DETAILED ACTION
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 .
Response to Arguments
Applicant's arguments filed April 6, 2026 have been fully considered but they are not persuasive. Regarding the election of species requirement set forth in the Office Action of January 16, 2026, Applicant traverses alleging that the examiner has not established that the species are independent or distinct or that examining the claims directed to all of the species would impose a serious search burden. Specifically, Applicant argues that the claims of the species share a common technical framework, that the features of the species overlap and therefore are not “independent and distinct” and that the examiner has not demonstrated that examining the claims together would impose a serious search burden.
While the claims may be directed to a common technical framework, as argued by Applicant, the examiner disagrees with Applicant’s position that the species are not independent or distinct or that searching all of the claims of all of the species would not impose a serious search burden. Even if species are related, restriction is proper if the species as claimed are mutually exclusive and are not obvious variants of each other. (See MPEP 806(f)). In the previous Office Action, the examiner stated that each species requires at least one mutually exclusive characteristic that is not required for the other species: “[f]or example, species A requires that the MC variable extracted by the IVM device comprises at least one functional parameter selected from a list, none of which are required by species B – S. Such mutually exclusive limitations are found in all of the species. In addition, none of these species are obvious variants of each other based on the current record.” Applicant has not disputed the examiner’s finding of mutual exclusivity nor has Applicant disputed the examiner’s position that the species are not obvious variants of one another.
Regarding Applicant’s contention that the examiner has not established that a serious search burden would be imposed, the examiner disagrees. In the previous Office Action, the examiner provided reasoning as to why there would be a serious search burden due to non-overlapping fields of search: “[f]or example, the field of search for the functional parameters of species B would be completely different from the field of search for the pathology types of species D. Likewise, the field of search for IVM devices that perform the analyses of the pelvic area, the cervix and vaginal wall of Species I would be completely different from the field of search for IVM devices that perform the Species J analyses of organs to be transplanted during an organ transplantation procedure to determine medical support that is needed during the procedure. Such serious search burdens exist for all of the species.” Therefore, the examiner has established that searching the claims of all of the species together would impose a serious search burden.
For all of the reasons, the election of species requirement is maintained.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The BRI of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
In the following, some of the terms in the claims have been given BRIs in light of the specification. These BRIs are used for purposes of searching for prior art and examining the claims, but cannot be incorporated into the claims. Should Applicant believe that different interpretations are appropriate, Applicant should point to the portions of the specification that clearly support a different interpretation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 and 30-37 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publ. Appl. No. 2007/0232874 A1 to Ince et al. (hereinafter referred to as “Ince”) in view of U.S. Publ. Appl. No. 2012/0269420 A1 to Najarian et al. (hereinafter referred to as “Najarian”) and further in view of U.S. Pat. No. 12,089,930 B2 to Hasan et al. (hereinafter referred to as “Hasan”).
Regarding claim 1, Ince discloses an intelligent vital microscopy, IVM, device (The BRI for IVM, based on para. [0059] of the present specification, is that it is any microscope capable of imaging or visualizing microcirculation. Para. [0011] of Ince discloses using Sidestream Dark Field (SDF) imaging technology in a handheld microscope to observe microcirculation) comprising:
a magnifying lens (Fig. 3, lens system 118) operably coupled to a focus mechanism (lens system 116 includes some type of focus system coupled thereto, para. [0107]) and an illumination source (Fig. 3, light source 20) for illuminating an organ surface (examination substrate 16; paras. [0105]-[0107] discuss the coupling of light from the source 20 onto the examination substrate 16 via an optics system comprising optical elements 100, 102, 104, 108, 114, 116, 112, and 118 of Fig. 3);
a receiver operably coupled to the magnifying lens, focus mechanism and illumination source and configured to receive at least one IVM image of a microcirculation, MC, of a subject (Fig. 2, the beam director 98 optically couples light reflected from the examination substrate 16 to the OPS imaging section 30 shown in Fig. 2, which constitutes a receiver. The image capture device 50 receives at least one IVM image of microcirculation coupled onto receiver 30 by beam director 98);
a learning processor coupled to the receiver (The BRI for “learning processor”, based on, for example, para. [0065] of the present specification, is that it is a processor that is trained to implement some type of machine learning artificial intelligence (AI). The processor 52 shown in Fig. 2 of Ince is coupled to the receiver and processes captured images, but Ince does not disclose that the processor 52 implements AI) and configured to:
process the at least one IVM image (The processor 52 processes IVM images captured by image capture device 50, para.[0098]),
identify single cellular oxygen transporting constituents of the MC embedded within flowing red blood cells, RBCs, of the at least one IVM image (Para. [0024] describes processes performed by the processor 52: “[i]nformation may be obtained about whether there is movement of the red blood cells in the microcirculation, whether the red blood cells are transporting oxygen (i.e. Hb saturation), and whether the tissue cells are getting enough oxygen (tissue CO.sub.2 measurement and/or NADH fluorescence imaging)”), and
extract at least one MC variable that describes oxygen transportation by RBCs in the MC (See above-quoted language from para. [0024] of Ince discussing the processor 52 measuring whether red blood cells are transporting oxygen; see also para. [0142] disclosing that the method may include “utilizing the microcirculatory flow information, the oxygen availability information, and the adequacy of oxygenation of tissue cells information, making an early and sensitive determination regarding states of shock, such as septic, hypovolemic, cardiogenic and obstructive septic shock, in patients, and guiding resuscitation therapies aimed at correcting this condition”), and
an output coupled to the learning processor and configured to output information based on the identification at least one MC variable (Para. [0098] discloses that the information obtained by the processor 52 is output to the display: “[t]he processor and display device 52 may be used to process information from the image capture device 50 and display the information in any number of ways”),
wherein the IVM device comprises an artificial intelligence, AI,-based communication circuit configured to communicate with a remote dataset to perform one or more analysis tasks (Ince does not explicitly disclose this limitation).
As indicated above, Ince does not explicitly disclose that the processor is trained to implement some type of machine learning AI. Najarian, in the same field of endeavor, discloses, in paras. [0026]-[0027] for example, using machine learning to train a predictive model on datasets stored in a database 15 to perform microcirculation analyses.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to configure the processor 52 of Ince to implement machine learning to perform the microcirculation measurements and analyses as taught by Najarian to perform the analyses tasks of Ince. One of ordinary skill in the art would have been motivated to make the modification to improve the robustness of the method and system of Ince by taking advantage of the well-known benefits of using trained machine learning models to detect and identify features in images. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (modifying the software executed by the processor 52 to implement a trained machine learning model such as a neural network, for example).
As indicated above, Ince does not explicitly disclose the limitation “wherein the IVM device comprises an artificial intelligence, AI,-based communication circuit configured to communicate with a remote dataset to perform one or more analysis tasks”. The BRI for this limitation, based on para. [00158] of the present specification, is that the processor of the IVM device communicates with a remote trained machine learning model that performs the analyses tasks and communicates the results to the IVM.
Hasan, in the same field of endeavor, discloses that a hand-held device acquires images of a finger and communicates with a remote computer that implements a machine learning predictive hemoglobin model that performs analyses tasks and communicates the results back to the point of care (POC) location where the hand-held device is being used (See Abstract and Col. 3, lines 47-52).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to configure the analyzing section 30 of Ince to communicate with a remote computer that implements a machine learning model that performs the analyses tasks and communicates the results back to the analyzing section 30 as taught by Hasan. One of ordinary skill in the art would have been motivated to make the modification to allow the processing and training to be offloaded from the IVM processor 52 of Ince to reduce processing overhead and to allow the remote model to handle processing tasks for multiple POC facilities. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (modifying the software executed by the processor 52 of the analyzing section to interface with a remote processor implementing a trained machine learning model).
Regarding claim 30, Ince discloses the at least one MC variable comprises a functional parameter of the microcirculation (Para. [0012]: “[t]he foregoing reflectance avoidance imaging systems, whether they utilize OPS, Mainstream Dark Field illumination, or SDF illumination, can be used to enable the comprehensive evaluation of the functional state of the microcirculation. This is achieved by an analysis of the moving cells in the images, which permits the quantitative measurement of red blood cell flow in the capillaries, as well as in the larger vessels of the microcirculation.”).
Regarding claim 31, Ince discloses that the functional parameter comprises functional capillary density, FCD (Para. [0013]: “[n]ext to the measurement of profusion, morphological characteristics of the microcirculation, such as functional capillary density and micro-vessel morphology, can be measured using reflectance avoidance imaging.”).
Regarding claim 32, Ince discloses that the functional parameter comprises tissue red blood cell perfusion (Paras. [0013] and [0023] disclose measuring tissue red blood cell perfusion and other functional parameters).
Regarding claim 33, Ince discloses that the identification comprises classification of a microcirculatory abnormality (Paras. [0013] and [0023] disclose classifying microcirculatory abnormalities, such as “abnormal red blood cell kinetics”).
Regarding claim 34, as indicated above, Ince does not explicitly disclose using a remote model or dataset. Hasan discloses that the remote predictive model executed by the remote processor “can, for example, be a cloud computer system or other types of wired or wireless networks.” (Col. 10, line 65-Col. 11, line 16). Hasan does not explicitly disclose that the cloud-based predictive model uses a cloud-based dataset. Najarian discloses that the machine learning predictive models that are used to assess microcirculatory functional parameters are trained using prior datasets to train the models that are stored in database 15 (Para. [0027]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to configure the analyzing section 30 of Ince to communicate with a remote cloud-based computer that implements machine learning model that performs the analyses tasks and communicates the results back to the analyzing section 30 as taught by Hasan. It would have also been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to train the remote model using a dataset of patients’ conditions and/or diseases as taught by Najarian. One of ordinary skill in the art would have been motivated to make the modifications to allow the processing and training to be offloaded from the IVM processor 52 of Ince to reduce processing overhead and to allow the remote model to handle processing tasks for multiple POC facilities. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (modifying the software executed by the processor 52 of the analyzing section to interface with a remote processor implementing a trained machine learning model).
Regarding claim 35, Ince does not explicitly disclose an AI-based communication circuit configured to update or train the processor 52. Najarian discloses that when the model is used to perform microcirculatory analyses for a new patient, the same information is used to further train the model (Para. [0027]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to configure the analyzing section 30 of Ince to communicate with a remote cloud-based computer that implements machine learning model that performs the analyses tasks and communicates the results back to the analyzing section 30 as taught by Hasan. It would have also been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to train the remote model using a dataset of patients’ conditions and/or diseases and to update the model with new patient data as taught by Najarian. One of ordinary skill in the art would have been motivated to make the modifications to allow the processing and training to be offloaded from the IVM processor 52 of Ince to reduce processing overhead, to allow the remote model to handle processing tasks for multiple POC facilities and to update the model as new patients are diagnosed. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (modifying the software executed by the processor 52 of the analyzing section to interface with a remote processor implementing a trained machine learning model that is updated as new patients are diagnosed).
Regarding claim 36, Ince discloses that at least one of the functional parameters is indicative of oxygen transport by red blood cells of the microcirculation (Para. [0025]: “[i]n some embodiments, reflectance spectrophotometry in conjunction with reflectance avoidance is used to assess the adequacy of oxygen availability. This may provide for the assessment of microcirculatory oxygen transport.”).
Regarding claim 37, as indicated above in the rejection of claim 1, Ince discloses that the microcirculatory assessments are displayed to the user on a display device, which constitutes outputting an indication of a condition of oxygen transport in the microcirculation.
Claim 29 is rejected under 35 U.S.C. 103 as being unpatentable over Ince in view of Najarian and Hasan and further in view of U.S. Publ. Appl. No. 2018/0242844 A1 to Liu et al. (hereinafter referred to as “Liu”).
Regarding claim 29, as indicated above, Ince does not explicitly disclose that the processor 52 comprises a trained artificial neural network. Liu, in the same field of endeavor, discloses using neural networks to process images acquired by an IVM device (an endoscope) to assess parameters of microcirculation (Paras. [0101], [0114] and [0116]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to configure the analyzing section 30 of Ince to use a neural network in the processor 52 of Ince or in a remote computer that implements a machine learning neural network model as taught by the combined teachings of Najarian, Hasan and Liu to perform the microcirculatory analyses tasks. One of ordinary skill in the art would have been motivated to make the modification to obtain the benefits of using machine learning neural networks to perform microcirculatory image segmentation and classification. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (modifying the software executed by the processor 52 or by a remote processor to implement a trained neural network).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Daniel J. Santos, whose telephone number is (571)272-2867. The examiner can normally be reached M-F 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matt Bella can be reached at (571)272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL J. SANTOS/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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