CTNF 18/910,050 CTNF 99349 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Notice to Applications This communication is in response to the Application filed on October 09, 2024 . Claims 1-12 are pending. Information Disclosure Statement The information disclosure statement(s) (IDS(s)) submitted on October 09, 2024 and September 08, 2025 are in compliance with the provisions of 27 CFR 1.97. Accordingly, the information disclosure statements are being considered and attached by the examiner. Specification 06-11 AIA The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1-12 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Patel et al. , US 20240144590 A1, (hereinafter “ Patel ”) . Regarding claim 1 , Patel teaches a non-transitory computer-readable recording medium storing a training program causing a computer to execute a process comprising ([0018] “System 100 includes processor(s) 102 and memory(ies) 106. Processor(s) 102 include one or more graphics processors, one or more general processors, and/or one or more digital signal processors. In some examples, memory(ies) 106 are one or more non-transitory computer-readable storage mediums (e.g., random access memory, flash memory) storing computer-readable instructions configured to be executed by processor(s) 102 to perform the techniques described below” ) : generating, for first data that includes a first object feature amount and position information of each of a plurality of target objects in first image data, at least one second data by substituting at least one first object feature amount of the plurality of target objects with a second object feature amount acquired for at least one other object classified into a same class as the target object in at least one second image data that is different from the first image data ([0034] “Each reference set of the plurality of second reference sets 308 may include a respective first object, a respective second object, and a respective relationship object.” ) ([0043] “At block 502, a speech input including a referenced virtual object is received. In some examples, image information associated with a device environment is received, a plurality of objects are identified from the image information, a plurality of relationships between objects in the plurality of objects are identified, and the plurality of second reference sets are generated based on the identified objects and identified plurality of relationships. In some examples, a first respective object and a second respective object are identified from the plurality of objects, and a relationship between the first respective object and the second respective object is identified, wherein the relationship defines a location of the first respective object relative to the second respective object.” wherein feature amount is the plurality of objects that are identified and position information is location of the first respective object relative to the second respective object) ([0005] “According to some embodiments, a speech input including a referenced virtual object is received. Based on the speech input, a first reference set is obtained. The first reference set is then compared to a plurality of second reference sets. Based on the comparison, a second reference set from the plurality of second reference sets is obtained. The second reference set may be identified based on a matching score between the first reference set and the second reference set. An object is then identified based on the second reference set. Based on the identified object, the referenced virtual object is displayed.” wherein substituting for a second object is obtaining the second reference set for the virtual object) ; and training an encoder by inputting the at least one second data to the encoder ([0032] “In particular, a sequence tagging model may be trained, which takes a natural language query as input and assigns respective tokens with a corresponding tags including the referenced virtual objects, relational objects, and landmark objects. A pre-trained encoder, such as a BERT encoder (Bi-directional Encoder Representation from Transformers) or modified BERT encoder may be utilized. A linear classification layer may, for example, be utilized on top of a final layer of the BERT encoder in order to predict token tags…The first reference set 304 may then be obtained by identifying the landmark object, which further includes a first object, a second object, and a positional relationship between the first and second object.” ) . Regarding claim 2 , Patel teaches the non-transitory computer-readable recording medium according to claim 1 , wherein in the process of training the encoder, the training program causes the computer to execute a process of training the encoder by inputting the first data and the second data to the encoder ([0032] “In particular, a sequence tagging model may be trained, which takes a natural language query as input and assigns respective tokens with a corresponding tags including the referenced virtual objects, relational objects, and landmark objects. A pre-trained encoder, such as a BERT encoder (Bi-directional Encoder Representation from Transformers) or modified BERT encoder may be utilized. A linear classification layer may, for example, be utilized on top of a final layer of the BERT encoder in order to predict token tags…The first reference set 304 may then be obtained by identifying the landmark object, which further includes a first object, a second object, and a positional relationship between the first and second object . ” ) . Regarding claim 3 , Patel teaches the non-transitory computer-readable recording medium according to claim 2 , wherein in the process of training the encoder, the training program causes the computer to execute a process of performing machine learning to increase a coincidence degree between a first relationship feature amount for a relationship between the plurality of target objects, which is obtained by inputting the first data to the encoder, and a second relationship feature amount for the relationship between the plurality of target objects, which is obtained by inputting the second data to the encoder ([0032] “In particular, a sequence tagging model may be trained, which takes a natural language query as input and assigns respective tokens with a corresponding tags including the referenced virtual objects, relational objects, and landmark objects. A pre-trained encoder, such as a BERT encoder (Bi-directional Encoder Representation from Transformers) or modified BERT encoder may be utilized. A linear classification layer may, for example, be utilized on top of a final layer of the BERT encoder in order to predict token tags…The first reference set 304 may then be obtained by identifying the landmark object, which further includes a first object, a second object, and a positional relationship between the first and second object. ”) ([0027] “Specifically, the relationship estimation network may be based on a Permutation Invariant Structured Prediction (PISP) model, utilizing visual features from an object detector and relying on class label distributions passed from a detector stage as input to a scene graph generation stage.” ) ([0045] “In some examples, comparing include comparing, for each reference set of the plurality of second reference sets, a first semantic similarity between the first object and the respective first object, a second semantic similarity between the second object and the respective second object, and a third semantic similarity between the first relationship object and the respective first relationship object. In some examples, comparing includes determining a distance between an object of the first reference set and an object of the plurality of second reference sets, and comparing the first reference set to the plurality of second reference sets based on the determined distance.” ) . Regarding claim 4 , Patel teaches the non-transitory computer-readable recording medium according to claim 1 , wherein the training program causes the computer to execute a process of acquiring the first object feature amount and the position information by inputting the first image data to a trained object detector ([0034] “Each reference set of the plurality of second reference sets 308 may include a respective first object, a respective second object, and a respective relationship object.” ) ([0032] “In particular, a sequence tagging model may be trained, which takes a natural language query as input and assigns respective tokens with a corresponding tags including the referenced virtual objects, relational objects, and landmark objects. A pre-trained encoder, such as a BERT encoder (Bi-directional Encoder Representation from Transformers) or modified BERT encoder may be utilized. A linear classification layer may, for example, be utilized on top of a final layer of the BERT encoder in order to predict token tags…The first reference set 304 may then be obtained by identifying the landmark object, which further includes a first object, a second object, and a positional relationship between the first and second object.” ) , and acquiring the second object feature amount by inputting the second image data to the trained object detector ([0034] “Each reference set of the plurality of second reference sets 308 may include a respective first object, a respective second object, and a respective relationship object.” ) ([0032] “ In particular, a sequence tagging model may be trained, which takes a natural language query as input and assigns respective tokens with a corresponding tags including the referenced virtual objects, relational objects, and landmark objects. A pre-trained encoder, such as a BERT encoder (Bi-directional Encoder Representation from Transformers) or modified BERT encoder may be utilized. A linear classification layer may, for example, be utilized on top of a final layer of the BERT encoder in order to predict token tags…The first reference set 304 may then be obtained by identifying the landmark object, which further includes a first object, a second object, and a positional relationship between the first and second object.” ) . Regarding claim 5 , the claim recites similar limitations to claim 1 but in the form of a method. Therefore, claim 5 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above). Regarding claim 6 , the claim recites similar limitations to claim 2 but in the form of a method. Therefore, claim 6 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above). Regarding claim 7 , the claim recites similar limitations to claim 3 but in the form of a method. Therefore, claim 7 recites similar limitations to claim 3 and is rejected for similar rationale and reasoning (see the analysis for claim 3 above). Regarding claim 8 , the claim recites similar limitations to claim 4 but in the form of a method. Therefore, claim 8 recites similar limitations to claim 4 and is rejected for similar rationale and reasoning (see the analysis for claim 4 above). Regarding claim 9 , the claim recites similar limitations to claim 1 but in the form of an apparatus. Therefore, claim 9 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above). Regarding claim 10 , the claim recites similar limitations to claim 2 but in the form of an apparatus. Therefore, claim 10 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above). Regarding claim 11 , the claim recites similar limitations to claim 3 but in the form of an apparatus. Therefore, claim 11 recites similar limitations to claim 3 and is rejected for similar rationale and reasoning (see the analysis for claim 3 above). Regarding claim 12 , the claim recites similar limitations to claim 4 but in the form of an apparatus. Therefore, claim 12 recites similar limitations to claim 4 and is rejected for similar rationale and reasoning (see the analysis for claim 4 above). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA PEARSON whose telephone number is (703)-756-5786. The examiner can normally be reached Monday - Friday 9:00 - 5:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emily Terrell can be reached on (571)- 270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /AMANDA H PEARSON/Examiner, Art Unit 2666 /MING Y HON/Primary Examiner, Art Unit 2666 Application/Control Number: 18/910,050 Page 2 Art Unit: 2666 Application/Control Number: 18/910,050 Page 3 Art Unit: 2666 Application/Control Number: 18/910,050 Page 4 Art Unit: 2666 Application/Control Number: 18/910,050 Page 5 Art Unit: 2666 Application/Control Number: 18/910,050 Page 6 Art Unit: 2666 Application/Control Number: 18/910,050 Page 7 Art Unit: 2666 Application/Control Number: 18/910,050 Page 8 Art Unit: 2666 Application/Control Number: 18/910,050 Page 9 Art Unit: 2666
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