CTFR 18/617,626 CTFR 82190 DETAILED ACTION 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. Response to Remarks The Office Action has been made issued in response to amendment filed April 02, 2026. Claims 1-16 are pending. Applicant’s arguments have been carefully and respectfully considered in light of the instant amendment, and are not persuasive. Accordingly, this action has been made FINAL. Claim Rejections – 35 USC section § 102/103 On pages 9-10 of the response, Applicant argued that the cited reference failed to disclose “wherein the element information is information indicating at least one of an imaging method, an imaging condition, and an imaging date and time related to imaging of the medical image”. However, Applicant's arguments with respect to the limitation “wherein the element information is information indicating at least one of an imaging method, an imaging condition, and an imaging date and time related to imaging of the medical image” have been considered but are moot in view of the new ground(s) of rejection. Claim Rejections - 35 USC § 103 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 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. 07-20-aia AIA 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 of this title, 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. 07-21 AIA Claim s 1-6, 9-12 and 15-16 are rejected under 35 U.S.C. 103(a) as being unpatentable over Li et al (NPL titled: Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation, [cited in IDS]) in view of Singh (Pub No.: US20210282730A1) . Regarding independent claim 1, Li teaches an information processing apparatus ( knowledge-driven encode, retrieve, paraphrase – see abstract ) comprising at least one processor ( GeForce GTX TITAN GPUs – see page 10, subsection - Training details, [p][001] ), wherein the processor is configured to ( Training details, [p][001] ): generate a graph structure ( encode module aims at encoding visual features as an abnormality graph – see page 5, subsection - Encode: visual feature to knowledge graph, [p][001] ) represented by a node ( an abnormality graph is represented as a set of nodes - see page 5, subsection - Encode: visual feature to knowledge graph, [p][001] ) indicating each of a plurality of pieces (note that each note have a size of N with initialized features - see page 5, subsection - Encode: visual feature to knowledge graph, [p][001] ) of element information used for medical diagnosis ( latent features of each node can be used to predict occurrence of the abnormality via an additional classification layer - see page 5, subsection - Encode: visual feature to knowledge graph, [p][001] ) and an edge connecting the nodes of the related pieces of element information ( a sequence of words can be formulated as a graph whose nodes are the individual words where edges – see page 3, subsection - GTR for multiple domains, [p][001] ); and generate a sentence related to the medical diagnosis based on the graph structure ( [p]araphrase serves for two purposes: 1) refine templates with enriched details and possibly new case-specific findings; 2) convert templates into more natural and dynamic expressions. The first purpose is achieved by modifying information in the templates that is not accurate for specific cases, and the second purpose is achieved by robust language modeling for the same content. These two goals supplement each other in order to generate accurate and robust reports - see page 5, subsection - Paraphrase: template sequence to repor t, [p][001] ), Li does not explicitly teach wherein the element information is information indicating at least one of an imaging method, an imaging condition, and an imaging date and time related to imaging of the medical image. Singh explicitly teaches wherein the element information is information indicating at least one of an imaging method, an imaging condition ( enhanced clinical imaging – see [p][0059] ), and an imaging date and time related to imaging of the medical image Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Li of have an information processing apparatus comprising at least one processor with the teachings of Singh wherein the element information is information indicating at least one of an imaging method, an imaging condition, and an imaging date and time related to imaging of the medical image. Wherein having Li wherein the element information is information indicating at least one of an imaging method, an imaging condition, and an imaging date and time related to imaging of the medical image. The motivation behind the modification would have been to automatically generate a radiology report from the results of the full CT scan and retrieves a sequence of templates according to the detected abnormalities via a Retrieve module since both Li and Singh are method and systems for generating reports bases on images wherein LI retrieves a sequence of templates according to the detected abnormalities via a Retrieve module and the words of the retrieved templates are further expanded and paraphrased into a report by a Paraphrase module which enriches the templates with details and corrects false information if any while Singh automatically generate a radiology report from the results of the full CT scan (Please see Li et al (NPL titled: Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation), Introduction, [p][003] and Singh et al (Pub No.: US20210282730A1), see abstract). Regarding claim 2 , Li in view of Singh teach the information processing apparatus according to claim 1, Li teaches wherein the element information is information indicating at least one of a name, a property, a measured value, a position, or an estimated disease name related to a region of interest included in a medical image ( [f]or example, atelectasis may be concluded if ”interval development of bandlike opacity in the left lung base” is present; consolidation and atelectasis may exist if there is ”streaky and patchy bibasilar opacities”, and ”triangular density projected over the heart” without ”typical findings of pleural effusion or pulmonary edema ” - see page 4, section - Knowledge-driven Encode, Retrieve, Paraphrase (KERP), [p][003] ). Regarding claim 3, Li in view of Singh teach the information processing apparatus according to claim 2, Li teaches wherein the region of interest is at least one of a region of a structure included in the medical image (f or e.g. left lung base - see page 4, section - Knowledge-driven Encode, Retrieve, Paraphrase (KERP), [p][003] ) or a region of an abnormal shadow included in the medical image. Regarding claim 4, Li in view of Singh teach the information processing apparatus according to claim 1, Li teaches wherein the processor is configured to connect the nodes indicating the plurality of pieces of element information regarding the same region of interest included in a medical image with the edge ( GTR evolves a target graph by recurrently performing Source Attention on a source graph and Self Attention on itself. The darkness of color of each graph node indicates the degree of attention the target node pays to – see Fig 2 and Fig 2. Description ). Regarding claim 5, Li in view of Singh teach the information processing apparatus according to claim 1, Li teaches wherein the processor is configured to connect the nodes indicating the pieces of element information regarding a plurality of different regions of interest included in a medical image ( visual features of an image as I 2 RD;W;H where D is the dimension of latent features – see page 4, subsection GTR for image input, [p][001] ) with the edge via a node indicating a physical correlation of the plurality of different regions of interest ( we design separate modules for the purpose of encoding visual features as an abnormality graph, retrieving templates based on the detected abnormalities, and rewriting Paraphrase (KERP templates according to case-specific scenario – see page 10 section -Knowledge-driven Encode, Retrieve, [p][002] ). Regarding claim 6 , Li in view of Singh teach the information processing apparatus according to claim 1, Li teaches wherein the processor is configured to connect the nodes indicating the pieces of element information regarding a plurality of different regions of interest ( visual features of an image as I 2 RD;W;H where D is the dimension of latent features – see page 4, subsection GTR for image input, [p][001] ) included in a medical image with an edge indicating a physical correlation of the plurality of different regions of interest ( we design separate modules for the purpose of encoding visual features as an abnormality graph, retrieving templates based on the detected abnormalities, and rewriting Paraphrase (KERP templates according to case-specific scenario – see page 10 section -Knowledge-driven Encode, Retrieve, [p][002] ). Regarding claim 9, Li in view of Singh teach the information processing apparatus according to claim 1, Li teaches wherein the processor is configured to: divide a plurality of the nodes and a plurality of the edges included in the graph structure into a plurality of groups ( [f]or example, a 2-dimentional image can be formulated as a graph whose nodes are pixels of the image where every node is connected with its neighboring pixel nodes – see page 3, subsection GTR for multiple domains, [p][001] ); generate a sentence for each group; and generate a sentence related to the medical diagnosis by combining a plurality of the sentences generated for each group ( a sequence of words can be formulated as a graph whose nodes are the individual words where edges among nodes are the consecutive relation among words - see page 3, subsection GTR for multiple domains, [p][001] ). Regarding claim 10, Li in view of Singh teach the information processing apparatus according to claim 1, Li teaches wherein the processor is configured to generate the sentence by inputting the generated graph structure to a trained model that has been trained in advance such that an input is the graph structure and an output is the sentence ( see page 6, subsection- Training details ). Regarding claim 11, Li in view of Singh teach the information processing apparatus according to claim 10, Li teaches wherein: the trained model is trained using a set of a permutation graph structure in which the node in the graph structure is permuted with a placeholder predetermined for each category of the element information indicated by the node ( see page 5, subsection - Learning and page 6, subsection -Training details ), and a sentence expressed including the placeholder as training data ( train the Paraphrase with ground truth templates - see page 5, subsection - Learning ), and the processor is configured to: generate the permutation graph structure in which the node in the generated graph structure is permuted with the placeholder ( Sampling the templates of maximum predicted probability breaks the connectivity of differentiable back-propagation of the whole encoderetrieve-paraphrase pipeline - see page 5, subsection - Learning ); generate the sentence expressed including the placeholder by inputting the permutation graph structure to the trained model ( our method is able to distill useful features for correctly classifying abnormalities and diseases. Given the high performance of Ours-2Graphs, we conduct following experiments using the knowledge graph trained with both abnormality and disease labels as initialization – see page 7, subsection - Results and Analyses, [p][001] ); and permute the placeholder included in the sentence with a character string indicated by the element information ( see page 7, subsection Medical report generation error analysis ). Regarding claim 12, Li in view of Singh teach the information processing apparatus according to claim 1, Li teaches wherein the processor is configured to: acquire a medical image ( input image - see Fig 3 ); and generate the element information based on the acquired medical image ( abnormal graph showing various disease conditions – see Fig 3 ). Regarding independent claim 15, Li teaches an information processing method ( knowledge-driven encode, retrieve, paraphrase – see abstract ): generate a graph structure ( encode module aims at encoding visual features as an abnormality graph – see page 5, subsection - Encode: visual feature to knowledge graph, [p][001] ) represented by a node ( an abnormality graph is represented as a set of nodes - see page 5, subsection - Encode: visual feature to knowledge graph, [p][001] ) indicating each of a plurality of pieces ( note that each note have a size of N with initialized features - see page 5, subsection - Encode: visual feature to knowledge graph, [p][001] ) of element information used for medical diagnosis ( latent features of each node can be used to predict occurrence of the abnormality via an additional classification layer - see page 5, subsection - Encode: visual feature to knowledge graph, [p][001] ) and an edge connecting the nodes of the related pieces of element information ( a sequence of words can be formulated as a graph whose nodes are the individual words where edges – see page 3, subsection - GTR for multiple domains, [p][001] ); and generate a sentence related to the medical diagnosis based on the graph structure ( [p]araphrase serves for two purposes: 1) refine templates with enriched details and possibly new case-specific findings; 2) convert templates into more natural and dynamic expressions. The first purpose is achieved by modifying information in the templates that is not accurate for specific cases, and the second purpose is achieved by robust language modeling for the same content. These two goals supplement each other in order to generate accurate and robust reports - see page 5, subsection - Paraphrase: template sequence to repor t, [p][001] ). Li does not explicitly teach wherein the element information is information indicating at least one of an imaging method, an imaging condition, and an imaging date and time related to imaging of the medical image. Singh explicitly teaches wherein the element information is information indicating at least one of an imaging method, an imaging condition ( enhanced clinical imaging – see [p][0059] ), and an imaging date and time related to imaging of the medical image Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Li of have an information processing apparatus comprising at least one processor with the teachings of Singh wherein the element information is information indicating at least one of an imaging method, an imaging condition, and an imaging date and time related to imaging of the medical image. Wherein having Li wherein the element information is information indicating at least one of an imaging method, an imaging condition, and an imaging date and time related to imaging of the medical image. The motivation behind the modification would have been to automatically generate a radiology report from the results of the full CT scan and retrieves a sequence of templates according to the detected abnormalities via a Retrieve module since both Li and Singh are method and systems for generating reports bases on images wherein LI retrieves a sequence of templates according to the detected abnormalities via a Retrieve module and the words of the retrieved templates are further expanded and paraphrased into a report by a Paraphrase module which enriches the templates with details and corrects false information if any while Singh automatically generate a radiology report from the results of the full CT scan (Please see Li et al (NPL titled: Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation), Introduction, [p][003] and Singh et al (Pub No.: US20210282730A1), see abstract). Regarding independent claim 16, Li teaches a ( knowledge-driven encode, retrieve, paraphrase – see abstract ) for causing a computer ( GeForce GTX TITAN GPUs – see page 10, subsection - Training details, [p][001] ) to execute: generate a graph structure ( encode module aims at encoding visual features as an abnormality graph – see page 5, subsection - Encode: visual feature to knowledge graph, [p][001] ) represented by a node ( an abnormality graph is represented as a set of nodes - see page 5, subsection - Encode: visual feature to knowledge graph, [p][001] ) indicating each of a plurality of pieces (note that each note have a size of N with initialized features - see page 5, subsection - Encode: visual feature to knowledge graph, [p][001] ) of element information used for medical diagnosis ( latent features of each node can be used to predict occurrence of the abnormality via an additional classification layer - see page 5, subsection - Encode: visual feature to knowledge graph, [p][001] ) and an edge connecting the nodes of the related pieces of element information ( a sequence of words can be formulated as a graph whose nodes are the individual words where edges – see page 3, subsection - GTR for multiple domains, [p][001] ); and generate a sentence related to the medical diagnosis based on the graph structure ( [p]araphrase serves for two purposes: 1) refine templates with enriched details and possibly new case-specific findings; 2) convert templates into more natural and dynamic expressions. The first purpose is achieved by modifying information in the templates that is not accurate for specific cases, and the second purpose is achieved by robust language modeling for the same content. These two goals supplement each other in order to generate accurate and robust reports - see page 5, subsection - Paraphrase: template sequence to repor t, [p][001] ). Li does not explicitly teach non-transitory computer-readable storage medium storing an information processing program and wherein the element information is information indicating at least one of an imaging method, an imaging condition, and an imaging date and time related to imaging of the medical image. Singh explicitly teaches non-transitory computer-readable storage medium ( 106, memory – see Fig 6 ) storing an information processing program ( non-transitory computer readable media may be used for the instructions – see [p][0094] ) and wherein the element information is information indicating at least one of an imaging method, an imaging condition ( enhanced clinical imaging – see [p][0059] ), and an imaging date and time related to imaging of the medical image Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Li of have an information processing apparatus comprising at least one processor with the teachings of Singh non-transitory computer-readable storage medium storing an information processing program and wherein the element information is information indicating at least one of an imaging method, an imaging condition, and an imaging date and time related to imaging of the medical image. Wherein having Li non-transitory computer-readable storage medium storing an information processing program and wherein the element information is information indicating at least one of an imaging method, an imaging condition, and an imaging date and time related to imaging of the medical image. The motivation behind the modification would have been to automatically generate a radiology report from the results of the full CT scan and retrieves a sequence of templates according to the detected abnormalities via a Retrieve module since both Li and Singh are method and systems for generating reports bases on images wherein LI retrieves a sequence of templates according to the detected abnormalities via a Retrieve module and the words of the retrieved templates are further expanded and paraphrased into a report by a Paraphrase module which enriches the templates with details and corrects false information if any while Singh automatically generate a radiology report from the results of the full CT scan (Please see Li et al (NPL titled: Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation), Introduction, [p][003] and Singh et al (Pub No.: US20210282730A1), see abstract) . 07-21 AIA Claim s 13-14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Li et al (NPL titled: Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation, [cited in IDS]) in view of Singh (Pub No.: US20210282730A1) as applied to claim 1 in view of Syeda-Mahmood et al (US Patent No.: US11244755B1) . Regarding claim 13, Li in view of Singh does not explicitly teach the information processing apparatus according to claim 1, further comprising an input unit, wherein the processor is configured to generate the element information based on information input via the input unit. Syeda-Mahmood explicitly teaches the information processing apparatus according to claim 1, further comprising an input unit ( input/output devices such as keyboard and point devices – see col 38, lines 30-31 ), wherein the processor is configured to generate the element information based on information input via the input unit ( see col 12, lines 30-35 ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Li as modified by Singh of have an information processing apparatus comprising at least one processor with the teachings of Syeda-Mahmood of having further comprising an input unit, wherein the processor is configured to generate the element information based on information input via the input unit. Wherein having Li further comprising an input unit, wherein the processor is configured to generate the element information based on information input via the input unit. The motivation behind the modification would have been to implement an automated medical imaging report generator by retrieves a sequence of templates according to the detected abnormalities via a Retrieve module since both Li and Syeda-Mahmood are method and systems for generating reports bases on images wherein LI retrieves a sequence of templates according to the detected abnormalities via a Retrieve module and the words of the retrieved templates are further expanded and paraphrased into a report by a Paraphrase module which enriches the templates with details and corrects false information if any while Syeda-Mahmood implements an automated medical imaging report generator. (Please see Li et al (NPL titled: Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation), Introduction, [p][003] and Syeda -Mahmood et al (US Patent No.: US11244755B1), col 1, lines 42-44). Regarding claim 14, Li in view of Singh does not explicitly teach the information processing apparatus according to claim 1, wherein the processor is configured to acquire the element information from an external device. Syeda-Mahmood explicitly teaches the information processing apparatus according to claim 1, wherein the processor is configured to acquire the element information from an external device ( client computing device 1110 may be a computing device at a medical imaging equipment location which performs the examination of the patient to capture the medical image data and provides the medical image data to the specially configured server computing device 1104 via one or more data network – see col 36, 21-26 ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Li as modified by Singh of have an information processing apparatus comprising at least one processor with the teachings of Syeda-Mahmood of having wherein the processor is configured to acquire the element information from an external device. Wherein having Li wherein the processor is configured to acquire the element information from an external device .. The motivation behind the modification would have been to implement an automated medical imaging report generator by retrieves a sequence of templates according to the detected abnormalities via a Retrieve module since both Li and Syeda-Mahmood are method and systems for generating reports bases on images wherein LI retrieves a sequence of templates according to the detected abnormalities via a Retrieve module and the words of the retrieved templates are further expanded and paraphrased into a report by a Paraphrase module which enriches the templates with details and corrects false information if any while Syeda-Mahmood implements an automated medical imaging report generator. (Please see Li et al (NPL titled: Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation), Introduction, [p][003] and Syeda -Mahmood et al (US Patent No.: US11244755B1), col 1, lines 42-44) . 07-21 AIA Claim s 7-8 are rejected under 35 U.S.C. 103(a) as being unpatentable over Li et al (NPL titled: Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation, [cited in IDS]) in view of Singh (Pub No.: US20210282730A1) as applied to claim 1 in view of NAKAMURA (Pub No.: 20190057503) . Regarding claim 7 , Li teaches the information processing apparatus according to claim 1, wherein the processor is configured to connect the nodes indicating the pieces of element information regarding regions of interest included in a plurality of medical images ( for e.g. left lung base - see page 4, section - Knowledge-driven Encode, Retrieve, Paraphrase (KERP), [p][003] ) captured at different imaging points in time with the edge via a node indicating a change over time in the regions of interest ( see page 5, subsection Experiments & Results ). Li in view of Singh does not explicitly teach of the same subject. NAKAMURA explicitly teaches of the same subject ( retrieval keywords to be retrieved may be a character string that means a change of a state of an anatomic region that is present in two or more medical images obtained by imaging the same patient at different times – see [p][0109]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Li as modified by Singh of have an information processing apparatus comprising at least one processor with the teachings of NAKAMURA of the same subject.. Wherein having Li of the same subject . The motivation behind the modification would have been to utilizing natural language to implement an automated medical imaging report generator by retrieves a sequence of templates according to the detected abnormalities via a Retrieve module since both Li and NAKAMURA are method and systems for generating reports bases on images wherein LI retrieves a sequence of templates according to the detected abnormalities via a Retrieve module and the words of the retrieved templates are further expanded and paraphrased into a report by a Paraphrase module which enriches the templates with details and corrects false information if any while NAKAMURA utilizes natural language analysis for the interpretation of a report to extract information relating to the size or shape of the anatomic region. (Please see Li et al (NPL titled: Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation), Introduction, [p][003] and NAKAMURA et al (Pub No.: 20190057503), [p][0016]). Regarding claim 8, Li in view of Singh and NAKAMURA teaches the information processing apparatus according to claim 1, Li teaches wherein the processor is configured to connect the nodes indicating the pieces of element information regarding regions of interest included in a plurality of medical images ( for e.g. left lung base - see page 4, section - Knowledge-driven Encode, Retrieve, Paraphrase (KERP), [p][003] ) Li in view of Singh does not explicitly teach of the same subject captured at different imaging points in time with an edge indicating a change over time in the regions of interest. NAKAMURA explicitly teaches of the same subject captured at different imaging points in time with an edge indicating a change over time in the regions of interest ([t]he retrieval keywords to be retrieved may be a character string that means a change of a state of an anatomic region that is present in two or more medical images obtained by imaging the same patient at different times. The character string indicating comparison of the anatomic regions may include a character string indicating a change in size of an organ itself, the presence or absence of a lesion in an organ region, and a change in size of the lesion, or the like, which may be any string that means information obtained by comparing the anatomic regions – see [p][0109]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Li as modified by Singh of have an information processing apparatus comprising at least one processor with the teachings of NAKAMURA of the same subject captured at different imaging points in time with an edge indicating a change over time in the regions of interest.. Wherein having Li of the same subject captured at different imaging points in time with an edge indicating a change over time in the regions of interest . The motivation behind the modification would have been to utilizing natural language to implement an automated medical imaging report generator by retrieves a sequence of templates according to the detected abnormalities via a Retrieve module since both Li and NAKAMURA are method and systems for generating reports bases on images wherein Li retrieves a sequence of templates according to the detected abnormalities via a Retrieve module and the words of the retrieved templates are further expanded and paraphrased into a report by a Paraphrase module which enriches the templates with details and corrects false information if any while NAKAMURA utilizes natural language analysis for the interpretation of a report to extract information relating to the size or shape of the anatomic region. (Please see Li et al (NPL titled: Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation), Introduction, [p][003] and NAKAMURA et al (Pub No.: 20190057503), [p][0016]). Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Inquiries Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRAE S ALLISON whose telephone number is (571)270-1052. The examiner can normally be reached on Monday-Friday 9am-5pm EST. 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, Chineyere Wills-Burns, can be reached on (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ANDRAE S ALLISON/Primary Examiner, Art Unit 2673 June 12, 2026 Application/Control Number: 18/617,626 Page 2 Art Unit: 2673 Application/Control Number: 18/617,626 Page 3 Art Unit: 2673 Application/Control Number: 18/617,626 Page 4 Art Unit: 2673 Application/Control Number: 18/617,626 Page 5 Art Unit: 2673 Application/Control Number: 18/617,626 Page 6 Art Unit: 2673 Application/Control Number: 18/617,626 Page 7 Art Unit: 2673 Application/Control Number: 18/617,626 Page 8 Art Unit: 2673 Application/Control Number: 18/617,626 Page 9 Art Unit: 2673 Application/Control Number: 18/617,626 Page 10 Art Unit: 2673 Application/Control Number: 18/617,626 Page 11 Art Unit: 2673 Application/Control Number: 18/617,626 Page 12 Art Unit: 2673 Application/Control Number: 18/617,626 Page 13 Art Unit: 2673 Application/Control Number: 18/617,626 Page 15 Art Unit: 2673 Application/Control Number: 18/617,626 Page 16 Art Unit: 2673 Application/Control Number: 18/617,626 Page 17 Art Unit: 2673 Application/Control Number: 18/617,626 Page 18 Art Unit: 2673 Application/Control Number: 18/617,626 Page 19 Art Unit: 2673 Application/Control Number: 18/617,626 Page 20 Art Unit: 2673 Application/Control Number: 18/617,626 Page 21 Art Unit: 2673
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