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 statement (IDS) submitted on 1 September 2023 and 11 August 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Terminal Disclaimer
The terminal disclaimer filed on 30 September 2025 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date Application No. 18/282,381 has been reviewed and is accepted. The terminal disclaimer has been recorded.
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.
Claims 1-3, 7-9, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Yagi (JP 2014026459) in view of Qian (Introducing self-organized maps (SOM) as a visualization tool for materials research and education, December 2019, Results in Materials, Volume 4).
Regarding claim 1, Yagi teaches a physical property map image generation apparatus comprising:
At least one memory that is configured to store instructions (Page 2, paragraph 8, the
CPU 211 that executes various programs and the program stored in the hard disk device 213 are read and executed by the CPU 211); and
At least one processor that is configured to execute the instructions (Page 2, paragraph
8, the CPU 211 that executes various programs and the program stored in the hard disk device 213 are read and executed by the CPU 211) to:
Generate, by using the physical property information, a self-organizing map on which each node is assigned a position in a map space and a physical property vector indicating a value related to a physical property quantity for each of a plurality of types of the physical properties of the product (Page 4, paragraph 8, the self-organizing map creation unit 22 described above creates a self-organizing map representing a similarity relationship by associating a plurality of types of physical property values with one node per substance among a plurality of nodes in the two-dimensional map);
While Yagi fails to disclose the following, Qian teaches:
Acquire, for each of a plurality of patterns of a material that can be used in a target process, physical property information indicating a physical property quantity for each of a plurality of physical properties of a product that can be generated in the target process (Section 2.1, the data is composed of many materials, and each data point represents a material with its mechanical, thermal, electrical and other properties);
Generate a physical property map image that represents each of the nodes arranged in the map space (Section 2.1, a SOM is trained based on the training data, in which red dots 1 to 12 are the training data points in the multi-dimensional space, axes from a to f represent the variables of training data. In our case, the red dots 1–12 represent 12 materials, and the variables a to f are the properties of materials), wherein the generation of the map image includes:
For each of a plurality of pieces of the material specification information, determining a node assigned to a physical property vector that is most similar to a physical property vector obtained from the physical property information corresponding to the material specification information, and assigning the determined node a specification vector indicating values related to the material specification represented by the material specification information (Section 2.1, Find the closest node to the chosen point in terms of the Euclidean distance in the multi-dimensional space. The node found in this step is called ‘Best Matching Unit’ (BMU) to the chosen training data point… Update the weights of all the BMUs as well as the weights of BMU’s neighbors in a way that makes them closer to their corresponding training data point); and
Performing cluster, coloring, or both for each of the nodes in the map image based on an assignment of the specification vectors to the nodes (Section 3.3, Fig.8. Cluster map produced from SOM algorithm containing 398 materials. The materials are individually represented by points that are color-coordinated to indicate the magnetic ability (a), the tolerance to strong acid (b), and material class (c)).
Qian and Yagi are both considered to be analogous to the claimed invention because they are in the same field of visualizing properties with self-organizing maps. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yagi by using Qian and acquiring material and physical property information to generate a heatmap by assigning each node a specification vector and performing clustering and coloring based on the specification vectors to the nodes. Doing so would allow for more accurate detection of materials within images.
Method claim 7 and CRM claim 13 correspond go apparatus claim 1. Therefore, claims 7 and 13 are rejected for the same reasons as used above.
Regarding claim 2, the combination of Yagi and Qian teaches the map image generation apparatus according to claim 1, wherein the generation of the map image includes:
Computing specification vectors to be assigned to nodes other than the determined nodes by performing an interpolation process based on an arrangement of the determined nodes in the map space and the specification vectors assigned to the determined nodes (Section 2.1, Steps 4-7, 4) Find the neighbors of the BMU. The neighbors are the nodes within a given neighborhood radius. Note that the neighborhood radius refers to the distance on the 2D topology of the grid. As shown in Fig. 2(b), we assume the side length of each little block in the grid is of 1 unit, thus, the neighborhood radius here is 2. The radius of neighborhood is a value that starts large, then diminishes in each iteration. 5) Repeat step 2 to 4 to identify the BMU and neighbors of BMU for all the points in the training data. 6) Update the weights of all the BMUs as well as the weights of BMU’s neighbors in a way that makes them closer to their corresponding training data point. One iteration is completed after this step. 7) Iterate until all the nodes in the map get close enough to the training data.).
Qian and Yagi are both considered to be analogous to the claimed invention because they are in the same field of visualizing properties with self-organizing maps. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yagi by using Qian and compute specification vectors to be assigned to nodes other than the determined nodes. Doing so would allow for a complete map that is able to detect materials other than solely the determined node.
Method claim 8 and CRM claim 14 correspond to apparatus claim 2. Therefore, claims 8 and 14 are rejected for the same reasons as used above.
Regarding claim 3, the combination of Yagi and Qian teaches the map image generation apparatus according to claim 1, wherein the generation of the map includes:
Performing the clustering of the nodes based on the assignment of the specification vectors to the respective nodes (Qian, The cluster map (Fig. 7(b)) indicates the different clusters of similar materials after running the SOM algorithm on our dataset); and
Including an indication by which clusters can be distinguished from each other into the map image (Yagi, page 7, paragraph 3, the shade of color on the map represents the distance between nodes as the height of the wall that is the boundary of the cluster; Yagi, page 7, paragraph 4, Looking at the overall self-organizing map shown in FIG. 6, although the boundary between each… there are about three Can be classified into clusters. In FIG. 6, the first classification range is surrounded by a dotted line, the second classification range is surrounded by a one-dot chain line, and the third classification range is surrounded by a twenty-one-dot chain line.).
Qian and Yagi are both considered to be analogous to the claimed invention because they are in the same field of visualizing properties with self-organizing maps. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yagi by using Qian and performing clustering of nodes based on the assignment of the specification vectors to the respective nodes. Doing so would allow for visualization of the different materials and their physical properties contained in the map.
Method claim 9 and CRM claim 15 correspond to apparatsu claim 3. Therefore, claims 9 and 15 are rejected for the same reasons as used above.
Claims 4, 10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yagi in view of Qian as applied to claims 1-3, 7-9, and 13-15 above and further in view of Bhatia (US 20210026722).
Regarding claim 4, the combination of Yagi and Qian teaches the map image generation apparatus according to claim 1. While the combination fails to disclose the following, Bhatia teaches:
Wherein in the map image, a color of each of the nodes is determined based on a magnitude of a component of at least one base color constituting the color (Paragraph 68, With reference now to FIG. 5, a graph 501 generated using a self organizing maps (SOM) algorithm shows sets of log data… those that are colored darkest have an anomaly score of 0.0), and
Wherein the generation of the map image includes determining the magnitude of the component of each of the base colors of the color assigned to the node by using a value that the specification vector assigned to the node indicates for a parameter corresponding to the base color (Paragraph 68, as the potential for data in a set of log data being anomalous increases, the shading for sets of log data lightens, until a white shading indicates a high likelihood that the data in that set of log data is in fact anomalous).
Bhatia and the combination of Yagi and Qian are both considered to be analogous to the claimed invention because they are in the same field of visualizing with self-organizing maps. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Yagi and Qian by using Bhatia and adjusting the magnitude (darkness/lightness) of the color of clustered nodes based on predetermined information. Doing so would allow for an easier to understand visualization of the values represented by the clustered nodes in the map image.
Method claim 10 and CRM claim 16 correspond to apparatus claim 4. Therefore, claims 10 and 16 are rejected for the same reasons as used above.
Claims 5-6, 11-12, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Yagi in view of Qian as applied to claims 1-3, 7-9, and 13-15 above and further in view of Ampanavos (US 20220067461).
Regarding claim 5, the combination of Yagi and Qian teaches the map image generation apparatus according to claim 1. While the combination fails to disclose the following, Ampanavos teaches:
Wherein the generation of the map image includes: acquiring target information representing a desired physical property of the product (Paragraph 79, the font-map-consumption system 103 selects italic fonts to provide for display within a visual depiction (e.g., based on user input requesting italic fonts or a font similar to another italic font)); and
Including, into the map image, a target indication by which a node assigned to a physical property vector that is most similar to the physical property vector obtained from the target information can be identified (Paragraph 65, In some embodiments, the font-map-creation system 102 compares feature vectors to identify best-matching nodes, and the font-map-consumption system 103 accesses information indicating the best-matching nodes to include with a visual depiction.).
Ampanavos and the combination of Yagi and Qian are both considered to be analogous to the claimed invention because they are in the same field of visualizing with self-organizing maps. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Yagi and Qian by using Ampanavos and indicating which node in the map image best matches the desired properties of a user input. Doing so would allow a user to interact with the map system and easily view the best node based on their input information that they desire.
Method claim 11 and CRM claim 17 correspond to apparatus claim 5. Therefore, claims 11 and 17 are rejected for the same reasons as used above.
Regarding claim 6, the combination of Yagi, Qian, and Ampanavos teaches the map image generation apparatus according to claim 5, wherein the generation of the map image includes includes, into the target indication, the material specification represented by the specification vector assigned to the node corresponding to the target indication (Ampanavos, Paragraph 65, Particularly, the font-map-consumption system 103 identifies a best-matching feature vector as a feature vector having a smallest difference (or distance in map space) from node weights corresponding to the single node. In addition, the font-map-consumption system 103 generates or identifies a visual depiction of a font corresponding to the best-matching feature vector to represent the single node within a visual depiction of the font map).
Ampanavos and the combination of Yagi and Qian are both considered to be analogous to the claimed invention because they are in the same field of visualizing with self-organizing maps. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Yagi and Qian by using Ampanavos and including into the target indication the material specification represented by the specification vector. Doing so would allow for effective visualization of the material specification information corresponding to the target indication.
Method claim 12 and CRM claim 18 correspond to apparatus claim 6. Therefore, claims 12 and 18 are rejected for the same reasons as used above.
Response to Arguments
Applicant’s arguments with respect to claims 1, 7, and 13 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/SNIGDHA SINHA/Examiner, Art Unit 2619
/JASON CHAN/Supervisory Patent Examiner, Art Unit 2619