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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04-10-2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. - An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following
three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth
paragraph:
the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a
nonstructural term having no specific structural meaning) for performing the claimed function;
the term "means" or "step" or the generic placeholder is modified by functional
language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"; and
the term "means" or "step" or the generic placeholder is not modified by sufficient
structure, material, or acts for performing the claimed function.
Use of the word "means" (or "step") in a claim with functional language creates a
rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C.
112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is
interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when
the claim limitation recites sufficient structure, material, or acts to entirely perform the recited
function.
Absence of the word "means" (or "step") in a claim creates a rebuttable presumption
that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35
U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under
35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation
recites function without reciting sufficient structure, material or acts to entirely perform the
recited function.
Claim limitations in this application that use the word "means" (or "step") are being
interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as
otherwise indicated in an Office action. Conversely, claim limitations in this application that do
not use the word "means" (or "step") are not being interpreted under 35 U.S.C. 112(f) or preAIA
35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
The following language is being interpreted under 35 U.S.C 112(f):
means for receiving 1) medical imaging metadata in a first format and 2)
instructions in Claim 10
means for converting the medical imaging metadata from the first format to a
second format based on the instructions using a machine learning based model in Claim 10
means for outputting the medical imaging metadata in the second format in
Claim 10
means for receiving one or more prompts comprising 1) the medical imaging
metadata in the first format and 2) the instructions in Claim 11
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA
35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure
described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C.
112(f) or pre-A IA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA
35 U.S.C. 112, sixth paragraph.
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.
Claim 10-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second
paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 10 limitation “means for receiving 1) medical imaging metadata in a first format and 2) instructions” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The limitation uses the term “means for”, recites the function of receiving medical imaging metadata in a first format, and does not recite sufficient structure for performing that function. Upon review of the written description, including the drawings, the specification fails to disclose corresponding structure for performing the entire claimed function and fails to clearly link or associate any disclosed structure with that function. Accordingly, one of the ordinary skill in the art would be unable to identify the structure corresponding to the recited “means”, thereby rendering the scope of the claim indefinite. Therefore, claim 10 is rejected under 35 U.S.C 112(b)
Claims 10 limitation “means for converting the medical imaging metadata from the first format to a second format based on the instructions using a machine learning based model” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The limitation uses the term “means for”, recites the function of converting the medical imaging metadata from the first format to a second format, and does not recite sufficient structure for performing that function. Upon review of the written description, including the drawings, the specification fails to disclose corresponding structure for performing the entire claimed function and fails to clearly link or associate any disclosed structure with that function. Accordingly, one of the ordinary skill in the art would be unable to identify the structure corresponding to the recited “means”, thereby rendering the scope of the claim indefinite. Therefore, claim 10 is rejected under 35 U.S.C 112(b)
Claims 10 limitation “means for outputting the medical imaging metadata in the second format” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The limitation uses the term “means for”, recites the function of outputting the medical imaging metadata in the second format, and does not recite sufficient structure for performing that function. Upon review of the written description, including the drawings, the specification fails to disclose corresponding structure for performing the entire claimed function and fails to clearly link or associate any disclosed structure with that function. Accordingly, one of the ordinary skill in the art would be unable to identify the structure corresponding to the recited “means”, thereby rendering the scope of the claim indefinite. Therefore, claim 10 is rejected under 35 U.S.C 112(b)
Claims 11 limitation “means for receiving one or more prompts comprising 1) the medical imaging metadata in the first format and 2) the instructions” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The limitation uses the term “means for”, recites the function of receiving one or more prompts in, and does not recite sufficient structure for performing that function. Upon review of the written description, including the drawings, the specification fails to disclose corresponding structure for performing the entire claimed function and fails to clearly link or associate any disclosed structure with that function. Accordingly, one of the ordinary skill in the art would be unable to identify the structure corresponding to the recited “means”, thereby rendering the scope of the claim indefinite. Therefore, claim 10 is rejected under 35 U.S.C 112(b)
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already
implicitly or inherently discloses the corresponding structure, material, or acts and clearly links
them to the function so that one of ordinary skill in the art would recognize what structure,
material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.0l(o) and 2181.
Dependent claims 12-14 are also rejected because they inherit the deficiencies of independent Claim 10.
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.
(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, 3, 4-6, 10, 12-15 and 17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Olivares et al. (US 2023/0162837 A1).
Regarding claim 1, Olivares explicitly discloses:
A computer-implemented method comprising:
receiving 1) medical imaging metadata in a first format and 2) instructions; (Olivares, ¶[0032]: “For example, an X-ray machine may generate an image data file for each instance in which a diagnostician "takes" an X-ray. Many other types of medical acquisition machines are also used.”, ¶[0039]: “The data object 600 may be, for example and without limitation, a radiological image or a patient's chart, in any medical data format.”, ¶[0050]: “Input activities are governed by the following rules: never receive a data object from memory, always output the data object in-memory, and never store the mentioned object in a queue”)
converting the medical imaging metadata from the first format to a second format based on the instructions using a machine learning based model; and (Olivares, ¶[0090-0091]: “Whenever all the metadata is ready, the DICOM image will be analyzed (at 906) in a DICOM Evaluator Activity 341.2 to determine if it contains a JPEG (low resolution image) or any other transfer syntax. In case the image is not a JPEG, it will be transcoded to an uncompressed format called VR Explicit Little Endian, in the first DICOM Transcoder Activity 331.1… A different circuit is traveled for another transaction 908 whenever the output at DICOM Evaluator Activity 341.2 determined (at 906) that the image is JPEG. In that case, a Repeater Activity 345.2 starts a new transaction 908 and will enqueue the image in two queues reusing the DICOM Enqueue Activity 361.2 for the "Hi-res" queue 30, and DICOM Enqueue Activity 361.3 for the "Low-res" queue.”)
outputting the medical imaging metadata in the second format. (Olivares, ¶[0090]: “The resulting image is used twice by a Repeater Activity 345.1, which starts a new transaction 910, with the following output: one for passing the image to a DICOM Enqueue Activity 361.2 associated to the queue 30, denominated "Hi-res", and the other output connects to a second DICOM Transcoder Activity 331.2 for converting the image to a JPEG, and then enqueue the converted image into the queue 20 called "Lo-res" by the DICOM Enqueue Activity 361.3.”)
Regarding claim 3, Olivares explicitly discloses:
wherein the machine learning based model receives as input the medical imaging metadata in the first format and the instructions and (Olivares, ¶[0032]: “For example, an X-ray machine may generate an image data file for each instance in which a diagnostician "takes" an X-ray. Many other types of medical acquisition machines are also used.”, ¶[0039]: “The data object 600 may be, for example and without limitation, a radiological image or a patient's chart, in any medical data format.”, ¶[0050]: “Input activities are governed by the following rules: never receive a data object from memory, always output the data object in-memory, and never store the mentioned object in a queue”)
generates as output the medical imaging metadata in the second format. (Olivares, ¶[0090]: “The resulting image is used twice by a Repeater Activity 345.1, which starts a new transaction 910, with the following output: one for passing the image to a DICOM Enqueue Activity 361.2 associated to the queue 30, denominated "Hi-res", and the other output connects to a second DICOM Transcoder Activity 331.2 for converting the image to a JPEG, and then enqueue the converted image into the queue 20 called "Lo-res" by the DICOM Enqueue Activity 361.3.”)
Regarding claim 4, Olivares explicitly discloses:
wherein the medical imaging metadata is metadata associated with an acquisition of one or more medical images of a patient. (Olivares, ¶[0034]: “A mechanism for processing clinical data, either images or metadata, in a uniform way that can be described as a graph of activities with homogeneous inputs and output gates”, ¶[0129]: “The DICOMWeb image storage 1506 automatically scans the image metadata and converts it into FHIR records consumable by the medical information viewing platform 1415 and other FHIR-compliant applications.”)
Regarding claim 5, Olivares explicitly discloses:
wherein the medical imaging metadata comprises patient health information of the patient and image acquisition parameters of the one or more medical images. (Olivares, ¶[0039]: “The data object 600 may be, for example and without limitation, a radiological image or a patient's chart, in any medical data format.”, ¶[0033]: “Additionally, other types of medical devices may acquire data for a given patient during a medical examination. Although there are many different types of machines and diagnostic tools available, the actual data contained within specific data files is not specifically important with respect to the needs for sharing that collected data. However, it is recognized that the different types of machines may collect data in different formats. Accordingly, an integration for these formats and continuity of managing related data files ( data files that are related based on medical diagnostic needs as opposed to technical similarities) in a comprehensive workflow is addressed in this disclosure.”)
Regarding claim 6, Olivares explicitly discloses:
wherein the first format and the second format are different implementations of a DICOM (digital imaging and communications in medicine) format. (Olivares, ¶[0039]: “Available medical data formats include, but are not limited to, HL 7 v2-all message types; HL 7 v3-all message types; HL 7 FHIR R4-all resource types; HL 7 FHIR RS-resource types; HL 7 CDA R2-all document types; DICOM (DIMSE)-all services; and DICOM (DICOMWeb)-all services”, (Olivares, ¶[0090-0091]: “Whenever all the metadata is ready, the DICOM image will be analyzed (at 906) in a DICOM Evaluator Activity 341.2 to determine if it contains a JPEG (low resolution image) or any other transfer syntax. In case the image is not a JPEG, it will be transcoded to an uncompressed format called VR Explicit Little Endian, in the first DICOM Transcoder Activity 331.1… A different circuit is traveled for another transaction 908 whenever the output at DICOM Evaluator Activity 341.2 determined (at 906) that the image is JPEG. In that case, a Repeater Activity 345.2 starts a new transaction 908 and will enqueue the image in two queues reusing the DICOM Enqueue Activity 361.2 for the "Hi-res" queue 30, and DICOM Enqueue Activity 361.3 for the "Low-res" queue.”)
Regarding claim 10, this claim is rejected under the same rationale with claim 1 as they are analogous claims.
Regarding claim 12, this claim is rejected under the same rationale with claim 3 as they are analogous claims.
Regarding claim 13, this claim is rejected under the same rationale with claim 4 as they are analogous claims.
Regarding claim 14, this claim is rejected under the same rationale with claim 5 as they are analogous claims.
Regarding claim 15, this claim is rejected under the same rationale with claim 1 as they are analogous claims.
Regarding claim 17, this claim is rejected under the same rationale with claim 6 as they are analogous claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2, 7-9, 11, 16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Olivares et al. (US 2023/0162837 A1) in view of Jaimovitch-Lopez et al. (“Can language models automate data wrangling?”).
Regarding claim 2, Olivares explicitly discloses all the limitations of claim 1 (as shown in the rejections above).
Olivares fails to disclose:
wherein the machine learning based model is an LLM (large language model) and
receiving 1) medical imaging metadata in a first format and 2) instructions comprises:
receiving one or more prompts comprising 1) the medical imaging metadata in the first format and 2) the instructions.
However, Jaimovitch-Lopez explicitly discloses:
wherein the machine learning based model is an LLM (large language model) and (Jaimovitch-Lopez, Pg. 2054, Section 1, ¶[4]: “In this paper we test experimentally whether language models can be used to solve typical problems in data wrangling, using different kinds of prompts”)
receiving 1) medical imaging metadata in a first format and 2) instructions comprises:
receiving one or more prompts comprising 1) the medical imaging metadata in the first format and 2) the instructions. (Jaimovitch-Lopez, Pg. 2054, Section 1, ¶[4]: “Some (few-shot) prompts
will have input-output examples and a single input ending the prompt, for which the language model will have to provide the output as a continuation of the prompt (e.g., Input:
‘marshap@gmail.com’ \ nOutput: ‘marshap’\ n\ nInput: ‘alant@hot-mail.com’\ nOutput:)”)
The combination of Olivares and Jaimovitch-Lopez are analogous art because they are in the same field of training time series data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, having the teachings of Olivares and Jaimovitch-Lopez before them, to modify the teachings of Olivares to include the teachings of Jaimovitch-Lopez to analyze how data wrangling automation can be improved by giving more information to the user about the reliability of the results given by the language models, using their probabilities and determining cutoffs
Regarding claim 7, Olivares explicitly discloses all the limitations of claim 1 (as shown in the rejections above).
Olivares fails to disclose:
wherein the instructions comprise instructions for converting the medical imaging metadata from the first format to the second format
However, Jaimovitch-Lopez explicitly discloses:
wherein the instructions comprise instructions for converting the medical imaging metadata from the first format to the second format. (Jaimovitch-Lopez, Pg. 2067, ¶[2]: “For instance, for the manipulation battery we will use a few-shot approach, while for the rest of tasks, we will not provide exemplars, but rather simple instructions about the task that we expect the language model to perform, thus following a zero-shot scenario.”, Pg. 2054, Section 1, ¶[4]: “In this paper we test experimentally whether language models can be used to solve typical problems in data wrangling, using different kinds of prompts. Some (few-shot) prompts will have input-output examples and a single input ending the prompt, for which the language model will have to provide the output as a continuation of the prompt (e.g., Input:‘marshap@gmail.com’ \ nOutput: ‘marshap’\ n\ nInput: ‘alant@hot-mail.com’\ nOutput:). For the transformation datasets, we compare the inference power of GPT-3 with other specialised tools on a benchmark of simple data wrangling problems.”)
Regarding claim 8, Olivares explicitly discloses all the limitations of claim 1 (as shown in the rejections above).
Olivares fails to disclose:
converting the medical imaging metadata from the first format to the second format in response to determining that the medical imaging metadata in the second format includes or is missing information for a class of patients that would influence a future processing step.
However, Jaimovitch-Lopez explicitly discloses:
converting the medical imaging metadata from the first format to the second format in response to determining that the medical imaging metadata in the second format includes or is missing information for a class of patients that would influence a future processing step. (Jaimovitch-Lopez, Pg. 2069, Section 3.2.4: “Here we will follow a zero-shot strategy where we will provide the language model with a prompt asking directly whether there are any outliers in a given set of data. For this battery we only include one prompt. We performed many preliminary tests to get good results. While we were looking for anomalies and not outliers, in the end we saw that the results were similar when we modified the prompt by asking for anomalies, oddities or abnormal phenomena in the data instead of using the word ‘outliers’. A couple of examples of the prompt follow: Are there any outliers in {70◦ F, 71◦ F, ..., 74◦ F}? Are there any outliers in {audi, chevrolet, dodge, ford, ..., volkswagen}?”, Pg. 2069, Section 3.2.5: “We use two prompts to make the language model infer the missing value from a set of examples. We use instances without missing values in the prompt and we leave the last line for the instance with the missing value”, Pg. 2057, Table 1: “Missing Data: Detect missing entries and understand missing data patterns for repair (i.e., imputing those missing entries with other values according to different rules)”)
Regarding claim 9, Olivares explicitly discloses all the limitations of claim 1 (as shown in the rejections above).
Olivares fails to disclose:
extracting information from a medical image associated with the medical imaging metadata using a machine learning based image assessment model; and
comparing the extracted information with the medical imaging metadata to confirm accuracy of the medical imaging metadata
However, Jaimovitch-Lopez explicitly discloses:
extracting information from a medical image associated with the medical imaging metadata using a machine learning based image assessment model; and (Jaimovitch-Lopez, Pg. 2057, Table 1: “Data Transformation Manipulate the shape of the data (e.g., switching the format of the table from a “wide” to a “long” format or vice versa) and extraction of relevant pieces of information from it (e.g., names of people or places, relationships, etc.)”)
comparing the extracted information with the medical imaging metadata to confirm accuracy of the medical imaging metadata. (Jaimovitch-Lopez, Pg. 2063, ¶[1]: “We will evaluate whether a system can distinguish between ordinal and non-ordinal attributes (just from their labels), summarized as accuracy, and then whether it orders them correctly (we will consider all the pairwise comparisons between attributes, aggregated into a single metric, Spearman correlation between the inferred order and the correct order).”, Pg. 2065, ¶[3]: “For each example, we repeat the procedure 10 times, and we measure the performance of the imputation comparing the predicted value with the actual value. In the case of the categorical attributes, we show the mean accuracy in imputing missing values. For the numerical attributes, we divide the estimated mean absolute error (MAE) by the standard deviation (σ) of the values of the feature. To make it more comparable with accuracy, we calculate its complementary”)
Regarding claim 11, this claim is rejected under the same rationale with claim 2 as they are analogous claims.
Regarding claim 16, this claim is rejected under the same rationale with claim 2 as they are analogous claims.
Regarding claim 18, this claim is rejected under the same rationale with claim 7 as they are analogous claims.
Regarding claim 19, this claim is rejected under the same rationale with claim 8 as they are analogous claims.
Regarding claim 20, this claim is rejected under the same rationale with claim 9 as they are analogous claims.
Conclusion
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/AMY TRAN/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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