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
This action is in response to the amendment filed on 03/16/2026. Claims 1, 6, 10-12, 14, and 18 are amended, and claims 2, 7, 8, and 19 are canceled. Claims 1, 3-6, and 9-18 are examined below.
Claim Rejections - 35 USC § 112
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 and 11 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.
The term “difference in content of a certain amount” in claim 6 is a relative term which renders the claim indefinite. The term “difference in content of a certain amount” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Regarding claim 11, the limitation “wherein the processing circuitry is further configured to create, as the document regarding the medical image of the target patient, a document in which an item having a difference between the plurality of medical documents, the difference being smaller than a difference of an item for which specific information is addable or any of options is selectable through the input operation, is described in a character color according to magnitude of the difference” is indefinite. The manner in which the limitation is written renders the metes and bounds of the claim unclear. For sake of examination, the Examiner shall interpret the limitation as color-coding portions of the document (e.g. report) associated with the target patient based on a level of variance in the medical documents used in creating the document.
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, 3-6, and 9-18 re rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more. Representative claim 1 recites (additional limitations crossed out)
obtain a medical image of a target patient and information indicating an area of interest in the medical image as accompanying information;
access a patient information database that stores medical images of a plurality of patients, make an evaluation of a similarity measure for the plurality of patients based on image analysis of the obtained accompanying information and the medical images of the plurality of patients, and extract similar patients similar to the target patient using the evaluation of the similarity measure;
obtain a plurality of medical images and a plurality of medical documents created for the extracted similar patients;
obtain a summary of the plurality of medical images by analyzing the medical images, and create a document regarding the medical image of the target patient based on the summary of the medical images, the obtained accompanying information, and a summary of the plurality of obtained medical documents; and
display the created document regarding the medical image of the target patient, wherein
narrow down a number of similar patients in accordance with the input operation;
narrow down a number of medical images and medical documents in accordance with the narrowing down of the number of similar patients; and
correct the document regarding the medical image of the target patient based on a summary of medical images and a summary of medical documents remaining after the narrowing down of the number of medical images and medical documents.
The above limitations, as drafted, are processes that, under their broadest reasonable interpretation, is a process that, under its broadest reasonable interpretation covers managing personal behavior or relationships or interactions between people, as well as performance of limitations by the human mind or with pen and paper. That is, other than reciting the claims as being performed by a “medical image analyzing apparatus comprising processing circuitry and an input interface”, nothing in the claims precludes the steps as being described as managing personal behavior or relationships or interactions between people, or performance of limitations by the human mind or with pen and paper. The claims, as written describe obtaining a medical image of a target patient and information indicating an area of interest in the medical image, accessing a patient information database that stores medical images of patients, extracting patients similar to the target patient, obtaining medical images and medical documents for the similar patients, obtaining a summary of the medical images, creating and displaying a document regarding the medical image of the target patient based at least on the summary, receiving an indication to correct the displayed document, narrowing down a number of the patients similar to the target patient and their associated medical images and documents based on the indication, and correcting the document based on a summary of the remaining medical images and documents. If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, describes steps that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a “medical image analyzing apparatus comprising processing circuitry and input interface” to perform the steps. This additional element is recited at a high level of generality (see at least paragraphs [0019]-[0024]) such that it amounts to no more than mere instructions to apply the exception using generic computing components. 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 therefore still directed to an abstract idea.
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 element of a “medical image analyzing apparatus comprising processing circuitry” to perform the claimed steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, the claims are not found to be patent eligible.
Claim 18 features limitations similar to those of claim 1, and is therefore also found to be directed to an abstract idea without significantly more.
Claims 3-6, and 9-17 are dependent on claim 1, and include all the limitations of claim 1. Therefore, they are also found to be directed to an abstract idea. Claim 3 states “wherein the processing circuitry is further configured to create the document regarding the medical image of the target patient using a large language model”. However the “model” is recited at a high level of generality and may reasonably be considered merely being applied to the judicial exception (i.e., “apply it”). The remaining dependent claims have not been found to integrate the judicial exception into a practical application, or provide significantly more than the abstract idea since they merely further narrow the abstract idea. Therefore, the dependent claims are found to be directed to an abstract idea without significantly more
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.
Claim(s) 1, 4-6, 9-10, 12-16, and 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Eswaran (US 2020/0019617).
Regarding claim 1, Eswaran discloses A medical image analyzing apparatus comprising:
processing circuitry configured to: obtain a medical image of a target patient and information indicating an area of interest in the medical image as accompanying information; (See at least Para. [0039] – “The general idea of how the system works is illustrated in FIG. 2. A radiology image 200 is obtained, e.g., using conventional imaging equipment, and supplied to the system 202 of this disclosure. The image 200 is considered the query image; that is, the medical professional seeks to find similar images to the image 200. The query image 200 is associated with medical information, metadata, reports, etc. (collectively "annotations").”)
Eswaran also discloses:
access a patient information database that stores medical images of a plurality of patients, make an evaluation of a similarity measure for the plurality of patients based on image analysis of the obtained accompanying information and the medical images of the plurality of patients, and extract similar patient similar to the target patient using the evaluation of the similarity measure;
obtain a plurality of medical images and a plurality of medical documents created for the extracted similar patients; and
See at least Para. [0022] – “Accordingly, once the set of similar images have been identified, relevant information is returned to the user from this set. This would normally include not only the images themselves, but also metadata associated with each of these images like radiology reports, clinical decisions made (e.g., prescribing of antibiotics, diuretics), classification diseases/conditions associated with the similar image, and information or statistics relating to a grouping/aggregation of these results.”, and Para. [0053] – “The software architecture of FIG. 4 can be realized in other formats and arrangements of the basic building blocks or objects. FIG. 5 illustrates one possible variation. In this configuration the query image 200 is received by a dispatcher/pooler 502 which includes a fetcher 406 which retrieves a candidate set of similar images from a data store or repository 500. The set of images is sent to a dispatcher 404 which sends the image query and the candidate images to a scoring module 408 which includes three different scorers 408A, 408B and 408C. Each module 408A, 408B and 408C uses a different modelling technique to generate the similarity score for the candidate images. These modelling techniques each capture or take into account two or more similarity attributes between the query image and the set of candidate similar radiology images, and the associated annotations, such as patient, diagnostic, and visual similarity. These attributes of similarity can be represented as coordinate axes in a multidimensional embedding space, see FIG. 11, where feature vectors of the image and associated annotations are used to plot the position of the query image and the candidate set of similar images in this feature space, and distance metrics or other types of modeling techniques described below are then used to generate similarity scores reflecting the similarity.”
obtain a summary of the plurality of medical images by analyzing the medical images, and create a document regarding the medical image of the target patient based on the summary of the medical images, the obtained accompanying information, and a summary of the plurality of obtained medical documents. (See at least Fig. 9, and accompanying text of Para. [0127])
display the created document regarding the medical image of the target patient, wherein
the medical image analyzing apparatus further comprises an input interface configured to receive an input operation for correcting the displayed document regarding the medical image of the target patient, and
the processing circuitry is further configured to:
narrow down a number of similar patients in accordance with the input operation;
narrow down a number of medical images and medical documents in accordance with the narrowing down of the number of similar patients; and
correct the document regarding the medical image of the target patient based on a summary of medical images and a summary of medical documents remaining after the narrowing down of the number of medical images and medical documents.
See at least Para. [0127] – “The bar 902 on the right hand has a condition "change in line or tube placement" and an option (X) to remove the condition if it is not relevant to the diagnosis of the query image.”, and “The bars 908 can be clicked in which case a filtering operation occurs which filters the similar images to only those represented in that selected bar.”, Para. [0129] – “In summary, once the set of similar images have been identified, relevant information is returned to the user from this set. This would normally include not only the images themselves, but also metadata associated with each of these images like radiology reports, clinical decisions made (e.g., prescribing of antibiotics, diuretics), classification diseases/conditions associated with the similar image, an information relating to a grouping/aggregation of these results.”; and Fig. 9.
Regarding claim 4, Eswaran discloses The medical image analyzing apparatus according to claim 1, wherein the processing circuitry is further configured, in addition to extract the similar patients using the evaluation of the similarity measure based on the image analysis, to compare the accompanying information regarding the target patient with a plurality of medical documents created for each of a plurality of past patients for each of a plurality of items, calculate distances between the accompanying information and the plurality of medical documents for each of the items, calculate degrees of similarity that are sums of the distances calculated for the items, and extract the similar patients from the plurality of past patients based on the calculated degrees of similarity. (See at least Para. [0066] – “As noted above, the system uses one or more scorers which receive the query image and the set of candidate similar radiology images (identified by the fetcher) and generates a similarity score between the query image and each candidate image, using the image data as well as underlying annotations (image metadata, reports, patient information etc.) associated with the images. The score can be computed for example based on pre-computed embedding and a standard distance metric (e.g., cosine or Euclidean distance) in the embedding space. For example, the scorer looks up the embedding of an image in a database and then uses a distance measure in the embedding space. See the discussion of FIG. 11, supra.”)
Regarding claim 5, Eswaran discloses The medical image analyzing apparatus according to claim 4, wherein the processing circuitry is further configured to extract patients having at least one of highest similarity measure and highest degrees of similarity among the plurality of past patients as the similar patients. (See at least Claim 1 – “a pooler receiving the similarity scores from the one or more scorers, ranking the candidate images, and returning a list of the candidate images reflecting the ranking;”)
Regarding claim 6, in light of the 112 rejection, Eswaran discloses The medical image analyzing apparatus according to claim 1, wherein the processing circuitry is further configured to:
extract a plurality of similar patients;
obtain a plurality of medical documents created for each of the plurality of similar patients; and
create the document regarding the medical image of the target patient based on a summary of two or more medical documents among the plurality of medical documents, contents of the two or more medical documents having a difference from each other by a certain amount or more among the plurality of medical documents.
(See at least Para. [0022] – “Accordingly, once the set of similar images have been identified, relevant information is returned to the user from this set. This would normally include not only the images themselves, but also metadata associated with each of these images like radiology reports, clinical decisions made (e.g., prescribing of antibiotics, diuretics), classification diseases/conditions associated with the similar image, and information or statistics relating to a grouping/aggregation of these results.”, Para. [0057] – “ Similar medical images to a query image are found by projecting the query image feature vectors into the embedding of FIG. 4 scoring the neighboring images by distance in the multidimensional space. For example, referring to FIG. 11, the cluster of images 1104 containing image 1102Arepresents a group of images which are similar in all three axes to a query image 1106 indicated by the star. In this example, if the query image was a chest X-ray positive for pneumothorax, patient was a smoker, etc. the query image would be positioned in the location of the star 408 and the images in the cluster 1104 would be scored lower (i.e., more similar) than for example the image 1102B which is further away.”, and Para. [0127] – “FIG. 9 is an illustration of another display of a query image 202 and the results 204.”, “Other aggregate statistics are shown in the region 910. The area 912 shows the most similar images to the query image. A load more icon 914 allows the user to load more images and a scroll bar allows the user to navigate down to the newly loaded images.”)
Regarding claim 9, Eswaran discloses The medical image analyzing apparatus according to claim 1, wherein
the processing circuitry is further configured to extract a plurality of similar patients, and
the summary of the medical documents includes items and styles common among a plurality of medical documents created for each of the plurality of similar patients.
(See at least Para. [0124] – “The groupings can involve the aggregation of relevant common text from radiology free text reports. For example, while there may not be a specific label indicating that an endotracheal tube is misplaced, we can aggregate together images that are associated with reports having common phrases that imply this condition to be present, for example reports having text entries "endotracheal tube at the level of the carina", "endotracheal tube tip terminates in right main bronchus", or "ET tube tip could be advanced a couple of centimeters for standard positioning." Attention mechanisms in the scorers can be used to identify portions of the free text reports, such as particular words or phrases, which contribute the most to the similarity score.”).
Regarding claim 10, Eswaran discloses The medical image analyzing apparatus according to claim 7, wherein
the processing circuitry is further configured to extract a plurality of similar patients,
the summary of the medical documents includes items and styles common among a plurality of medical documents created for each of the plurality of similar patients, and
the processing circuitry is further configured to create, as the document regarding the medical image of the target patient, a document in which an item and a style common among the plurality of medical documents are included and specific information is addable through the input operation or any of options is selectable through the input operation for an item having a difference among the plurality of medical documents.
See at least Para. [0124] – “The groupings can involve the aggregation of relevant common text from radiology free text reports. For example, while there may not be a specific label indicating that an endotracheal tube is misplaced, we can aggregate together images that are associated with reports having common phrases that imply this condition to be present, for example reports having text entries "endotracheal tube at the level of the carina", "endotracheal tube tip terminates in right main bronchus", or "ET tube tip could be advanced a couple of centimeters for standard positioning." Attention mechanisms in the scorers can be used to identify portions of the free text reports, such as particular words or phrases, which contribute the most to the similarity score.”), and Para. [0127] – “FIG. 9 is an illustration of another display of a query image 202 and the results 204. This configuration emphasizes summarizing similar patient data over time, but still provides individual instances of similar images. The horizontal bars 900 identify similar conditions, with the counts (numbers) of similar images that were returned. For example, the first bar has a condition "pneumothorax" and a count of 13, the second bar has a condition of "pulmonary embolism" and a count of 31. The bar 902 on the right hand has a condition "change in line or tube placement" and an option (X) to remove the condition if it is not relevant to the diagnosis of the query image.”
Regarding claim 12, Eswaran discloses The medical image analyzing apparatus according to claim 2, wherein the processing circuitry is further configured to further display the plurality of medical documents created for the similar patients and a plurality of medical images created for the similar patients. (See at least Fig. 9, Items 904 and 912)
Regarding claim 13, Eswaran discloses The medical image analyzing apparatus according to claim 1, wherein the processing circuitry is further configured to obtain at least one of an examination purpose, a patient attribute, and an examination finding as the accompanying information. (See at least Para. [0067] – “The scorers implement a modelling technique to generate the similarity score that can capture similarity on many different axes (e.g., diagnostic, visual, patient, etc.) Diagnostic, visual and patient attributes are some of the many signals that could be important on specific axes of similarity, but these three are not meant to be an exhaustive list.”)
Regarding claim 14, Eswaran discloses The medical image analyzing apparatus according to claim 7, wherein the processing circuitry is further configured to obtain, as the accompanying information, at least one of an examination purpose, a patient attribute, and an examination finding, and input information based on the input operation for correcting the medical document of the target patient. (See at least Para. [0067] – “The scorers implement a modelling technique to generate the similarity score that can capture similarity on many different axes (e.g., diagnostic, visual, patient, etc.) Diagnostic, visual and patient attributes are some of the many signals that could be important on specific axes of similarity, but these three are not meant to be an exhaustive list.”, and Para. [0127] – “The bar 902 on the right hand has a condition "change in line or tube placement" and an option (X) to remove the condition if it is not relevant to the diagnosis of the query image.”, and “The bars 908 can be clicked in which case a filtering operation occurs which filters the similar images to only those represented in that selected bar.”)
Regarding claim 15, Eswaran discloses The medical image analyzing apparatus according to claim 1, wherein the processing circuitry is further configured to calculate a score of each of the plurality of medical documents created for the similar patients and obtain, based on the calculated score, a medical document to be used to create the document regarding the medical image of the target patient from among the plurality of medical documents. (See at least Para. [0066] – “As noted above, the system uses one or more scorers which receive the query image and the set of candidate similar radiology images (identified by the fetcher) and generates a similarity score between the query image and each candidate image, using the image data as well as underlying annotations (image metadata, reports, patient information etc.) associated with the images. The score can be computed for example based on pre-computed embedding and a standard distance metric (e.g., cosine or Euclidean distance) in the embedding space. For example, the scorer looks up the embedding of an image in a database and then uses a distance measure in the embedding space. See the discussion of FIG. 11, supra.”, and Para. [0122] – “As explained in FIG. 3, once the similar images are retrieved, scored and ranked, they are presented to the user. The information that is returned to a user includes not just the similar images (and associated annotations, e.g. metadata, reports or excerpts thereof), but also information that can be culled, inferred or aggregated from the result set of the similar images, such as the statistics that can be computed from the query images.”
Regarding claim 16, The medical image analyzing apparatus according to claim 15, wherein the processing circuitry is further configured to calculate, based on a plurality of indices related to calculation of scores, scores of the plurality of medical documents for each of the indices, combine the calculated scores for the indices to calculate a final score of each of the plurality of medical documents, and obtain, based on the calculated final score, a medical document to be used to create the document regarding the medical image of the target patient from among the plurality of medical documents. (See at least Para. [0099]-[0102], and [0114]-[0115])
Claim 18 features limitations similar to those of claim 1, and is therefore rejected using the same rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eswaran (US 2020/0019617) in view of “Retrieval Augmented Chest X-Ray Report Generation using OpenAI GPT models.” by Mercy Ranjit, available May 5, 2023, hereinafter referred to as Ranjit1.
Regarding claim 3, Eswaran does not explicitly disclose The medical image analyzing apparatus according to claim 1, wherein the processing circuitry is further configured to create the document regarding the medical image of the target patient using a large language model. (See at least Ranjit, page 3 – “As the image and text embeddings were aligned during the contrastive pre-training, the most relevant text radiology text (reports or sentences) is retrieved for an input x-ray image based on the similarity of the input image embeddings to the radiology report embeddings. A consolidated radiology report impression is generated from the filtered set of records using the OpenAI text-davinci-003, gpt-3.5-turbo and gpt-4 models.”, and page 5 – “We in this work endow the LLMs with the index of radiology report text and use it as a knowledge base to allow LLMs generate a radiology report impression for an input radiology image. To enable the multimodal retrieval of Images and Text, we use the contrastively pretrained vision language model from CXR-ReDonE [Ramesh et al. (2022)] which improved the work of CXR-RepaiR [Endo et al. (2021)] for multimodal retrievals to see if augmented generation on top of these retrievals can further push the report generation benchmark.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Eswaran to utilize the teachings of Ranjit since both are within the same field of endeavor (i.e. analysis of radiology reports ), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.)
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eswaran (US 2020/0019617) in view of “Trust it or not: Confidence-guided automatic radiology report generation” by Yixin Wang, available February 3, 2022, hereinafter referred to as Wang2.
Regarding claim 11, Eswaran does not explicitly disclose The medical image analyzing apparatus according to claim 10, wherein
the processing circuitry is further configured to create, as the document regarding the medical image of the target patient, a first document,
the first document has a first item having a difference between the plurality of medical documents, the difference being smaller than a difference of a second item for which specific information is addable or any of options is selectable through the input operation, and
the first item is described in a character color according to magnitude of the difference.
(In light of the 112 rejection, Wang teaches this. See at least Page 4 – “ The textual uncertainty can be obtained by the variance of semantic similarity scores among generated diagnostic reports. Combined with medical word embeddings (such as BioWordVec [50]), WRD [20] can be a feasible similarity measurement for reports.”, and Pages 8-9 – “Fig. 5(b)-(d) show the meanings of sentence-level uncertainty, where one case is presented, along with its several predictions MC-R1 to MC-RT under T times MC dropout sampling and the reference report MC-R among them. Different colors represent the uncertainty values of the corresponding sentences. Our proposed reference report covers every information in the ground truth including lungs, pleural effusion, heart and spine with different uncertainties. One can easily observe that the sentence describing spine has the highest uncertainty, which can be explained through the uncertain predicted sentence on spine from MC - R1 to MC - RT . This suggests that the model cannot make sure whether the spine is normal or not. Such an uncertainty should be warned by the report generation system so that doctors can make further decisions based on it.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Eswaran to utilize the teachings of Wang since it may assist clinicians in decision-making in regards to radiology reports.)
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eswaran (US 2020/0019617) in view of “The QUEST for quality online health information: validation of a short quantitative tool” by Julie Robillard, published October 19, 2018, hereinafter referred to as Robillard3.
Regarding claim 17, Eswaran does not explicitly disclose The medical image analyzing apparatus according to claim 16, wherein the plurality of indices includes at least two of a record of use of a medical document, man-hours required to create the medical document, credibility of content of the medical document, and proficiency of a creator of the medical document. (See at least pages 3-4 of Robillard – “There is currently no singular quantitative tool that has undergone a validation process, can be used for a broad range of health information, and strikes a balance between ease of use, concision and comprehensiveness (Fig. 1). To address these gaps, we developed the QUality Evaluation Scoring Tool (QUEST). The QUEST quantitatively measures six aspects of the quality of online health information: authorship, attribution, conflict of interest, currency, complementarity, and tone (Fig. 2), yielding an overall quality score between 0 and 28. Attribution is measured through two items, yielding a seven-item evaluation for six measures of health information quality. The criteria were chosen based on a review of existing tools used to evaluate the quality of online information by Chumber et al. [29], Sandvik et al. [28], and Silberg et al. [30]; content analysis was used to capture the overarching categories assessed by these tools [31].”, “When applying the QUEST, each of the seven quality items is assigned a weighted score. The weighting of each criterion was developed based on two factors: (i) how critical it is to the overall quality of the article, established by a preliminary analysis of a sample of websites, and (ii) consideration of the criterion’s ethical implications. One criterion, attribution, is measured through a two-step process by identifying (1) the presence of references to scientific studies and, (2) the type of studies referenced, if any (e.g., animal models, observational studies, meta-analyses, clinical trials). The second item, which assigns a ranking based on the types of studies included, is in accordance with the GRADE criteria for clinical evidence [32]. This item is scored as a support to the overall quality of the health information presented, not as a judgment of the referenced studies’ quality.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Eswaran to utilize the teachings of Robillard since it would ensure the validity of the reports utilized in Eswaran. The Examiner further notes that the language “wherein the plurality of indices includes at least two of a record of use of a medical document, man-hours required to create the medical document, credibility of content of the medical document, and proficiency of a creator of the medical document” is simply a label for the indices and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the particular type of indices) which does not explicitly alter or impact the steps of the method (i.e., calculating a score based on indices) does not patentably distinguish the claimed invention from the prior art in terms of patentability. Therefore, it would have been obvious to a person of ordinary skill in the art at the time of invention to have the indices of Eswaran be one of the particular indices disclosed in claim 17 because the particular type of indices do not functionally alter or relate to the steps of the method and merely labeling the indices differently from that of the prior art does not patentably distinguish the claimed invention.)
Response to Arguments
Applicant's arguments regarding claims rejected under 35 U.S.C. 112(b) have been fully considered but they are not persuasive. Applicant argues with substance:
Regarding claim 6, Applicant argues that “a person skilled in the art would understand that two documents differ from each other by an amount such that two documents would be considered as different”. This is not persuasive. The limitation states, “…contents of the two or more medical documents having a difference from each other by a certain amount”. The “difference” of contents is relative because there are no parameters in determining what actually establishes said difference. For example, the contents two documents concerning X-rays may be considered the exact same in that they are both in regards to X-rays. However, the contents may be considered as different if the parameter was concerning X-rays of a particular body part (i.e., one document is X-ray of foot, while the other is X-ray of hand).
Applicant’s arguments concerning claim 11 are not persuasive as the amendments fail to clarify the metes and bounds of the claim.
Applicant's arguments regarding claims rejected under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant argues that the claims are not directed to a mental process due to reciting “an apparatus having an input interface which receives an input operation and having processing circuitry which creates and displays the document regarding the medical image of the target patient”. This is not persuasive. As indicated in the body of the 101 rejection above, the recitation of these additional elements amounts to no more than mere instructions to apply the exception using generic computing components.
Applicant argues that the claims provide technical improvement in the process of creating the document and the quality of the document by reducing processing and creating the document using more appropriate information. This is not persuasive. Any alleged reduction in processing is an inherent result of processing less data. Further, even if more “appropriate” information was used, this is not a technical solution as the functionality of any involved computing elements remain unchanged.
Applicant’s argument that the claims provide an improvement over conventional keyword-based search systems is not persuasive as it is merely a conclusory statement.
Applicant's arguments regarding claims rejected under 35 U.S.C. 102 have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant argues that the Eswaran does not disclose claim 8 (now incorporated into claim 1). Applicant argues that that “image 204 is not both a medical image and medical report of the target patient”. This is not persuasive. Applicant points to paragraph [0022] – “Accordingly, once the set of similar images have been identified, relevant information is returned to the user from this set. This would normally include not only the images themselves, but also metadata associated with each of these images like radiology reports, clinical decisions made (e.g., prescribing of antibiotics, diuretics), classification diseases/conditions associated with the similar image, and information or statistics relating to a grouping/aggregation of these results.” This indicates that medical images and medical reports are provided. Next the Examiner points to paragraph [0127] which states, in part, “The bars 908 can be clicked in which case a filtering operation occurs which filters the similar images to only those represented in that selected bar.” Based on this, it is apparent that the system provides the filtered images and their associated metadata which includes reports (i.e., provides a corrected document).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Google’s SMILY is reverse image search for cancer diagnosis” by Devin Coldewey, available July 19, 20194 – discloses a user searching for images similar to a submitted medical image, as well as removing images with particular features.
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 KYLE G ROBINSON whose telephone number is (571)272-9261. The examiner can normally be reached Monday - Thursday, 7:00 - 4:30 EST; Friday 7:00-11:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached at 571-272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KYLE G ROBINSON/Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
1 Available at https://arxiv.org/pdf/2305.03660
2 Available at https://arxiv.org/pdf/2106.10887
3 Available at https://link.springer.com/article/10.1186/s12911-018-0668-9
4 Available at https://techcrunch.com/2019/07/19/googles-smily-is-reverse-image-search-for-cancer-diagnosis/
Prosecution Insights