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 .
Notice of Reply
This communication is responsive to the amendment(s) and/or argument(s) filed 6/19/26. The previous ground(s) of objection and/or rejection is/are withdrawn. The following new and/or reiterated ground(s) of rejection is/are set forth hereinbelow.
Claim Rejections - 35 USC § 112(b)
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 1-14 and 16 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 1 positively recites the limitation "the computer system" in line 8. There is insufficient antecedent basis for this limitation in the claim. Depending claims 2-14 and 16 do not remedy and inherit the indefiniteness.
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 1-14 and 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 acquiring esophageal measurement pressure and geometry data, 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 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 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 the acquisition of data (i) is merely well-known, routine, and conventional pre-solution data gathering activity and/or (ii) the data being acquired from the subject using a functional lumen imaging probe (ii-a) does not require the probe itself in the scope of the claim and instead merely requires acquiring data additional elements are required and/or (ii-b) assuming arguendo the functional lumen imaging probe may be required as an additional element it is a well-known routine, and conventional pre-solution data gathering means.
Depending claims 2-14 and 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.
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) 1-14 and 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) acquiring esophageal measurement data (FLIP panometry data) from the subject using a functional lumen imaging probe (FLIP panometry probe) (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 (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 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 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).
Response to Arguments
Applicant’s arguments, see page 9, filed 6/19/26, with respect to the transitory signal 101 in view of the newly amended claims have been fully considered and are persuasive. The transitory signal 101 rejections of the newly amended claims have been withdrawn.
Applicant's arguments regarding the abstract idea 101 and 102 rejections filed 6/19/26 have been fully considered but they are not persuasive.
In response to Applicants arguments regarding the abstract idea 101, the Examiner respectfully disagrees and notes the following in response:
Applicant argues “The claims are not directed to a mental process” because “Claim 1 as amended now recites "acquiring esophageal measurement data from the subject using a functional lumen imaging probe." This amendment reframes the claim by requiring active data acquisition using a specific medical device.”. The Examiner respectfully disagrees noting acquiring data is a well-known, routine, and conventional insignificant pre-solution data gathering activity as an additional element not amounting to significantly more, as set forth hereinabove. The claims do not positive recite, for example at least, method steps of providing a functional lumen imaging probe, using the probe to gather data, and acquiring the data from resultant data from the probe. The probe itself is not part of the claimed method and conversely, merely the data acquiring is required, while data acquisition and transferring is well-known, routine, and conventional.
In response to applicant's argument that the claim is statutory, it is noted that the features upon which applicant relies (i.e., “training a neural network includes optimizing network parameters (e.g., weights, biases, or both) based on minimizing a loss function, initializing the neural network, computing or estimating initial network parameters, inputting training data to generate output, evaluating the quality of classified feature data by passing them to a loss function to compute an error, and updating the neural network based on the calculated error using backpropagation methods“, “esophageal measurement data indicate measurements of pressure and/or geometry of the subject's esophagus“, “specific trained parameters-weights, biases, and other network parameters-that enable the classification function. The specification describes that training a neural network includes optimizing network parameters based on minimizing a loss function, computing initial network parameters, and updating the neural network based on calculated error using backpropagation methods“, and/or “The method generates classified feature data that classify the FLIP data as being indicative of an upper gastrointestinal disorder in the subject“) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Regarding Applicant’s assertions that the steps may not be performed mentally, the Examiner respectfully notes, as broadly as recited, it is well within the grasp of a mind of an individual to learn from and train themselves on algorithms relating to particular data and generate mental results therefrom. Reiterating the example hereinabove, a gastrointestinal medical professional is well-trained on the meaning of esophageal pressure data as it may relate to physiological underpinnings, spending years of study learning to correlate measurements with potential physiological conditions, such that their mind may generate a mental output (or via pen and paper) of upper GI disorders in the presence of data, particularly as broadly as claimed.
In response to Applicants arguments regarding the 102, the Examiner respectfully disagrees and notes the following in response:
As broadly as claimed, absent any structural limitation to the contrary, and absent any special definition in the instant Specification upon which Applicant does not appear to rely, Carlson’s rules-based classification scheme performed by human clinicians may fairly and reasonably be considered at least a machine learning algorithm as a human-computer interaction comprising accessing a trained machine learning algorithm (the algorithmic, CNN, random-forest classifying human(s) using bagging techniques of gradient-based trees such as in Fig 2) with the computer system (the FLIP computer and post-hoc result diagnostics supervised by the algorithmic classifying human(s)) to further train the algorithm (the human) based on generated classified output agreement of high-resolution manometry results with diagnosis.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “a neural network, such as a convolutional neural network, that is focused on heat maps estimated, computed, or otherwise determined from esophageal measurement data can be used to classify the esophageal measurement data into one of three distinct patterns”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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
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