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 .
Status of the Application
Claims 1-18 are currently pending in this case and have been examined and addressed below. This communication is a Non-Final Rejection in response to the Amendments to the Claims and Remarks filed on 12/04/2025.
Claims 1-15 are currently amended.
Claims 16-18 are added.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/25/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 – 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Claims 1-6 and 16 are drawn to a machine. Claims 7-12 and 17 are drawn to a process. As such, claims 1-12 and 16-17 are drawn to one of the statutory categories of invention.
Claims 13-15 and 18 are recited to comprise a computer-readable medium having various features that, under the broadest reasonable interpretation, may be entirely embodied in transitory forms of signal transmission. Claims 13-15 and 18 is rejected because it does not sufficiently recite a non-transitory computer readable storage medium. The United States Patent and Trademark Office (USPTO) is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re Zletz, 893 F.2d 319(Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. §101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. §101, Aug. 24, 2009; p. 2.
The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under 35 U.S.C. §101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. §101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. §101 by adding the limitation "non-transitory" to the claim. Cf. Animals – Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation "non-human" to a claim covering a multi-cellular organism to avoid a rejection under 35 U.S.C. §101). Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se. The limited situations in which such an amendment could raise issues of new matter occur, for example, when the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473 (Fed. Cir. 1998). In furtherance of compact prosecution, Examiner will further consider the claims under 35 USC § 101 as if the claims were amended to be directed towards a non-transitory computer-readable medium and not signal per se. (Step 1: YES, for claims 1-12 and 16-17, NO for claims 13-15 and 18).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Independent Claim 1: A system for diagnosing an esophageal disease, the system comprising:
at least one processor;
and at least one memory storing instructions which, when executed by the at least one processor, cause the system to:
access, during a procedure involving an in-vivo device located within a person, pH data measured by the in-vivo device relating to an esophageal disease;
evaluate, during the procedure while the in-vivo device is located within the person, a diagnosis for the esophageal disease for the person by applying a trained machine learning model to the pH data measured by the in-vivo device;
and communicate, during the procedure while the in-vivo device is located within the person, the diagnosis for the esophageal disease.
Independent Claim 7: A computer-implemented method for diagnosing an esophageal disease, the method comprising:
accessing, during a procedure involving an in-vivo device located within a person, pH data measured by the in-vivo device relating to an esophageal disease;
evaluating, during the procedure while the in-vivo device is located within the person, a diagnosis for the esophageal disease for the person by applying a trained machine learning model to the pH data measured by the in-vivo device;
and communicating, during the procedure while the in-vivo device is located within the person, the diagnosis for the esophageal disease.
Independent Claim 13: A computer-readable medium comprising instructions which, when executed by at least one processor of a system, cause the system to:
access, during a procedure involving an in-vivo device located within a person, pH data measured by the in-vivo device relating to an esophageal disease;
evaluate, during the procedure while the in-vivo device is located within the person, a diagnosis for the esophageal disease for the person by applying a trained machine learning model to the pH data measured by the in-vivo device;
and communicate, during the procedure while the in-vivo device is located within the person, the diagnosis for the esophageal disease.
(Examiner notes: The above claim terms underlined are additional elements that fall under Step 2A - Prong Two analysis section detailed below)
These steps amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or behaviors; and managing human mental activity (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people). Therefore, accessing pH data relating to a esophageal disease during a procedure, evaluating a diagnosis for the esophageal disease during the procedure, and communicating the diagnosis for the esophageal disease during the procedure are directed to managing personal interactions or personal behavior.
The dependent claim 2 is directed to access, during the procedure, event information relating to events of the person which occur during the procedure, the event information comprising an indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure.
The dependent claim 3 is directed to accessing the event information relating to events of the person which occur during the procedure comprises receiving the event information, the indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure, is not entered by the person.
The dependent claim 4 is directed to pH data collected over a predetermined time duration that is less than twenty-four hours.
The dependent claim 5 is directed to pH data collected over a first predetermined time duration and a second predetermined time duration which is longer than the first predetermined time duration.
The dependent claim 6 is directed to evaluate, at a first time during the procedure, a first diagnosis for the esophageal disease for the person, wherein pH data over the first predetermined time duration is available at the first time but pH data over the second predetermined time duration is not yet available at the first time, determine that the first diagnosis does not meet confidence criteria, evaluate, at a second time during the procedure, a second diagnosis for the esophageal disease for person, wherein the second time is after the first time, and wherein pH data over the second predetermined time duration is available at the second time, determine that the second diagnosis meets confidence criteria, provide the second diagnosis as the diagnosis for the esophageal disease for the person.
The dependent claim 8 is directed to accessing, during the procedure, event information relating to events of the person which occur during the procedure, the event information comprising an indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure
The dependent claim 9 is directed to accessing the event information relating to events of the person which occur during the procedure comprises receiving the event information, the indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure, is not entered by the person.
The dependent claim 10 is directed to pH data collected over a predetermined time duration that is less than twenty-four hours.
The dependent claim 11 is directed to pH data collected over a first predetermined time duration and a second predetermined time duration which is longer than the first predetermined time duration.
The dependent claim 12 is directed to evaluate, at a first time during the procedure, a first diagnosis for the esophageal disease for the person, wherein pH data over the first predetermined time duration is available at the first time but pH data over the second predetermined time duration is not yet available at the first time, determine that the first diagnosis does not meet confidence criteria, evaluate, at a second time during the procedure, a second diagnosis for the esophageal disease for person, wherein the second time is after the first time, and wherein pH data over the second predetermined time duration is available at the second time, determine that the second diagnosis meets confidence criteria, provide the second diagnosis as the diagnosis for the esophageal disease for the person.
The dependent claim 14 is directed to access, during the procedure, event information relating to events of the person which occur during the procedure, the event information comprising an indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure, wherein accessing the event information relating to events of the person which occur during the procedure comprises receiving the event information.
The dependent claim 15 is directed to data collected over a predetermined time duration that is less than twenty-four hours.
The dependent claim 16 is directed to account for abnormal pH readings due to at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure.
The dependent claim 17 is directed to accounting for abnormal pH readings due to at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure.
The dependent claim 18 is directed to account for abnormal pH readings due to at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure.
Each of these steps of the preceding dependent claims 2-6, 8-12, and 14-18 only serve to further limit or specify the features of independent claims 1, 7, and 13 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
As such, the Examiner concludes that the preceding claims recite an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
Claim 1 recites the use of a processor, only recites the processor as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Claims 1-2, 6, and 16 recite the use of a at least one memory storing instructions which, when executed by the at least one processor, in this case to access, pH data relating to an esophageal disease, evaluate a diagnosis for the esophageal disease for the person, communicate the diagnosis for the esophageal disease, access event information relating to events of the person during the procedure, only recites the at least one memory storing instructions which, when executed by the at least one processor as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Claims 1, 6-8, and 12-14 recite an in-vivo device located within a person, as being used in its ordinary capacity and is merely a tool to execute the abstract idea (MPEP § 2106.05(f)(2)).
Claims 1, 3-5, 7, 9-11, 13, and 15 recite the use of pH data measured by the in-vivo device, as being used in its ordinary capacity and is merely a tool to execute the abstract idea (MPEP § 2106.05(f)(2)).
Claims 1, 3, 7, 9, and 13-14 recite the use of a applying a trained machine learning model, in this case to , only recites the applying a trained machine learning model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claims 3, 9, and 14 recite the use of a mobile device and a wearable device separate from the mobile device, only as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Claims 4, 10, and 15 recite the use of a trained machine learning model comprises a trained deep learning neural network, only as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claims 5-6 and 11-12 recite the use of a the trained machine learning model is one model among a plurality of trained machine learning models, the plurality of trained machine learning models further comprising a first model different from the trained machine learning model, only recites the trained machine learning model is one model among a plurality of trained machine learning models, the plurality of trained machine learning models further comprising a first model different from the trained machine learning model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claims 13 and 18 recite the use of a computer-readable medium comprising instructions which, when executed by at least one processor of a system, in this case to access, pH data relating to an esophageal disease, evaluate a diagnosis for the esophageal disease for the person, communicate the diagnosis for the esophageal disease, access event information relating to events of the person during the procedure, only recites the computer-readable medium comprising instructions which, when executed by at least one processor of a system as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception.
As discussed above in “Step 2A – Prong 2”, the identified additional elements, such as the processor, at least one memory storing instructions which, when executed by the at least one processor, in-vivo device located within a person, pH data measure by the in-vivo device, applying a trained machine learning model, mobile device, wearable device separate from the mobile device, trained machine learning model comprises a trained deep learning neural network, the trained machine learning model is one model among a plurality of trained machine learning models, the plurality of trained machine learning models further comprising a first model different from the trained machine learning model, and computer-readable medium comprising instructions which, when executed by at least one processor of a system in independent claims 1, 7, and 13 and dependent claims 2-6, 8-12, and 14-18 are {Excel Sheet} equivalent to adding the words “apply it” on a generic computer. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the computer and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”). Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements are directed to generic computer component and functions being used to perform the abstract idea.
Applicant’s own disclosure in paragraph [0072] acknowledges that the “processor or controller 505 that may be or include, for example, one or more central processing unit processor(s) (CPU), one or more Graphics Processing Unit(s) (GPU or GPGPU), and/or other types of processor, such as a microprocessor, digital signal processor, microcontroller, programmable logic device (PLD), field programmable gate array (FPGA), or any suitable computing or computational device…memory 520 may be or may include, for example, one or more Random Access Memory (RAM), read-only memory (ROM), flash memory, volatile memory, non-volatile memory, cache memory, and/or other memory devices. The memory 520 may store, for example, executable instructions that carry out an operation (e.g., executable code 525) and/or data”. Paragraphs [0077-0078] disclose “Referring to FIG. 6, there is a diagram of various devices and systems of a computing configuration and communications between the devices and systems. The systems include a kit 610 that includes an in-vivo device 612…In the kit 610, the in-vivo device 612 and the ex-vivo device 614 can communicate with each other using radio frequency (RF) transceivers. Persons skilled in the art will understand how to implement RF transceivers and associated electronics for interfacing with RF transceivers. In various embodiments, the RF transceivers can be designed to use frequencies that experience less interference or no interference from common communications devices, such as cordless phones”. Additionally, paragraph [0092] acknowledges “a trained machine learning model, such as deep neural network or a model which includes a deep learning neural network. A deep learning neural network is a machine learning model that does not require feature engineering. Rather, a deep learning neural networks can use a large amount of input data to learn correlations, such as learning correlations between input data and the presence or absence of an esophageal or gastrointestinal disease such as GERD”. Paragraphs [0081] and [0089] disclose “mobile device 622 may be referred to herein as a mobile device 622 and can include, without limitation, a smartphone, a laptop, or a tablet, among others. The mobile device 622 can be any mobile device used by a patient, including a mobile device owned by the patient or a mobile device loaned to the patient for the CE procedure… a wearable device, such as by a smartwatch or bracelet”. Furthermore, paragraph [0039] discloses “a computer-readable medium includes instructions which, when executed by at least one processor of a system”.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-18 are not eligible subject matter under 35 USC 101.
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 1-2, 7-8, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Duval et al. (US-11244454-B2)[hereinafter Duval], in view of Kwiatek et al. (“The Bravo™ pH capsule system”)[hereinafter Kwiatek].
As per Claim 1, Duval discloses a system for diagnosing an esophageal disease in column 2 lines 13-20 and column 3 line 57-column 4 line 8 (a system for diagnosing a gastrointestinal tract disease (synonymous to the esophageal disease)), the system comprising: at least one processor in paragraphs column 5 lines 43-63 (a processor); and at least one memory storing instructions which, when executed by the at least one processor in column 4 lines 9-15 and column 5 lines 43-63 and column 6 lines 7-18 (a memory storing instructions, executed by the processor), cause the system to: access, during a procedure involving an in-vivo device located within a person, data measured by the in-vivo device relating to an esophageal disease in column 4 line 9-column 5 line 42 (access, during a procedure involving an imaging device (synonymous to an in-vivo device) located within a person, wherein the imaging device is a part of an implantable monitor, data measured by the imagining device relating to a GI tract disease); evaluate, during the procedure while the in-vivo device is located within the person, a diagnosis for the esophageal disease for the person by applying a trained machine learning model to the data measured by the in-vivo device in column 6 lines 40-column 7 lines 44 and column 10 lines 15-49 (evaluate, during the procedure while the imaging device is located within the person, a diagnosis for the GI tract disease for the person by applying trained machine learning model to the data measured by the imaging device); and communicate, during the procedure while the in-vivo device is located within the person, the diagnosis for the esophageal disease in column 7 lines 47-54 and column 9 line 63-column 10 line 14 and column 10 lines 36-49 (communicate, during the procedure while the imaging device is located within the person, the diagnosis for the GI tract disease).
Duval discloses accessing and evaluating, during a procedure, data measured by an in-vivo device relating to an esophageal disease, but does not disclose the data being pH data. However, Kwiatek discloses access, during a procedure involving an in-vivo device located within a person, pH data measured by the in-vivo device relating to an esophageal disease in the 3rd paragraph of the Introduction on page 156, 1st paragraph of the Bravo™ wireless pH monitoring system on pages 156-157, and the Duration of pH monitoring section on page 158 (access, during pH monitoring involving the Bravo TM wireless pH capsule (synonymous to a procedure involving in-vivo device located within a person), pH data measured by the Bravo TM wireless pH capsule relating to gastroesophageal reflux disease (synonymous to an esophageal disease)); evaluate, during the procedure while the in-vivo device is located within the person, a diagnosis for the esophageal disease for the person by the pH data measured by the in-vivo device in the 3rd paragraph of the Introduction on page 156 and the Duration of pH monitoring section on page 158 (evaluate, during pH monitoring (synonymous to procedure) involving the Bravo TM wireless pH capsule, gastroesophageal reflux disease for the patient by using pH data measured by the Bravo TM wireless pH capsule).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the pH data of Kwiatek for the accessing data and using the data to evaluate for an esophageal disease means of the Duval. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
As per Claim 2, Duval and Kwiatek disclose the system of claim 1, wherein the instructions, when executed by the at least one processor, further cause the system to: Duval also discloses access, during the procedure while the in-vivo device is located within a person, event information relating to events of the person which occur during the procedure, the event information comprising an indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure in column 8 line 23-column 9 line 21 (access, during the procedure while the imaging device is located within a person, measurements (synonymous to event information) relating to activity of the GI tract (synonymous to events of the person) which occur during the procedure, the measurements include gastric motility (synonymous to an indication of an eating, sleeping, or exercising event)).
As per Claim 7, Duval discloses a computer-implemented method for diagnosing an esophageal disease in column 2 lines 13-20 and column 3 line 57-column 4 line 8 and column 5 lines 43-63 (a computer-implemented method for diagnosing a gastrointestinal tract disease (synonymous to the esophageal disease)), the method comprising: accessing, during a procedure involving an in-vivo device located within a person, data measured by the in-vivo device relating to an esophageal disease in column 4 line 9-column 5 line 42 (access, during a procedure involving an imaging device (synonymous to an in-vivo device) located within a person, wherein the imaging device is a part of an implantable monitor, data measured by the imagining device relating to a GI tract disease); evaluating, during the procedure while the in-vivo device is located within the person, a diagnosis for the esophageal disease for the person by applying a trained machine learning model to the data measured by the in-vivo device in column 6 lines 40-column 7 lines 44 and column 10 lines 15-49 (evaluate, during the procedure while the imaging device is located within the person, a diagnosis for the GI tract disease for the person by applying trained machine learning model to the data measured by the imaging device); and communicating, during the procedure while the in-vivo device is located within the person, the diagnosis for the esophageal disease in column 7 lines 47-54 and column 9 line 63-column 10 line 14 and column 10 lines 36-49 (communicate, during the procedure while the imaging device is located within the person, the diagnosis for the GI tract disease).
Duval discloses accessing and evaluating, during a procedure, data measured by an in-vivo device relating to an esophageal disease, but does not disclose the data being pH data. However, Kwiatek discloses accessing, during a procedure involving an in-vivo device located within a person, pH data measured by the in-vivo device relating to an esophageal disease in the 3rd paragraph of the Introduction on page 156, 1st paragraph of the Bravo™ wireless pH monitoring system on pages 156-157, and the Duration of pH monitoring section on page 158 (access, during pH monitoring involving the Bravo TM wireless pH capsule (synonymous to a procedure involving in-vivo device located within a person), pH data measured by the Bravo TM wireless pH capsule relating to gastroesophageal reflux disease (synonymous to an esophageal disease)); evaluating, during the procedure while the in-vivo device is located within the person, a diagnosis for the esophageal disease for the person by the pH data measured by the in-vivo device in 3rd paragraph of the Introduction on page 156 and the Duration of pH monitoring section on page 158 (evaluate, during pH monitoring involving the Bravo TM wireless pH capsule, gastroesophageal reflux disease for the patient by using pH data measured by the Bravo TM wireless pH capsule).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the pH data of Kwiatek for the accessing data and using the data to evaluate for an esophageal disease means of the Duval. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
As per Claim 8, Duval and Kwiatek disclose the computer-implemented method of claim 7, Duval also discloses further comprising: accessing, during the procedure while the in-vivo device is located within a person, event information relating to events of the person which occur during the procedure, the event information comprising an indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure in column 8 line 23-column 9 line 21 (access, during the procedure while the imaging device is located within a person, measurements (synonymous to event information) relating to activity of the GI tract (synonymous to events of the person) which occur during the procedure, the measurements include gastric motility (synonymous to an indication of an eating, sleeping, or exercising event)).
As per Claim 13, Duval discloses a computer-readable medium comprising instructions which, when executed by at least one processor of a system in column 4 lines 9-15 and column 5 lines 43-63 and column 6 lines 7-18 (a computer readable medium including instructions, executed by the processor of a system), cause the system to: access, during a procedure involving an in-vivo device located within a person, data measured by the in-vivo device relating to an esophageal disease in paragraphs column 4 line 9-column 5 line 42 (access, during a procedure involving an imaging device (synonymous to an in-vivo device) located within a person, wherein the imaging device is a part of an implantable monitor, data measured by the imagining device relating to a GI tract disease); evaluate, during the procedure while the in-vivo device is located within the person, a diagnosis for the esophageal disease for the person by applying a trained machine learning model to the data measured by the in-vivo device in paragraphs column 6 lines 40-column 7 lines 44 and column 10 lines 15-49 (evaluate, during the procedure while the imaging device is located within the person, a diagnosis for the GI tract disease for the person by applying trained machine learning model to the data measured by the imaging device); and communicate, during the procedure while the in-vivo device is located within the person, the diagnosis for the esophageal disease in column 7 lines 47-54 and column 9 line 63-column 10 line 14 and column 10 lines 36-49 (communicate, during the procedure while the imaging device is located within the person, the diagnosis for the GI tract disease).
Duval discloses accessing and evaluating, during a procedure, data measured by an in-vivo device relating to an esophageal disease, but does not disclose the data being pH data. However, Kwiatek discloses access, during a procedure involving an in-vivo device located within a person, pH data measured by the in-vivo device relating to an esophageal disease in the 3rd paragraph of the Introduction on page 156, 1st paragraph of the Bravo™ wireless pH monitoring system on pages 156-157, and the Duration of pH monitoring section on page 158 (access, during pH monitoring involving the Bravo TM wireless pH capsule (synonymous to a procedure involving in-vivo device located within a person), pH data measured by the Bravo TM wireless pH capsule relating to gastroesophageal reflux disease (synonymous to an esophageal disease)); evaluate, during the procedure while the in-vivo device is located within the person, a diagnosis for the esophageal disease for the person by the pH data measured by the in-vivo device in the 3rd paragraph of the Introduction on page 156 and the Duration of pH monitoring section on page 158 (evaluate, during pH monitoring involving the Bravo TM wireless pH capsule, gastroesophageal reflux disease for the patient by using pH data measured by the Bravo TM wireless pH capsule).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the pH data of Kwiatek for the accessing data and using the data to evaluate for an esophageal disease means of the Duval. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claims 3, 9, 14, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Duval et al. (US-11244454-B2)[hereinafter Duval], in view of Kwiatek et al. (“The Bravo™ pH capsule system”)[hereinafter Kwiatek], in view of Wexler et al. (US-20200375549-A1)[hereinafter Wexler].
As per Claim 3, Duval and Kwiatek disclose the system of claim 2, Duval also discloses wherein the evaluating the diagnosis for the esophageal disease for the person comprises applying the trained machine learning model to the data measured by the in-vivo device and to the indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure in column 6 lines 40-column 7 lines 44 and column 8 line 23-column 9 line 21 and column 10 lines 15-49 (evaluating the diagnosis for the GI tract disease for the person by applying trained machine learning model to the data measured by the imaging device and to the measurements relating to activity of the GI tract indication of gastric motility measurements during the procedure).
Duval discloses evaluating the diagnosis for the esophageal disease by applying a trained machine learning model to the data measured by the in-vivo device, but does not disclose the data being pH data. However, Kwiatek discloses wherein the evaluating the diagnosis for the esophageal disease for the person comprises the pH data measured by the in-vivo device and to the indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure in the 3rd paragraph of the Introduction on page 156 and the Duration of pH monitoring section on page 158 (evaluating gastroesophageal reflux disease for the patient by using pH data measured by the Bravo™ wireless pH capsule during pH monitoring and the indication of sipping acidic beverages (synonymous to an eating event) during the pH monitoring).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the pH data of Kwiatek for the accessing data and using the data to evaluate for an esophageal disease means of the Duval. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
The combination of Duval and Kwiatek discloses accessing the event information relating to events of the person which occur during the procedure, but fails to disclose the event information being received from the mobile device of a person. However, Wexler discloses wherein accessing the event information relating to events of the person which occur during the procedure comprises receiving the event information from a mobile device of the person in paragraphs [0014] and [0032] and [0036-0037] (accessing the blood glucose data (synonymous to the event information) relating to events of the person which occur during the biomonitoring (synonymous to a procedure) includes receiving the blood glucose data from a user device of the person, wherein the user device includes a mobile device (Examiner notes that the blood glucose data is correlated with a meal intake or physical activity event)), wherein the indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure, is not entered by the person and is generated by at least one of: the mobile device of the person or a wearable device separate from the mobile device in paragraphs [0014] and [0032] and [0036-0041] (the indication of the meal intake (synonymous to an eating event during the procedure) or a physical activity event (synonymous to an exercising event) is not entered by the person and is generated by the user device).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for diagnosing an esophageal disease, as disclosed by Duval and Kwiatek, to be combined with receiving event information from a mobile device of the person, as disclosed by Wexler, for the purpose of providing real-time analytics, personalized analytics, or forecasting in a rapid, reliable, and accurate manner [0003-0005].
As per Claim 9, Duval and Kwiatek disclose the computer-implemented method of claim 8, Duval also discloses wherein the evaluating the diagnosis for the esophageal disease for the person comprises applying the trained machine learning model to the data measured by the in-vivo device and to the indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure in column 6 lines 40-column 7 lines 44 and column 8 line 23-column 9 line 21 and column 10 lines 15-49 (evaluating the diagnosis for the GI tract disease for the person by applying trained machine learning model to the data measured by the imaging device and to the measurements relating to activity of the GI tract indication of gastric motility measurements during the procedure).
Duval discloses evaluating the diagnosis for the esophageal disease by applying a trained machine learning model to the data measured by the in-vivo device, but does not disclose the data being pH data. However, Kwiatek discloses wherein the evaluating the diagnosis for the esophageal disease for the person comprises the pH data measured by the in-vivo device and to the indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure in the 3rd paragraph of the Introduction on page 156 and the Duration of pH monitoring section on page 158 (evaluating gastroesophageal reflux disease for the patient by using pH data measured by the Bravo™ wireless pH capsule during pH monitoring and the indication of sipping acidic beverages (synonymous to an eating event) during the pH monitoring).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the pH data of Kwiatek for the accessing data and using the data to evaluate for an esophageal disease means of the Duval. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
The combination of Duval and Kwiatek discloses accessing the event information relating to events of the person which occur during the procedure, but fails to disclose the event information being received from the mobile device of a person. However, Wexler discloses wherein accessing the event information relating to events of the person which occur during the procedure comprises receiving the event information from a mobile device of the person in paragraphs [0014] and [0032] and [0036-0037] (accessing the blood glucose data (synonymous to the event information) relating to events of the person which occur during the biomonitoring (synonymous to a procedure) includes receiving the blood glucose data from a user device of the person, wherein the user device includes a mobile device (Examiner notes that the blood glucose data is correlated with a meal intake or physical activity event)), wherein the indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure, is not entered by the person and is generated by at least one of: the mobile device of the person or a wearable device separate from the mobile device in paragraphs [0014] and [0032] and [0036-0041] (the indication of the meal intake (synonymous to an eating event during the procedure) or a physical activity event (synonymous to an exercising event) is not entered by the person and is generated by the user device).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for diagnosing an esophageal disease, as disclosed by Duval and Kwiatek, to be combined with receiving event information from a mobile device of the person, as disclosed by Wexler, for the purpose of providing real-time analytics, personalized analytics, or forecasting in a rapid, reliable, and accurate manner [0003-0005].
As per Claim 14, Duval and Kwiatek disclose the computer-readable medium of claim 13, wherein the instructions, when executed by the at least one processor, further cause the system to: Duval also discloses access, during the procedure while the in-vivo device is located within a person, event information relating to events of the person which occur during the procedure, the event information comprising an indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure in column 8 line 23-column 9 line 21 (access, during the procedure while the imaging device is located within a person, measurements (synonymous to event information) relating to activity of the GI tract (synonymous to events of the person) which occur during the procedure, the measurements include gastric motility (synonymous to an indication of an eating, sleeping, or exercising event)), and wherein evaluating the diagnosis for the esophageal disease for the person comprises applying the trained machine learning model to the data measured by the in-vivo device and to the indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure in column 6 lines 40-column 7 lines 44 and column 8 line 23-column 9 line 21 and column 10 lines 15-49 (evaluating the diagnosis for the GI tract disease for the person by applying trained machine learning model to the data measured by the imaging device and to the measurements relating to activity of the GI tract indication of gastric motility measurements during the procedure).
Duval discloses evaluating the diagnosis for the esophageal disease by applying a trained machine learning model to the data measured by the in-vivo device, but does not disclose the data being pH data. However, Kwiatek discloses wherein evaluating the diagnosis for the esophageal disease for the person comprises the pH data measured by the in-vivo device and to the indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure in the 3rd paragraph of the Introduction on page 156 and the Duration of pH monitoring section on page 158 (evaluating gastroesophageal reflux disease for the patient by using pH data measured by the Bravo™ wireless pH capsule during pH monitoring and the indication of sipping acidic beverages (synonymous to an eating event) during the pH monitoring).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the pH data of Kwiatek for the accessing data and using the data to evaluate for an esophageal disease means of the Duval. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
The combination of Duval and Kwiatek discloses accessing the event information relating to events of the person which occur during the procedure, but fails to disclose the event information being received from the mobile device of a person. However, Wexler discloses wherein accessing the event information relating to events of the person which occur during the procedure comprises receiving the event information from a mobile device of the person in paragraphs [0014] and [0032] and [0036-0037] (accessing the blood glucose data (synonymous to the event information) relating to events of the person which occur during the biomonitoring (synonymous to a procedure) includes receiving the blood glucose data from a user device of the person, wherein the user device includes a mobile device (Examiner notes that the blood glucose data is correlated with a meal intake or physical activity event)), wherein the indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure, is not entered by the person and is generated by at least one of: the mobile device of the person or a wearable device separate from the mobile device in paragraphs [0014] and [0032] and [0036-0041] (the indication of the meal intake (synonymous to an eating event during the procedure) or a physical activity event (synonymous to an exercising event) is not entered by the person and is generated by the user device).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of diagnosing an esophageal disease, as disclosed by Duval and Kwiatek, to be combined with receiving event information from a mobile device of the person, as disclosed by Wexler, for the purpose of providing real-time analytics, personalized analytics, or forecasting in a rapid, reliable, and accurate manner [0003-0005].
As per Claim 16, Duval, Kwiatek, and Wexler disclose the system of claim 3, wherein in the evaluating the diagnosis for the esophageal disease for the person, the instructions, when executed by the at least one processor.
Duval does not disclose the following limitations. However, Kwiatek discloses account for abnormal pH readings due to at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure in the 3rd paragraph of the Limitations section on page 159 (account for abnormal acid reflux events due to sipping acidic beverages (synonymous to an eating event) during the monitoring (Examiner note that abnormal acid reflux events indicate abnormal pH readings)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for diagnosing an esophageal disease, as disclosed by Duval and Kwiatek, to be combined with receiving event information from a mobile device of the person, as disclosed by Wexler, for the purpose of improving diagnostic accuracy along with patient compliance [abstract on page 156].
As per Claim 17, Duval, Kwiatek, and Wexler disclose the computer-implemented method of claim 9, wherein the evaluating the diagnosis for the esophageal disease for the person.
Duval does not disclose the following limitations. However, Kwiatek discloses accounting for abnormal pH readings due to at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure in the 3rd paragraph of the Limitations section on page 159 (account for abnormal acid reflux events due to sipping acidic beverages (synonymous to an eating event) during the monitoring (Examiner note that abnormal acid reflux events indicate abnormal pH readings)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for diagnosing an esophageal disease, as disclosed by Duval and Kwiatek, to be combined with receiving event information from a mobile device of the person, as disclosed by Wexler, for the purpose of improving diagnostic accuracy along with patient compliance [abstract on page 156].
As per Claim 18, Duval, Kwiatek, and Wexler disclose the computer-readable medium of claim 14, wherein in the evaluating the diagnosis for the esophageal disease for the person, the instructions, when executed by the at least one processor.
Duval does not disclose the following limitations. However, Kwiatek discloses cause the system to: account for abnormal pH readings due to at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure in paragraphs 3rd paragraph of the Limitations section on page 159 (account for abnormal acid reflux events due to sipping acidic beverages (synonymous to an eating event) during the monitoring (Examiner note that abnormal acid reflux events indicate abnormal pH readings)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of diagnosing an esophageal disease, as disclosed by Duval and Kwiatek, to be combined with receiving event information from a mobile device of the person, as disclosed by Wexler, for the purpose of improving diagnostic accuracy along with patient compliance [abstract on page 156].
Claims 4, 10, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Duval et al. (US-11244454-B2)[hereinafter Duval], in view of Kwiatek et al. (“The Bravo™ pH capsule system”)[hereinafter Kwiatek], in view of SOBOL et al. (EP3735681A1)[hereinafter Sobol], in view of Wexler et al. (US-20200375549-A1)[hereinafter Wexler].
As per Claim 4, Duval and Kwiatek disclose the system of claim 1.
The combination of Duval and Kwiatek discloses applying the trained machine learning model to the pH data, but does not disclose the trained machine learning model being a neural network and the pH data collected over a predetermined time duration that is less than 24 hours. However, Sobol discloses wherein the trained machine learning model comprises a trained deep learning neural network configured to be applied to pH data collected over a predetermined time duration that is less than twenty-four hours in paragraphs [0030] and [00116] and [00124-00125] and [00140] and [00193] and [00243] (the machine learning classification model includes a deep learning neural network is applied to data collected over a predetermined period that is less than 24 hours).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for diagnosing an esophageal disease, as disclosed by Duval and Kwiatek, to be combined with the trained machine learning model including a trained deep learning neural network applied to data collected over a predetermined time duration less than twenty-four hours, as disclosed by Sobol, for the purpose of improving the ability to track and identify salient indicators of changing health [0002-0006].
The combination of Duval, Kwiatek, and Sobol discloses pH data measured by the in-vivo device over at least the predetermined time duration, but does not disclose applying the trained deep learning neural network to the pH data measured by the in-vivo device over at least the predetermined time duration. However, Wexler discloses wherein the pH data measured by the in-vivo device comprises pH data measured by the in-vivo device over at least the predetermined time duration, such that the trained deep learning neural network is applied to the pH data measured by the in-vivo device over at least the predetermined time duration in paragraphs [0033-0034] and [0049] and [0080] (blood glucose data measured by the implanted blood glucose sensor (synonymous to the in-vivo device) includes data measured over the predetermined time intervals, such that the machine learning models (synonymous to the trained deep learning neural network) is applied to the blood glucose data measured by the implanted blood glucose sensor over the predetermined time intervals).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a system for diagnosing an esophageal disease, as disclosed by Duval, Kwiatek, and Sobol, to be combined with the trained machine learning model including a trained deep learning neural network applied to data collected over a predetermined time duration less than twenty-four hours, as disclosed by Wexler, for the purpose of providing real-time analytics, personalized analytics, or forecasting in a rapid, reliable, and accurate manner [0003-0005].
As per Claim 10, Duval and Kwiatek disclose the computer-implemented method of claim 7.
The combination of Duval and Kwiatek discloses applying the trained machine learning model to the pH data, but does not disclose the trained machine learning model being a neural network and the pH data collected over a predetermined time duration that is less than 24 hours. However, Sobol discloses wherein the trained machine learning model comprises a trained deep learning neural network configured to be applied to pH data collected over a predetermined time duration that is less than twenty-four hours in paragraphs [0030] and [00116] and [00124-00125] and [00140] and [00193] and [00243] (the machine learning classification model includes a deep learning neural network is applied to data collected over a predetermined period that is less than 24 hours).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of method for diagnosing an esophageal disease, as disclosed by Duval and Kwiatek, to be combined with the trained machine learning model including a trained deep learning neural network applied to data collected over a predetermined time duration less than twenty-four hours, as disclosed by Sobol, for the purpose of improving the ability to track and identify salient indicators of changing health [0002-0006].
The combination of Duval, Kwiatek, and Sobol discloses pH data measured by the in-vivo device over at least the predetermined time duration, but does not disclose applying the trained deep learning neural network to the pH data measured by the in-vivo device over at least the predetermined time duration. However, Wexler discloses wherein the pH data measured by the in-vivo device comprises pH data measured by the in-vivo device over at least the predetermined time duration, such that the trained deep learning neural network is applied to the pH data measured by the in-vivo device over at least the predetermined time duration in paragraphs [0033-0034] and [0049] and [0080] (blood glucose data measured by the implanted blood glucose sensor (synonymous to the in-vivo device) includes data measured over the predetermined time intervals, such that the machine learning models (synonymous to the trained deep learning neural network) is applied to the blood glucose data measured by the implanted blood glucose sensor over the predetermined time intervals).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a method for diagnosing an esophageal disease, as disclosed by Duval, Kwiatek, and Sobol, to be combined with the trained machine learning model including a trained deep learning neural network applied to data collected over a predetermined time duration less than twenty-four hours, as disclosed by Wexler, for the purpose of providing real-time analytics, personalized analytics, or forecasting in a rapid, reliable, and accurate manner [0003-0005].
As per Claim 15, Duval and Kwiatek disclose the computer-readable medium of claim 13.
The combination of Duval and Kwiatek discloses applying the trained machine learning model to the pH data, but does not disclose the trained machine learning model being a neural network and the pH data collected over a predetermined time duration that is less than 24 hours. However, Sobol discloses wherein the trained machine learning model comprises a trained deep learning neural network configured to be applied to data collected over a predetermined time duration that is less than twenty-four hours in paragraphs [0030] and [00116] and [00124-00125] and [00140] and [00193] and [00243] (the machine learning classification model includes a deep learning neural network is applied to data collected over a predetermined period that is less than 24 hours).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of diagnosing an esophageal disease, as disclosed by Duval and Kwiatek, to be combined with the trained machine learning model including a trained deep learning neural network applied to data collected over a predetermined time duration less than twenty-four hours, as disclosed by Sobol, for the purpose of improving the ability to track and identify salient indicators of changing health [0002-0006].
The combination of Duval, Kwiatek, and Sobol discloses pH data measured by the in-vivo device over at least the predetermined time duration, but does not disclose applying the trained deep learning neural network to the pH data measured by the in-vivo device over at least the predetermined time duration. However, Wexler discloses wherein the pH data measured by the in-vivo device comprises pH data measured by the in-vivo device over at least the predetermined time duration, such that the trained deep learning neural network is applied to the pH data measured by the in-vivo device over at least the predetermined time duration in paragraphs [0033-0034] and [0049] and [0080] (blood glucose data measured by the implanted blood glucose sensor (synonymous to the in-vivo device) includes data measured over the predetermined time intervals, such that the machine learning models (synonymous to the trained deep learning neural network) is applied to the blood glucose data measured by the implanted blood glucose sensor over the predetermined time intervals).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of diagnosing an esophageal disease, as disclosed by Duval, Kwiatek, and Sobol, to be combined with the trained machine learning model including a trained deep learning neural network applied to data collected over a predetermined time duration less than twenty-four hours, as disclosed by Wexler, for the purpose of providing real-time analytics, personalized analytics, or forecasting in a rapid, reliable, and accurate manner [0003-0005].
Subject Matter Free of the Prior Art
The following is an examiner’s statement of subject matter free of the prior art:
The limitations in claims 5 and 11 stating: wherein the trained machine learning model is one model among a plurality of trained machine learning models, the plurality of trained machine learning models further comprising a first model different from the trained machine learning model, wherein the first model is configured to be applied to pH data collected by the in-vivo device over a first predetermined time duration, and wherein the trained machine learning model is configured to be applied to pH data collected by the in-vivo device over a second predetermined time duration longer than the first predetermined time duration is free of the prior art. The broadest reasonable interpretation of the claim language requires applying a model for data taken over a period of time and applying a separate model for additional data over a longer period of time. The most remarkable prior arts of record are as follows:
Goetz et al. (US 20210295996 A1)[hereinafter Goetz] teaches on using a plurality of models to diagnose a disease, while Lanius et al. (US 20210398677 A1)[hereinafter Lanius] teaches on a model applied to laboratory data collected over different durations of time. Neither Duval, Kwiatek, Goetz or Lanius teach on a first model and a trained machine learning model applied to pH data collected by the in-vivo device over a predetermined time, wherein the first model is different from the trained machine learning model and the predetermined time duration corresponding to the data collected using the first model is a shorter duration than the predetermined time duration corresponding to the data collected using the trained model. Therefore, claims 5 and 11 are free of the prior art.
The limitations in claims 6 and 12 stating: wherein in evaluating the diagnosis for the esophageal disease for the person, the instructions, when executed by the at least one processor, cause the system to: evaluate, at a first time during the procedure while the in-vivo device is located within the person, a first diagnosis for the esophageal disease for the person using the first model of the plurality of trained machine learning models, wherein pH data over the first predetermined time duration is available at the first time but pH data over the second predetermined time duration is not yet available at the first time; determine that the first diagnosis does not meet confidence criteria; evaluate, at a second time during the procedure while the in-vivo device is located within the person, a second diagnosis for the esophageal disease for the person using the trained machine learning model, wherein the second time is after the first time, and wherein pH data over the second predetermined time duration is available at the second time; determine that the second diagnosis meets confidence criteria; and provide the second diagnosis as the diagnosis for the esophageal disease for the person is free of the prior art. The broadest reasonable interpretation of the claim language requires applying a model for data taken over a period of time, disregarding that data and then applying a separate model for additional data over a longer period of time. The most remarkable prior arts of record are as follows:
Duval teaches evaluating a first diagnosis with a first model, determining that the first diagnosis does not meet confidence criteria, evaluating a second diagnosis with a second model, and determining that the second diagnosis meets the confidence criteria. Neither, Duval, Kwiatek, Goetz, or Lanius teaches on applying a model for data taken over a first period of time, disregarding that data and then applying a second model for the data taken over a period of time longer than the previous period of time, wherein the data over the first period of time is available at the first time but the data over the second period of time is only available at the second period of time. Therefore, claims 6 and 12 are free of the prior art.
Response to Arguments
Applicant's arguments, see Pages 9-11, “Claim Rejections - 35 U.S.C. § 101“, filed 12/04/2025 with respect to claims 1, 7, and 13 have been fully considered but they are not persuasive.
Applicant argues that the amended claims do not recite any abstract idea of organizing human activity. Examiner respectfully disagrees. The amended claim limitations are directed to evaluating a diagnosis for a disease. The claims merely recite accessing pH data relating to a esophageal disease during a procedure, evaluating a diagnosis for the esophageal disease during the procedure, and communicating the diagnosis for the esophageal disease during the procedure, which are activities performed by medical staff, which falls into the abstract grouping of certain methods of organizing human activity because it is the business relations of medical staff and patients. Additionally, the claim limitations involve managing personal behaviors or interactions between people.
Applicant argues that the amended claims integrate the abstract idea into a practical application by providing an improvement to the technology for diagnosing esophageal disease. Examiner respectfully disagrees. The claims do not recite an improvement to the technology for diagnosing esophageal disease. The claims merely recite accessing pH data relating to a esophageal disease during a procedure, evaluating a diagnosis for the esophageal disease during the procedure, which are a part of the abstract idea. An improvement to the abstract ideas of accessing pH data relating to a esophageal disease during a procedure, evaluating a diagnosis for the esophageal disease during the procedure does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(II) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology."). The courts indicated in TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48, that gathering and analyzing information using conventional techniques and providing the output is not sufficient to show an improvement to technology. The claim language and instant application fails to provide details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Here, the improvement is to accessing pH data relating to a esophageal disease during a procedure, evaluating a diagnosis for the esophageal disease during the procedure. There is no indication in the disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Merely adding generic computer components to perform the method is not sufficient.
Applicant argues that the amended claims are directed to something “significantly more” than the idea itself by the claim limitations accessing pH data relating to a esophageal disease during a procedure, evaluating a diagnosis for the esophageal disease during the procedure. Examiner respectfully disagrees. The amended claim limitations are directed to accessing pH data relating to a esophageal disease during a procedure, evaluating a diagnosis for the esophageal disease during the procedure. The use of the processor, at least one memory storing instructions which, when executed by the at least one processor, in-vivo device located within a person, pH data measure by the in-vivo device, applying a trained machine learning model, mobile device, wearable device separate from the mobile device, trained machine learning model comprises a trained deep learning neural network, the trained machine learning model is one model among a plurality of trained machine learning models, the plurality of trained machine learning models further comprising a first model different from the trained machine learning model, and computer-readable medium comprising instructions which, when executed by at least one processor of a system to carry out the steps of the abstract idea is merely applying the abstract idea to general purpose computer components which amounts to mere instructions to apply the exceptions, see MPEP 2106.05(f)(2). The courts indicated in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984, that “a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer” is not enough to qualify as significantly more.
Applicant’s arguments, see Pages 11-12, “Claim Rejections - 35 U.S.C. § 102“, and Pages 12-14, “Claim Rejections - 35 U.S.C. § 103”, filed 12/04/2025 with respect to claims 1-15 have been fully considered.
With regards to claims 1-2, 5-8, and 11-14, Applicant argues that Duval fails to teach or suggest the amended limitations of the independent claims. Specifically, Duval does not suggest using pH measurements to evaluate a diagnosis for the esophageal disease. Examiner find this persuasive. Therefore, the rejection of 09/04/2025 has been withdrawn. However, upon further consideration a new grounds of rejection is made over Duval in view of Kwiatek. As per the rejections of claims 3, 9, and 14, Applicant argues Duval and Wexler fail to teach or suggest the amended limitations. Examiner points Applicant to the updated rejection and citations in the 103 rejections above. The combination of Duval and Kwiatek disclose “wherein the evaluating the diagnosis for the esophageal disease for the person comprises applying the trained machine learning model to the pH data measured by the in-vivo device and to the indication of at least one of: an eating event of the person during the procedure, a sleeping event of the person during the procedure, or an exercise event of the person during the procedure.” Duval discloses in column 6 lines 40-column 7 lines 44, column 8 line 23-column 9 line 21, and column 10 lines 15-49 that evaluating the diagnosis for the GI tract disease by applying a trained machine learning model to the data measured by the imaging device inside the person’s body and to the GI tract activity measurements indicating gastric motility measurements. Kwiatek discloses in the Introduction on page 156, the Duration of pH monitoring on page 158, and the Limitation section on page 159 that the pH data measured by the pH capsule and the indication of sipping acidic beverages are used to evaluate gastroesophageal reflux disease.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hung Cao, “An Implantable, Batteryless, and Wireless Capsule With Integrated Impedance and pH Sensors for Gastroesophageal Reflux Monitoring”, (2012) teaches a device for monitoring gastroesophageal reflux disease (GERD).
Luis A. de Souza, “A survey on Barrett's esophagus analysis using machine learning”, (2018) teaches on machine learning for Barrett’s esophagus diagnosis and treatment.
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/K.N.W./
Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682