DETAILED ACTION
Notice to Applicant
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is in response to the amendment filed 12/18/25. Claims 1, 3-4, 6-19, and 21-22 are pending.
The IDS filed 12/18/25 has been considered.
Claim Objections
Claim 1 is objected to because of the following informalities: a possible typographical in the amended language “corresponding to a section of medial report.” Appropriate correction is required.
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, 3-4, 6-19, and 21-22 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.
35 USC 101 enumerates four categories of subject matter that Congress deemed to be appropriate subject matter for a patent: processes, machines, manufactures and compositions of matter. As explained by the courts, these “four categories together describe the exclusive reach of patentable subject matter. If a claim covers material not found in any of the four statutory categories, that claim falls outside the plainly expressed scope of Section 101 even if the subject matter is otherwise new and useful.” In re Nuijten, 500 F.3d 1346, 1354, 84 USPQ2d 1495, 1500 (Fed. Cir. 2007). Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter? Applicant’s claims fall within at least one of the four categories of patent eligible subject matter because claims 1-10 and 11-22, are drawn to methods.
Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101 (i.e., process, machine, manufacture, or composition of matter) in Step 1 does not complete the eligibility analysis. Claims drawn only to an abstract idea, a natural phenomenon, and laws of nature are not eligible for patent protection. As described in MPEP 2106, subsection III, Step 2A of the Office’s eligibility analysis is the first part of the Alice/Mayo test, i.e., the Supreme Court’s “framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l,134 S. Ct. 2347, 2355, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. at 77-78, 101 USPQ2d at 1967-68).
In 2019, the United States Patent and Trademark Office (USPTO) prepared revised guidance (2019 Revised Patent Subject Matter Eligibility Guidance) for use by USPTO personnel in evaluating subject matter eligibility. The framework for this revised guidance, which sets forth the procedures for determining whether a patent claim or patent application claim is directed to a judicial exception (laws of nature, natural phenomena, and abstract ideas), is described in MPEP sections 2106.03 and 2106.04.
As explained in MPEP 2106.04(a)(2), the 2019 Revised Patent Subject Matter Eligibility Guidance explains that abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes. Moreover, this guidance explains that a patent claim or patent application claim that recites a judicial exception is not ‘‘directed to’’ the judicial exception if the judicial exception is integrated into a practical application of the judicial exception. A claim that recites a judicial exception, but is not integrated into a practical application, is directed to the judicial exception under Step 2A and must then be evaluated under Step 2B (inventive concept) to determine the subject matter eligibility of the claim.
Step 2A asks: Does the claim recite a law of nature, a natural phenomenon (product of nature) or an abstract idea? (Prong One) If so, is the judicial exception integrated into a practical application of the judicial exception? (Prong Two) A claim recites a judicial exception when a law of nature, a natural phenomenon, or an abstract idea is set forth or described in the claim. While the terms “set forth” and “describe” are thus both equated with “recite”, their different language is intended to indicate that there are different ways in which an exception can be recited in a claim. For instance, the claims in Diehr set forth a mathematical equation in the repetitively calculating step, while the claims in Mayo set forth laws of nature in the wherein clause, meaning that the claims in those cases contained discrete claim language that was identifiable as a judicial exception. The claims in Alice Corp., however, described the concept of intermediated settlement without ever explicitly using the words “intermediated” or “settlement.” A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
In the instant case, claims 1-22 recite(s) a method and system for certain methods of organizing human activities, which is subject matter that falls within the enumerated groupings of abstract ideas described in MPEP 2106.04 (2019 Revised Patent Subject Matter Eligibility Guidance) Certain methods of organizing human activities includes fundamental economic practices, like insurance; commercial interactions (i.e. legal obligations, marketing or sales activities or behaviors, and business relations). Organizing human activity also encompasses managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions.) The recited method and system are drawn to generating/updating a medical imaging exam report.
In particular, claim 1 recites a method comprising:
determining a type of a medical imaging exam;
determining a finding item template associated with the determined type of medical imaging exam, wherein each of a plurality of finding item templates includes different sets of one or more finding items and associated finding item criteria;
for each phrase in the text input, evaluate the finding item criteria of the determined finding item template with reference to the phrase, provides a matching finding item for each phrase, based on likelihoods of the phrase matching with particular finding items, and not other finding items that are not included in the determined finding item template; and a
associating the phrase to a matching finding item; and
automatically updating a report to include the phrases associated with the matching finding items.
Similarly, claim 11 recites a method comprising:
determining one or more attributes of a medical imaging exam;
generating a finding item template including one or more finding items associated with the one or more attributes of the medical imaging exam, wherein each of the one or more finding items is associated with corresponding finding item criteria;
for each phrase in the text input, evaluate the finding item criteria of the determined finding item template with reference to the phrase, provide(s) a matching finding item for each phrase; and
associating the phrase to a matching finding item; and
automatically updating a report to include the phrases associated with the matching finding items.
This judicial exception is not integrated into a practical application because the claim language does not recite any improvements to the functioning of a computer, or to any other technology or technical field (See MPEP 2106.04(d)(1); see also MPEP 2106.05(a)(I-II)). Moreover, the claims do not integrate the judicial exception into a practical application because the claimed invention does not: apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)); effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)); or apply or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment see MPEP 2106.05(e). (Considerations for integration into a practical application in Step 2A, prong two and for recitation of significantly more than the judicial exception in Step 2B)
While abstract ideas, natural phenomena, and laws of nature are not eligible for patenting by themselves, claims that integrate these exceptions into an inventive concept are thereby transformed into patent-eligible inventions. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2354, 110 USPQ2d 1976, 1981 (2014) (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71-72, 101 USPQ2d 1961, 1966 (2012)). Thus, the second part of the Alice/Mayo test is often referred to as a search for an inventive concept. Id. An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting “the Government’s invitation to substitute Sections 102, 103, and 112 inquiries for the better established inquiry under Section 101”). As made clear by the courts, the “‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the Section 101 categories of possibly patentable subject matter.” Intellectual Ventures I v. Symantec Corp.,838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9).
As described in MPEP 2106.05, Step 2B of the Office’s eligibility analysis is the second part of the Alice/Mayo test, i.e., the Supreme Court’s “framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. _, 134 S. Ct. 2347, 2355, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. 66, 101 USPQ2d 1961 (2012)). Step 2B asks: Does the claim recite additional elements that amount to significantly more than the judicial exception? The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The additional steps amount to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Examples of insignificant extra-solution activity include mere data gathering, selecting a particular data source or type of data to be manipulated, and insignificant application. In the instant case, claims 1 and 11 additionally recite “receiving text input from a user of the computing system.” The additional step amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering)
Claims 1 and 11 recites additional limitation(s), including: a computing system having one or more hardware computer processors; a one or more non-transitory computer readable storage device storing software instructions executable by the computing system. However, these additional elements are generic components performing functions well-understood, routine and conventional activities that amount to no more than implementing the abstract idea with a computerized system.
The generic nature of the computer system used to carryout steps of the recited method is underscored by the system description in the instant application, which discloses: “The computing system 150 may take various forms. In one embodiment, the computing system 150 may be a computer workstation having modules 151, such as software, firmware, and/or hardware modules. In other embodiments, modules 151 may reside on another computing device, such as a web server… the computing system 150 comprises one or more of a server, a desktop computer, a workstation, a laptop computer, a mobile computer, a Smartphone, a tablet computer, a cell phone, a personal digital assistant, a gaming system, a kiosk, any other device that utilizes a graphical user interface, including office equipment, automobiles, industrial equipment, and/or a television, for example. In one embodiment, for example, the computing system 150 comprises a tablet computer that provides a user interface responsive to contact with a human hand/finger or stylus” (par. 25-26)
The application explains: “The computing system 150 may include one or more hardware computing processors 152. The computer processors 152 may include central processing units (CPUs) and may further include dedicated processors such as graphics processor chips, or other specialized processors.” (see par. 28)
The specification further discloses: “These computer readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.” (par. 96)
Such language underscores that the applicant's perceived invention/ novelty focuses on the computerized implementation of the abstract idea, not the underlying structure of generic system components.
Furthermore, the courts have recognized certain computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05 (d) (II)). Among these are the following features, which are recited in claim 1 and claim 11:
- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
- Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Claims 1 and 11 also recite a categorization model and “applying a categorization model configured to.” As currently drafted, the recitation of models and a generative machine learning model in the claims fails to recite significantly more, and amounts to 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., 573 U.S. at 225-26, 110 USPQ2d at 1984. (See MPEP 2106.05(A))
The recitation of “applying a categorization model” recites only the idea of a solution or outcome, but the claims fail to recite details of how a solution to a problem is accomplished. Moreover, as drafted, the claims invoke the use of the “applying a categorization model” merely as a tool to perform an existing process. More specifically, the claims are drawn to determining where text should be included in a medical report to comply with a particular format (template)and updating the report accordingly.
Claims 2-10 are dependent from Claim 1 and include(s) all the limitations of claim(s) 1. However, the additional limitations of the claims 2-10 fail to recite significantly more than the abstract idea. More specifically, the additional limitations further define the abstract idea with additional steps or details regarding data types; or additional steps which amount to insignificant extra solution activities. Therefore, claim(s) 2-10 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claims 12-22 are dependent from Claim 11 and include(s) all the limitations of claim(s) 11. However, the additional limitations of the claims 12-22 fail to recite significantly more than the abstract idea. More specifically, the additional limitations further define the abstract idea with additional steps or details regarding data types; or additional steps which amount to insignificant extra solution activities. Therefore, claim(s) 12-22 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Because Applicant’s claimed invention recites a judicial exception that is not integrated into a practical application and does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself, the claimed invention is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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, 3-4, 6-19, and 21-22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Paik et al (WO 2022212771 A2)
Claim 1. Paik discloses a computerized method, performed by a computing system having one or more hardware computer processors and one or more non-transitory computer readable storage device storing software instructions executable by the computing system to perform the computerized method (par. 9-10; par. 84-85); comprising:
determining a type of a medical imaging exam; (par. 15-determining modality; time stamps; par. 111; par. 226-hanging protocol definition including matching criteria for modality)
determining a finding item template associated with the determined type of medical imaging exam, (par. 155- The reporter module can perform functions including report templates and macros, speech-to-text input, and dictaphone-based template field navigation; par. 156-the user can perform one or more of the following: (1) select anatomic regions in images to navigate through report template fields, (2) select report template fields to navigate images) wherein each of a plurality of finding item templates includes:
different sets of one or more finding items and associated finding item criteria, each corresponding to a section of a medical report:; (par. 155- The reporter module can perform functions including report templates and macros, speech-to-text input, and dictaphone-based template field navigation; par. 156-the user can perform one or more of the following: (1) select anatomic regions in images to navigate through report template fields, (2) select report template fields to navigate images; par. 262-263) and
finding item criteria associated with respective finding items wherein each of the finding item criteria includes a character string or rule for identifying phrases that should be associated with the corresponding finding item. (par. 22- determining whether information is clinically relevant information comprises detecting one or more keyword and/or applying one or more rules; par. 261-262: the reports are split into sections (e.g., Clinical History, Findings, Impressions) using regex pattern matching. In some cases, the pipeline focuses on free-text in the Findings and Impressions sections. This text can be further split into sentences. These extracted findings can be analyzed using the Information Model (1.5) to determine what classes of entities to search for in these sentences, and the Ontology (1.6) for specific instances of classes.)
receiving text input from a user of the computing system; (par. 127: when the user selects a particular finding (e.g., from the list of possible findings), a structured representation of the finding is generated; par. 128: the structured representation of the imaging finding is converted into natural text for insertion into the report; par. 158:The reporter module can perform functions including report templates and macros, speech-to-text input, and dictaphone-based template field navigation…The user can move the reporter text input cursor to the desired template field using the next/previous buttons on the dictaphone or the mouse);
for each phrase in the text input, (par. 169-The reporter text input cursor is then placed in the field corresponding to that descriptor. If the result is a background label, a catch-all template field (e.g., “Additional Information:”) may be selected.)
applying a categorization model configured to evaluate the finding item criteria of the determined finding item template with reference to the phrase, wherein the categorization model provides a matching finding item for each phrase, based on likelihoods of the phrase matching with particular finding items, and not other finding items that are not included in the determined finding item template; (par. 121-122; Par. 126-128: par. 126: neural network is made up of a sequence of modules with classifiers that generate input based on an input medical image and the output generated by the previous classifier. In this example, the classifiers of this neural network carry out image segmentation, labeling of the segmented parts of the image, and then identifies pathologies for the labeled segments (e.g., a lesion, stenosis, fracture, etc.) in sequence with the segmentation output being used in combination with the original image by a classifier that performs labeling of the identified image segments, and the classifier identifying pathologies using the labeled image segments and the original image; par. 140- a given point in the image would have a map of probabilities (or probability-like quantities) for each possible finding. Given the point specified by the user, the AI system can return a rank-ordered list of possible findings in decreasing order. The list may be truncated at a given probability level or length. With a verbal "yes" or "no" command or by clicking on the appropriate button, the user is able to choose whether or not the AI system should automatically generate the text for this finding and insert it into the report ) and
associating the phrase to a matching finding item; and (par. 121-122- the AI- assisted reporting would only need “disc bulge” to be dictated while either pointing at or looking at the T1-T2 disc… The user’s dictation can be converted into the corresponding text to be incorporated into a medical report. The dictation can be literally converted into text verbatim, and alternatively or in combination, the text or dictation can be “translated” or “interpreted” to generate the final text that is incorporated into a report.; par. 140- the user would be able to determine by visual similarity which of the CBIR results matches the current query and as above, the report text would be automatically generated and inserted into the report; par. 151: the linked text is “Posterior/posterior inferior labral tear with adjacent paralabral cyst along the posterior inferior glenoid measuring approximately 1.2cm in thickness and 2.0cm in length.” The user would be able to search via specific text included in this hyperlink, e.g. “labral tear,” by the heading or headings of the text, e.g. “Labrum” or “Findings,” or via representation matching allowed by the ontology, e.g. understanding that “posteroinferior” corresponds to the same thing as “posterior inferior,” or more general understanding allowed by the ontology, e.g. understanding that a request for the “posterior superior” or “posterosuperior” labrum should navigate to a location a short distance in the superior direction. FIG. 40 shows an illustrative example of user selection of coordinate within an image (e.g., x/y/z coordinate) and the corresponding generation of an associated finding that is connected to the image coordinate via a URL hyperlink. )
automatically updating a report to include the phrases associated with the matching finding items. (par. 121-122: The user’s dictation can be converted into the corresponding text to be incorporated into a medical report ; par. 127-128- the structured representation of the imaging finding is converted into natural text for insertion into the report; par. 137- the system can make a quantitative comparison (for instance, percent change) and generate the text and insert into the report automatically (e.g., a computer-finding that is a quantitative measurement).
Claim 3 Paik teaches the computerized method of claim 1, wherein each of the finding item criteria comprises an artificial intelligence model configured to identify phrases conceptually associated with the corresponding finding item. (par. 250- when previous reports frequently show a particular text with corresponding ontological presentations that match the general rule, a macro can be generated that enables the original text to be included; par. 264- regex pattern matching is used to filter for sentences which may contain “frames” of interest; that is, phrases that contain observations and other elements of the Information Model. These filtered sentences can then be tokenized and parsed using natural language processing programs/libraries such as NLTK; par. 26)
Claim 4 Paik teaches the computerized method of claim 3, wherein the artificial intelligence model comprises one or more large language model. (par. 81- Algorithms that can be used in the processes or subsystems include various models such as computer vision and natural language processing algorithms; par. 138; par. 187- an NLP-based system which detects incongruence in inconsistency between a user’s explicit interaction with the outputs of the AI models (through the described UI components) and the words they dictate into the diagnostic report.)
Claim 6 Paik teaches the computerized method of claim 1, wherein matching finding items are determined based on confidence levels that the phrase matches respective finding items. (par. 166; par. 221)
Claim 7. Paik discloses the computerized method of claim 6, wherein a finding item with a highest confidence level is selected as the matching finding item. (par. 103-scoring functions a greater score for a particular match- The scoring function can contain a sequence-to-sequence score that computes a cost of each vertebral label to each image region. For instance, if a particular image region contained a vertebra with an inferiorly projecting spinous process, the algorithm may assign it a greater score for a match with a thoracic vertebral label (T1-T12) than for other matches. The scoring function can also contain an intra-sequence score for both image regions and for vertebral labels; par. 166; par. 221)
Claim 8 Paik discloses the computerized method of claim 1, wherein the categorization model further includes one or more deterministic rules configured to associate a phrase with a finding item. (par. 22- determining whether information is clinically relevant information comprises detecting one or more keyword and/or applying one or more rules; par. 228- the image study metadata attribute values are examined in order to find the single matching protocol, either by simple attribute values matching rules or by machine learning applied to attribute values or other high level information regarding the study; par. 250)
Claim 9. Paik teaches the computerized method of claim 8, wherein the deterministic rules are evaluated in response to unsuccessfully identifying a matching finding item based on the finding item criteria. (par. 22- determining whether information is clinically relevant information comprises detecting one or more keyword and/or applying one or more rules; par. 244- Given a new case, the software is capable of matching the new series against the rules specified here and offering backups if they are available. Furthermore, in at least one embodiment, the software is capable of understanding the characteristics of prior cases and choosing a particular “most relevant” prior, where relevance may be defined by anatomy or pathology considerations. In some embodiments, the presence of relevant text in the report on a prior case is used to inform the selection)
Claim 10 Paik discloses the computerized method of claim 1, wherein the text input is parsed into phrases by a large language model. (par. 128- a query might return “A mild neuroforaminal stenosis is observed at the C2-3 level” from an existing database of parsed findings while a production rule like “<anatomy> has <severity> <observation>” might return “C2-C3 foramen has mild stenosis;” par. 245-parsing prior reports from a radiologist)
Claim 11 Paik discloses a computerized method, performed by a computing system having one or more hardware computer processors and one or more non-transitory computer readable storage device storing software instructions executable by the computing system to perform the computerized method, (par. 9-10; par. 84-85) comprising:
determining one or more attributes of a medical imaging exam; (par. 15-determining modality; time stamps; par. 111; par. 226-hanging protocol definition including matching criteria for modality)
generating a finding item template including one or more finding items each corresponding to a section of a medical report:; (par. 155- The reporter module can perform functions including report templates and macros, speech-to-text input, and dictaphone-based template field navigation; par. 156-the user can perform one or more of the following: (1) select anatomic regions in images to navigate through report template fields, (2) select report template fields to navigate images; par. 262-263), wherein each of the one or more finding items is associated with corresponding finding item criteria; (par. 155- 157: The reporter module can perform functions including report templates) and at least some of the finding item criteria include a character string or rule for identifying phrases that should be associated with the corresponding finding item (par. 22- determining whether information is clinically relevant information comprises detecting one or more keyword and/or applying one or more rules; par. 261-262: the reports are split into sections (e.g., Clinical History, Findings, Impressions) using regex pattern matching. In some cases, the pipeline focuses on free-text in the Findings and Impressions sections. This text can be further split into sentences. These extracted findings can be analyzed using the Information Model (1.5) to determine what classes of entities to search for in these sentences, and the Ontology (1.6) for specific instances of classes.)
receiving text input from a user of the computing system; (par. 127: when the user selects a particular finding (e.g., from the list of possible findings), a structured representation of the finding is generated; par. 128: the structured representation of the imaging finding is converted into natural text for insertion into the report; par. 158:The reporter module can perform functions including report templates and macros, speech-to-text input, and dictaphone-based template field navigation…The user can move the reporter text input cursor to the desired template field using the next/previous buttons on the dictaphone or the mouse)
for each phrase in the text input (par. 169);
applying a categorization model configured to evaluate the finding item criteria of the determined finding item template with reference to the phrase, wherein the categorization model provides a matching finding item for each phrase; (par. 121-122; par. Par. 126-128: par. 126: neural network is made up of a sequence of modules with classifiers that generate input based on an input medical image and the output generated by the previous classifier. In this example, the classifiers of this neural network carry out image segmentation, labeling of the segmented parts of the image, and then identifies pathologies for the labeled segments (e.g., a lesion, stenosis, fracture, etc.) in sequence with the segmentation output being used in combination with the original image by a classifier that performs labeling of the identified image segments, and the classifier identifying pathologies using the labeled image segments and the original image; par. 140- a given point in the image would have a map of probabilities (or probability-like quantities) for each possible finding. Given the point specified by the user, the AI system can return a rank-ordered list of possible findings in decreasing order. The list may be truncated at a given probability level or length. With a verbal "yes" or "no" command or by clicking on the appropriate button, the user is able to choose whether or not the AI system should automatically generate the text for this finding and insert it into the report ) and
associating the phrase to a matching finding item; (par. 121-122- the AI- assisted reporting would only need “disc bulge” to be dictated while either pointing at or looking at the T1-T2 disc… The user’s dictation can be converted into the corresponding text to be incorporated into a medical report. The dictation can be literally converted into text verbatim, and alternatively or in combination, the text or dictation can be “translated” or “interpreted” to generate the final text that is incorporated into a report.; par. 140- the user would be able to determine by visual similarity which of the CBIR results matches the current query and as above, the report text would be automatically generated and inserted into the report; par. 151: the linked text is “Posterior/posterior inferior labral tear with adjacent paralabral cyst along the posterior inferior glenoid measuring approximately 1.2cm in thickness and 2.0cm in length.” The user would be able to search via specific text included in this hyperlink, e.g. “labral tear,” by the heading or headings of the text, e.g. “Labrum” or “Findings,” or via representation matching allowed by the ontology, e.g. understanding that “posteroinferior” corresponds to the same thing as “posterior inferior,” or more general understanding allowed by the ontology, e.g. understanding that a request for the “posterior superior” or “posterosuperior” labrum should navigate to a location a short distance in the superior direction. FIG. 40 shows an illustrative example of user selection of coordinate within an image (e.g., x/y/z coordinate) and the corresponding generation of an associated finding that is connected to the image coordinate via a URL hyperlink. ) and
automatically updating a report to include the phrases associated with the matching finding items. (par. 121-122: The user’s dictation can be converted into the corresponding text to be incorporated into a medical report ; par. 127-128- the structured representation of the imaging finding is converted into natural text for insertion into the report; par. 137- the system can make a quantitative comparison (for instance, percent change) and generate the text and insert into the report automatically (e.g., a computer-finding that is a quantitative measurement).
Claim 12 Paik teaches the computerized method of claim 11, wherein the one or more attributes of the medical imaging exam are the finding items in a report template. (par. 22- determining whether information is clinically relevant information comprises detecting one or more keyword and/or applying one or more rules; par. 155; par. 228- the image study metadata attribute values are examined in order to find the single matching protocol, either by simple attribute values matching rules or by machine learning applied to attribute values or other high level information regarding the study; par. 250-252)
Claim 13 Paik discloses the computerized method of claim 12, wherein the report template is automatically selected based on an exam type. (par. 247-249)
Claim 14 Paik discloses the computerized method of claim 12, wherein the report template includes default text for one or more of the finding items. (par. 120-122; par. 125- This is a unique identifier for each type of anatomy in the software’s ontology. This identifier is what is used to set the anatomic context globally such that all other user actions occur in this context. In some cases, when a user selects (e.g., using the mouse or eye-tracking) a part of the anatomy shown on the image, the user is presented with a list possible clinical findings relevant to that part of the anatomy. In one embodiment, the list comprises possible findings in decreasing order of prevalence without consideration of the imaging appearance for this particular patient. In some cases, the list is curtailed at a user adjustable length (e.g., top 10 findings); par. 216-217)
Claim 15. Paik discloses the computerized method of claim 12, further comprising: generating a report based on the report template, including each of the finding items in the report template and any matching text associated with respective finding items as determined by the categorization model. (par. 121-122: The user’s dictation can be converted into the corresponding text to be incorporated into a medical report ; par. 127-128- the structured representation of the imaging finding is converted into natural text for insertion into the report; par. 137- the system can make a quantitative comparison (for instance, percent change) and generate the text and insert into the report automatically (e.g., a computer-finding that is a quantitative measurement; ; (par. 155- The reporter module can perform functions including report templates and macros, speech-to-text input, and dictaphone-based template field navigation).
Claim 16 Paik teaches the computerized method of claim 15, wherein the report includes default text associated with one or more of the finding items. (par. 121-122; par. Par. 126-128)
Claim 17 Paik discloses the computerized method of claim 12, further comprising: generating a conclusion or recommendation based at least one the matching text associated with the finding items, wherein the conclusion or recommendation includes at least some of the matching text. (par. 247-a data set comprising one or more previous reports can be used to understand common phrases contained therein. In some embodiments, an algorithm is used to identify common phrases based simply on the statistical rate of occurrence of the phrase…if the phrases “Suspraspinatus normal, infraspinatus normal, teres minor normal, subscapularis normal” all appear, the ontology could recognize this to mean that the rotator cuff is normal, and provide the macro recommendation “rotator cuff normal” to autocomplete to this entire phrase.)
Claim 18 Paik teaches the computerized method of claim 11, wherein the finding items are further determined based on one or more user, user attribute, system, or system attribute. (par. 296-customizing reporting templates and selection of templates based on user preferences/attributes; par. 359-a keyword match with inference (instead of heuristics) can be performed. In this case, a layer of inference is introduced that maps from keywords to formulas for findings and leverages those mappings, along with anatomic context, to limit the number of findings users could mean)
Claim 19 Paik teaches the computerized method of claim 11, wherein the categorization model is configured to compare text input with only those finding item criteria of the finding item template, and not compare text input with finding item criteria of a plurality of other finding items that are not included in the finding item template. (. par. 121-122; Par. 126-128; par. 174-175)
Claim 21 Paik discloses the computerized method of claim 11, further comprising: generating a user interface configured to receive user input updating first finding item criteria associated with a first finding item. (par. 174-176)
Claim 22 Paik teaches the computerized method of claim 21, wherein the first finding item criteria includes one or more character strings and the update indicates one or more of: removal of one or more of the character strings, modification of one or more of the character strings, or addition of a new character string. (editing of findings: par. 10- processor is configured to automatically populate a portion of said medical report based on a determination of congruence between said feature and said input. In some cases, said processor is configured to present said computer-generated finding to said user for acceptance and optionally editing, wherein an accepted computer-generated finding is automatically populated into said portion of said report; par. 150- the section of text in the reporter that is linked is automatically suggested. In some embodiments, the section of text in the reporter can be specified or edited by the user.)
Response to Arguments
Applicant's arguments filed 12/18/25 have been fully considered but they are not persuasive.
(A) Applicant argues that the claim rejections under 35 USC 101. In particular, applicant argues that the claims any judicial exception is integrated into practical application, and that the claims provide an improvement in technology.
In response, the examiner disagrees. In accordance with MPEP 2106.05 (a), if it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01. In contrast, the court in Affinity Labs of Tex. v. DirecTV, LLC relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible. 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016).
An important consideration in determining whether a claim is directed to an improvement in technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other Step 2B considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.
In the instant case, the claims are drawn to the abstract idea of generating/ updating a medical finding report. Applicant’s claimed invention suggests an improvement to the abstract idea, but does not reflect an improvement to a technological field.
(B) Applicant argues that the Paik reference does not disclose “finding item template” as claimed by applicant.
In response, the examiner disagrees. While applicant asserts that the templates of the claimed invention are different than those in Paik, it is not clear from the arguments or the claim language how the two templates differ.
Moreoever, it is difficult to understand the “granularity” of the terms used to describe the various elements. Examiner is not entirely certain what a “finding item” includes and how it is distinct from finding item “criteria.”
The applicant’s specification does not provide a particular definition of a “finding item,” and instead explains: “Such templates often include a list of “finding items,” where a “finding item” is any portion, such as a pre-defined section or part, of a report. For example, in a report of a chest radiograph, a list of finding items may include LUNGS, HEART, MEDIASTINUM, PLEURA, BONES, TUBES/LINES, UPPER ABDOMEN, OTHER, each with their own default text descriptions. For example, for the finding item LUNGS, the default text may be “No pneumonia, mass, or other abnormality.” Although in this example, the finding items are anatomical regions, finding items need not be anatomically based.”
The claims have been given the broadest reasonable interpretation and prior art applied accordingly.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ellingsworth (US 20070282824 A1)-discloses a system and method for classifying structured and unstructured documents.
Glottmann et al (US 20200321100 A1) discloses systems and methods for the automated analysis of radiological information. Systems and methods include receiving medical images and textual data, generating enhanced medical image data by applying an artificial intelligence module to the received medical images, generating structured text data by applying a natural language processing module to the received textual data, and generating improved medical image reports and/or alerts based on the generated enhanced medical image data and the generated structured text data.
McKinney et al (US 20210065859 A1)-discloses a method of generating structured labels for free-text medical reports, such as doctor notes, and associating such labels with medical images, such as chest X-rays which are adjunct to the medical reports.
THIS ACTION IS MADE FINAL. 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 Rachel L Porter whose telephone number is (571)272-6775. The examiner can normally be reached M-F, 10-6:30.
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/Rachel L. Porter/
Primary Examiner, Art Unit 3684
Prosecution Insights