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
Applicant’s election of Group I, drawn to the process of generating classified feature data of an upper gastrointestinal disorder, Species B1, embodiment drawn to Figure 4, and Species B2, embodiment drawn to Figure 13, in the reply filed on 9/16/25 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)).
Claims 17-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 9/16/25.
Applicant is reminded that upon the cancelation of claims to a non-elected invention, the inventorship must be corrected in compliance with 37 CFR 1.48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 37 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 37 CFR 1.76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i).
Information Disclosure Statement
The information disclosure statement (IDS) submission(s) is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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:
(A) 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 non-structural term having no specific structural meaning) for performing the claimed function;
(B) 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
(C) 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 pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim Objections
Claim 1 is objected to because of the following informalities: there appears to be a missing “and” at the end of line 10, or the like. 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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. A method of accessing data, accessing a trained machine learning algorithm, and applying data to the trained machine learning algorithm may reasonably be considered to be performable mentally within the human mind and/or by pen and paper.
For sole independent claims 1, the claim(s) recite(s) a process of accessing esophageal measurement pressure and geometry data with a computer system, accessing a trained machine learning algorithm that classifies data features, and applying data to the trained machine learning algorithm using the computer system to output a classified feature indicative of upper gastrointestinal disorder. As broadly as structurally claimed these steps may be reasonably considered as the judicial exception of a mental process performable within the human mind, including by observation, evaluation, judgement and opinion forming, or by a human using pen and paper (see MPEP 2106.04(a)(2) subsection III). For example, at least, these limitations are nothing more than a gastrointestinal medical professional capturing data, printing it out, and using the data to mentally extract, classify or learn from data features to determine an upper GI disorder.
This judicial exception is not integrated into a practical application because the process steps as broadly as structurally claimed are not tied to nor required to be performed, executed, or programmed on a special purpose computer. Further, the judicial exception is not even required to be performed on or tied to a mere generic processing device, controller, or the like. Conversely, the claims merely require accessing and applying data to a computer system with a trained machine learning system. Neither the computer nor trained machine learning algorithm are positively or explicitly required and instead are inferentially and/or implicitly required at best given they only need be accessed. Further, the human mind is we a well known computing system and gastrointestinal medical professionals are well known to be trained in evaluating esophageal measurement data.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because either (i) no additional elements are required or (ii), assuming arguendo the computer system is required with a trained machine learning algorithm, that additional element could reasonably be considered well-known, routine and conventional amounting to insignificant data access and computational activity as broadly as claimed.
Depending claims 2-16 inherit and do not remedy the non-statutory deficiency noted above. Despite further specifying steps relating to the machine learning, classified features being indicative of various upper GI disorders, or esophageal data used, for similar rationales as above the claims do not integrate into a practical nor do they recite additional elements amounting to significantly more.
Claims 1-16 are also rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because, as broadly as structurally claimed, the process may be considered a transitory signal per se, reading on naturally occurring phenomenon of transient signals.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Carlson et al. (9/20/25 IDS NPL Cite No 3, hereinafter Carlson).
For claim 1, Carlson discloses a method for generating classified feature data indicative of an upper gastrointestinal disorder in a subject based on esophageal measurement data acquired from the subject's esophagus (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2), the method comprising:
(a) accessing esophageal measurement data (FLIP panometry data) with a computer system (computer with machine learning model) (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2), wherein the esophageal measurement data comprise measurements of pressure within the subject's esophagus and changes in a geometry of the subject's esophagus (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2);
(b) accessing a trained machine learning algorithm with the computer system (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2), wherein the trained machine learning algorithm has been trained on training data in order to generate classified feature data from esophageal measurement data (Pages 3-9, especially 7) (Figures 1-5) (Tables 1-2); and
(c) applying the esophageal measurement data to the trained machine learning algorithm using the computer system (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2), generating output as classified feature data that classify the esophageal measurement data as being indicative of an upper gastrointestinal disorder in the subject (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2).
For claim 2, Carlson discloses the method of claim 1, wherein the trained machine learning algorithm comprises a neural network.
For claim 3, Carlson discloses the method of claim 2, wherein the neural network is a convolutional neural network (supervised machine learning models based on boosting/bagging techniques of gradient-based trees as a classifier) (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2).
For claim 4, Carlson discloses the method of claim 1, wherein the training data include labeled data comprising esophageal measurement data labeled as corresponding to a contractile response pattern (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2).
For claim 5, Carlson discloses the method of claim 4, wherein the contractile response pattern comprises a distention-induced contractile response pattern (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2).
For claim 6, Carlson discloses the method of claim 5, wherein the distention-induced contractile response pattern comprises at least one of a repetitive antegrade contractions (RAC) pattern, an absent contractile response (ACR) pattern, a repetitive retrograde contractions (RRC) pattern, an impaired or disordered contraction (IDCR) pattern, or a spastic contractile (SCR) pattern (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2).
For claim 7, Carlson discloses the method of claim 6, wherein the SCR pattern comprises at least one of a sustained occluding contraction (SOC) pattern or a sustained LES contraction (sLESC) pattern (Pages 3-9, especially 4) (Figures 1-5) (Tables 1-2).
For claim 8, Carlson discloses the method of claim 1, wherein the trained machine learning algorithm is trained on the training data in order to identify a contractile response pattern in the esophageal measurement data and to generate the classified feature data based on the contractile response pattern identified in the esophageal measurement data (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2).
For claim 9, Carlson discloses the method of claim 8, wherein the contractile response pattern comprises a distention-induced contractile response pattern (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2).
For claim 10, Carlson discloses the method of claim 9, wherein the distention-induced contractile response pattern comprises at least one of a repetitive antegrade contractions (RAC) pattern, an absent contractile response (ACR) pattern, a repetitive retrograde contractions (RRC) pattern, an impaired or disordered contraction (IDCR) pattern, or a spastic contractile (SCR) pattern (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2).
For claim 11, Carlson discloses the method of claim 10, wherein the SCR pattern comprises at least one of a sustained occluding contraction (SOC) pattern or a sustained LES contraction (sLESC) pattern. (Pages 3-9, especially 4, 8-9) (Figures 1-5) (Tables 1-2)
For claim 12, Carlson discloses the method of claim 1, further comprising computing an esophagogastric junction distensibility index (EGJ-DI) value from the esophageal measurement data (Pages 3-9, especially 4) (Figures 1-5) (Tables 1-2), and wherein step (c) also includes applying the EGJ-DI value to the trained machine learning algorithm in order to generate the output as the classified feature data (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2, especially Table 1).
For claim 13, Carlson discloses the method of claim 1, wherein the trained machine learning algorithm comprises a random forest model supervised machine learning models based on boosting/bagging techniques of gradient-based trees as a classifier) (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2).
For claim 14, Carlson discloses the method of claim 1, wherein the esophageal measurement data comprise measurements of pressure within the subject's esophagus and changes in a diameter of the subject's esophagus and esophagogastric junction (EGJ) (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2).
For claim 15, Carlson discloses the method of claim 14, wherein the esophageal measurement data are acquired from the subject using a functional lumen imaging probe (Pages 3-9, especially 8-9) (Figures 1-5) (Tables 1-2).
For claim 16, Carlson discloses the method of claim 1, wherein the classified feature data comprise a probability score representative of a probability that the esophageal measurement data are indicative of the upper gastrointestinal disorder in the subject (Pages 3-9, especially 5) (Figures 1-5) (Tables 1-2, especially Table 2).
Conclusion
The cited prior art made of record on the accompanying PTO-892 and not relied upon is considered pertinent to applicant's disclosure, relating to means for classifying esophageal pressure measurement data for upper GI disorders.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jeffrey G. Hoekstra whose telephone number is (571)272-7232. The examiner can normally be reached Monday through Thursday from 5am-3pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles A. Marmor II can be reached at (571)272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Jeffrey G. Hoekstra
Primary Examiner
Art Unit 3791
/JEFFREY G. HOEKSTRA/ Primary Examiner, Art Unit 3791