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
Regarding 35 U.S.C. 101
Applicant's arguments filed 07/02/2026 have been fully considered but they are not persuasive.
For example, applicant argues “the limitations of the claims do no t fall into any of these enumerated groupings of abstract ideas, thus, the claims do not recite an abstract idea” (See argument 2 of REMARKS pg. 9) and “at pages 13-14, the rejection summarizes the independent claims and asserts that these claims encompass a mental process and do not ‘preclude the limitation from being performed by a human, mentally or with pen and paper’. This is incorrect, and there is nothing in the record other than these incorrect conclusory assertions that shows the combination of features in the independent claims can be performed by a human, mentally or with pen and paper” and “the inability to practicably perform these features in the human mind is described in the Background of the instant application, and exemplified by the absence of these types of features being performed in the human mind in any of the documents cited in the art rejections” (see argument 3 of REMARKS pg. 9). Examiner notes that, as previously noted in the previous Office Actions, Examiner respectfully disagrees in that the claim limitations as currently recited are broadly recited and are not precluded from being performable in the human mind and the record clearly identifies elements of the claims which can be performed by a human, mentally or with pen and paper (i.e. identifying, computing, generating, comparing, and identifying) with reasoning as to how a person could look at an ultrasound frame and perform these steps (see the previous OA and rejection maintained below for more detail). Applicant’s arguments are considered merely conclusory without providing any evidence/arguments as to why the cited elements are not capable of being performed by a human, mentally or with pen and paper and it is noted that applicant’s arguments cannot replace evidence where evidence is necessary. Furthermore, regarding the background of the invention, it is noted that the background merely describes that the evaluation of pleural line changes in lung ultrasound is subjective, however, does not reasonably state nor suggest that the claimed features of identifying pixels forming a segmentation mask corresponding to a plueral line, computing pixel distributions, generating a thickness metric of the pleural line, comparing the thickness metric and identifying thickening as recited by the claims cannot be performed in the human mind or with the aid of pen and paper. Applicant’s arguments are thus considered merely conclusory without providing specific evidence/arguments as to why the cited features cannot be performed in the mind. Furthermore, it is noted that the absence of features being performed in the human mind by the cited art is not a factor to be considered when examining under 101. First it is noted that prior art and 101 evaluations are different and considered on their own merits and further it is noted that the prior art using a computer/processor to perform such recited limitations does not mean that the elements are not practically performed in the mind. Furthermore, it is noted that Sehgal discloses that the predictive model, built on quantitative echo-line features, serves as an alternative to visual clinical assessment where an observer looks for A-lines in normal lungs and B-lines in COVID-19 cases and discloses Regions of interest (ROIs) defining lung areas showing A-lines in normal cases and B-lines in COVID-19 cases were outlined manually by an expert in [0056], thus the prior art explicitly discloses that the predictive model which performs the steps of the claims including identification of pixels and computing of pixel distributions replaces what could be done by an observer/expert. Examiner notes that the above arguments are nearly identical to those presented previously and applicant has not provided any specific remarks to examiner’s response in the Office Action mailed on 03/02/2026.
Applicant further argues in pg. 10 that segmentation of a pleural line and knowing the image pixels that constitute the pleural line are not practicably performed in a human mind. Examiner respectfully disagrees with this assertion as a person could reasonably know image pixels that constitute the pleural line and segment (e.g. identify/distinguish) the pleural line as a pleural line and pixels therein are readily recognizable in an ultrasound as a bright, hyperechoic line at an interface between the chest wall and the lung. Furthermore, examiner notes that references such as Xu (US 20200359991 A1) which discloses a user may identify a pleural line upon visual examination of a live ultrasound image displayed on the user interface ([0043]), Liu (CN 122199382 A) which discloses that a trained U-Net deeply learns the training data of the semantic segmentation model which is composed of a diaphragm B-type ultrasonic image precisely marked by expert, the marked content is pixel-level precise pleural line, and Alkan (US 20250302442 A) which discloses training images comprising at least a subset of images annotated for such features by a doctor distinguishing features from one another (e.g. distinguishing A-lines from B-lines, B-lines from the pleural line, the pleural line from rib shadows, etc.) provide evidence that a person could reasonably segment/know image pixels that constitute the pleural line, and further provide evidence that a person could reasonably identify pixels forming a segmentation mask corresponding to the pleural line and compute, from the segmentation mask, pixel distributions in the pleural line by column and row as recited by the currently amended claims.
Regarding applicant’s arguments on pg. 11 that “none of the claims specified in the MPEP to recite a mental process are analogous to the pending claims” with specific mention of Electric Power Group, University of Utah Research Foundation v. Ambry Genetics and “Classen Immunotherapies, Inc. v. Biogen IDEC”. As noted previously, the application is examined on its own merits and the cited cases are merely exemplary of mental processes but mental processes are not limited by such examples. Nonetheless, as noted above, the instant claims would be considered analogous to the cited case law. Specifically, the claims are directed to merely gathering/collecting data and analyzing it (via identification, computation, generation of a metric, comparison, and identification) which is similar to each of the cited case law directed towards collecting data and analyzing it/comparing data and making determinations. Applicant’s arguments that these are not analogous are merely conclusory and do not specifically point to each of the cited claim limitations and how they are anything other than collecting and analyzing/comparing data.
Applicant further argues “as such, the amended claims require image-space processing of a digital segmentation mask and derivation of thickness metrics through column-wise and row-wise pixel distributions. The claims before and after the herein-contained amendments do not merely recite observing a pleural line and forming a judgment regarding thickening. Rather, the amended claims require generation of a segmentation mask, computation of pixel distributions across rows and columns of that mask and derivation of a thickness metric from those pixel distributions. These are image-processing operations performed on digital image structures and are not observations, evaluations judgments, or opinions that can practicably performed in the human mind. The amended claims do not recite merely counting pixels, and instead require a much more specific computational operation” (REMARKS pg. 12). Examiner first notes that the claims do not recite any generation of a segmentation mask, but rather identifying pixels forming a segmentation mask. Where such identification of pixels is recognized as an element which is reasonably performed by a human. Applicant’s arguments that the limitations are image-processing operations performed on digital image structures and are not observations, evaluations, judgments, or opinions are considered merely conclusory and applicant has not provided any evidence as to how identifying of pixels, computation of pixel distributions, and derivation of a thickness metric are not performable by a human or with the aid of pen and paper. Examiner notes that pixel identification of a pleural line (or segmentation mask thereof) is specifically disclosed by Liu as noted above as being performed by an expert and such pixel identification necessarily results in a computation of pixel distributions in rows/columns (i.e. the combination of all pixels forming the pleural line). Examiner further notes that a person could reasonably uses such pixel identification to compute pixel distributions in rows/columns accordingly and generate/derive a thickness metric therefrom without any specific image processing operations (which are not explicitly nor implicitly recited by the claims) that are so complex that they could not be performed by a human.
Applicant further agues “on the other hand, support for eligibility of claims such as those in the instant application is provided by example 47 in the 2024 AI Examples. That is, in the pending claims, features such as in claim 1 require the types of granular data processing specified in these claims. Features of at least claims 2, 7-8 and 10 further define the granular processing supportive of patent eligibility under the guidance provided by the U.S. Patent and Trademark Office, such as by Example 47. The granularity is described in the specification in terms of the discussion of segmenting and processing pixels, and nothing like this has been found ineligible in the various decisions cited as guidance to Examiners in the MPEP.” (See argument 4 of REMARKS on pg. 13).
In response to applicant's argument that the granularity is described in the specification in terms of discussion of segmenting and processing, it is noted that such granularity in processing is not explicitly nor implicitly recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). In other words, the claims are recited with such high generality that they do not require any specific granularity that precludes such limitations from being performable in the mind. Furthermore, regarding applicant’s arguments that the support for eligibility of the claims of the instant application is provided by example 47, examiner respectfully disagrees. First it is noted that applicant’s arguments are not specific as to which claim from example 47 (1, 2, or 3) applicant seems to find are analogous to the instant claims. Specifically, it is noted that claim 1 of example 47 is found to be eligible because it is merely directed to hardware components and does not recite any abstract ideas, claim 2 of example 47 which examiner notes while not exactly the same fact pattern is closest of the three claims of example 47 to the instant claims and is found to be ineligible, and claim 3 is found to be eligiblereasons is that it recites additional elements which integrate the abstract idea into a practical application (e.g. dropping/blocking limitations) where it is noted that the instant application does not recite any additional elements which may be considered for determining whether the claim as a whole is integrated into a practical application. Applicant’s arguments with respect to example 47 are overly vague and do not provide any specificity as to the similarities between example 47 nor the claims presented therein which would be analogous to the instant claims for providing eligibility under 101. For at least these reasons, applicant’s arguments are not found persuasive.
Applicant further argues “support for eligibility of the claims such as those in the instant application can be found in, for example, the two following precedential judicial opinions” citing to SRI International, Inc, v. Cisco Systems, Inc. and Packet Intelligence, Inc. v. Netscout Systems Texas which were confirmed as not involving any abstract idea and further argues that the cited features in the pending claims “require the types of granular data processing required in these cases” (See argument 5 of REMARKS pg. 12). Examiner respectfully disagrees in that applicant’s arguments are merely conclusory without providing specific evidence/arguments as to how the instant claim limitations “require the types of granular data processing required” in the cited cases. Applicant’s arguments do not replace evidence where evidence is necessary. It is noted that although the cited cases are identified as not being directed to abstract ideas, the reasons identified by the courts are not relatable to the instant claims and applicant has not specifically pointed to the ‘types of granular data processing’ that are required for the precedential cases that relate to the instant application. More specifically of the precedential cases cited by applicant are directed to packet-level security monitoring having specific features and processing techniques which lead to solving technological problems arising in computer networks, where such specific features/processing techniques do not relate to nor are recited in the instant claims. In other words, there is no nexus between such a specific techniques/elements recited by the claims of these cited cases and the instant claims. For at least these reasons, applicant’s arguments are not found persuasive.
Applicant’s arguments that “a key question that must then be answered is to whether the claims recite specific features as to how the abstract idea is performed or otherwise achieved, and especially in a way that provides a technology solution to a technology problem and points to specific decisions from the Court of Appeals for the Federal Circuit” and argues “the instant application implicitly or explicitly describes multiple different technical problems solved by the technical solutions in the pending claims. These technical problems are expressly identified in the Background, which explains that evaluation of pleural line changes in Lung ultrasound is subjective, that under-trained users often lack confidence in assessment, that no objective method existed for providing indications of pleural line changes, and that longitudinal monitoring requires automated standardized and explainable assessment” pointing to three technical problems and technical solutions including “the claimed method transforms ultrasound image data into a segmentation mask, computes row-wise and column-wise pixel distributions, derives a thickness metric, and automatically identifies pleural thickening from the metric”, the claims generate a reproducible thickness metric from the segmentation mask and compare that metric to predetermined healthy-reference values” and “claim 10 aggregates thickness metrics across frames before comparison” (See argument 6 of REMARKS pg. 14). Examiner first notes that there is no transformation of ultrasound image data into a segmentation mask as argued by applicant, but rather the claim recites identifying pixels forming a segmentation mask. Furthermore, examiner respectfully disagrees with applicant’s assertion that the claim results in argued solutions in that the claims are broadly recited without providing any specificity as to how the recited steps are performed such that they cannot be performed in the mind by human or pen and paper and further are broadly recited such that there is no nexus between the recited elements and the supposed improvement described in the background. In other words, the claims do not recite any specificity such that identifying…, computing…, generating…,comparing… and identifying… as recited by the claims lead to the supposed improvement of objective assessments that are automated, standardized, and understandable to users. Furthermore, it is noted that the judicial exception alone cannot provide the improvement (see MPEP 2106.05(a)). Thus applicant’s arguments do not specifically describe how the claims as a whole (e.g. including additional elements) leads to the supposed improvement. In other words, applicant’s arguments are considered merely conclusory without providing specific evidence/arguments as to why the claim as a whole leads to the cited improvement. Finally, it is noted that nothing in the claims implicitly nor explicitly requires any automation nor standardization as argued for the improvement. For at least these reasons, applicant’s arguments are not found persuasive.
For all of the reasons listed above, applicant’s arguments are not found persuasive and 101 rejection is maintained/updated in light of the amendments.
Regarding 35 U.S.C. 112
Examiner notes that although claim 1 is amended such that the count of pixels has been deleted, claim 7 remains unclear as to whether the count of pixels is intended to be the same as or different from the pixel distributions recited in claim 1.
Examiner further notes that although claim 8 has been amended, claim 8 remains unclear in light of amendments made to claim 7 in which the generation of the thickness metric comprises calculating a plurality of pleural line metrics (see below rejection for further detail).
Furthermore, new 112(b) rejections are necessitated by amendment.
Regarding prior art
Applicant's arguments filed 07/02/2026 have been fully considered but they are not persuasive.
For example, applicant argues “Sehgal never computes pixel distributions by row and Colum. Instead, thickness calculation in Sehgal is expressed in terms of region geometry. Table 3 in Sehgal defines thickness using y-ranges across a segmented region and averages them. Sehgal may identify a pleural line region and may derive thickness-related features from that region. However, Sehgal nowhere discloses computing pixel distributions in the pleural line by column and row, much less generating a thickness metric from a segmentation mask via such column-wise and row-wise pixel distribution as specifically recited in claim 1” and “Seghal discloses determining values of morphological features, but not generating a thickness metric from a segmentation mask via row-wise and column-wise pixel distributions” (REMARKS pg. 17-18). Examiner respectfully disagrees in that as noted by applicant Sehgal defines thickness using y-ranges across a segmented region and averages them. Such y-ranges and averaging thereof across each horizontal value is considered a pixel distribution in its broadest reasonable interpretation in that it is a computation of the distributions of pixels by row (i.e. in the y-range) and column (at east horizontal coordinate), furthermore, it is noted that the segmentation which grows the region to include object pixels of similar grayscale in [0054] is considered to be a computation of a pixel distribution by row and column as well as the disclosure with respect to table 4 and disclosure in [0091] disclosing the count of homogeneous runs of gray level so that element (I,j) is the number of homogeneous runs of j pixels with intensity i). Furthermore, regarding applicants arguments that Seghal does not disclose generating a thickness metric from a segmentation mask via row-wise and column-wise pixel distributions, examiner notes that citations of Seghal explicitly disclose description and formulas for pleural-line (p-line) features including thickenss/thickness variation in at least [0090] and in [0020] following the detection of the pleural line (p-line), a computing device may extract a variety of features such as quantitative features describing thickness. Examiner notes that such thickness or thickness variation of the pleural line are considered thickness metrics and are from the segmentation mask (i.e. the segmented pleural line) via the pixel distributions (i.e. the distribution of pixels across the segmentation mask). Applicant has only pointed to the morphological features without expressly providing evidence that the thickness and/or thickness variation previously cited by examiner are not considered thickness metrics nor any explanation as to how they are not from the segmentation mask via pixel distributions other than merely saying so. Applicant arguments do not replace evidence where evidence is necessary (MPEP 2145).
Applicant further argues “Sehgal also does not identify ‘thickening’ based on comparison of the thickness metric to a healthy reference value as in claim 1. Sehgal extracts features to generate a disease indication, but does not compare a thickness metric to a predetermined value characterizing a healthy pleural line and identifying thickening based on that comparison as in claim 1” (REMARKS pg. 18). Examiner respectfully disagrees in that Sehgal explicitly teaches in [0027] The disclosed approach involves identifying specific ultrasound image features that differentiate normal and abnormal pleural lines and in [0046] that the thickness of p-lines was larger in COVID-19 cases compared to normal. Examiner notes that such a determination of the thickness being larger compared to normal especially considering the values is necessarily a comparison between the thickness metric (i.e. thickness of COVID-19 cases) and a healthy reference value (i.e. normal), such a determination is necessarily an identification of thickening. Examiner also notes that fig. 2 explicitly depicts a comparison of computer-based pleural line (p-line) features of COVID-19 and normal cases, therefore, Sehgal expressly discloses comparing the thickness metric to a healthy reference value. Applicant has not provided any specific arguments as to the citations previously provided by examiner (e.g. the comparison of thickness to normal/healthy values).
For at least the reasons listed above, applicant’s arguments with respect to the teachings of Sehgal are not found persuasive.
Applicant further argues “Sehgal has not been shown to be prior art to the instant application…. While Sehgal claims priority to U.S. provisional patent application 63/247,362, filed September 23, 2021, the features for which Sehgal has been cited have not been shown present, let alone in proper context, in the priority provisional patent application” (REMARKS pg. 18). As noted multiple times previously, the cited features of Sehgal are explicitly disclosed in the provisional application. Specifically, examiner notes that the cited paragraphs of Sehgal are identical to those disclosed in the provisional application with the exception of paragraph [0001] in the PGPub which claims priority to the provisional. Therefore each paragraph cited in PGPub can be referenced by the preceding paragraph in the provisional (e.g. cited paragraph [0020] is supported by paragraph [0019] of the provisional application).
Examiner advises applicant to fully review examiner’s remarks as well as the prior art upon filing a subsequent response and specifically responding to examiner’s notes/remarks in order to better advance prosecution.
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.
Claims 1-4, 6-18, and 20-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception in the form of an abstract idea without significantly more.
In a test for patent subject matter eligibility, the claims pass Step 1 (see 2019 Revised Patent Subject Matter Eligibility), as they are related to a process, machine, manufacture, or composition of matter.
When assessed under Step2A, Prong I, Independent claims 1, 11, and 14 are found to recite a judicial exception (i.e. abstract idea). In this instance, claims 1 and 11 recite the limitations “obtain/obtaining an ultrasound frame of a lung and identify/identifying a pleural line in the lung”, “identify/identifying pixels forming a segmentation mask corresponding to the pleural line”, “compute/computing, from the segmentation mask, pixel distributions in the pleural line by column and row”, “generating a thickness metric of the pleural line form the segmentation mask via the pixel distributions”, “compare/comparing the thickness metric to a predetermined value (that characterizes a normal, healthy pleural line claim 1)”, “identify/identifying a thickening in the pleural line based on comparing the thickness metric of the pleural line to the predetermined value”. The cited limitations, under their broadest reasonable interpretation, encompass a mental process (i.e. abstract idea) of obtaining, identifying, computing, generating, comparing, and identifying which can be performed in the mind or by a human using a pen and a paper (e.g. observation, evaluation, judgment, opinion). In other words, a person could reasonably obtain an ultrasound frame by observation (e.g. by looking at an ultrasound image/frame), identify a pleural line by observation/evaluation, identify pixels via observation/evaluation, compute pixel distributions via observation/evaluation, generate a thickness metric via evaluation, compare the thickness metric to a predetermined value by observation/evaluation/judgment, and identify a thickening in the pleural line by observation/evaluation. Examiner notes that with the exception of generic computer-implemented steps (e.g. an ultrasound controller recited in claim 11 and a computer apparatus in claim 14), there is nothing in the claims that preclude the limitations from being performed by a human, mentally or with pen and paper, thus the cited limitation(s) recites a judicial exception (MPEP 2106.04(a)) and the claim must be reviewed under Step 2A, Prong II to determine patent eligibility.
Step 2A, Prong II determines whether any claim recites an additional element that integrates the judicial exception into a practical application. Independent claims recites the following additional element(s):
Obtain/obtaining an ultrasound frame of a lung (claims 1, 11, and 14)
An ultrasound controller comprising a memory that stores instructions and a processor that executes the instructions (claim 11)
A tangible non-transitory computer readable storage medium (claim 14)
The additional elements in the cited independent claims are not found to integrate the judicial exception into a practical application. In this case, obtain/obtaining an ultrasound frame of a lung is alternatively considered an additional element which amounts to merely insignificant extra-solution activity of data gathering where the data is an ultrasound frame of a lung which is considered to merely link the judicial exception to a field of use (i.e. ultrasound imaging of a lung), an ultrasound controller and non-transitory computer readable storage medium are found to merely be generic components of an ultrasound system and/or amount to merely performing/applying the judicial exception on a generic computer or in a computer environment. These elements are seen as adding insignificant extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use. Therefore, under step 2A Prong II the judicial exception is not integrated into a practical application by additional elements of independent claims 1, 11, and 14 and the claims must be reviewed under Step 2B to determine patent eligibility.
Step 2B determines where a claim amounts to significantly more.
The additional element(s) listed above do not amount to significantly more than the judicial exception. In this instance, as noted above the additional elements amount to merely insignificant extra-solution activity of data gathering in the field of ultrasound lung imaging and applying the judicial exception with a generic computer. Additionally there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Therefore, under Step 2B in a test for patent subject matter eligibility, the judicial exception of the independent claim(s) do not amount to significantly more and the independent claim(s) remain patent ineligible.
Dependent claims 2-4, 6-10, 12-13, 15-18, and 20-21 further limit the abstract idea of independent claims 1, 11, and 14. When analyzed as a whole, these claims are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed towards an abstract idea and do not sufficiently integrate the subject matter into a practical application or recite elements which constitute significantly more than the abstract ideas identified. The dependent claims are directed toward additional elements which encompass abstract ideas
In this instance, dependent claims recite the following limitations:
Determining a degree of the thickening in the pleural line based on the thickness metric of the pleural line (claims 2 and 15)
Calculating a plurality of pleural line metrics including a count of pixels in the pleural line in each column and row of a display (claim 7)
Comparing each of the plurality of pleural line metrics to a corresponding predetermined value (claim 8)
Detecting ultrasound imaging of the lung (claim 9)
Aggregating the thickness metric of the pleural line identified in the lung from the ultrasound frame with another thickness metric of the pleural line identified in the lung from another ultrasound frame to obtain an aggregated quantification of the pleural line (claim 10)
Identifying the thickening in the pleural line based on comparing the aggregated thickness metric of the pleural line to the predetermined value (claim 10)
determine a severity level associated with the pleural line, determine a pleural line as being either normal or abnormal, determine based on generating the thickness metric an assessment of an overall condition of the lung, or determine based on generating the thickness metric a likelihood of one or more pathological conditions (claim 21)
The cited limitation(s), under their broadest reasonable interpretation, encompass mental processes (i.e. abstract idea) which can be performed in the mind or by a human using a pen and a paper (e.g. observation, evaluation, judgment, opinion). In other words, a human could reasonably determine a degree of the thickening by observation/evaluation, calculate a plurality of pleural line metrics by observation/evaluation, compare each of the plurality of pleural line metrics to a corresponding predetermined value by observation/evaluation, detect ultrasound imaging of the lung by observation/evaluation, aggregate thickness metrics from different images by observation/evaluation, identify the thickening in the plural line based on comparing by observation/evaluation, determine a degree of the thickness based on metrics of the image features by observation/evaluation, and determine a severity level, a pleural line as normal or abnormal, determine an assessment of an overall condition, or determine a likelihood of one or more pathological conditions by observation/evaluation. Examiner notes that with the exception of generic computer-implemented steps (e.g. a processor/computer apparatus) there is nothing in the claims that preclude the limitation from being performed by a human, mentally or with pen and paper, thus the claimed limitation is considered to be directed towards a judicial exception (MPEP 2106.04(a)).
Under Step 2A, Prong II dependent claims 2-10, 12-13, and 15-21 present additional elements which only further narrow the judicial exceptions (e.g. claims 2-3, 12, and 16-17 which further recite outputting an indication which amounts to merely insignificant extra-solution activity of outputting data/results and further recite data superimposed on an ultrasound frame on a display which amounts to merely displaying data/result on a generic component of an ultrasound system (i.e. a display) and further narrow the nature of the count of pixels, claim 9 which merely recites automatically executing a software program to perform the method of claim 1 which amounts to merely applying the judicial exception on a generic computer, claims 13 and 18 which merely recite applying a trained artificial intelligence model to the thickness metric which amounts to merely applying a generic computer) and provide no additional element which are found to integrate the judicial exception into a practical application.
These dependent claims include no additional claims that are sufficient to amount to significantly more than the judicial exception. Additionally, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. As discussed above with respect to integration of the abstract idea into a practical application, the additional claims do not provide any additional elements that would amount to significantly more than the judicial exception. Under Step 2B, these claims are not patent eligible.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6-8 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 6 and 20 recite the limitation “a segmentation of the pleural line”. It is unclear if the segmentation is the same as the segmentation mask corresponding to the pleural line or if this is a different segmentation. For examination purposes, it has been interpreted that they may be the same or different, however, clarification is required.
Claim 7 recites the limitation “generating the thickness metric further comprises calculating a plurality of pleural line metrics including a count of pixels in the pleural line in each column and row of a display”. The limitation is unclear as claim 1 recites the limitation “computing, from the segmentation mask, pixel distributions in the pleural line by column and row” and it would appear that the pixel distributions by column and row could be the same as the count of pixels in the pleural line. The relationship between the pixel distributions in the pleural line by column and row and the count of pixels in the pleural line is further made unclear as the disclosure does not provide any description of computing pixel distributions by column and row, but only discloses counting the number of pixels that include the pleural line in each column and row of the display in [0047], thus it appears that computation of the pixel distributions and the counting of pixels in the column and row are one in the same, however, the claim appears to introduce counting of the pixels as a new element included in the generation of the thickness metric. If the counting of pixels in the pleural line is intended to be different from the computing of pixel distributions, examiner notes that there does not appear to be any support for such computing of pixel distributions in the pleural line by column and row and would therefore lack sufficient written description under 35 U.S.C. 112(a). The limitation is further unclear as to whether the pleural line metrics are included in or the same as the thickness metric or if they are different/distinct metrics ultimately used in the generation of the thickness metric. For examination purposes, it has been interpreted that the pleural line metrics may be the same as or different form the thickness metric and the count of pixels may be the same as or different from the distribution, however, clarification is required.
Claim 8 recites the limitation “wherein the comparing further comparing each of the plurality of pleural line metrics other than the thickness metric to a corresponding predetermined value other than the predetermined value which is compared to the thickness metric”. Examiner notes that claim 7 is amended to recite that generating the thickness metric comprises calculating a plurality of pleural line metrics. It is noted that in an instance where the pleural line metrics are included in the thickness metric or otherwise the same as the thickness metric, it is unclear how comparing each of the pleural line metrics other than the thickness metric is done to a corresponding predetermined value other than the predetermined value which is compared to the thickness metric. For examination purposes, it has been interpreted that the pleural line metrics are different from the thickness metric, however, clarification is required.
Claim Rejections - 35 USC § 102
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)(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.
(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.
Claims 1-4, 6-9, 11-18 and 20-21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated Sehgal et al. (US 20230090858 A1), hereinafter Sehgal.
Regarding claims 1, 11, and 14,
Sehgal discloses a system (at least fig. 8 and corresponding disclosure in at least [0092]) for processing an ultrasound frame, comprising:
an ultrasound controller (at least fig. 8 (800) and corresponding disclosure in at least [0093]) comprising a memory ([0104] which discloses the mass storage device 828 or other computer-readable storage media may also be encoded with computer-executable instructions) that stores instructions and a processor (at least fig. 8 (804) and corresponding disclosure in at least that executes the instructions, wherein, when executed by the processor, the instructions cause the ultrasound controller ([0104] which discloses the computing device 800 may have access to computer-readable storage media storing computer-executable instructions, which, when executed by the computing device 800, may perform the methods described herein related to one or more of imaging, machine learning, analyzing imaging, indicating diseases and/or conditions, or a combination thereof) to:
obtain an ultrasound frame of a lung ([0052] which discloses a retrospective pilot study was conducted on 20 B-mode ultrasound images that were used to evaluate the proposed quantitative analysis. Ten images were acquired from COVID-19 patients and another independent 10 images were acquired from normal cases Qualitative imaging findings and the results of diagnostic reverse transcription polymerase chain reaction tests were included with the images. The images were received for analysis, and analyzed without patient-related information. The images usually are acquired in a video clip format that includes many frames of the scanned area and [0060] which discloses lung ultrasound images) and identify a pleural line in the lung ([0018]-[0019] disclosing the pleural line segmentation thus identification)
identify pixels forming a segmentation mask corresponding to the pleural line ([0019] which discloses pleural lines (e.g., or pleural line regions) may be segmented. If the segmentation is semi-automated, the segmentation may be based on selection using a “wand” tool. Following the selection of a pixel within the pleural line with the wand (e.g., or cursor), the algorithm automatically grows the region to include object pixels of similar grayscale within a tolerance range. Only minimal input from the user is requested: to click and then validate the segmentation, making corrections in a few cases where the segmented margin is not acceptable)
compute, from the segmentation mask, pixel distributions in the pleural line by column and row([0021] which discloses quantitative features were extracted as grayscale first-order statistics, and determined by run length and gray-level co-occurrence matrices (GLCM) and table 4 describing the run length matrix includes a number of homogeneous runs of j pixels with intensity i and GLCM is a matrix which records the counts of pixel intensity combinations occurring between computed over 8 directions up and down, left and right, diagonals [0055] which discloses the software extracts quantitative features describing the depth (thickness), margin morphology, brightness, and heterogeneity. See also Table 3 depicting the formulas used for determining the thickness and thickness variation, where max(yx) – min (yx) would be a subtraction of the maximal y coordinate (i.e. pixel) from the minimum y coordinate (i.e. pixel) found in the pleural line for each x coordinate value, thus is understood to be a computation of pixel distributions by column and row. Additionally/alternatively, it is noted that growing the region to include object pixels of similar grayscale within a tolerance range as part of the segmentation process disclosed in [0019] is considered to compute, from the segmentation mask, pixel distributions in the pleural line by column and row);
generate a thickness metric of the pleural line from the segmentation mask via the pixel distributions ([0021] and [0055] disclosing the extraction of quantitative features describing depth (thickness) and table 3 depicting formulas used for determining the thickness and thickness variation found in the pleural line (thus from the segmentation mask via the pixel distributions and [0020] which discloses following detection of the pleural line (p-line), from the segmentation mask, a computing device may extract a variety of features, such as quantitative features describing thickness. The thickness parameters may measure the nonuniform widening of the pleural line)
compare the thickness metric of the pleural line to a predetermined value that characterizes a normal, healthy pleural line ([0046] which discloses thickness of p-lines was larger in COVID-19 cases (6.27±1.45 mm) compared to normal (1.00±0.19 mm), P<0.001 and [0062] which discloses the thickness of p-lines was larger on average in COVID-19 cases (6.27±1.45 mm) compared to normal (1.00±0.19 mm), P<0.001. P-line thickness variation was also larger on average in COVID-19 cases, 2.86±0.64 mm compared to 0.26±0.07 mm, P<0.001 Among features describing p-line margin morphology, projected intensity deviation showed the largest difference between COVID-19 cases (4.08±0.32) and normal (0.43±0.06), P<0.001. From the echo-line features, only 2 features, gray-level non-uniformity and run-length non-uniformity, showed a significant difference between normal cases (0.32±0.06, 0.59±0.06) and COVID-19 (0.22±0.02, 0.39±0.05), P=0.04, respectively. All features together for p-line showed perfect sensitivity and specificity of 100%; whereas, echo-line features had a sensitivity of 90% and specificity of 70%. Observer agreement for p-lines (ICC=0.65 to 0.85) was higher than for echo-line features (ICC=0.42 to 0.72))
identify thickening in a pleural line based on comparing a quantification of a pleural line to a predetermined value ([0083] which discloses fig. 7A shows an example of a COVID-19 confirmed case showing pleural thickening and irregularity with the presence of focal B-lines. Examiner notes that by determining the thickness of p-lines was larger in COVID-19 cases compared to normal a thickening in the pleural line is necessarily identified)
Examiner notes that the system of Sehgal would further perform the method of claim 1 having corresponding method steps and would comprise the tangible non-transitory computer readable storage medium of claim 14 having corresponding functions.
Regarding claims 2 and 15,
Sehgal further discloses wherein the ultrasound controller is caused to:
Determine a degree of the thickening in the pleural line based on the thickness metric of the pleural line ([0046] thickness of p-lines was larger in COVID-19 cases (6.27±1.45 mm) compared to normal (1.00±0.19 mm), P<0.001 and [0062] which discloses the thickness of p-lines was larger on average in COVID-19 cases (6.27±1.45 mm) compared to normal (1.00±0.19 mm), P<0.001. P-line thickness variation was also larger on average in COVID-19 cases, 2.86±0.64 mm compared to 0.26±0.07 mm, P<0.001) where such a thickness evaluation compared to normal is considered a degree of thickening).
Regarding claims 3 and 16,
Sehgal further discloses wherein the computer program, when executed by the processor, causes the computer apparatus to further: output an indication of the thickening of the pleural line ([0092] which discloses outputting an indication of the conditions (i.e. indication of a disease) and [0066] which discloses The statistically significant features included the thickness, thickness variation (TV), projected intensity deviation (PID), nonlinearity, tortuosity, and heterogeneity. Any one or combination of these features may be used to identify a condition of a subject, thus it is noted that outputting an indication of a condition (i.e. disease) is considered an indication of the thickening of the pleural line see also at least fig. 1A showing a confirmed COVI-19 case with plural thickening and irregularity and the right side depicting an outlined pleural line which is detected with a semi-automated segmentation as disclosed in [0060]).
Regarding claims 4 and 17,
Sehgal further discloses wherein the indication is output as data superimposed on the ultrasound frame on a display (see at least fig. 1A right panel and [0060] which discloses a pleural line detected with semiautomated segmentation in a confirmed COVID-19 case) and wherein the count of pixels in the pleural line includes a count of pixels in the pleural line in each column and row of the display ([0021] which discloses quantitative features were extracted as grayscale first-order statistics, and determined by run length and gray-level co-occurrence matrices (GLCM) and table 4 describing the run length matrix includes a number of homogeneous runs of j pixels with intensity i and GLCM is a matrix which records the counts of pixel intensity combinations occurring between computed over 8 directions up and down, left and right, diagonals [0055] which discloses the software extracts quantitative features describing the depth (thickness), margin morphology, brightness, and heterogeneity. See also Table 3 depicting the formulas used for determining the thickness and thickness variation, where max(yx) – min (yx) would be a subtraction of the maximal y coordinate (i.e. pixel) from the minimum y coordinate (i.e. pixel) found in the pleural line for each x coordinate value, thus is understood to be a count of pixels in the pleural line. Additionally/alternatively, it is noted that growing the region to include object pixels of similar grayscale within a tolerance range as part of the segmentation process disclosed in [0019] is considered to determine a count of pixels in the pleural line. Examiner thus notes that the count of pixels includes a count of pixels in each column and row (i.e. all of the pixels in the pleural line) of the display)
Regarding claims 6 and 20,
Sehgal further discloses wherein the thickness metric corresponds to at least one of a measure of a width of the pleural line, thickness of a segmentation of the pleural line, or area of the segmentation of the pleural line ([0020] which discloses following the detection of the pleural line (p-line), a computing device may extract a variety of features, such as morphological features, quantitative features describing thickness. The thickness parameters may measure the nonuniform widening of the pleural line and [0046] thickness of p-lines was larger in COVID-19 cases (6.27±1.45 mm) compared to normal (1.00±0.19 mm), P<0.001 and [0062] which discloses the thickness of p-lines was larger on average in COVID-19 cases (6.27±1.45 mm) compared to normal (1.00±0.19 mm), P<0.001. P-line thickness variation was also larger on average in COVID-19 cases, 2.86±0.64 mm compared to 0.26±0.07 mm, P<0.001. see also [0083] which discloses fig. 7A shows an example of a COVID-19 confirmed case showing pleural thickening and irregularity with the presence of focal B-lines. Quantitative pleural line features detected the case accurately as COVID-19).
Regarding claim 7,
Sehgal further teaches wherein generating the thickness metric further comprises: calculating a plurality of pleural line metrics including a count of pixels in the pleural line in each column and row of a display ([0021] which discloses quantitative features were extracted as grayscale first-order statistics, and determined by run length and gray-level co-occurrence matrices (GLCM) and table 4 describing the run length matrix includes a number of homogeneous runs of j pixels with intensity i and GLCM is a matrix which records the counts of pixel intensity combinations occurring between computed over 8 directions up and down, left and right, diagonals [0055] which discloses the software extracts quantitative features describing the depth (thickness), margin morphology, brightness, and heterogeneity. See also Table 3 depicting the formulas used for determining the thickness and thickness variation, where max(yx) – min (yx) would be a subtraction of the maximal y coordinate (i.e. pixel) from the minimum y coordinate (i.e. pixel) found in the pleural line for each x coordinate value, thus is understood to be a count of pixels in the pleural line. Additionally/alternatively, it is noted that growing the region to include object pixels of similar grayscale within a tolerance range as part of the segmentation process disclosed in [0019] is considered to determine a count of pixels in the pleural line. Examiner thus notes that the plurality of metrics includes a count of pixels in each column and row (i.e. all of the pixels in the pleural line) of the display)
Regarding claim 8,
Sehgal further discloses comparing each of the plurality of pleural line metrics other than the thickness metric to a corresponding predetermined value other than the predetermined value which is compared to the thickness metric ([0046] which discloses results: Six of 7 p-line features showed a significant difference between normal and COVID-19 cases. Thickness of p-lines was larger in COVID-19 cases (6.27±1.45 mm) compared to normal (1.00±0.19 mm), P<0.001. Among features describing p-line margin morphology, projected intensity deviation showed the largest difference between COVID-19 cases (4.08±0.32) and normal (0.43±0.06), P<0.001. From the echo-line features, only 2 features, gray-level non-uniformity and run-length non-uniformity, showed a significant difference between normal cases (0.32±0.06, 0.59±0.06) and COVID-19 (0.22±0.02, 0.39±0.05), P=0.04, respectively. See also fig. 2)
Regarding claim 9,
Sehgal further discloses further comprising:
Detecting ultrasound imaging of the lung ([0107] which discloses the imaging device may scan a subject and generate imaging data based on the scan. The imaging data may be sent to the computing device. Examiner thus notes that the method necessarily includes detecting ultrasound imaging of the lung in some capacity (e.g. by sending the imaging data to the computer, by
Automatically executing a software program to perform the method of claim 1 based on detecting the ultrasound imaging of the lung ([0107] which discloses the computing device may process the imaging data by performing segmentation to determine one or more pleural line regions. The computing device may analyze the pleural line regions using a model, rules, a machine learning model, and/or the like. The pleural line regions may have various features recognized computed as values by the methods herein. The resulting values of features may be used to categorize the subject as having a condition, a disease, a disease level, or a combination thereof).
Regarding claim 12,
Sehgal further discloses further comprising:
A display ([0105] which discloses input/output controller may provide output to a display, such as a computer monitor, a flat-panel display…) that displays an indication of the thickening of the pleural line (at least [0115] which discloses and see also at least fig. 1A showing a confirmed COVI-19 case with plural thickening and irregularity and the right side depicting an outlined pleural line which is detected with a semi-automated segmentation as disclosed in [0060]), wherein the count of pixels in the pleural line includes a count of pixels in the pleural line in each column and row of the display ([0021] which discloses quantitative features were extracted as grayscale first-order statistics, and determined by run length and gray-level co-occurrence matrices (GLCM) and table 4 describing the run length matrix includes a number of homogeneous runs of j pixels with intensity i and GLCM is a matrix which records the counts of pixel intensity combinations occurring between computed over 8 directions up and down, left and right, diagonals [0055] which discloses the software extracts quantitative features describing the depth (thickness), margin morphology, brightness, and heterogeneity. See also Table 3 depicting the formulas used for determining the thickness and thickness variation, where max(yx) – min (yx) would be a subtraction of the maximal y coordinate (i.e. pixel) from the minimum y coordinate (i.e. pixel) found in the pleural line for each x coordinate value, thus is understood to be a count of pixels in the pleural line. Additionally/alternatively, it is noted that growing the region to include object pixels of similar grayscale within a tolerance range as part of the segmentation process disclosed in [0019] is considered to determine a count of pixels in the pleural line. Examiner thus notes that the count of pixels includes a count of pixels in each column and row (i.e. all of the pixels in the pleural line) of the display)
Regarding claims 13 and 18,
Sehgal further discloses applying a trained artificial intelligence model to the thickness metric of the pleural line to compare the thickness metric of the pleural line to the predetermined value and to identify the thickening ([0092] which discloses additionally, the computing device may be configured to implement a machine learning model configured to recognize features of pleural lines, categorize images based on imaging features (e.g., pleural lines, morphology of pleural lines), and/or the like and [0107] which discloses the computing device may analyze the pleural line regions using a model, rules, a machine learning model, and/or the like. Examiner notes that to compare the thickness metric of the pleural line to the predetermined value and to identify the thickening are considered an intended use of such application of the trained artificial intelligence model and it is noted that applying the trained artificial intelligence model is capable of being used to compare the thickness metric of the pleural line and to identify the thickening)).
Regarding claim 21,
Sehgal further discloses wherein the computer program, when executed by the processor, further causes the computer apparatus to:
At least one of determine a severity level associated with the pleural line, determine a pleural line as being either normal or abnormal, determine based on generating the thickness metric, an assessment of an overall condition of the lung, or determine based on generating the thickness level a likelihood of one or more pathological conditions ([0104] which discloses the computing device 800 may perform the methods described herein relating to indicating diseases and/or conditions and [0107] which discloses The pleural line regions may have various features recognized computed as values by the methods herein. The resulting values of features may be used to categorize the subject as having a condition, a disease, a disease level, or a combination thereof. See also [0109] and at least claims 1-3)
Claim Rejections - 35 USC § 103
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.
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.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Sehgal in view of Arntfield et al. (US 20230148996 A1), hereinafter Arntfield.
Regarding claim 10,
Sehgal teaches the elements of claim 1 as previously stated. Sehgal fails to explicitly teach further comprising:
Aggregating the thickness metric of the pleural line identified in the lung from the ultrasound frame with another thickness metric of the pleural line identified in the lung from another ultrasound frame to obtain an aggregated thickness metric of the pleural line, and
Identifying the thickening in the pleural line based on comparing the aggregated thickness metric of the pleural line to the predetermined value.
In a similar field of endeavor involving ultrasound lung imaging, teaches aggregating a quantification from an ultrasound frame and a quantification from another ultrasound frame to obtain an aggregated quantification ([0127] which discloses if individual images are processed separately at 1135, the aggregated outputs for the encounter may be averaged or otherwise combined to generate a combined output for the encounter and [0130] At 1135, an output tensor can be generated using an output neural network. In some cases, the output neural network is a 3-layer fully connected network with SoftMax activation. [0131] The output tensor may represent a probability of the presence of a first condition of the plurality of conditions in the at least one ultrasound image).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have modified Sehgal to include aggregating the thickness metric from the ultrasound frame with a quantification from another ultrasound frame to obtain an aggregated quantification as taught by Arntfield in order to enhance the accuracy of the thickness metric of Sehgal. A person having ordinary skill in the art would have recognized the benefit of averaging data analysis output across multiple images to improve the accuracy of the quantification obtained from said images.
It would have been further obvious to a person having ordinary skill in the art before the effective filing date to have modified Sehgal, as currently modified, to compare the aggregated thickness metric to the predetermined value of Sehgal, as currently modified, (e.g. the predetermined value of Sehgal) in order to determine the thickening accordingly. Such a modification would thereby provide enhanced accuracy of the prediction of thickness for the pleural line of the subject by using more accurate thickness evaluation.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BROOKE L KLEIN whose telephone number is (571)270-5204. The examiner can normally be reached Mon-Fri 7:30-4.
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/BROOKE LYN KLEIN/Primary Examiner, Art Unit 3797
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