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 .
This is in response to the application filed on March 11, 2026, claims 1-7 are now pending for examination in the application.
Response to Arguments
This office action is in response to amendment filed 02/10/2026. In this action 1, Claim(s) 1-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree et al. (US Pub. No. 20240386015) in view of Hu et al. (US Pub. No. 20190318405). The Crabtree et al. reference has been added to address the amendment of for each of the objects, obtaining, based on the object clustering hierarchy diagram, an object index value comprising a top-level index component identifying the target leaf category and additional index components corresponding to clustering categories to which the object is assigned at respective clustering levels of the object clustering hierarchy diagram.
Applicant’s arguments:
In regards to claim 1 on Page(s) 9, applicant argues “As amended, Claim 1 does not merely recite generic computer components executing an abstract idea; rather, it is directed to a specific technological improvement in the functioning of an object database retrieval system. Specifically, Claim 1 recites: "obtaining multimodal features of each object under a target leaf category, wherein the multimodal features comprise at least a text feature vector, an image feature vector, and an efficiency feature vector; performing multi-level clustering on the objects under the target leaf category based on the multimodal features to obtain an object clustering hierarchy diagram comprising clustering categories organized across a plurality of clustering levels; for each of the objects, obtaining, based on the object clustering hierarchy diagram, an object index value comprising a top-level index component identifying the target leaf category and additional index components corresponding to clustering categories to which the object is assigned at respective clustering levels of the object clustering hierarchy diagram; and storing, in an index repository associated with the object database, the object index values in association with the objects to enable retrieval of a target object based on the object index values.”
Examiner’s Reply:
The examiner respectfully disagrees and would like to point out that human mind using a computer as a tool is fully capable of generating an object index and extracting features. A human would be able to follow these steps along with any needed additional elements (eg obtaining features, storing index). The examiner notes that the computer (being used as a generic tool) as recited in the claims is being used for indexing for improve product retrieval during e-commerce. The use of interface elements does not improve the functioning of a computer. Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claim 1-7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below, on Claim Rejections - 35 USC 101 accordance with the "2019 Revised Patent Subject Matter Eligibility Guidance" (published on 1/7/2019 in Fed, Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the "2019 PEG").
Step 1. in accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted the claim method (claims 1-5), medium(s) (claim 6), and device (claim 7) is/are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mental Processes & Mathematical Concepts enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 6, and 7 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claims 1 recites the following limitations directed towards a Mental Processes & Mathematical Concepts:
performing multi-level clustering on the objects under the target leaf category based on the multimodal features to obtain an object clustering hierarchy diagram comprising clustering categories organized across a plurality of clustering levels (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to perform clustering);
(The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to perform identifying a category)
to clustering categories to which the object is assigned at respective clustering levels of the object clustering hierarchy diagram (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to perform clustering).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1, 6, and 7:
one or more processors (i.e., as a generic processor performing a generic computer function); and
one or more computer-readable memories (i.e., as a generic processor performing a generic computer function);
obtaining multimodal features of each object under a target leaf category, wherein the multimodal features comprise at least a text feature vector, an image feature vector, and an efficiency feature vector (recites insignificant extra solution activity that amounts to mere data gathering);
for each of the objects, obtaining, based on the object clustering hierarchy diagram,
storing, in an index repository associated with the object database, the object index values in association with the objects to enable retrieval of a target object based on the object index values (recites insignificant extra solution activity that amounts to storing data).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 6, and 7 are rejected under 35 U.S.C. 101.
With respect to claim(s) 2:
Step 2A, prong one of the 2019 PEG:
selecting a target clustering category from the object clustering hierarchy diagram that matches the incremental object based on the multimodal features (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to selecting a target);
adding the incremental object to the target clustering category to update the object clustering hierarchy diagram (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to updating a diagram).
Step 2A Prong Two Analysis:
obtaining the multimodal features of an incremental object under the leaf category (recites insignificant extra solution activity that amounts to mere data gathering);
obtaining the object index value for the incremental object based on the updated object clustering hierarchy diagram (recites insignificant extra solution activity that amounts to mere data gathering).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 3:
Step 2A, prong one of the 2019 PEG:
selecting a candidate clustering category from the bottom-level clustering categories based on a similarity distance between the multimodal features of the incremental object and the category feature vectors (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to selecting a category);
determining the candidate clustering category as the target clustering category for the incremental object based on a number of existing objects in the candidate clustering category and/or the number of objects in a sibling clustering category of the candidate clustering category, or selecting the target clustering category from the sibling clustering categories, wherein the sibling clustering category is a clustering category under a parent category of the candidate clustering category (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to determining a category).
Step 2A Prong Two Analysis:
obtaining category feature vectors for each bottom-level clustering category in the object clustering hierarchy diagram (recites insignificant extra solution activity that amounts to mere data gathering).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4:
Step 2A, prong one of the 2019 PEG:
concatenating the obtained feature vectors of the object to form the corresponding multimodal features of the object (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to concatenating a vector).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5:
Step 2A, prong one of the 2019 PEG:
determining a top-level index value for the object based on the target leaf category (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to determine an index value);
determining hierarchical index values corresponding to each hierarchical clustering category
to which the object belongs (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to determine an index value);
concatenating the top-level index component and the additional index components in descending order of the respective clustering levels to generate the object index value (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to concatenating an index value).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
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(s) 1-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree et al. (US Pub. No. 20240386015) in view of Hu et al. (US Pub. No. 20190318405).
With respect to claim 1, Crabtree et al. teaches a method for establishing an object index, comprising:
obtaining multimodal features of each object under a target leaf category, wherein the multimodal features comprise at least a text feature vector, an image feature vector, and an efficiency feature vector (Paragraph 112 discloses map different modalities into a common vector space and extract features from each modality (e.g., text embeddings, audio spectrograms, image features));
performing multi-level clustering on the objects under the target leaf category based on the multimodal features to obtain an object clustering hierarchy diagram comprising clustering categories organized across a plurality of clustering levels (Paragraph 301 discloses the subsystem 3220 can apply unsupervised learning methods such as clustering (e.g., K-means, hierarchical clustering) and topic modeling (e.g., Latent Dirichlet Allocation) to discover semantic categories and hierarchies and Paragraph 336 discloses employing indexing techniques, such as inverted indices and graph indices, to enable fast retrieval of relevant ontological information during the search process);
for each of the objects, obtaining, based on the object clustering hierarchy diagram, an object index value comprising a top-level index component identifying the target leaf category and additional index components corresponding to clustering categories to which the object is assigned at respective clustering levels of the object clustering hierarchy diagram (Paragraph 301 discloses the subsystem 3220 can apply unsupervised learning methods such as clustering (e.g., K-means, hierarchical clustering) and topic modeling (e.g., Latent Dirichlet Allocation) to discover semantic categories and hierarchies and Paragraph 336 discloses employing indexing techniques, such as inverted indices and graph indices, to enable fast retrieval of relevant ontological information during the search process). Crabtree et al. does not explicitly disclose storing, in an index repository associated with the object database, the object index values in association with the objects to enable retrieval of a target object based on the object index values.
However, Hu et al. discloses storing, in an index repository associated with the object database, the object index values in association with the objects to enable retrieval of a target object based on the object index values (Paragraph 70 discloses the product recognition model may be used to build a product index to return information regarding buying options).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Crabtree et al. with Hu et al. This would have facilitated indexing for e-commerce. See Hu et al. Paragraph(s) 2-4.
The Crabtree et al. reference as modified by Hu et al. teaches all the limitations of claim 1. With respect to claim 2, Hu et al. teaches the method of claim 1, wherein, after establishing the object index value for each of the objects based on the object clustering hierarchy diagram, the method further comprises:
obtaining the multimodal features of an incremental object under the leaf category (Paragraph 86 discloses PQ feature, some category matching, and the image caption. PQ features can provide an efficient mechanism to speed up calculations in which millions of image candidates can be ranked based on feature vector distances);
selecting a target clustering category from the object clustering hierarchy diagram that matches the incremental object based on the multimodal features (Paragraph 86 discloses PQ feature, some category matching, and the image caption. PQ features can provide an efficient mechanism to speed up calculations in which millions of image candidates can be ranked based on feature vector distances);
adding the incremental object to the target clustering category to update the object clustering hierarchy diagram (Paragraph 67 discloses a commerce website 508 may offer a plurality of products for sale and include images for the products. The commerce website 508 may be crawled to gather product information. In some example embodiments, the commerce website is searched periodically for one or more of the fashion items being classified (e.g., products that can be identified). Thus, the product-recognition program performs a search specifying a particular product. The information returned by the commerce website 508 is then parsed and the obtained information used to update the training data and, optionally, the product information (e.g., shopping options));
obtaining the object index value for the incremental object base discloses d on the updated object clustering hierarchy diagram (Paragraph 67 discloses a commerce website 508 may offer a plurality of products for sale and include images for the products. The commerce website 508 may be crawled to gather product information. In some example embodiments, the commerce website is searched periodically for one or more of the fashion items being classified (e.g., products that can be identified). Thus, the product-recognition program performs a search specifying a particular product. The information returned by the commerce website 508 is then parsed and the obtained information used to update the training data and, optionally, the product information (e.g., shopping options)).
The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Crabtree et al. reference and the Hu et al. reference is applicable to dependent claim 2.
The Crabtree et al. reference as modified by Hu et al. teaches all the limitations of claim 2. With respect to claim 3, Hu et al. teaches the method of claim 2, wherein selecting a target clustering category from the object clustering hierarchy diagram that matches the incremental object based on the multimodal features comprises:
obtaining category feature vectors for each bottom-level clustering category in the object clustering hierarchy diagram (Paragraph 84 discloses quantize a dense feature vector into a set of discrete visual words, which are essentially a clustering of similar feature vectors into clusters, using a joint k-means algorithm);
selecting a candidate clustering category from the bottom-level clustering categories based on a similarity distance between the multimodal features of the incremental object and the category feature vectors (Paragraph 86 discloses PQ features can provide an efficient mechanism to speed up calculations in which millions of image candidates can be ranked based on feature vector distances. A PQ procedure can allow for image searching to be performed in real-time. With respect to signal processing and data processing, by real-time is meant completing some signal/data processing within a time that is sufficient to keep up with an external process, such as conducting an image search on a query image received from a communication channel in an acceptable user timeframe such as, but not limited to, a range within a second);
determining the candidate clustering category as the target clustering category for the incremental object based on a number of existing objects in the candidate clustering category and/or the number of objects in a sibling clustering category of the candidate clustering category, or selecting the target clustering category from the sibling clustering categories, wherein the sibling clustering category is a clustering category under a parent category of the candidate clustering category (Paragraph 112 discloses candidate for visual search results 806 may be ranked not only based on relevance to a detected object but also relevance to the image content as a whole).
The Crabtree et al. reference as modified by Hu et al. teaches all the limitations of claim 1. With respect to claim 4, Hu et al. teaches the method of claim 1, wherein the multimodal features of the object are obtained by:
concatenating the obtained feature vectors of the object to form the corresponding multimodal features of the object (Paragraph 84 discloses a text-like representation for the image feature vector is generated. To accomplish this search, a technique known in the vision area as visual words is employed. This technique allows a system to quantize a dense feature vector into a set of discrete visual words, which are essentially a clustering of similar feature vectors into clusters, using a joint k-means algorithm).
The Crabtree et al. reference as modified by Hu et al. teaches all the limitations of claim 1. With respect to claim 5, Hu et al. teaches the method of claim 1, wherein the object clustering hierarchy diagram is used to describe the hierarchical clustering categories to which the object belongs during the multi-level clustering process, and establishing the object index value based on the object clustering hierarchy diagram comprises:
concatenating the top-level index component and the additional index components in descending order of the respective clustering levels to generate the object index value (Paragraph 107 discloses Categorical object classifications, provided by neural network image classifiers (e.g., implemented by the object detection model 802) are important features, and are stored in indices for not only content retrieval but also ranking of retrieved content).
With respect to claim 6, it is rejected on grounds corresponding to above rejected claim 1, because claim 6 is substantially equivalent to claim 1.
With respect to claim 7, it is rejected on grounds corresponding to above rejected claim 1, because claim 7 is substantially equivalent to claim 1.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-PUB 20180165554 is directed to SEMISUPERVISED AUTOENCODER FOR SENTIMENT ANALYSIS [0017] Clustering acts to effectively reduce the dimensionality of a data set by treating each cluster as a degree of freedom, with a distance from a centroid or other characteristic exemplar of the set. In a non-hybrid system, the distance is a scalar, while in systems that retain some flexibility at the cost of complexity, the distance itself may be a vector. Thus, a data set with 10,000 data points, potentially has 10,000 degrees of freedom, that is, each data point represents the centroid of its own cluster. However, if it is clustered into 100 groups of 100 data points, the degrees of freedom is reduced to 100, with the remaining differences expressed as a distance from the cluster definition. Cluster analysis groups data objects based on information in or about the data that describes the objects and their relationships. The goal is that the objects within a group be similar (or related) to one another and different from (or unrelated to) the objects in other groups. The greater the similarity (or homogeneity) within a group and the greater the difference between groups, the “better” or more distinct is the clustering.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS E ALLEN whose telephone number is (571)270-3562. The examiner can normally be reached Monday through Thursday 830-630.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.E.A/Examiner, Art Unit 2154
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154
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