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 .
Election/Restrictions
Claims 19 and 20 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected group, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 1/14/2026. The restriction is made final herein.
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.
The USPTO “Interim Guidelines for Examination of Patent Applications for Patent Subject Matter Eligibility” (Official Gazette notice of 22 November 2005), Annex IV, reads as follows:
Descriptive material can be characterized as either "functional descriptive material" or "nonfunctional descriptive material." In this context, "functional descriptive material" consists of data structures and computer programs which impart functionality when employed as a computer component. (The definition of "data structure" is "a physical or logical relationship among data elements, designed to support specific data manipulation functions." The New IEEE Standard Dictionary of Electrical and Electronics Terms 308 (5th ed. 1993).) "Nonfunctional descriptive material" includes but is not limited to music, literary works and a compilation or mere arrangement of data.
When functional descriptive material is recorded on some computer-readable medium it becomes structurally and functionally interrelated to the medium and will be statutory in most cases since use of technology permits the function of the descriptive material to be realized. Compare In re Lowry, 32 F.3d 1579, 1583-84, 32 USPQ2d 1031, 1035 (Fed. Cir. 1994) (claim to data structure stored on a computer readable medium that increases computer efficiency held statutory) and Warmerdam, 33 F.3d at 1360-61, 31 USPQ2d at 1759 (claim to computer having a specific data structure stored in memory held statutory product-by-process claim) with Warmerdam, 33 F.3d at 1361, 31 USPQ2d at 1760 (claim to a data structure per se held nonstatutory).
In contrast, a claimed computer-readable medium encoded with a computer program is a computer element which defines structural and functional interrelationships between the computer program and the rest of the computer which permit the computer program's functionality to be realized, and is thus statutory. See Lowry, 32 F.3d at 1583-84, 32 USPQ2d at 1035.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Claim 1 defines an apparatus However, while the preamble defines an apparatus, the body of the claim lacks definite structure indicative of a physical apparatus. The claim only provides “processing circuitry” to provide structure of an apparatus. However, the specification indicates that the invention may be embodied as pure software, and that the circuitry can be implemented by means of a computer program (paragraph 78 of the PGPub). Therefore, the claim as a whole appears to be nothing more than an apparatus of software elements, thus defining functional descriptive material per se.
Functional descriptive material may be statutory if it resides on a “non-transitory computer-readable medium or computer-readable memory”. The claim(s) indicated above lack structure, and do not define a computer readable medium and are thus non-statutory for that reason (i.e., “When functional descriptive material is recorded on some computer-readable medium it becomes structurally and functionally interrelated to the medium and will be statutory in most cases since use of technology permits the function of the descriptive material to be realized” – Guidelines Annex IV). The scope of the presently claimed invention encompasses products that are not necessarily computer readable, and thus NOT able to impart any functionality of the recited program. The examiner suggests:
1. Amending the claim(s) to embody the program on “non-transitory computer-readable medium” or equivalent; assuming the specification does NOT define the computer readable medium as a “signal”, “carrier wave”, or “transmission medium” which are deemed non-statutory; or
2. Adding structure to the body of the claim that would clearly define a statutory apparatus.
Any amendment to the claim should be commensurate with its corresponding disclosure.
It is noted that claim 18 is considered eligible subject matter. Even if the claim were considered an abstract idea, the claim provides a practical application, i.e. medical image analysis.
Claim Objections
Claim 1 is objected to because of the following informalities: Claim 1 contains a period In line 6 which appears to be extraneous/ unintended. Appropriate correction is required.
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 3-15 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.
Claim 3 recites the limitation “the processing” in line 1. IT is unclear as to which processing the applicant is referring to, because the applicant previously claims processing twice: in line 2 of claim 1, and a step of “process at least…” in line 7.
Claim 3 recites the limitation "the main machine learning model" in line 4. There is insufficient antecedent basis for this limitation in the claim. If the applicant is referring to “the main trained machine learning model”, please keep terms consistent.
Claim 3 recites the limitation "the outputs" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites the limitation "the output of an auxiliary trained machine learning model" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "the output of the main machine learning model" in the last two lines. It is unclear as to which output the applicant is referring to, because the applicant previously claims “outputs”.
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.
Claims 1-9 and 16-18 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by U.S. Patent Application Publication No. 20190188848 (Madani et al) .
Regarding claim 18, Madani et al discloses a method (fig. 5) of image processing apparatus (fig. 4) comprising: receiving medical image data (fig. 5, item 510, “receive medical image”); receiving text data including clinical information relating to the medical image data (fig. 5, item 510 “receive…associated medical report”); specifying a target processing region of the medical image data based on the text data, i.e. anomalous region coordinates (fig. 5, item 510); and processing at least the specified target processing region of the medical image data (fig. 5, item 570-590).
Claim 1 is rejected for the same reasons as claim 18. Thus, the arguments analogous to that presented above for claim 18 are equally applicable to claim 1. Claim 1 distinguishes from claim 18 only in that claim 1 is a medical image processing apparatus with processing circuitry configured to carry out a broader version of claim 1, in which a space derived from the medical image data could be specified instead of the target processing region. Because all the required limitations are addressed above, and because Madani et al discloses an apparatus comprising processing circuitry (fig. 4), prior art applies.
Regarding claim 2, Madani et al discloses the processing of at least the specified target processing region comprises performing a segmentation process to segment a specified anatomical feature and/or pathology, i.e. annotation of contours of anomalous regions (page 9, paragraph 80).
Regarding claim 3, Madani et al discloses the processing comprises applying a main trained machine learning model to the medical image data (fig. 1, item 190), and the main trained machine learning model is configured to include or receive conditioning information, i.e. the input of LTSM fig. 1, item 180, or the weights of page 6, paragraph 51, to condition outputs of the main machine learning model such that the outputs depend on both the medical image data and the conditioning information (fig. 1, item 190 outputs depend on fig. 1, item 120 and weights of MLP, page 5, paragraph 51, and additionally on the LSTM data of fig. 1, item 180).
Regarding claim 4, Madani et al discloses the processing circuitry is configured to apply an auxiliary trained machine learning model to the text data thereby to specify the target processing region (fig. 1, item 140- 150, 170, 180).
Regarding claim 5, Madani et al discloses the auxiliary trained machine learning model comprises a text encoder network that is configured to generate embeddings for tokens that represent text of the text data (fig. 1, item 170), and to combine the embeddings into a vector by concatenation (page 5, paragraph 50) that is or forms part of the conditioning information for the main machine learning model, i.e. conditioning of fig. 1, item 180).
Regarding claim 6, Madani et al discloses the processing circuitry is configured to apply an additional conditioning process so as to ensure or encourage that spatial information in the text data is taken into account by the main machine learning model, i.e. the spatial data of the character and word recognition of text carried out in fig. 1, item 140.
Regarding claim 7, Madani et al discloses the additional conditioning process is such as to alter an attention vector representing spatial attention that is input to or included in the main machine learning model, i.e. weights of the MLP (page 6, paragraph 51) which provide the amount of attention required for each feature and thus altering the attention vector by multiplying it by the weight and representing the spatial attention/ importance of the vector (page 6, paragraph 51).
Regarding claim 8, Madani et al discloses the additional conditioning process is such as to condition the main machine learning model with a representation of a shape or other property of a pathology or other feature of interest, because the weights are learned with ground truth images and their annotated features of interest (page 6, paragraph 51).
Regarding claim 9, Madani et al discloses the additional conditioning process is such as to condition the main machine learning model using a signed distance field or other loss function (Page 4, paragraph 32).
Regarding claim 16, Madani et al discloses the text data comprise at least one radiology report and/or clinician notes or other user notes (fig. 1, item 130).
Regarding claim 17, Madani et al discloses the image data comprises at least one of magnetic resonance imaging (MRI) data, CT (computed tomography) data, cone-beam CT data, X-ray data (fig. 1, item 120), ultrasound data, positron emission tomography (PET) data or single photon emission computed tomography (SPECT) data.
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.
Claims 10 and 11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Madani et al in view of U.S. Patent Application Publication No. 20210279863 (Jackenkow et al).
Regarding claim 10, Madani et al discloses all of the claimed elements as set forth above and incorporated herein by reference. Madani et al further discloses training of the auxiliary trained machine learning model (page 4, paragraph 32).
Madani et al does not disclose expressly training comprises applying a penalty to a loss function in order to train against structured data.
Jackenkow et al discloses training comprises applying a penalty to a loss function in order to train against structured data (page 6, paragraph 118).
Madani et al & Jackenkow et al are combinable because they are from the same field of endeavor, i.e. training neural networks.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply a penalty in a loss function.
The suggestion/motivation for doing so would have been provide a more robust system by encouraging learning an informative function..
Therefore, it would have been obvious to combine the apparatus of Madani et al with the learning with loss function penalty of Jackenkow et al to obtain the invention as specified in claim 10.
Regarding claim 11, Madani discloses training of the main machine learning model (page 4, paragraph 34) comprises applying a penalty to a loss function in order to train against structured data. Jackenkow et al discloses training comprises applying a penalty to a loss function in order to train against structured data (page 6, paragraph 118).
Claims 12 and 13 are rejected under 35 U.S.C. 103(a) as being unpatentable over Madani et al in view of U.S. Patent Application Publication No. 20090310836 (Krishnan et al).
Regarding claim 12, Madani et al discloses all of the claimed elements as set forth above and incorporated herein by reference. Madani et al discloses the machine learning model is trained using training data (page 6, paragraph 53)
Madani et al does not disclose expressly the training data includes counterfactual training data.
Krishnan et al discloses the training data includes counterfactual training data, negative training data (Page 4, paragraph 42).
Madani et al & Krishnan et al are combinable because they are from the same field of endeavor, i.e. medical image processing.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use negative training data.
The suggestion/motivation for doing so would have been to provide a more accurate recognition.
Therefore, it would have been obvious to combine the apparatus of Madani et al with the negative training data of Krishnan et al to obtain the invention as specified in claim 12.
Regarding claim 13, Krishnan et al discloses the training data comprises at least some training data that represent counterfactual examples (page 4, paragraph 42) or other property that encourage the main machine learning model to use or be more influenced by the conditioning information and/or the output of an auxiliary trained machine learning model.
Claims 14 and 15 are rejected under 35 U.S.C. 103(a) as being unpatentable over Madani et al in view of U.S. Patent Application Publication No. 20210166807 (Quennesson et al).
Regarding claim 14, Madani et al discloses all of the claimed elements as set forth above and incorporated herein by reference. Madani et al further discloses the processing circuitry is configured to receive user input (page 7, paragraph 58).
Madani et al does not disclose expressly alter at least one of spatial attention mechanisms of the main machine learning model in dependence on the user input and/or augment or otherwise modify the text data based on the user input.
Quennesson et al discloses otherwise modifying the text data, corresponding to the medical report, based on the user input (pages 12-13, paragraph 159).
Madani et al & Quennesson et al are combinable because they are from the same field of endeavor, i.e. medical image and report processing.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the text data.
The suggestion/motivation for doing so would have been to provide a more robust system by allowing a user to correct incorrect data.
Therefore, it would have been obvious to combine the apparatus of Madani et al with the modifying of text data of Quennesson et al to obtain the invention as specified in claim 14.
Regarding claim 15, Madani et al discloses the processing circuitry is configured to receive user input (page 7, paragraph 58). Quennesson et al discloses use the user input in a feedback loop (page 13, paragraph 163) that comprises modifying at least one of input to the auxiliary machine learning model, since the correction is reprocessed in the NLP module (page 13, paragraph 163), and recalculating the output of the main machine learning model by retraining the model for verification (page 13, paragraph 153).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHLEEN YUAN DULANEY whose telephone number is (571)272-2902. The examiner can normally be reached M1:9am-5pm, th1:9am-1pm, fri1 9am-3pm, m2: 9am-5pm, t2:9-5 th2:9am-5pm, f2: 9am-5pm.
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/KATHLEEN Y DULANEY/Primary Examiner, Art Unit 2666 1/26/2026