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 Amendment
The previous 112(f) claim interpretation has been withdrawn.
In light of the amendments, the claims are rejected under 35 U.S.C. 101.
In light of the amendments, the claims are rejected under 35 U.S.C. 103.
Notice to Applicant
In the amendment dated 01/21/2026, the following has occurred: claims 1, 11-14, 17, and 21 have been amended; claims 2-9, 15-16, and 18-20 remain unchanged; and claim 22 has been added.
Claims 1-9 and 11-22 are pending.
Effective Filing Date: 09/22/2023
Response to Arguments
Claim Interpretation:
Applicant amended the claims to overcome the previous claim interpretation. Examiner withdraws the previous claim interpretation.
35 U.S.C. 101 Rejections:
Step 2A, Prong One:
Applicant argues that the claims do not recite an abstract idea classified under mathematical concepts. The present claims however are directed towards certain methods of organizing human activity.
Step 2A, Prong Two:
Applicant argues that the present claims provide an objective readout and spares the user from the task of manually selecting and evaluating different options. Preventing a manual task by automating it is not a technical improvement, it can be considered to be closer to taking an abstract idea and “applying it” using generic computer components.
Furthermore, using additional data in the form of supplementary information in order to make a prediction is an improvement but an improvement to the data. Improving the data is in line with an improvement to the abstract idea involving making a determination based on data.
35 U.S.C. 102/103 Rejections:
Applicant argues that the new amendments to the independent claims involving the supplementary information being both received and used when making a prediction is not taught with the previously-cited references. Examiner however respectfully would like to point out that the reception of supplementary information is taught by the McGinnis et al. reference, though Applicant argues that Examiner is incorrectly categorizing the received annotations as supplementary information. The supplementary information is only described as “supplementary information” within the claims, and this is broad. If Applicant wants weight over what the supplementary information is, Examiner suggests amending the claims to add detail as to what it is. As for the usage of this supplementary to generate a treatment response, Applicant states that McGinnis et al. is completely silent on the cell classification model generating a treatment response based on a whole slide image and the whole slide image labelled by the expert. Examiner has used another reference to address this limitation.
The dependent claims are being argued as allowable based on the independent claims. Examiner however respectfully disagrees based on the assessment of the independent claims above.
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 1-9 and 11-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-9, 11-16, and 18-22 are drawn to a method and claim 17 is drawn to a system, each of which is within the four statutory categories. Claims 1-9 and 11-22 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites a computer-implemented method for providing a treatment response prediction for a patient suffering from a cancerous disease, the computer-implemented method comprising:
1) obtaining a whole slide image of the patient, the whole slide image showing a tissue sample relating to the cancerous disease;
2) obtaining, from a) a healthcare information system, a supplementary information associated with the patient;
3) generating a treatment response prediction for one or more treatment options by applying a prediction function to the whole slide image and the supplementary information; and
4) providing the treatment response prediction.
Claim 1 recites, in part, performing the steps of 2) obtaining a supplementary information associated with the patient, 3) generating a treatment response prediction for one or more treatment options by applying a prediction function to the whole slide image and the supplementary information, and 4) providing the treatment response prediction. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes how one can make a prediction based on data. Independent claim 17 recites similar limitations and is also directed to an abstract idea under the same analysis.
Depending claims 2-16 and 18-21 include all of the limitations of claim 1, and therefore likewise incorporate the above described abstract idea. Depending claim 4 adds the additional steps of “providing a certainty calculation module configured to output a certainty measure for a corresponding treatment response prediction, the certainty measure measuring a confidence of the corresponding treatment response prediction”, “applying the certainty calculation module to obtain a certainty measure for the treatment response prediction”, and “providing the certainty measure for the treatment response prediction”; claim 5 adds the additional step of “outputting the certainty measure together with the treatment response prediction to a user via a user interface”; claim 6 adds the additional steps of “determining, based on the certainty measure, whether or not the treatment response prediction is conclusive” and “in response to the treatment response prediction being inconclusive determining a piece of information relating to the patient that is different than the whole slide image and which is suited for rendering the treatment response prediction conclusive”; claim 7 adds the additional step of “accessing a healthcare information system including healthcare data of the patient”, “processing the piece of information to provide an updated treatment response prediction and an updated certainty measure”, and “providing the updated treatment response prediction and the updated certainty measure”; claim 8 adds the additional step of “accessing a healthcare information system comprising healthcare data of the patient”, “determining whether the piece of information is available in the healthcare information system”, and “generating, via a user interface, a corresponding notification to a user in response to the piece of information not being available”; claim 10 adds the additional step of “obtaining, from a healthcare information system, supplementary information associated with the patient, wherein the prediction function is further configured to derive the treatment response prediction additionally based on the supplementary information, and the applying includes additionally applying the prediction function to the supplementary information”; claim 11 adds the additional steps of “providing a data extraction module configured to search an electronic medical record of the patient for the supplementary information, the data extraction module including a large language model, and wherein the obtaining of the supplementary information includes accessing the electronic medical record of the patient in the healthcare information system” and “applying the data extraction module to the electronic medical record to obtain the supplementary information”; claim 13 adds the additional steps of “providing an image analysis module configured to extract a radiological observable from radiology image data; and applying the image analysis module on the radiology image data to obtain the radiological observable, wherein the prediction function is further configured to derive the treatment response prediction additionally based on the radiological observable” and “applying includes additionally applying the prediction function to the radiological observable”; claim 20 adds the additional steps of “determining, based on the certainty measure, whether or not the treatment response prediction is conclusive” and “in response to the treatment response prediction being inconclusive determining a piece of information relating to the patient that is different than the whole slide image and which is suited for rendering the treatment response prediction conclusive”; and claim 21 adds the additional steps of “accessing a healthcare information system including healthcare data of the patient”, “processing the piece of information to provide an updated treatment response prediction”, and “providing the updated treatment response prediction”. Additionally, the limitations of depending claims 2-3, 9, 12, 14-16, and 18-19 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. Thus, depending claims 2-16 and 18-21 are nonetheless directed towards fundamentally the same abstract idea as independent claim 1 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) a healthcare information system and b) at least one processor (in claim 17) to perform the claimed steps.
Additionally, claims 1 and 17 include the additional element step of 1) “obtaining a whole slide image of the patient, the whole slide image showing a tissue sample relating to the cancerous disease”.
The a) healthcare information system and b) at least one processor in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification, paragraph [0312] where there is discussion of a general purpose computer executing functions, see MPEP 2106.05(f)).
The 1) “obtaining a whole slide image of the patient, the whole slide image showing a tissue sample relating to the cancerous disease” adds insignificant extra-solution activity to the abstract idea which amounts to mere data gathering, see MPEP 2106.05(g).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a) a healthcare information system and b) at least one processor to perform the claimed steps and the additional element step of 1) “obtaining a whole slide image of the patient, the whole slide image showing a tissue sample relating to the cancerous disease” amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity) and mere instructions to apply the exception using generic computer components that do not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain methods of organizing human activity steps. Specifically, MPEP 2106.05(d) and MPEP 2106.05(f) recite that the following limitations are not significantly more:
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); and
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
The additional element step of 1) “obtaining a whole slide image of the patient, the whole slide image showing a tissue sample relating to the cancerous disease” in these steps add insignificant extra-solution activity/pre-solution activity in the form of WURC activity to the abstract idea. The following is an example of a court decision demonstrating computer functions as well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives whole image slide image data, and transmits the data to computing components over a network, for example the Internet.
Furthermore, the current invention provides a treatment response prediction utilizing a) a healthcare information system and b) at least one processor, thus these computing components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Mere instructions to apply an exception using generic computer components or insignificant extra-solution activity in the form of WURC activity cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1-9 and 11-22 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
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.
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.
Claims 1-3, 12-14, 17-19, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2025/0285457 to McGinnis et al. in view of U.S. Patent No. 12,586,685 to Vladimirova et al.
As per claim 1, McGinnis et al. teaches a computer-implemented method for providing a treatment response prediction for a patient suffering from a cancerous disease, (see: paragraph [0004] where there is such a method) the computer-implemented method comprising:
--obtaining a whole slide image of the patient, (see: paragraph [0021] and 202 of FIG. 2A where there is receiving of a whole slide image of a patient) the whole slide image showing a tissue sample relating to the cancerous disease; (see: paragraph [0019] where there is a whole slide image showing tissue sample related to a cancerous disease)
--obtaining, from a healthcare information system, a supplementary information associated with the patient; (see: paragraph [0053] where there is obtaining of supplementary information in the form of annotations associated with the image)
--generating a treatment response prediction for one or more treatment options by applying a prediction function to the whole slide image; (see: 208 of FIG. 2A and paragraph [0032] where the diagnosis and treatment engine uses the composition profile to generate a prediction of a response to a treatment. Also see: paragraph [0030] where the composition profile includes data of the slide image) and
--providing the treatment response prediction (see: paragraphs [0019] – [0020] and 258 of FIG. 2B where there is providing of a prediction of a response to a treatment).
McGinnis et al. may not further, specifically teach:
--generating a treatment response prediction for one or more treatment options by applying a prediction function to the supplementary information.
Vladimirova et al. teaches:
--generating a treatment response prediction for one or more treatment options by applying a prediction function to the supplementary information (see: Abstract where both a molecular data (supplementary) and an image data are being received and then used to generate a treatment response prediction).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to generate a treatment response prediction for one or more treatment options by applying a prediction function to the supplementary information as taught by Vladimirova et al. in the method as taught by McGinnis et al. with the motivation(s) of improving the prediction accuracy (see: column 13, lines 3-16 of Vladimirova et al.).
As per claim 2, McGinnis et al. and Vladimirova et al. in combination teaches the method of claim 1, see discussion of claim 1. McGinnis et al. further teaches wherein the one or more treatment options comprise at least one of:
--a radiotherapy treatment,
--an immunotherapy treatment, (see: paragraph [0019] where there is an immunotherapy treatment)
--a chemotherapy treatment, or
--a treatment by surgical intervention.
As per claim 3, McGinnis et al. and Vladimirova et al. in combination teaches the method of claim 1, see discussion of claim 1. McGinnis et al. further teaches wherein the treatment response prediction comprises at least one of:
--a predicted susceptibility of the cancerous disease to the one or more treatment options,
--a probability for a reoccurrence of the cancerous disease based on the one or more treatment options, or
--a predicted survival rate of the patient with the one or more treatment options (see: paragraph [0032] where the treatment response is based on a variety of clinical outcomes including overall survival).
As per claim 12, McGinnis et al. and Vladimirova et al. in combination teaches the method of claim 1, see discussion of claim 1. McGinnis et al. further teaches wherein the supplementary information includes radiology image data depicting a manifestation of the cancerous disease in a body of the patient (see: paragraph [0022] where there is an annotation of data that may depict a manifestation of a disease).
As per claim 13, McGinnis et al. and Vladimirova et al. in combination teaches the method of claim 12, see discussion of claim 12. McGinnis et al. further teaches:
--providing an image analysis module configured to extract a radiological observable from radiology image data; (see: paragraph [0025] where information is being extracted from an image, thus this extraction is being provided) and
--applying the image analysis module on the radiology image data to obtain the radiological observable, (see: paragraph [0025] where information is being extracted from an image, thus this extraction is being applied to the image) wherein
--the prediction function is further configured to derive the treatment response prediction additionally based on the radiological observable, (see: paragraph [0038] where the extracted image data is used for determining a treatment response prediction) and
--the generating the treatment response includes additionally applying the prediction function to the radiological observable (see: paragraph [0038] where the extracted image data is used for determining a treatment response prediction).
As per claim 14, McGinnis et al. and Vladimirova et al. in combination teaches the method of claim 13, see discussion of claim 13. McGinnis et al. further teaches wherein the radiological observable comprises at least one of:
--a tumor burden of the patient in the radiology image data, (see: paragraphs [0032] and [0040] where there is a determination of a disease burden)
--a visual characteristic of a lesion depicted in the radiology image data, or
--a temporal evolution of a lesion depicted in the radiology image data.
As per claim 17, McGinnis et al. teaches a system for providing a treatment response prediction for a patient suffering from a cancerous disease, the system comprising:
--at least one processor (see: paragraph [0058] where there is such a processor) configured to cause the system to
--obtain a whole slide image of the patient, (see: paragraph [0021] and 202 of FIG. 2A where there is receiving of a whole slide image of a patient) the whole slide image showing a tissue sample relating to the cancerous disease, (see: paragraph [0019] where there is a whole slide image showing tissue sample related to a cancerous disease)
--obtain from a healthcare information system, supplementary information associated with the patient; (see: paragraph [0053] where there is obtaining of supplementary information in the form of annotations associated with the image)
--generate a treatment response prediction for one or more treatment options by applying a prediction function to the whole slide image; (see: 208 of FIG. 2A and paragraph [0032] where the diagnosis and treatment engine uses the composition profile to generate a prediction of a response to a treatment. Also see: paragraph [0030] where the composition profile includes data of the slide image) and
--provide the treatment response prediction (see: paragraphs [0019] – [0020] and 258 of FIG. 2B where there is providing of a prediction of a response to a treatment).
McGinnis et al. may not further, specifically teach:
--generating a treatment response prediction for one or more treatment options by applying a prediction function to the supplementary information.
Vladimirova et al. teaches:
--generating a treatment response prediction for one or more treatment options by applying a prediction function to the supplementary information (see: Abstract where both a molecular data (supplementary) and an image data are being received and then used to generate a treatment response prediction).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to generate a treatment response prediction for one or more treatment options by applying a prediction function to the supplementary information as taught by Vladimirova et al. in the system as taught by McGinnis et al. with the motivation(s) of improving the prediction accuracy (see: column 13, lines 3-16 of Vladimirova et al.).
As per claim 18, McGinnis et al. and Vladimirova et al. in combination teaches the method of claim 1, see discussion of claim 1. McGinnis et al. further teaches a non-transitory computer program product comprising program elements that induce a computing unit of a system to perform the method of claim 1, when the program elements are loaded into a memory of the computing unit (see: paragraph [0006] where there is a non-transitory medium where there are instructions in memory. The program is loaded into memory here).
As per claim 19, McGinnis et al. and Vladimirova et al. in combination teaches the method of claim 1, see discussion of claim 1. McGinnis et al. further teaches a non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by at least one processor of a system, cause the system to perform the method according to claim 1 (see: paragraph [0006] where there is a non-transitory medium where there are instructions in memory).
As per claim 22, McGinnis et al. and Vladimirova et al. in combination teaches the method of claim 1, see discussion of claim 1. McGinnis et al. further teaches wherein the generating the treatment response prediction includes
--extracting one or more features from whole slide images, the one or more features including one or more abstract feature, (see: paragraphs [0030] and [0031] where there is extraction of features from the image to get a composition profile) and
--generating the treatment response prediction based on the one or more features. (see: 208 of FIG. 2A and paragraph [0032] where the diagnosis and treatment engine uses the composition profile to generate a prediction of a response to a treatment. Also see: paragraph [0030] where the composition profile includes data of the slide image).
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2025/0285457 to McGinnis et al. in view of U.S. Patent No. 12,586,685 to Vladimirova et al as applied to claim 1, and further in view of U.S. 2023/0109108 to Banerjee et al.
As per claim 4, McGinnis et al. and Vladimirova et al. in combination teaches the method of claim 1, see discussion of claim 1. The combination may not further, specifically teaches:
1) --providing a certainty calculation module configured to output a certainty measure for a corresponding treatment response prediction, the certainty measure measuring a confidence of the corresponding treatment response prediction;
2) --applying the certainty calculation module to obtain a certainty measure for the treatment response prediction; and
3) --providing the certainty measure for the treatment response prediction.
Banerjee et al. teaches:
1) --providing a certainty calculation module configured to output a certainty measure for a corresponding treatment response prediction, (see: paragraph [0129] where there is providing of a module to output a certainty metric) the certainty measure measuring a confidence of the corresponding treatment response prediction; (see: paragraph [0129] where there is a measure of a confidence of a determination. The determination being related to a prediction response was taught in the independent claim rejection)
2) --applying the certainty calculation module to obtain a certainty measure for the treatment response prediction; (see: paragraph [0129] where the module for outputting the certainty metric is being applied) and
3) --providing the certainty measure for the treatment response prediction (see: paragraph [0129] where there is providing of a module to output a certainty metric)
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to 1) provide a certainty calculation module configured to output a certainty measure for a corresponding treatment response prediction, the certainty measure measuring a confidence of the corresponding treatment response prediction, 2) apply the certainty calculation module to obtain a certainty measure for the treatment response prediction, and 3) provide the certainty measure for the treatment response prediction as taught by Banerjee et al. in the method as taught by McGinnis et al. and Vladimirova et al. in combination with the motivation(s) of helping establish trust in the output (see: paragraph [0178] of Banerjee et al.).
As per claim 5, McGinnis et al., Vladimirova et al., and Banerjee et al. in combination teaches the method of claim 4, see discussion of claim 4. Banerjee et al. further teaches wherein the providing the certainty measure comprises:
--outputting the certainty measure together with the treatment response prediction to a user via a user interface (see: paragraph [0129] where there is outputting of a module to output a certainty metric).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 4, and incorporated herein.
Claims 6 and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2025/0285457 to McGinnis et al. in view of U.S. Patent No. 12,586,685 to Vladimirova further in view of U.S. 2023/0109108 to Banerjee et al. as applied to claims 4-5, and further in view of U.S. 2019/0043619 to Vaughn et al.
As per claim 6, McGinnis et al., Vladimirova et al., and Banerjee et al. in combination teaches the method of claim 4, see discussion of claim 4. The combination may not further, specifically teach:
--determining, based on the certainty measure, whether or not the treatment response prediction is conclusive; and
--in response to the treatment response prediction being inconclusive
--determining a piece of information relating to the patient that is different than the whole slide image and which is suited for rendering the treatment response prediction conclusive.
Vaughn et al. teaches:
--determining, based on the certainty measure, whether or not the treatment response prediction is conclusive; (see: paragraph [0185] where there is a determination of whether a prediction is conclusive or not. The prediction being related to a treatment response was taught in claim 1) and
--in response to the treatment response prediction being inconclusive
--determining a piece of information relating to the patient that is different than the whole slide image and which is suited for rendering the treatment response prediction conclusive (see: paragraph [0185] where, in response to a determination of being inconclusive, there is a determination of which information is suited for rendering a conclusive prediction. The prediction being related to a treatment response and the usage of a whole slide image were taught in claim 1).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to determine, based on the certainty measure, whether or not the treatment response prediction is conclusive and in response to the treatment response prediction being inconclusive determining a piece of information relating to the patient that is different than the whole slide image and which is suited for rendering the treatment response prediction conclusive as taught by Vaughn et al. in the method as taught by McGinnis et al., Vladimirova et al., and Banerjee et al. in combination with the motivation(s) of improving the sensitivity and specificity for determinations (see: Abstract of Vaughn et al.).
As per claim 20, McGinnis et al., Vladimirova et al., and Banerjee et al. in combination teaches the method of claim 5, see discussion of claim 5. The combination may not further, specifically teach:
--determining, based on the certainty measure, whether or not the treatment response prediction is conclusive; and
--in response to the treatment response prediction being inconclusive
--determining a piece of information relating to the patient that is different than the whole slide image and which is suited for rendering the treatment response prediction conclusive.
Vaughn et al. teaches:
--determining, based on the certainty measure, whether or not the treatment response prediction is conclusive; (see: paragraph [0185] where there is a determination of whether a prediction is conclusive or not. The prediction being related to a treatment response was taught in claim 1) and
--in response to the treatment response prediction being inconclusive
--determining a piece of information relating to the patient that is different than the whole slide image and which is suited for rendering the treatment response prediction conclusive (see: paragraph [0185] where, in response to a determination of being inconclusive, there is a determination of which information is suited for rendering a conclusive prediction. The prediction being related to a treatment response and the usage of a whole slide image were taught in claim 1).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to determine, based on the certainty measure, whether or not the treatment response prediction is conclusive and in response to the treatment response prediction being inconclusive determining a piece of information relating to the patient that is different than the whole slide image and which is suited for rendering the treatment response prediction conclusive as taught by Vaughn et al. in the method as taught by McGinnis et al., Vladimirova et al., and Banerjee et al. in combination with the motivation(s) of improving the sensitivity and specificity for determinations (see: Abstract of Vaughn et al.).
As per claim 21, McGinnis et al., Vladimirova et al., Banerjee et al., and Vaughn et al. in combination teaches the method of claim 6, see discussion of claim 6. Vladimirova et al. further teaches:
--retrieving the piece of information from the healthcare information system; (see: Abstract where there is reception of information from a system)
--processing the piece of information to provide an updated treatment response prediction; (see: Abstract where there is processing of this information to generate a prediction. Predictions can be performed over and over, thus this claim language describes a second prediction with this invention) and
--providing the updated treatment response prediction (see: Abstract where there is providing a prediction based on the information. Predictions can be performed over and over, thus this claim language describes a second prediction with this invention).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2025/0285457 to McGinnis et al. in view of U.S. Patent No. 12,586,685 to Vladimirova et al as applied to claim 1, and further in view of U.S. 2024/0305588 to Chu et al.
As per claim 11, McGinnis et al. and Vladimirova et al. in combination teaches the method of claim 1, see discussion of claim 1. The combination may not further, specifically teach:
--providing a data extraction module configured to search an electronic medical record of the patient for the supplementary information, the data extraction module including a large language model, and wherein
--the obtaining of the supplementary information includes
--accessing the electronic medical record of the patient in the healthcare information system, and
--applying the data extraction module to the electronic medical record to obtain the supplementary information.
Chu et al. teaches:
--providing a data extraction module configured to search an electronic medical record of the patient for the supplementary information, (see: paragraph [0015] where there is extraction of data from an EMR) the data extraction module including a large language model, (see: paragraph [0021] where there is a LLM) and wherein
--the obtaining of the supplementary information includes
--accessing the electronic medical record of the patient in the healthcare information system, (see: paragraph [0015] where there is extraction of data from an EMR, thus there is accessing of an EMR) and
--applying the data extraction module to the electronic medical record to obtain the supplementary information (see: paragraph [0015] where there is extraction of data from an EMR, thus there is application of an extraction module ).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to provide a data extraction module configured to search an electronic medical record of the patient for the supplementary information, the data extraction module including a large language model, and wherein the obtaining of the supplementary information includes accessing the electronic medical record of the patient in the healthcare information system, and applying the data extraction module to the electronic medical record to obtain the supplementary information as taught by Chu et al. in the method as taught by McGinnis et al. and Vladimirova et al. in combination with the motivation(s) of providing information (see: paragraph [0015] of Chu et al.).
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2025/0285457 to McGinnis et al. in view of U.S. Patent No. 12,586,685 to Vladimirova et al as applied to claim 1, and further in view of U.S. 2020/0384289 to Smith et al.
As per claim 15, McGinnis et al. and Vladimirova et al. in combination teaches the method of claim 1, see discussion of claim 1. The combination may not further, specifically teach wherein the one or more treatment options comprise at least two different radiotherapy treatment options, the at least two different radiotherapy treatment options differing in at least one of:
--a dose distribution,
--a dose threshold limit,
--a dose rate,
--a fractionation,
--a usage of proton/photon or electron radiation, or
--a usage of a co-planar or non-co-planar beam.
Smith et al. teaches:
--wherein the one or more treatment options comprise at least two different radiotherapy treatment options, the at least two different radiotherapy treatment options differing in at least one of:
--a dose distribution,
--a dose threshold limit,
--a dose rate, (see: paragraph [0081] where there is a dose rate)
--a fractionation, (see: paragraph [0079] where there is fractionation)
--a usage of proton/photon or electron radiation, or
--a usage of a co-planar or non-co-planar beam.
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the one or more treatment options includes a radiotherapy treatment with a dose rate greater than 40 Gy/sec as taught by Smith et al. in the method as taught by McGinnis et al. and Vladimirova et al. in combination with the motivation(s) of being a type of treatment (see: paragraphs [0081] and [0083] of Smith et al.).
As per claim 16, McGinnis et al. and Vladimirova et al. in combination teaches the method of claim 1, see discussion of claim 1. The combination may not further, specifically teach wherein the one or more treatment options includes a radiotherapy treatment with a dose rate greater than 40 Gy/sec.
Smith et al. teaches:
--wherein the one or more treatment options includes a radiotherapy treatment with a dose rate greater than 40 Gy/sec (see: paragraph [0081] where there is a dose rate greater than 40 Gy/sec).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the one or more treatment options includes a radiotherapy treatment with a dose rate greater than 40 Gy/sec as taught by Smith et al. in the method as taught by McGinnis et al. and Vladimirova et al. in combination with the motivation(s) of being a type of treatment (see: paragraphs [0081] and [0083] of Smith et al.).
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.
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684