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 .
DETAILED ACTION
1. The following is a non-final, First Office Action on the merits. Claims 1-14 and 22-28 are pending. Applicant’s election of Group I (claims 1-14) in the reply filed on 11/17/2025 without traverse is acknowledged. The claims in the nonelected Group II (15-21) have been canceled. Claims 22-28 are new.
Claim Objections
2. Claims 4, 11 and 25 is objected to because of the following informalities:
Dependent 4, 11 and 25 recite “…….identifying the keywords associated with the particular contextual environment based on or more of…..” There seems to be missing the word “one” in the claims. For the purpose of examination, the Examiner construes that the claims would have been correctly written as “…..identifying the keywords associated with the particular contextual environment based on one or more of….”. Appropriated correction is required.
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.
3. The claimed invention (Claims 1-14 and 22-28) is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) abstract ideas including “Certain Methods of Organizing Human Activity”, and/or “Mathematical Concepts”, which has/have been identified/found by the courts as abstract ideas in MPEP 2106.04(a). This judicial exception is not integrated into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because it/they is/are recited at a high level of generality and/or are recited as performing generic computer functions routinely used in the computer applications:
4. Step 1: Does the Claim Fall within a statutory Category?
Claims 1-8: Yes, these claims recite one or more non-transitory computer readable media, which is interpreted as a system because it recites one or more hardware processors, and therefore are directed to the statutory class of machine.
Claims 8-14: Yes, these claims are method and therefore are directed to the statutory class of process.
Claims 22-28: Yes, these claims are system, which recites one or more hardware processors….., and therefore are directed to the statutory class of machine and article of manufacture.
5. Step 2A prong 1, Step 2A prong 2 and Step 2B:
Independent claim 22 (Step 2A, Prong I): is directed to an abstract idea of “Certain Methods of Organizing Human Activity”, and/or “Mathematical Concepts”:
Steps/Limitations 1-3 of computing a target feature vector representing a target contextual environment for placement of a content item (step/limitation 1); clusters feature vectors representing contextual environments, clusters the target feature vector in a same cluster as a particular feature vector representing a particular contextual environment of a plurality of candidate contextual environments (step/limitation 2); responsive to determining that the target feature vector is clustered in a same cluster as the particular feature vector, selecting the particular contextual environment for the placement of the content item (step/limitation 3) falls within “Certain Methods of Organizing Human Activity” grouping of abstract idea because these steps mainly describe the concepts of commercial or legal interactions (include subject matter relating to agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and/or managing personal behavior or relationships or interactions between people (including following rules or instructions).
Further, step/limitation 1 of computing a target feature vector representing a target contextual environment for placement of a content item (step/limitation 1) also fall under Mathematical Concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) grouping of abstract idea because there must be mathematical operations/ algorithms involved in order to calculate/computer a target feature vector.
Independent claim 22, Step 2A (Prong II): Accordingly, the claim recites an abstract idea(s) as pointed out above. This judicial exception(s) is/are not integrated into a practical application. In particular, the claim recites underlined additional elements (i.e., one or more hardware processor; one or more non-transitory computer-readable media; and program instructions stored on the one or more non-transitory computer-readable media that, when executed by the one or more hardware processors, cause the system to perform operations….; a virtual universe/the virtual environment; a clustering-type machine learning model/the clustering-type machine learning model….) to perform abstract steps/limitations 1-3 mentioned above. The additional element(s) in all of the steps is/are recited at a high-level of generality such that it amounts no more than mere instructions of computers or other machinery merely as a tool to perform/apply the judicial exception(s) of steps/limitations 1-3 mentioned above; thus, they do not integrate the identified abstract idea into a practical application. See MPEP 2106.05(f). Further, in claim 22, the last step/limitation of “causing a display of the content item within the particular contextual environment of the virtual universe” is merely transmitting data/ sending data/displaying data, which is considered as “insignificant extra solution activity”; thus, it does not integrate the identified abstract idea into a practical application. See MPEP 2106.05(g). Accordingly, again, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Again, the claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Again, as discussed above with respect to integration of the abstract idea into a practical application, again, the additional element of using generic computer components (i.e., one or more hardware processor; one or more non-transitory computer-readable media; and program instructions stored on the one or more non-transitory computer-readable media that, when executed by the one or more hardware processors, cause the system to perform operations….; a virtual universe/the virtual environment; a clustering-type machine learning model/the clustering-type machine learning model….) to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. see MPEP 2106.05(f). For the above mentioned reasons, viewed the claim as a whole, the additional elements/additional steps/additional limitations individually and in combination do not integrate the identified abstract idea into a practical application. Furthermore, there is neither improvement to another technology or technical field nor an improvement to the functioning of the computer itself.
Independent claim 22 (step 2B): The underlined additional elements in claim 22 of (i.e., one or more hardware processor; one or more non-transitory computer-readable media; and program instructions stored on the one or more non-transitory computer-readable media that, when executed by the one or more hardware processors, cause the system to perform operations….; a virtual universe/the virtual environment; a clustering-type machine learning model/the clustering-type machine learning model...) is/are recited at a high level of generality and/or are recited as performing generic computer functions routinely used in the computer applications; thus they are not significantly more than the identified abstract idea. In other word, the underlined additional elements “i.e., one or more hardware processor; one or more non-transitory computer-readable media; and program instructions stored on the one or more non-transitory computer-readable media that, when executed by the one or more hardware processors, cause the system to perform operations….; a virtual universe/the virtual environment; a clustering-type machine learning model/the clustering-type machine learning model.….” is/are amounts no more than mere instructions of computers or other machinery merely as a tool to perform/apply the judicial exception(s) of steps/limitations 1-3 mentioned above; thus, they are not significantly more than the identified abstract idea. see MPEP 2106.05(f). Further, in claim 22, the last step/limitation of “causing a display of the content item within the particular contextual environment of the virtual universe” is merely transmitting data/ sending data/displaying data, which is considered as “insignificant extra solution activity”; thus, is not significantly more than the identified abstract idea. See MPEP 2106.05(g). Again, the claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
When revaluating the last step/limitation of claim 22 mentioned above of “causing a display of the content item within the particular contextual environment of the virtual universe” in step 2B here, this transmitting data/ sending data is also well-understood, routine and conventional activities. The use of generic computer to display data/transmit data/send data through an unspecified generic computer does not impose any meaningful limit on the computer implementation of the abstract idea, and is/are considered as well-understood, routine, conventional activity. According to MPEP 2106.05 (d), elements that the Courts have recognized as well-understood, routine, conventional activity in particular fields are e.g., "Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”.
Thus, evidences has been provided to show these additional elements are well-understood, routine, conventional activity according to MPEP 2106.07 (a) (III). Therefore, for the above mentioned reasons, viewed as a whole, even in combination, the above additional steps/additional elements/additional limitations do not amount to significantly more/do not provide an inventive concept. Furthermore, there is neither improvement to another technology or technical field nor an improvement to the functioning of the computer itself.
As per independent claims 1 and 8: Alice Corp. also establishes that the same/similar analysis should be used for all categories of claims. Therefore, one or more non-transitory computer readable media claim 1 and a method claim 8 are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same/similar reasons as the system claim(s) 22. The additional underlined components (i.e., one or more non-transitory computer readable media comprising instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to perform operations ..…..; a virtual universe/the virtual universe; a clustering-type machine learning model/the clustering-type machine learning model) described in independent claims 1 and 8 add nothing of substance to the underlying abstract idea. They are merely using as tools to implement the identified abstract idea and/or are general link to technological environment. Thus, are not significantly more than the identified abstract idea. At best, the claim(s) are merely providing an environment to implement the abstract idea.
Dependent claims 2-7, 9-14 and 23-28 are merely add further details of the abstract steps/elements recited in claims 1, 8 and 22 without including an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Therefore, Looking at the limitations as an ordered combinations adds nothing that is not already present when looking at the elements taken individually. Furthermore, there is neither improvement to another technology or technical field nor an improvement to the functioning of the computer itself. Therefore, dependent claims 2-7, 9-14 and 23-28 are also non-statutory subject matter.
Claim Rejections - 35 USC § 103
6. 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.
7. Claims 1, 7-8, 14, 22 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over MAEDER et al; (US 2024/0062490 A1), in view of Kußmaul et al; (US 2020/0310939 A1):
8. Independent claims 1, 8 and 22: Maeder teaches a method and system comprising:
identifying a target feature vector (e.g., location feature/contextual features/ contextual information that is used in the embedding techniques in fig. 5 paras 0225-0245 especially paras 0236- 0237, fig. 6 paras 0252-0263 especially paras 0262 0263, fig. 7 paras 0274-0278, fig. 2 especially paras 0106, fig. 3 paras 0146-0149 in context with paras 0195-0198) representing a target contextual environment in a virtual universe for placement of a content item (e.g., object in paras 0036, 0103-0106, 0146-0153, 0157-0158, 0166-0173, 0208-0210, 0216-0245, 0272-0278) (step/limitation 1) {At least paras 0020, 0036, fig. 5 paras 0218-0245, fig. 6 paras 0252-0263, fig. 7 paras 0274-0282 in context with fig. 2 paras 0102-0111, fig. 3 paras 0146-0212 and fig. 4 paras 0146-0153};
applying a machine learning model (paras 0101-0108, 0236-0245, 0262-0263) that map/match feature vectors (paras 0106, 0236-0237, 0240-0241, 0262-0263 see embedding techniques) representing contextual environments, wherein the machine learning model map/match the target feature vector (e.g., location feature/contextual features/ contextual information that is used in the embedding techniques in fig. 5 paras 0236-0245 especially para 0237, fig. 6 paras 0252-0263 especially paras 0262 0263, fig. 7 paras 0274-0278, fig. 2 especially paras 0106, fig. 3 paras 0146-0149 in context with paras 0195-0198) with a particular feature vector (e.g., contextual information/contextual feature and placement space attribute/ placement space feature in para 0236-0245 especially paras 0241-0245 in context with para 0150, and also paras 0252-0263 in context with para 0150, Abstract) representing a particular contextual environment of a plurality of candidate contextual environments (e.g., candidate placement spaces in fig. 5 paras 0215-0245, fig. 4 paras 0146-0151, fig. 3 paras 0166-0169 in context with paras 0013, 0102-0106) in the virtual universe (part of step/limitation 2) {At least Abstract, fig. 5 paras 0215-0245 especially paras 0228-0245, fig. 4 paras 0146-0151, fig. 3 paras 0166-0169, 0159-0173, fig. 2 paras 0101-0108 in context with Abstract, paras 0020, 0036. Also see fig. 6 paras 0247-0264 and fig. 7 paras 0266-0280};
responsive to determining that the target feature vector is matched (map/similar) with the particular feature vector (fig. 5 especially paras 0241-0243, fig. 6 especially paras 0260-0263, in context with fig. 3 paras 0146-0151 especially paras 0150-0151 and fig. 2 paras 0101-0111), selecting the particular contextual environment for the placement of the content item in the virtual universe (part of step/limitation 3) {At least Abstract, fig. 5 especially paras 0241-0243, fig. 6 especially paras 0260-0263, in context with fig. 3 paras 0146-0151 especially paras 0150-0151, fig. 2 paras 0101-0111 and paras 0020, 0036}, and
causing a display of the content item within the particular contextual environment of the
virtual universe (step/limitation 4) {At least Abstract, paras 0150-0151, 0232, 0020, 0036} .
However, Maeder does not explicitly teach the underlined features: applying a clustering-type machine learning model that clusters feature vectors representing contextual environments, wherein the clustering-type machine learning model clusters the target feature vector in a same cluster as a particular feature vector representing a particular contextual environment of a plurality of candidate contextual environments in the virtual universe” (part of step 2), and responsive to determining that the target feature vector is clustered in a same cluster as the particular feature vector, selecting the particular contextual environment for the placement of the content item in the virtual universe (part of step 3).
Kußmaul teaches a general concept of applying a clustering-type machine learning model that clusters feature vectors, wherein the clustering-type machine learning model clusters a target feature vector in a same cluster as a particular feature vector of a plurality of feature vectors {At least fig. 5 paras 0062-0069 especially paras 0068-0069}.
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claim invention to modify “applying a machine learning model that map/match feature vectors representing contextual environments, wherein the machine learning model map/match the target feature vector with a particular feature vector representing a particular contextual environment of a plurality of candidate contextual environments in the virtual universe” of Maeder to include “applying a clustering-type machine learning model that clusters feature vectors, wherein the clustering-type machine learning model clusters a target feature vector in a same cluster as a particular feature vector of a plurality of feature vectors”, taught by Kußmaul so that the combination of Maeder and Kußmaul would yield “applying a clustering-type machine learning model that clusters feature vectors representing contextual environments, wherein the clustering-type machine learning model clusters the target feature vector in a same cluster as a particular feature vector representing a particular contextual environment of a plurality of candidate contextual environments in the virtual universe; and responsive to determining that the target feature vector is clustered in a same cluster as the particular feature vector, selecting the particular contextual environment for the placement of the content item in the virtual universe”. One would be motivated to do this in order to enable another option of using machine model (e.g., via clustering-type machine learning model) to determine/identify similarity between two sets of data/ grouping the similar sets of data for targeting the content/ advertisement to the user, which in turn would enhance the efficiency, flexibility and effectiveness of the overall combination system of Maeder and Kußmaul.
9. Claims 7, 14 and 28: The combination Maeder and Kußmaul teaches the claimed invention as in claims 1, 8 and 22 respectively. The combination further teaches wherein the particular contextual environment comprises a sub-environment (e.g., placement space in Abstract paras 0144-0151, 0167-0173) of the virtual universe (e.g., digital environments, mix reality (MR) environments in Abstract, para 0002-0003, 0005) {Maeder: At least Abstract, 0002-0042 in context with figs 3-7}.
10. Claims 2-4, 9-11 and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Maeder et al; (US 2024/0062490 A1), in view of Kußmaul et al; (US 2020/0310939 A1), and further in view of Finn et al; (US 2009/0063168 A1):
11. Claims 2, 9 and 23: The combination of Maeder and Kußmaul teaches the claimed invention as in claims 1, 8 and 22 respectively. Maeder further teaches computing the particular feature vector based on category (e.g., contextual features/ contextual information comprising a category in paras 0254, 0275 in context with paras 0262-0263, and/or physical location and physical environment of the user and proximity point of interest/POI in paras 0195-0198, and/or placement space features in paras 0169, 0222) associated with the particular contextual environment {At least fig. 5 paras 0220-0245, fig. 6 paras 0252-0263 and fig. 7 paras 0272-0280 in context with paras 0195-0198}.
However, Maeder does not explicitly teach the underlined feature: “computing the particular feature vector based on keywords associated with the particular contextual environment”
Kußmaul teaches a general concept of computing a particular feature vector based on keywords {At least fig. 5 paras 0062-0069 especially paras 0068-0069}.
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claim invention to modify “computing the particular feature vector based on category associated with the particular contextual environment” of Maeder to include “computing a particular feature vector based on keywords”, taught by Kußmaul. One would be motivated to do this in order to enrich the features (e.g., include keywords option) that is used to determine the data related to the user, which in turn would help targeting the content/ advertisement to the user more relevantly to increase content/advertisement’s effectiveness.
However, the combination of Maeder and Kußmaul especially Kußmau’s keywords are not explicitly “associated with the particular contextual environment”.
Finn teaches keywords are associated with a particular contextual environment (e.g., sports car in virtual world in para 0036) {At least paras 0035-0036, 0039, claims 1-2 and 5}.
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claim invention to modify “computing the particular feature vector based on category associated with the particular contextual environment; and computing a particular feature vector based on keywords” of the combination of Maeder and Kußmaul to include “keywords are associated a particular contextual environment”, taught by Finn so that the combination of Maeder, Kußmaul and Finn would yield “computing the particular feature vector based on keywords associated with the particular contextual environment” . One would be motivated to do this in order to enable another option (e.g., identifying the keywords associated with the particular contextual environment …) to identify the contextual feature environment for targeting content to the user. Since keywords in the contextual environment allows for more precise targeting of contents, as it can identify specific keywords that are relevant to the user’s environment, making the user experience more relevant and engaging.
12. Claims 3, 10 and 24: The combination of Maeder, Kußmaul and Finns teaches the claimed invention as in claims 2, 9 and 23 respectively. The combination of further teaches identifying the category/keywords associated with the particular contextual environment {Maeder: e.g., contextual features/ contextual information comprising a category in paras 0254, 0275 in context with paras 0262-0263, and/or physical location and physical environment of the user and proximity point of interest/POI in paras 0195-0198, and/or placement space features in paras 0169, 0222} in context with {Finn: At least paras 0035-0036, 0039, claims 1-2 and 5}} by:
identifying physical characteristics corresponding to the particular contextual environment by scraping content data of the particular contextual environment {Maeder: At least fig. 5 paras 0220-0245 especially paras 0222-0227 and fig. 6 paras 0247-0264 especially paras 0253-0254}, in context with {Finn: At least paras 0035-0036, 0039, claims 1-2 and 5}; and
determining the keywords/category based on the physical characteristics {Maeder: At least fig. 5 paras 0220-0245 and fig. 6 paras 0247-0264, and fig. 7 paras 0272-0280}, in context with {Finn: At least paras 0035-0036, 0039, claims 1-2 and 5}.
13. Claims 4, 11 and 25: The combination of Maeder, Kußmaul and Finn teaches the claimed invention as in claims 2, 9 and 23 respectively. The combination further teaches wherein the operations further comprise identifying the category/keywords associated with the particular contextual environment {Maeder: At least fig. 5 paras 0220-0245 especially paras 0222-0227 and fig. 6 paras 0247-0264 especially paras 0253-0254}, in context with {Finn: At least paras 0035-0036, 0039, claims 1-2 and 5} based on one or more of:
metadata associated with the particular contextual environment {Maeder: At least paras 0169, 0225-0245}, and {Finn: At least paras 0035-0036, 0039, claims 1-2 and 5};
metadata associated with objects included the particular contextual environment {Maeder: At least paras 0226-0227 in context with para 0225}, and {Finn: At least paras 0035-0036, 0039, claims 1-2 and 5}; and
code associated with the particular contextual environment.
14. Claims 5-6, 12-13, 26 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Maeder et al; (US 2024/0062490 A1), in view of Kußmaul et al; (US 2020/0310939 A1), in view of Finn et al; (US 2009/0063168 A1), and further in view of Rathod {US 2018/0350144 A1):
15. Claims 5, 12 and 26: The combination of Maeder, Kußmaul and Finn teaches the claimed invention as in claims 4, 11 and 25 respectively. The combination does not explicitly teach the underlined features: “wherein the metadata associated with the particular contextual environment and the metadata associated with the objects comprise sentiment information.”
Rathod teaches metadata associated with a particular contextual environment and metadata associated with objects comprise sentiment information (e.g., mood, emotions, expression in paras 0124, 0298) {At least paras 0124, 0298}.
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claim invention to modify “the metadata associated with the particular contextual environment and the metadata associated with the objects” of the combination of Maeder, Kußmaul and Finn to include “metadata associated with a particular contextual environment and metadata associated with objects comprise sentiment information”, taught by Rathod. One would be motivated to do this in order to determine/collect the user sentiment information in order to better targeting content/ advertisement to the user.
16. Claims 6, 13 and 27: The combination of Maeder, Kußmaul and Finn teaches the claimed invention as in claims 2, 9 and 23 respectively. The combination further teaches
wherein the operations further comprise identifying the category/keywords associated with the particular contextual environment based on metadata associated with user behavior {Maeder: At least fig. 5 paras 0220-0245 especially paras 0222-0227 and fig. 6 paras 0247-0264 especially paras 0253-0254, see category}, in context with {Finn: At least paras 0035-0036, 0039, claims 1-2 and 5, see keywords}.
However, the combination does not explicitly teach the underlined features: “the metadata indicating one or more of user risk-taking behavior information, user goal-completion behavior information, and user spending behavior information.
Rathod teaches metadata indicating one or more of user goal-completion behavior information {At least paras 0343-0346 in context with para 0263}, and user spending behavior information {At least paras 0337-0342 in context with para 0263}.
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claim invention to modify “wherein the operations further comprise identifying the keywords associated with the particular contextual environment based on metadata associated with user behavior” of the combination of Maeder, Kußmaul and Finn to include “metadata indicating one or more of user goal-completion behavior information, and user spending behavior information”, taught by Rathod. One would be motivated to do this in order to capture additional specific behavior information (e.g., user goal-completion behavior information, and/or user spending behavior information) of the user for better targeting content/ advertisement to the user, which in turn would increase the content/ advertisement’s effectiveness.
Prior Art that is pertinent to Applicant’s disclosure
17. The prior art made of record is considered pertinent to applicant's disclosure.
Mayster et al; (US 2022/0198771 A1), wherein teaches Virtual spaces can be added to both 2D and 3D representations of physical spaces allowing for enhancements of those spaces. The virtual spaces can belong to business locations, such as a bakery or coffee shop, and content for those spaces can be controlled by the owner or user of that location. Virtual spaces belonging to public spaces can be provided as digital space for content providers. Valuation of the virtual space can occur based on various requests and use metrics of that virtual space and provide a marketplace for the addition of virtual content. Layers or groups of virtual spaces can be created and enabled or disabled by a user viewing the representation with the virtual space. Other rules associated with the virtual spaces or content for display in a virtual space can be provided or stored in a database to enable dynamic display of the content and use of the virtual spaces.
UKAI et al; (WO 2023/176445 A1), wherein teaches Provided is a technology with which the basis for image similarity can be explained on a conceptual basis. In this image processing device, a plurality of feature vectors (in a feature space) are acquired, said feature vectors being outputted from a trained model in response to the input of a plurality of input images to the trained model, and said trained model resulting from performing machine learning (step S21). A hierarchical plurality of clusters is generated by subjecting the plurality of feature vectors to hierarchical clustering processing (step S22). A partial space or vector corresponding to a specific cluster among the plurality of clusters is then extracted as a concept (concept expression) of the specific cluster.
Sivanandan et al; (US 2013/0124311 A1), wherein teaches Systems and methods for dynamic integration of advertisements in 3D virtual environments may provide contextual placement of advertising assets into those environments. The advertising assets may include 3D models of products or advertisements. The selection of appropriate advertising assets during runtime may be dependent on context-sensitive metadata associated with placeholders tagged in the virtual environment by a virtual environment authoring application, and corresponding metadata associated with available advertising assets. The metadata may include classification attributes, visual attributes, physical attributes, behavioral attributes, or interactivity attributes. The selection of appropriate advertising assets may be further dependent on user information and/or game session information captured at runtime. Additional advertising assets may be dynamically selected in response to interactions with the advertising assets, or dependent on changes in status of a user or game session. The methods may be implemented as program instructions stored on computer-readable storage media, executable by a CPU and/or GPU.
Agrawal et al; (US 2024/0331025 A1), wherein teaches Techniques are described with respect to a system, method, and computer product for bidding on a virtual space. An associated method includes analyzing a first virtual collaborative domain and in response to the analysis, assigning a potentiality score of the virtual space for bidding of the first virtual collaborative domain. The method further including assigning a potentiality score of the virtual space for bidding of the first virtual collaborative domain; conducting a virtual auction for the virtual space; and in response to finalizing the virtual auction, infusing a second virtual collaborative domain with the first virtual collaborative domain.
Herling et al; (US 2021/0132683 A1), wherein teaches Disclosed herein are related to a system and a method for porting a physical object in a physical space into a virtual reality. In one approach, the method includes detecting an input device in a physical space relative to a user of the input device. In one approach, the method includes presenting, by a display device to the user, a virtual model of the detected input device in a virtual space at a location and an orientation. The location and the orientation of the virtual model in the virtual space may correspond to a location and an orientation of the input device in the physical space relative to the user. In one approach, the method includes visually providing relative to the virtual model in the virtual space, through the display device, spatial feedback on the user's interaction with the input device in the physical space.
KAISSER et al; (US 2016/0026919 A1), wherein teaches A computer-implemented method, computer program product, and systems for event detection. The computer system for event detection includes an interface component configured to receive data entries from a social media data storage wherein the data entries have associated time values and location values. The received data entries are stored in a data storage component. A cluster creator of a clustering component can create a cluster with cluster data entries wherein the cluster data entries are received data entries having time values within a range of a time interval and having location values within a range of a location interval. A cluster evaluator can then determine a cluster value for the cluster by computing an event-specific cluster feature vector as input to a machine learning algorithm wherein the machine learning algorithm calculates the cluster value. If the cluster value exceeds an event detection threshold value an event is detected.
Further see other reference in PTO-892 form.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Thuy Nguyen whose telephone number is 571-272-4585 and fax number is 571-273-4585. The examiner can normally be reached on Mon-Thurs, 8:30 am to 5: 00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Ilana Spar can be reached on 571-270-7537. The FAX number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THUY N NGUYEN/
Primary Examiner, Art Unit 3622.