DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after allowance or after an Office action under Ex Parte Quayle, 25 USPQ 74, 453 O.G. 213 (Comm'r Pat. 1935). Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on 01/22/2026 has been entered.
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 1-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) and does not include additional elements that amount to significantly more than the judicial exception.
Step 2A, Prong One:
The claims recite mental processes, namely, observations/evaluations and decisions that could be performed conceptually in the human mind, including “acquiring a tomographic image of a subject”, “acquiring a plurality of measurement values…”, “acquiring a sentence…”, and “selecting at least some of the plurality of measurement values.”
Step 2A, Prong Two:
The claims are “information processing” and include steps like “acquiring a tomographic image…”, “acquiring a plurality of measurement values…”, “acquiring a sentence…”, “selecting at least some of the plurality of measurement values..” These are generic computer functions involving data gathering, decision-making, and output. Merely applying an abstract idea on a generic computer or using conventional speech recognition does not integrate the exception into a practical application. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); Credit Acceptance Corp. v. Westlake Servs., 859 F.3d 1044 (Fed. Cir. 2017).
The claims do not recite an improvement to the functioning of the computer or to another technology/technical field. The use of “an interpretation report screen” are invoked as tools to output information pertaining to measurement values. There is no recitation of a specific, technological improvement.
Constraints like “displaying, on an interpretation report screen, the at least some of the plurality of measurement values” are post-solution activity that do not meaningfully limit the abstract idea.
Step 2B:
Beyond the abstract ideas, the claims recite generic computer implementation: acquiring an image, acquiring data, selecting a subset of data, and displaying information. The specification, as reflected by the claim language, does not require any unconventional hardware or a particular machine.
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.
Claim(s) 1-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rao et al. (IDS: US 20210158936 A1).
Regarding claims 1, 16, and 17, Rao teaches:
“acquire a tomographic image of a subject, wherein the tomographic image includes an abnormal shadow” (par. 0078; ‘The medical scan image data 410 can include one or more image slices 412, for example, corresponding to a single x-ray image, a plurality of cross-sectional, tomographic images of a scan such as a CT scan, or any plurality of images taken from the same or different point at the same or different angles.’
“acquire a plurality of measurement values associated with the abnormal shadow at a plurality of different points in time” (par. 0058; ‘This calculated distance, error, or other measured discrepancy in each category can be used to quantify state change data, indicate a new classifier in one or more categories, to determine if a certain category has become more or less severe, or otherwise determine how the abnormality has changed over time.’; par. 0252; ‘In particular, the lesion size, shape, diameter, and/or volume, and/or other characteristics of the lesion such as other abnormality classification data 445 can be determined for each scan, and the changes in these features over time can be measured and tracked.’)
“acquire a sentence commenting on the plurality of measurement values associated with the abnormal shadow” (par. 0054, abnormality annotation data; ‘Abnormality annotation data can be generated by identifying one or more of abnormalities in the medical scan by utilizing a computer vision model that is trained on a plurality of training medical scans.’; par. 0334; ‘In the example shown, the medical scan viewing system 3100 analyzes the medical scan 3120 in the segmented region and determines the possibility of an enlarged lymph node, which is indicated in the text box 3134.’; par. 0060; ‘The medical scan annotator system 106 can be used to gather annotations of medical scans based on review of the medical scan image data by users of the system such as radiologists or other medical professionals.’);
“select at least some of the plurality of measurement values based on a phrase from the sentence” (par. 0095; ‘The interactive interface feature data 471, slice subset 472, slice order data 473, slice cropping data 474, and/or the density window data 475 can be selected via user input and/or generated automatically by one or more subsystems 101, for example, based on the abnormality annotation data 442 and/or based on performance score data of different interactive interface versions.’); and
“display, on an interpretation report screen, the at least some of the plurality of measurement values” (par. 0095; ‘Display parameter data 470 can indicate parameters indicating an optimal or preferred display of the medical scan by an interactive interface 275 and/or formatted report for each abnormality and/or for the scan as a whole.’; par. 0099; ‘This can be utilized when displaying similar scans to a user via interactive interface 275 and/or can be utilized when generating report data 449 that includes similar scans, for example, in conjunction with the medical scan assisted review system 102.’).
Regarding claim 2 (dep. on claim 1), Rao further teaches:
“select the at least some of the plurality of measurement values based on a phrase that expresses a change over time in the measurement value included in the sentence” (par. 0058; ‘In various embodiments where several past scans are available, such state change data can be determined over time, and statistical data showing growth rate changes over time or malignancy changes over time can be generated, for example, indicating if a growth rate is lessening or worsening over time.’).
Regarding claim 3 (dep. on claim 1), Rao further teaches:
“time information indicating a point in time of measurement is added to a measurement value of plurality of measurement values” (par. 0274; ‘The medical scan entries 3005 and 3006 can be received at the same time or different times for processing. For example, as medical scan entries 3005 and 3006 correspond to different scan dates, they can be sent to the medical scan lesion tracking system for processing as scans are taken for the patient.’), and
the at least one processor is configured to:
“create a plot diagram including the at least some selected measurement values using the measurement value and the time information as variables” (par. 0265; ‘The lesion area of lesion 3010 can be calculated for each image slice, as illustrated in the discrete plot 3032 of slice index vs lesion area. Plot 3032 can be utilized to determine volume as the area under the curve of plot 3034 to perform a trapezoidal Riemann sum approximation of lesion volume, where the x-axis measures cross-sectional distance, or width, from slice 0. This can be determined by multiplying the slice index of the x-axis of plot 3032 by the slice thickness to determine the x-value of each of the coordinates plotted in plot 3032.’); and
“cause a display to display the plot diagram” (par. 0269; ‘For example, the lesion measurement change data can be displayed as text and/or can be displayed visually in conjunction with the image data 410 of medical scan entries 3005 and/or 3006 by utilizing the medical scan assisted review system 102.’).
Regarding claim 4 (dep. on claim 3), Rao further teaches:
“in a case where an instruction is received” (par. 0281; ‘In some embodiments, before execution of the inference function on the one or more medical scans of the new patient, a user interacting with the interface displayed by the display device can select a projected time span from a discrete set of options, and/or can enter any projected time span.’);
“create the plot diagram including all of the plurality of acquired measurement values” (par. 0281; ‘In some embodiments, before execution of the inference function on the one or more medical scans of the new patient, a user interacting with the interface displayed by the display device can select a projected time span from a discrete set of options, and/or can enter any projected time span.’); and
“cause the display to display the plot diagram” (par. 0281; ‘In some embodiments, before execution of the inference function on the one or more medical scans of the new patient, a user interacting with the interface displayed by the display device can select a projected time span from a discrete set of options, and/or can enter any projected time span.’).
Regarding claim 5 (dep. on claim 1), Rao further teaches:
“time information indicating a point in time of measurement is added toa measurement value of plurality of measurement values” (par. 0280; ‘For example, lesion change prediction data can be generated for one year, two years, and three years in the future, and the prediction data for each projected time span can be sent to the client device for display. In some embodiments, the interface can prompt the user to select one of the set of different projected time spans, and the prediction data for the selected one of the projected time spans will be displayed accordingly.’), and
“the at least one processor is configured to select the measurement value to which the time information indicating the point in time of measurement determined based on the phrase related to the measurement value is added” (par. 0280; ‘In some embodiments, the interface can prompt the user to select one of the set of different projected time spans, and the prediction data for the selected one of the projected time spans will be displayed accordingly.’).
Regarding claim 8 (dep. on claim 1), Rao further teaches:
“wherein the at least one processor is configured to select at least some of the plurality of measurement values according to the number of measurement values determined based on the phrase related to the measurement value” (par. 0059; ‘The medical scan report labeling system 104 can be used to automatically assign medical codes to medical scans based on user identified keywords, phrases, or other relevant medical condition terms of natural text data in a medical scan report of the medical scan, identified by users of the medical scan report labeling system 104.’).
Regarding claim 7 (dep. on claim 1), Rao further teaches:
“select at least some of the plurality of measurement values based on a phrase that expresses a disease name included in the sentence” (par. 0272; ‘The lesion change measurement data can be utilized to determine RECIST evaluation data based on the change in the lesion in a more recent scan when compared to a prior scan. In particular, the lesion change measurement data can be utilized to indicate if the lesion is “Complete Response”, “Partial Response”, “Stable Disease”, or “Progressive Disease”. In cases where three or more scans are evaluated for a patient, the RECIST evaluation data can reflect changes over time.’).
Regarding claim 8 (dep. on claim 1), Rao further teaches:
“select at least some of the plurality of measurement values based on a phrase that expresses a purpose of examination included in the sentence” (par. 0111; ‘Alternatively, the medical scan diagnosing system 108 can automatically retrieve a medical scan from the medical scan database that is selected based on a request received from a user for a particular scan and/or based on a queue of scans automatically ordered by the medical scan diagnosing system 108 or another subsystem based on scan priority data 427.’).
Regarding claim 9 (dep. on claim 1), Rao further teaches:
“determine whether to select the measurement value based on a result of comparison between the measurement value included in the sentence and a predetermined threshold value” (par. 0284; ‘The lesion location determined in the detection data, the lesion diameter, area and/or volume determined in the lesion measurement data, and/or abnormality classification data 445 for one or more abnormality classifier categories 444 can be compared to corresponding portions of the human assessment data by performing a similarity function, by computing a difference in values, by determining whether or not the values match or otherwise compare favorably, and/or by computing a Euclidean distance between feature vectors of the human assessment data and the automated assessment data.’).
Regarding claim 10 (dep. on claim 1), Rao further teaches:
“determine whether to select at least two measurement values included in the sentence based on a result of comparison between a difference between the at least two measurement values and a predetermined threshold value” (par. 0284; ‘The lesion location determined in the detection data, the lesion diameter, area and/or volume determined in the lesion measurement data, and/or abnormality classification data 445 for one or more abnormality classifier categories 444 can be compared to corresponding portions of the human assessment data by performing a similarity function, by computing a difference in values, by determining whether or not the values match or otherwise compare favorably, and/or by computing a Euclidean distance between feature vectors of the human assessment data and the automated assessment data.’).
Regarding claim 11 (dep. on claim 1), Rao further teaches:
“select the measurement value that satisfies a predetermined condition from among the plurality of measurement values” (par. 0284; ‘The lesion location determined in the detection data, the lesion diameter, area and/or volume determined in the lesion measurement data, and/or abnormality classification data 445 for one or more abnormality classifier categories 444 can be compared to corresponding portions of the human assessment data by performing a similarity function, by computing a difference in values, by determining whether or not the values match or otherwise compare favorably, and/or by computing a Euclidean distance between feature vectors of the human assessment data and the automated assessment data.’).
Regarding claim 12 (dep. on claim 1), Rao further teaches:
“in a case where a difference between at least two measurement values included in the plurality of measurement values satisfies a predetermined condition, select the at least two measurement values” (par. 0284; ‘The lesion location determined in the detection data, the lesion diameter, area and/or volume determined in the lesion measurement data, and/or abnormality classification data 445 for one or more abnormality classifier categories 444 can be compared to corresponding portions of the human assessment data by performing a similarity function, by computing a difference in values, by determining whether or not the values match or otherwise compare favorably, and/or by computing a Euclidean distance between feature vectors of the human assessment data and the automated assessment data.’).
Regarding claim 13 (dep. on claim 1), Rao further teaches:
“time information indicating a point in time of measurement is added to the measurement value” (par. 0231; ‘These medial scans and reports can be anonymized as well, where the dates and/or times detected in these medical scans and/or medical reports offset by the same determined amount, randomized or pseudo-randomized for particular patient ID number, for example, based on performing a hash function on the patient ID number.’), and
“the at least one processor is configured to select at least some of the plurality of measurement values that are continuous in time series order” (par. 0086; ‘In some embodiments, abnormality classifier categories 444 can be assigned one or more non-binary values, such as one or more continuous or discrete values indicating a likelihood that the corresponding classifier category 444 is present.’).
Regarding claim 14 (dep. on claim 1), Rao further teaches:
“time information indicating a point in time of measurement is added to the measurement value” (par. 0231; ‘These medial scans and reports can be anonymized as well, where the dates and/or times detected in these medical scans and/or medical reports offset by the same determined amount, randomized or pseudo-randomized for particular patient ID number, for example, based on performing a hash function on the patient ID number.’), and
“the at least one processor is configured to select at least some of the plurality of measurement values that are discrete in time series order” (par. 0265; ‘The lesion area of lesion 3010 can be calculated for each image slice, as illustrated in the discrete plot 3032 of slice index vs lesion area.’).
Regarding claim 15 (dep. on claim 1), Rao further teaches:
“the measurement value is at least one of a size of a lesion or a signal value at a part of the lesion in a medical image obtained by imaging the lesion” (par. 0260; ‘For a lesion 3010 detected in the image data of medical scan entry 3005, the lesion diameter measurement function can include performing a lesion diameter calculation on each of the image slice subset 3030 to generate a set of diameter measurements. Generating the lesion diameter measurement for the lesion of medical scan entry 3005 can include selecting a maximum of the set of diameter measurements.’).
Regarding claim 18 (dep. on claim 1), Rao further teaches:
“acquire the sentence commenting on the plurality of measurement values associated with the abnormal shadow” (par. 0105; ‘Alternatively or in addition to identifying particular scans of the training set, the training set data 621 can identify training set criteria, such as necessary scan classifier data 420, necessary abnormality locations, classifiers, or other criteria corresponding to abnormality annotation data 442, necessary confidence score data 460, for example, indicating that only medical scans with diagnosis data 440 assigned a truth flag 461 or with confidence score data 460 otherwise comparing favorably to a training set confidence score threshold are included, a number of medical scans to be included and proportion data corresponding to different criteria, or other criteria used to populate a training set with data of medical scans.’) comprising:
“generate a sentence commenting on the plurality of measurement values associated with the abnormal shadow based on a trained model trained from machine learning” (par. 0157; ‘The annotating system 2612 can generate annotation data by performing an inference function on the de-identified medical scan, utilizing the model parameters received from the central server system 2640.’).
Conclusion
Other pertinent prior art are cited in the PTO-892 for the applicant's consideration.
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MARK . VILLENA
Examiner
Art Unit 2658
/MARK VILLENA/Examiner, Art Unit 2658
Prosecution Insights