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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/13/2023 have been considered by the examiner.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-4, 9, 11-12, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Vailaya et al. (US Pub. No. 2005/0278321 A1) in view of Regev et al. (US Pub. No. 2012/0041955 A1) in view of Brito et al. (US Pub. No. 2014/0003717 A1).
Regarding claim 1, Vailaya discloses, a method comprising: initializing a plurality of clusters based on the plurality of images, wherein each cluster of the plurality of clusters includes an exemplar image that is one of the plurality of images; (See Vailaya ¶75, “In one example, a technique referred to as group-average-linkage hierarchical clustering is performed. According to this technique, each document is first placed into an individual cluster, so that the total number of initial clusters equals the total number of documents.” Since each cluster has only one image so it is an exemplar image.)
computing a form similarity score (See Vailaya ¶75. “A comparison is then made on a cluster-to-cluster basis, using a similarity measure (such as a proximity score, for example), to determine which clusters are the most similar, as determined by the highest similarity or proximity score.”)
merging the two clusters into a same cluster of the plurality of clusters; (See Vailaya ¶75, “Once two clusters have been combined into a single cluster (as in forming a cluster having two documents in the first round of the procedure).”)
Vailaya discloses the above limitations, but he fails to disclose receiving a plurality of images of forms; aligning exemplar images from two clusters of the plurality of clusters; computing a form similarity score based on the alignment of the exemplar images; when the form similarity score is above a predetermined threshold, merging the two clusters into a same cluster of the plurality of clusters; and identifying form images from one cluster of the plurality of clusters as being versions of a same template form based on the form images being in the one cluster.
However, Regev discloses, receiving a plurality of images of forms; (See ¶52 “Feature extractor 35 analyzes each document retrieved by crawler 34.” Further see Regev ¶28, “For example, the finance department of an organization uses various documents, all of which share the same topic--finance--but which may differ in their function and format: There may be, for example, procedures, internal forms, external forms, human resources (HR) forms, standard purchase orders, fixed-price purchase orders, executive presentations, financial statements, memos, etc.”)
aligning exemplar images from two clusters of the plurality of clusters alignment of the exemplar images; (See Regev ¶11, “Typically, the embedded object features include a respective shape of each of the embedded objects. Computing the measure of the distance may include aligning each of the embedded objects in a first document with a corresponding embedded object in a second document, and computing an association score between the aligned embedded objects.”)
when the form similarity score is above a predetermined threshold, merging the two clusters into a same cluster of the plurality of clusters; (See Regev ¶64, “The distance functions are measures of the difference (or inversely, the similarity) between the candidate document and the input document and may include content feature distance, format feature distance, and metadata feature distance. Alternatively, other suitable groups of distance measures may be computed at this step. If the distance functions are below certain predetermined thresholds for all candidate documents (i.e., large distance between the input document and the candidate documents), the classifier assigns the input document to a new cluster.” Since distance is inversely proportion to similarity this is the same as the similarity is above the threshold.)
and identifying form images from one cluster of the plurality of clusters as being versions of a same template form based on the form images being in the one cluster. (See Regev ¶49, “Server 22 first arranges documents 52 in version (initial) clusters 62, such that all the documents in any given cluster 62 are considered likely to be versions of the same document.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the merging form documents based on thresholding their similarity as suggested by Regev to Vailaya’s merging of documents clusters based on similarity. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is improved document form cluster reliability. Merging only clusters of document forms that meet a specific similarity criterion ensures that the resulting clusters are cohesive and genuinely related. This leads to more reliable and potentially more meaningful clusters.
Vailaya and Regev disclose the above limitations but they fail to disclose aligning exemplar images based on keypoints of the exemplar images; computing a form similarity score based on the alignment of the exemplar images;
However, Brito discloses, aligning exemplar images
computing a form similarity score based on the alignment of the exemplar images; (See Brito ¶42, “In that regard, the fingerprint histogram of the test image 12 is compared to each of the fingerprint histograms 26 of the template model database 14 and similarity scores are generated.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the aligning images based on keypoints as suggested by Brito to Vailaya and Regev’s aligning of images of documents. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is because keypoints, such as corners, edges, or distinctive texture patterns, are designed to be stable and reliably detectable even when images undergo geometric transformations (rotation, scaling, translation, perspective changes). This enables accurate alignment despite differences in how the images were captured.
Regarding claim 3, Vailaya, Regev, and Brito disclose, the method of claim 1, wherein the similarity score is based on a sub- comparisons of predetermined regions of each of the plurality of images, (See Regev ¶11, “Typically, the embedded object features include a respective shape of each of the embedded objects. Computing the measure of the distance may include aligning each of the embedded objects in a first document with a corresponding embedded object in a second document, and computing an association score between the aligned embedded objects.”)
wherein the predetermined regions are based on regions of a set of template forms that are most indicative of variations between a plurality of form versions. (See Regev ¶114, “FIG. 5 is a flow chart that schematically illustrates a method for extracting and comparing heading features, in accordance with an embodiment of the present invention. The heading features relate both to heading styles, i.e., to the format of the headings, and to heading content, i.e., to the text of the headings. These heading features are a strong indicator of the document format, which unites documents belonging to the same type.”)
Regarding claim 3, Vailaya, Regev, and Brito disclose, the 4. The method of claim 3, wherein the predetermined regions are based on the keypoints in the plurality of images. (See Brito ¶39, “The method 50 of FIG. 5 includes receiving 52 the test image 12 and the template model database 14. Curvilinear objects are then extracted 54 from the test image 12, and line-art junctions are extracted 56 from the curvilinear objects. … Thereafter, a set, such as a predetermined number, of fingerprints are generated 58 from the extracted line-art junctions, the line-art junctions being employed as keypoints.”
Regarding claim 9, Vailaya discloses, a system comprising: A processor: A memory including instructions that when executed cause the processor to: (See Vailaya ¶49, “A "processor" references any hardware and/or software combination which will perform the functions required of it. … Where the processor is programmable, suitable programming can be communicated from a remote location to the processor, or previously saved in a computer program product (such as a portable or fixed computer readable storage medium, whether magnetic, optical or solid-state device based).”)
initializing a plurality of clusters based on the plurality of images, wherein each cluster of the plurality of clusters includes an exemplar image that is one of the plurality of images; (See Vailaya ¶75, “In one example, a technique referred to as group-average-linkage hierarchical clustering is performed. According to this technique, each document is first placed into an individual cluster, so that the total number of initial clusters equals the total number of documents.” Since each cluster has only one image so it is an exemplar image.)
computing a form similarity score (See Vailaya ¶75. “A comparison is then made on a cluster-to-cluster basis, using a similarity measure (such as a proximity score, for example), to determine which clusters are the most similar, as determined by the highest similarity or proximity score.”)
merging the two clusters into a same cluster of the plurality of clusters; (See Vailaya ¶75, “Once two clusters have been combined into a single cluster (as in forming a cluster having two documents in the first round of the procedure).”)
Vailaya discloses the above limitations, but he fails to disclose receiving a plurality of images of forms; aligning exemplar images from two clusters of the plurality of clusters; computing a form similarity score based on the alignment of the exemplar images; when the form similarity score is above a predetermined threshold, merging the two clusters into a same cluster of the plurality of clusters; and identifying form images from one cluster of the plurality of clusters as being versions of a same template form based on the form images being in the one cluster.
However, Regev discloses, receive a plurality of images of forms; (See ¶52 “Feature extractor 35 analyzes each document retrieved by crawler 34.” Further see Regev ¶28, “For example, the finance department of an organization uses various documents, all of which share the same topic--finance--but which may differ in their function and format: There may be, for example, procedures, internal forms, external forms, human resources (HR) forms, standard purchase orders, fixed-price purchase orders, executive presentations, financial statements, memos, etc.”)
align exemplar images from two clusters of the plurality of clusters
when the form similarity score is above a predetermined threshold, merge the two clusters into a same cluster of the plurality of clusters; (See Regev ¶64, “The distance functions are measures of the difference (or inversely, the similarity) between the candidate document and the input document and may include content feature distance, format feature distance, and metadata feature distance. Alternatively, other suitable groups of distance measures may be computed at this step. If the distance functions are below certain predetermined thresholds for all candidate documents (i.e., large distance between the input document and the candidate documents), the classifier assigns the input document to a new cluster.” Since distance is inversely proportion to similarity this is the same as the similarity is above the threshold.)
and identify form images from one cluster of the plurality of clusters as being versions of a same template form based on the form images being in the one cluster. (See Regev ¶49, “Server 22 first arranges documents 52 in version (initial) clusters 62, such that all the documents in any given cluster 62 are considered likely to be versions of the same document.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the merging form documents based on thresholding their similarity as suggested by Regev to Vailaya’s merging of documents clusters based on similarity. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is improved document form cluster reliability. Merging only clusters of document forms that meet a specific similarity criterion ensures that the resulting clusters are cohesive and genuinely related. This leads to more reliable and potentially more meaningful clusters.
Vailaya and Regev disclose the above limitations but they fail to disclose align exemplar images based on keypoints of the exemplar images; compute a form similarity score based on the alignment of the exemplar images;
However, Brito discloses, align exemplar images
compute a form similarity score based on the alignment of the exemplar images; (See Brito ¶42, “In that regard, the fingerprint histogram of the test image 12 is compared to each of the fingerprint histograms 26 of the template model database 14 and similarity scores are generated.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the aligning images based on keypoints as suggested by Brito to Vailaya and Regev’s aligning of images of documents. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is because keypoints, such as corners, edges, or distinctive texture patterns, are designed to be stable and reliably detectable even when images undergo geometric transformations (rotation, scaling, translation, perspective changes). This enables accurate alignment despite differences in how the images were captured.
Regarding claim 11, Vailaya, Regev, and Brito disclose, the system of claim 9, wherein the similarity score is based on a sub- comparisons of predetermined regions of each of the plurality of images, wherein the predetermined regions are based on regions of a set of template forms that are most indicative of variations between a plurality of form versions. (See the rejection of claim 3 as it is equally applicable for claim 11 as well.)
Regarding claim 12, Vailaya, Regev, and Brito disclose, the system of claim 11, wherein the predetermined regions are based on the keypoints in the plurality of images. (See the rejection of claim 4 as it is equally applicable for claim 12 as well.)
Regarding claim 17, Vailaya discloses, one or more non-transitory computer-readable mediums having executable instructions stored thereon that, when executed by one or more processors, perform the operations of: (See Vailaya ¶49, “Where the processor is programmable, suitable programming can be communicated from a remote location to the processor, or previously saved in a computer program product (such as a portable or fixed computer readable storage medium, whether magnetic, optical or solid-state device based). For example, a magnetic or optical disk may carry the programming, and can be read by a suitable disk reader communicating with each processor at its corresponding station.”)
initializing a plurality of clusters based on the plurality of images, wherein each cluster of the plurality of clusters includes an exemplar image that is one of the plurality of images; (See Vailaya ¶75, “In one example, a technique referred to as group-average-linkage hierarchical clustering is performed. According to this technique, each document is first placed into an individual cluster, so that the total number of initial clusters equals the total number of documents.” Since each cluster has only one image so it is an exemplar image.)
computing a form similarity score (See Vailaya ¶75. “A comparison is then made on a cluster-to-cluster basis, using a similarity measure (such as a proximity score, for example), to determine which clusters are the most similar, as determined by the highest similarity or proximity score.”)
merging the two clusters into a same cluster of the plurality of clusters; (See Vailaya ¶75, “Once two clusters have been combined into a single cluster (as in forming a cluster having two documents in the first round of the procedure).”)
Vailaya discloses the above limitations, but he fails to disclose receiving a plurality of images of forms; aligning exemplar images from two clusters of the plurality of clusters; computing a form similarity score based on the alignment of the exemplar images; when the form similarity score is above a predetermined threshold, merging the two clusters into a same cluster of the plurality of clusters; and identifying form images from one cluster of the plurality of clusters as being versions of a same template form based on the form images being in the one cluster.
However, Regev discloses, receiving a plurality of images of forms; (See ¶52 “Feature extractor 35 analyzes each document retrieved by crawler 34.” Further see Regev ¶28, “For example, the finance department of an organization uses various documents, all of which share the same topic--finance--but which may differ in their function and format: There may be, for example, procedures, internal forms, external forms, human resources (HR) forms, standard purchase orders, fixed-price purchase orders, executive presentations, financial statements, memos, etc.”)
aligning exemplar images from two clusters of the plurality of clusters measure of the distance may include aligning each of the embedded objects in a first document with a corresponding embedded object in a second document, and computing an association score between the aligned embedded objects.”)
when the form similarity score is above a predetermined threshold, merging the two clusters into a same cluster of the plurality of clusters; (See Regev ¶64, “The distance functions are measures of the difference (or inversely, the similarity) between the candidate document and the input document and may include content feature distance, format feature distance, and metadata feature distance. Alternatively, other suitable groups of distance measures may be computed at this step. If the distance functions are below certain predetermined thresholds for all candidate documents (i.e., large distance between the input document and the candidate documents), the classifier assigns the input document to a new cluster.” Since distance is inversely proportion to similarity this is the same as the similarity is above the threshold.)
and identifying form images from one cluster of the plurality of clusters as being versions of a same template form based on the form images being in the one cluster. (See Regev ¶49, “Server 22 first arranges documents 52 in version (initial) clusters 62, such that all the documents in any given cluster 62 are considered likely to be versions of the same document.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the merging form documents based on thresholding their similarity as suggested by Regev to Vailaya’s merging of documents clusters based on similarity. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is improved document form cluster reliability. Merging only clusters of document forms that meet a specific similarity criterion ensures that the resulting clusters are cohesive and genuinely related. This leads to more reliable and potentially more meaningful clusters.
Vailaya and Regev disclose the above limitations but they fail to disclose aligning exemplar images based on keypoints of the exemplar images; computing a form similarity score based on the alignment of the exemplar images;
However, Brito discloses, aligning exemplar images
computing a form similarity score based on the alignment of the exemplar images; (See Brito ¶42, “In that regard, the fingerprint histogram of the test image 12 is compared to each of the fingerprint histograms 26 of the template model database 14 and similarity scores are generated.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the aligning images based on keypoints as suggested by Brito to Vailaya and Regev’s aligning of images of documents. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is because keypoints, such as corners, edges, or distinctive texture patterns, are designed to be stable and reliably detectable even when images undergo geometric transformations (rotation, scaling, translation, perspective changes). This enables accurate alignment despite differences in how the images were captured.
Regarding claim 19, Vailaya, Regev, and Brito disclose, the one or more non-transitory computer-readable mediums of claim 17, wherein the similarity score is based on a sub-comparisons of predetermined regions of each of the plurality of images, wherein the predetermined regions are based on regions of a set of template forms that are most indicative of variations between a plurality of form versions. (See the rejection of claim 3 as it is equally applicable for claim 19 as well.)
Regarding claim 20, Vailaya, Regev, and Brito disclose, the one or more non-transitory computer-readable mediums of claim 19, wherein the predetermined regions are based on the keypoints in the plurality of images. (See the rejection of claim 4 as it is equally applicable for claim 20 as well.)
Claims 2, 10, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Vailaya et al. (US Pub. No. 2005/0278321 A1) in view of Regev et al. (US Pub. No. 2012/0041955 A1) in view of Brito et al. (US Pub. No. 2014/0003717 A1) and in further view of Mitura et al. (US Pub. No. 2013/0148898 A1).
Regarding claim 2, Vailaya, Regev, and Brito disclose, the method of claim 1, but they fail to disclose, determining a single template form associated with each of the plurality of clusters via registration of a single image of each of the plurality of clusters of forms against a set template forms; and assign all forms in each cluster to a particular template form based on said determining.
However, Mitura discloses, further comprising: determining a single template form associated with each of the plurality of clusters via registration of a single image of each of the plurality of clusters of forms against a set template forms; (See Mitura ¶40, “comparing normalized facial images in the cluster with clusters present in patterns database 220. In one embodiment, the identification of facial images is based on a distance calculation from a normalized input facial image to reference images in the patterns database 220. In an embodiment, distance calculations comprise of discrete cosine transforms.”)
and assign all forms in each cluster to a particular template form based on said determining. (See Mitura ¶40, “Once the clusters have been named in the cluster cache 210, they may be stored in a cluster database 216. The metadata associated with the facial images in the clusters may be updated when previously unknown facial images in the cluster are identified.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the identification of clusters of images based on matching (i.e. registration) of a normalized image (i.e. exemplary image) to a database of identified clusters as suggested by Mitura to Vailaya, Regev, and Brito’s identification of clusters of image template forms. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is because it provides more efficient labeling by allowing you to leverage a small, labeled dataset of images to guide the learning process on a much larger unlabeled cluster dataset, which is more efficient than manually labeling every single image.
Regarding claim 10, Vailaya, Regev, Brito, and Mitura disclose, the system of claim 9, wherein the instructions further comprise: determining a single template form associated with each of the plurality of clusters via registration of a single image of each of the plurality of clusters of forms against a set template forms; and assign all forms in each cluster to a particular template form based on said determining. (See the rejection of claim 2 as it is equally applicable for claim 10 as well.)
Regarding claim 18, Vailaya, Regev, Brito, and Mitura disclose, the one or more non-transitory computer-readable mediums of claim 17, further comprising: determining a single template form associated with each of the plurality of clusters via registration of a single image of each of the plurality of clusters of forms against a set template forms; and assign all forms in each cluster to a particular template form based on said determining. (See the rejection of claim 2 as it is equally applicable for claim 18 as well.)
Claims 5, 13, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Vailaya et al. (US Pub. No. 2005/0278321 A1) in view of Regev et al. (US Pub. No. 2012/0041955 A1) in view of Brito et al. (US Pub. No. 2014/0003717 A1) and in further view of Kletter et al. (US Pub. No. 2009/0324100 A1).
Regarding claim 5, Vailaya, Regev, and Brito disclose, the method of claim 1, but they fail to disclose, wherein said computing is based on a threshold of matching vector elements, wherein the vector elements describe each of the plurality of images.
However, Kletter discloses, wherein said computing is based on a threshold of matching vector elements, wherein the vector elements describe each of the plurality of images. (See Kletter ¶42, “At query time, FIG. 4 illustrates performing a real-time image query 400 for a particular query image 410, by identifying keypoint locations 420 in the particular query image 410 and computing fingerprint information 430 for each query keypoint from local groups of query keypoints, matching the query fingerprints 440 to the existing Fan Tree fingerprint data 480 to determine the best matching document or set of documents within the collection. … Each time the query fingerprint matches one of the retrieved fingerprint records, a count of the number of matching fingerprints for that document is incremented. … Depending on the application, the Fingerprint score analysis module 490; may select a single document with the highest overall score, or it may alternatively select all documents having an overall score higher than a given value.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include determining a similarity score based on matching keypoints of image fingerprints or vector elements using a threshold number of fingerprints as suggested by Kletter to Vailaya, Regev, and Brito’s similarity score calculation using keypoints. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is because unique local keypoints that create fingerprints provide fast and robust comparison against a large collection of images.
Regarding claim 13, Vailaya, Regev, Brito, and Kletter disclose, the system of claim 9, wherein said compute instruction is based on a threshold of matching vector elements, wherein the vector elements describe each of the plurality of images. (See the rejection of claim 5 as it is equally applicable for claim 13 as well.)
Regarding claim 21, Vailaya, Regev, Brito, and Kletter disclose, the one or more non-transitory computer-readable mediums of claim 17, wherein said computing is based on a threshold of matching vector elements, wherein the vector elements describe each of the plurality of images. (See the rejection of claim 5 as it is equally applicable for claim 21 as well.)
Allowable Subject Matter
Claims 6-8, 14-16, and 22-24 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.
Regarding claim 6, the method of claim 5, wherein said computing further includes: assigning a class label to each vector element in the respective image. (The disclosed prior of record fails to disclose the limitations of this claim.)
Regarding claim 7, the method of claim 5, wherein said computing is performed using a trained machine learning model that receives vectors and outputs a confidence of similarity score. (The disclosed prior of record fails to disclose the limitations of this claim.)
Regarding clam 8, this claim is objected to since it depends from objected claim 7.
Regarding claims 14-16 and 22-24, these claims are objected to, since they are similar to objected claims 6-8.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
Namba, Isao (US Pub. No. 2003/0172058 A1) An input section inputs a document set. A normalization section calculates a similarity as a relative value between documents, with respect to combinations of a plurality of documents, in the document set. The normalization section employs the tf.multidot.idf method. In tf.multidot.idf method, a document vector and a significance of a word included in the document is used to perform normalization to convert each similarity to an absolute value.
Diao, Qian (US Pub. No. 2012/0232788 A1) A method of operation of a navigation system includes: extracting navigation-related web documents having a point of interest; generating formatting sequences of the navigation-related web documents; selecting a user-defined percentile representing reciprocal fraction of an expected number of clusters; calculating a threshold value for a first cluster with the threshold value to be equal to the user-defined percentile of a first normalized distribution of sample comparison values between the first cluster and formatting sequence samples from the formatting sequences, the first cluster is from the clusters; computing an associated comparison value between a first formatting sequence from the formatting sequences and the first cluster; grouping the first formatting sequence with the first cluster when the associated comparison value exceeds the threshold value for the first cluster; and generating a travel route for the point of interest related to the first cluster for displaying on a device.
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/DAVID PERLMAN/Primary Examiner, Art Unit 2673