within the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chopra et al. (US 2021/0073267) disclose of identifying attributes from multiple attribute groups within target digital images utilizing a deep cognitive attribution neural network. See Figures 3A and 4.
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/STEPHEN G SHERMAN/Primary Examiner, Art Unit 2621
21 August 2026
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