DETERMINATION OF A SOFT TISSUE-RELATED PROPERTY FROM X-RAY IMAGING DATA

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 Office action is in response to communications filed on 3/10/2025. Claims 1-15 are currently pending within this application. Information Disclosure Statement 2. The information disclosure statement(s) filed on 3/10/2025 and 10/27/2025 is/are in compliance with the provisions of 37 CFR 1.97, and has/have been considered and a copy/copies is/are enclosed with this Office action. Claim Rejections – 35 USC § 102 3. 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 – (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. 4. Claims 1-5 and 7-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li (US PGPub 2024/0013395) [hereafter Li]. 5. As to claim 1, Li discloses an apparatus (joint space quantification system shown in Figures 1-2) for determining a soft tissue-related property of a bone constellation (as shown in Figure 3) from X-ray imaging data, the apparatus comprising: a memory (non-transitory CRM 180) that stores a plurality of instructions; and a processor (hardware computer processing unit within server 160/processor 120) coupled to the memory and configured to execute the plurality of instructions to: obtain the X-ray imaging data (medical image 210) of a region of interest (as shown in Figure 3A) including the bone constellation (bones 40); and utilize a 3D pose estimation computer model (CM) (bone modeling unit 220 and bone distance quantification unit 230) into which the X-ray imaging data is input to determine a pose estimation of the bone constellation (bone model 240 as shown in Figure 3B); and determine at least one geometric parameter (bone distances 250) defined between bones of the bone constellation in the pose estimation (as shown in Figure 5), wherein the soft tissue-related property of the bone constellation is determined based on the at least one geometric parameter (Paragraphs 0004, 0006, 0032-0048, 0056, a server and/or computing device includes a processing unit that performs instructions stored within a memory enabling the server and/or processor to function as a joint space quantification system that includes a bone modeling unit that receives an input medical CT image including an imaged part of the body containing a plurality of bones, such as the hand shown in Figure 3A, and generates a 3D representation of each bone in the medical image indicative of the size, shape, and location of each bone in order to input the bone model into a bone distance quantification unit that derives bone distances between bones of the group of bones in the CT image in order to measure bone/joint distances and diagnose ligament tears/soft tissue abnormalities existing between the identified bones that are conditions affecting joint spacing by comparing the bone distances to with distances indicating conditions/ailments negatively affecting joint spacing within a bone distance analytics unit). 6. As to claim 2, Li discloses the apparatus is further configured to trigger an output of at least the soft tissue-related property (Paragraphs 0006, 0034, 0046-0047, 0056, the comparisons and bone distances calculated using the generated bone model are output to a graphical user interface). 7. As to claim 3, Li discloses the apparatus is further configured to explain and/or visualize the soft tissue-related property in or based on the 3D pose estimation computer model (Paragraphs 0006, 0034, 0046-0047, 0056, the comparisons and bone distances calculated using the generated bone model are output to a graphical user interface). 8. As to claim 4, Li discloses the apparatus is further configured to determine, from the at least one soft tissue-related property, a soft tissue-related lesion and/or abnormality (Paragraphs 0046-0048, conditions affecting joint spacings are determined based on the measured distances between the bones). 9. As to claim 5, Li discloses the apparatus is further configured to compare the at least one geometric parameter and/or the pose estimation with a corresponding threshold to determine the soft tissue-related lesion and/or abnormality (Paragraph 0048, the measured distances are compared to thresholds indicating various diagnosed conditions). 10. As to claim 7, Li discloses the apparatus is further configured to combine the at least one geometric parameter and/or the soft tissue-related property with patient-related data to determine the soft-tissue-related lesion and/or abnormality (Paragraphs 0046-0048, the measured distances are compared with patient related data including distances calculated using previous captured medical images of the same patient, and/or other patients diagnosed with specific conditions that affect the joint spacing). 11. As to claim 8, Li discloses the apparatus (100) is further configured to trigger an alert to be output, based on the determined soft-tissue-related lesion and/or abnormality (Paragraphs 0006, 0034, 0046-0047, 0056, the severity of the condition determined based on comparisons and bone distances calculated using the generated bone model are output to a graphical user interface). 12. As to claim 9, Li discloses the 3D pose estimation computer model is parameterized by parameters including one or more of a flexion angle of the bone constellation, a viewing direction of the bone constellation, an X-ray detector position relative to the bone constellation, a translation in an X-ray detector plane, a rotation in an X-ray detector plane, a scaling in an X-ray detector plane, and the geometric parameter, wherein the 3D pose estimation computer model is trained to regress the one or more parameters (Paragraphs 0041, 0043-0045, the bone modeling unit and the bone distance quantification unit are parameterized and trained to regress the distance between any two bones in a bone model of a medical image). 13. As to claim 10, Li discloses the at least one geometric parameter comprises one or more of a distance between the bones, an orthogonal translation, and a rotation (Paragraphs 0041-0046, the geometric parameter calculated is a distance between the imaged and identified bones). 14. As to claim 11, Li discloses the apparatus is further configured to determine an axis between two neighboring bones (as shown in Figures 5A-5K) of the bone constellation and to measure a distance between the two neighboring bones along the axis (Paragraphs 0043-0044, the axis generated by the centroid of each bone within the medical image bone model is identified and used to calculate the distance between the corresponding centroids of the bones). 15. As to claim 12, Li discloses the at least one geometric parameter comprises a distance between the bones, and a tree or graph (as shown in Figure 4) is used to encode a number of distances between bones in the 3D pose estimation computer model, wherein the bones are vertices and the distances are modelled by edges (Paragraphs 0042-0044, a graph/table is used to encode a number of distanced between the bones in the 3D bone model measured where the centroid of each bone within the medical image bone model is identified and used to calculate the distance between the corresponding centroids of the bones). 16. As to claim 13, Li discloses the at least one geometric parameter (centroid distance between bones) is a variable parameter of the 3D pose estimation computer model (Paragraphs 0040-0044, the calculated distance between the detected bones differs depending upon the input medical image and any condition present affecting the distance therebetween). 17. As to claim 14, Li discloses a computer-implemented method for determining a soft tissue-related property of a bone constellation from X-ray imaging data, comprising: obtaining the X-ray imaging data (medical image 210) of a region of interest (as shown in Figure 3A) including the bone constellation (bones 40); and utilizing a 3D pose estimation computer model (CM) (bone modeling unit 220 and bone distance quantification unit 230) into which the X-ray imaging data is input to determine a pose estimation of the bone constellation (bone model 240 as shown in Figure 3B); and determining at least one geometric parameter (bone distances 250) defined between bones of the bone constellation in the pose estimation (as shown in Figure 5), wherein the soft tissue-related property of the bone constellation is determined based on the at least one geometric parameter (Paragraphs 0004, 0006, 0032-0048, 0056, a server and/or computing device includes a processing unit that performs instructions stored within a memory enabling the server and/or processor to function as a joint space quantification system that includes a bone modeling unit that receives an input medical CT image including an imaged part of the body containing a plurality of bones, such as the hand shown in Figure 3A, and generates a 3D representation of each bone in the medical image indicative of the size, shape, and location of each bone in order to input the bone model into a bone distance quantification unit that derives bone distances between bones of the group of bones in the CT image in order to measure bone/joint distances and diagnose ligament tears/soft tissue abnormalities existing between the identified bones that are conditions affecting joint spacing by comparing the bone distances to with distances indicating conditions/ailments negatively affecting joint spacing within a bone distance analytics unit). 18. As to claim 15, Li discloses a non-transitory computer readable medium (storage media 180) for storing executable instructions, which when executed by a processor (computing device 120 and server 160), are configured to carry out the method of claim 14 (Paragraphs 0033-0034). Claim Objections 19. Claim 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion 20. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL S OSINSKI whose telephone number is (571) 270-3949. The examiner can normally be reached on Monday - Friday, 10:00am - 6:00pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oneal Mistry can be reached on (313) 446-4912. 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. MO /MICHAEL S OSINSKI/Primary Examiner, Art Unit 2674 10/9/2026