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 .
Detailed Action
2. Claims 1-7,10-11,26-39 and 43-52 are pending.
Information Disclosure Statement
3. The information disclosure statements (IDS) submitted on 06/18/2026 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, an initialed and dated copy of Applicant's IDS form SB08 filed 06/18/2026 is attached to the instant Office action.
Response to Amendment
4. This action is in response to the Amendment filed on 06/16/2026. The amendment has been entered. Claims 1,5,6,10,26-28,30,32-33,38-39,43-48, and 52 have been amended and claims 19-25 and 40-42 have been canceled. Claims 1-7,10-11,26-39, and 43-52 are pending, with claims 1,10,26,28,33,38, and 43 being independent in the instant application.
Response to Arguments
5. Claim objections on claims 10,38 and 43 being withdrawn in this current office action in view of the amended claims.
Applicant's Arguments/Remarks filed on 06/16/2026 on page 11-15 regarding 35 U.S.C. 102 and 35 U.S.C. 103 rejections have been fully considered and are found persuasive in view of the amended claims and presented Arguments/Remarks by the Applicant. However, a new ground of rejections is necessitated by Applicant's claim amendments. Therefore, the previous rejections regarding 35 U.S.C. 102 and 35 U.S.C.103 are being amended in this current office action. (See analysis below Claim Rejections-35 U.S.C. §102 and Claim Rejections-35 U.S.C. §103).
Examiner Notes
6. Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
7. Claims 43 and 46-52 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by an NPL “Multiscale Modeling of Gastrointestinal Electrophysiology and Experimental Validation” by Peng Du et al. (hereinafter Du1, date of publication 2010).
Regarding Claim 43, Du1 teaches a method performed by one or more computing systems for identifying physical mesh readings of electrical activity of a digestive system of a patient, (Du1 disclosed in page 16 section 3.: “To create an anatomically realistic geometry of the GI organs, finite element fitting techniques have been developed to fit a geometric mesh to a data cloud identifying the organ of interest outline, e.g., the stomach, from a high-resolution image set, such as the visible human data or from clinical imaging systems … The surface mesh of the organ is then created from the data cloud by an iterative linear fitting technique, and a volumetric mesh of the organ wall can be subsequently constructed in order to better represent known anatomical features. … A tube of radius 10 mm was then extruded from the intestinal centerline to form a volumetric mesh.” This disclosure teaches the claim element “physical mesh”.
In page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The major advantage of HR mapping over previous sparse-electrode approaches is that the high spatial density of simultaneous sampling affords a detailed spatiotemporal understanding of the slow wave propagation. … The recent development of a flexible printed circuit board (PCB) electrode now promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, …”).
Du1 teaches the method comprising: accessing a physical mesh mapping library that maps library digestive electrogram (EDG) to associated library physical mesh readings of electrodes of an electrode physical mesh within a digestive system, each library EDG and its associated library physical mesh readings corresponding to the same electrical activity of the digestive system; (Du1 disclosed in page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The recent development of a flexible printed circuit board (PCB) electrode now promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, …”. In page 37, Fig. 7 discussed about the electrode platforms used to record GI slow waves from the serosal surface of the GI tract. (A) PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. The disclosure “data cloud” in page 16 relates to the claim element “mapping library”.
Further, page 35 Fig. 5 (b) discussed about simulated slow waves in an abnormal stomach with a decoupled antral slow wave behavior. The decoupled slow waves in the antrum propagated in the retrograde direction, opposing the normal wave front in the antegrade direction. Up to three wave fronts could be identified at 0 s.).
Du1 teaches receiving a patient EDG of the patient; (Du1 disclosed in page 23 section IV. (1st para): “anatomically realistic tissue structures containing the ICC network and SMCs could be obtained from human patients or animal models of dysmotility disorders, and biophysically based ICC models with the correct entrainment behavior could be integrated into those structures to investigate pathological differences in slow wave propagation.” It has been discussed in page 37, Fig. 7 (A) about the electrode platforms used to record GI slow waves from the serosal surface of the GI tract. PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. The PCB electrodes are used to record GI slow waves at high spatiotemporal resolutions).
Du1 teaches identifying physical mesh readings derived from library EDGs based on similarity to of the patient EDG to one or more library EDGs; (Du1 disclosed in page 20: “The major advantage of HR mapping over previous sparse-electrode approaches is that the high spatial density of simultaneous sampling affords a detailed spatiotemporal understanding of the slow wave propagation. … In the second study, HR mapping was used to define the origin and propagation of slow wave activity in the canine stomach, providing new descriptions of a high-amplitude, high-velocity activity in association with the pacemaker site, and the presence of multiple simultaneously propagating wave fronts. promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, and can be mass produced with high fidelity and low cost (Fig. 7A).” Further, in page 37, Fig. 7 (B) discussed about the “Laparoscopic electrodes” (the wand) that can be inserted through a port during keyhole surgery, a number of electrodes were embedded into the shaft of the recording device, which then made contact with the GI serosal surface to record slow wave activity. The wand electrode is minimally invasive and offers an efficient way to take snapshots of slow waves along the GI tract during surgery).
and Du1 teaches outputting an indication of the identified physical mesh readings, wherein an EDG represents electrical activity collected via electrodes placed cutaneously. (Du1 disclosed in page 16 section 3.: “To create an anatomically realistic geometry of the GI organs, finite element fitting techniques have been developed to fit a geometric mesh to a data cloud identifying the organ of interest outline, e.g., the stomach, from a high-resolution image set, such as the visible human data or from clinical imaging systems … The surface mesh of the organ is then created from the data cloud by an iterative linear fitting technique, and a volumetric mesh of the organ wall can be subsequently constructed in order to better represent known anatomical features. … A tube of radius 10 mm was then extruded from the intestinal centerline to form a volumetric mesh.” Further, in page 37, Fig. 7 (B) discussed about the “Laparoscopic electrodes” (the wand) that can be inserted through a port during keyhole surgery, a number of electrodes were embedded into the shaft of the recording device, which then made contact with the GI serosal surface to record slow wave activity).
Regarding Claim 46, Du1 teaches the method of claim 43, wherein the physical mesh mapping library includes simulated EDGs and simulated physical mesh readings generated by running simulations of electrical activity of digestive system and generating simulated physical mesh readings and simulated EDGs based on the simulated electrical activity. (Du1 disclosed in page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The recent development of a flexible printed circuit board (PCB) electrode now promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, …”. In page 37, Fig. 7 discussed about the electrode platforms used to record GI slow waves from the serosal surface of the GI tract. (A) PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. The disclosure “data cloud” in page 16 relates to the claim element “mapping library”.
Further, in page 13 section C. (1st para): “the modeling techniques used to simulate slow waves are also beginning to take on a multiscale representation of the underlying electrophysiological processes that contribute to the generation of GI slow waves. A multiscale model of gastric slow waves includes mathematical models of ICCs and SMCs and their interactions and contributions at the tissue and the whole-body biophysical scales”).
Regarding Claim 47, Du1 teaches the method of claim 43, wherein the physical mesh mapping library includes EDGs and physical mesh readings collected from patients. (Du1 disclosed in page 16 section 3.: “To create an anatomically realistic geometry of the GI organs, finite element fitting techniques have been developed to fit a geometric mesh to a data cloud identifying the organ of interest outline, e.g., the stomach, from a high-resolution image set, such as the visible human data or from clinical imaging systems … The surface mesh of the organ is then created from the data cloud by an iterative linear fitting technique, and a volumetric mesh of the organ wall can be subsequently constructed in order to better represent known anatomical features. … A tube of radius 10 mm was then extruded from the intestinal centerline to form a volumetric mesh.” This disclosure teaches the claim element “physical mesh”.
In page 37, Fig. 7 (A) discussed about the electrode platforms used to record GI slow waves from the serosal surface of the GI tract. PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. The PCB electrodes are used to record GI slow waves at high spatiotemporal resolutions. In same page Fig. 7 (B) discussed about the “Laparoscopic electrodes” (the wand) that can be inserted through a port during keyhole surgery, a number of electrodes were embedded into the shaft of the recording device, which then made contact with the GI serosal surface to record slow wave activity. The wand electrode is minimally invasive and offers an efficient way to take snapshots of slow waves along the GI tract during surgery).
Regarding Claim 48, Du1 teaches the method of claim 43, wherein the physical mesh mapping library maps library EDGs or library physical mesh readings to characteristics of the digestive system from which the library EDGs were generated. (Du1 disclosed in page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The recent development of a flexible printed circuit board (PCB) electrode now promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, …”. In page 37, Fig. 7 discussed about the electrode platforms used to record GI slow waves from the serosal surface of the GI tract. (A) PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. Further, in page 35 Fig. 5 (b) discussed about simulated slow waves in an abnormal stomach with a decoupled antral slow wave behavior. The decoupled slow waves in the antrum propagated in the retrograde direction, opposing the normal wave front in the antegrade direction. Up to three wave fronts could be identified at 0 s. This disclosure teaches the claim limitation “one or more characteristic of the digestive system”).
Regarding Claim 49, Du1 teaches the method of claim 48 wherein a characteristic is a type of digestive disorder. (Du1 disclosed in page 1 under ‘Abstract’ regarding “GI motility disorders”, further in page 4 section 2.b. disclosed: “the slow wave activity of both the ICC network and smooth muscle layer becomes disorganized, and the resulting “dysrhythmia” is considered to be a contributing pathophysiological factor in many hypomotility disorders such as gastroparesis.” These disclosures are related to “type of digestive disorder”).
Regarding Claim 50, Du1 teaches the method of claim 43 wherein a characteristic is location of a digestive disorder. (Du1 disclosed in page 3 section 1. (last para): “The manifestation of slow waves in SMCs is periodic, occurring at approximately three cycles per minute (cpm) in the human stomach, 10–12 cpm in the duodenum, and 8–9 cpm in the terminal ileum.1 The systematic propagation of slow waves in broad wave fronts over the GI tract organs at these frequencies confer a critical coordinating effect on GI motility patterns, which follow the slow wave pattern in much of the gut”).
Regarding Claim 51, Du1 teaches the method of claim 48 wherein a characteristic is an indication of a treatment. (Du1 disclosed in page 23 section IV (2nd para): “The advent of appropriate HR mapping tools for the accurate evaluation of human GI tract activity, and the flexible PCB electrodes in particular, will continue to guide more physiologically realistic modeling of GI motility. In related work, the flexible PCBs have already been applied to study the entrainment of slow waves following gastric pacing, providing an enhanced understanding of the effects of stimulation on slow wave activity, and enabling the validation of multiscale frameworks for simulating stimulation outcomes”).
Regarding Claim 52, Du1 teaches the method of claim 43 wherein the physical mesh mapping library maps library EDGs or library physical mesh readings to pacing locations. (Du1 disclosed in page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The major advantage of HR mapping over previous sparse-electrode approaches is that the high spatial density of simultaneous sampling affords a detailed spatiotemporal understanding of the slow wave propagation. … The recent development of a flexible printed circuit board (PCB) electrode now promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, …”. In page 37, Fig. 7 (A) discussed about the electrode platforms used to record GI slow waves from the serosal surface of the GI tract. PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. The PCB electrodes are used to record GI slow waves at high spatiotemporal resolutions. In same page Fig. 7 (B) discussed about the “Laparoscopic electrodes” (the wand) that can be inserted through a port during keyhole surgery, a number of electrodes were embedded into the shaft of the recording device, which then made contact with the GI serosal surface to record slow wave activity. The wand electrode is minimally invasive and offers an efficient way to take snapshots of slow waves along the GI tract during surgery).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham, v. John Deere Co., 383 U.S.1.148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
8. Claims 1, 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over an NPL “A Multiscale Tridomain Model for Simulating Bioelectric Gastric Pacing” by Shameer Sathar et al. (hereinafter Sathar, this paper available online on 2015), in view of an NPL paper “A Tissue Framework for Simulating the Effects of Gastric Electrical Stimulation and In Vivo Validation” by Peng Du et al. (hereinafter Du, NPL published on 2009) and further in view of Jackson et al. (Pub. No. US2021/0186605A1).
Regarding Claim 1, Sathar teaches a method performed by one or more computing systems for modeling electrical activity of a digestive system, (Sathar disclosed in page 2685 section I: “The phasic contractions in the gastrointestinal tract are controlled and coordinated by an electrical activity termed slow waves (SW). Interstitial cells of Cajal (ICC) generate and propagate these events, within a syncytial cell network. These signals in turn activate the adjacent smooth muscle cells (SMC). … In this study, we present an efficient anatomically realistic human stomach model for simulating bioelectric pacing activity. Importantly, to simulate the effects of pacing protocols on SW activity, … normal SW activity and pacing induced activity has been efficiently simulated in the context of inter-mingled ICC and SMC cells with anisotropic axes of electrical conduction.”).
Sathar teaches the method comprising: running simulations to simulate electrical activity of the digestive system, each simulation based on a set of characteristic values of characteristics of the digestive system and a simulated pacing location within the digestive system; (Sathar disclosed in page 2686 section I (left col. last para of section I): “In this study, we present an efficient anatomically realistic human stomach model for simulating bioelectric pacing activity. Importantly, to simulate the effects of pacing protocols on SW activity, we introduce a novel multicell tridomain formulation of the governing equations such that the resulting finite-element discretization is symmetrical, generating positive-definite systems of linear equations that may be efficiently solved. … normal SW activity and pacing induced activity has been efficiently simulated in the context of inter-mingled ICC and SMC cells …”. In page 2689 section E.: “Gastric pacing successfully entrained gastric SW activity in the model as shown in Fig. 5. The pacing activity progressively took over the antegrade activity by inducing a retrograde propagation. As observed in experimental studies, the retrograde propagation incrementally captured greater portions of the stomach. … The rate at which a region was entrained increased with pacing frequency as shown in Fig. 6. The y-axis indicates the increase in the entrained region in terms of a distance for each successive cycle (e.g., C2–C1). The formation of a collision region was defined by both pacing location and relative frequencies. Fig. 6 shows that for constant tissue electrical properties, the cycle-to cycle increase in the entrained region was linearly dependent on the pacing interval.”
It has been discussed in 2685 section I. (1st para) that the phasic contractions in the gastrointestinal tract are controlled and coordinated by an electrical activity termed slow waves (SW). Interstitial cells of Cajal (ICC) generate and propagate these events, within a syncytial cell network. The disclosure above “Gastric pacing successfully entrained gastric SW activity in the model as shown in Fig. 5” corresponds to claim limitation “simulated pacing location within the digestive system”).
However, Sathar doesn’t explicitly teach the limitations “for each of a plurality of simulations, generating a simulated digestive electrogram (EDG) representing electrical activity of the digestive system based on the simulated electrical activity of that simulation, the simulated EDG representing an EDG collected cutaneously; generating a characteristics mapping library that includes mappings of the simulated EDGs to one or more characteristic values of the set of characteristic values and the simulated pacing location of the simulation from which the simulated EDGs were generated; receiving a patient pacing EDG collected cutaneously while pacing at that patient pacing location; determining that patient pacing location based on a simulated EDG that is similar to the patient pacing EDG based on satisfying a similarity criterion; and outputting the determined pacing location”.
Du teaches for each of a plurality of simulations, generating a simulated digestive electrogram (EDG) representing electrical activity of the digestive system based on the simulated electrical activity of that simulation, the simulated EDG representing an EDG collected cutaneously; (Du disclosed in page 5 section B. “Experimental validation studies were conducted in a porcine model and ethical approval for porcine experiments was obtained from the local institutional committee … Recordings were performed in two female weaner crossbreed pigs …”. In page 6 section III A. “Slow-wave propagation maps and gastric electrograms from the simulated and experimental studies are compared in Fig. 3. Overall, the simulated slow-wave activity produced good agreement with the recording of normal slow waves, in terms of frequency and propagation velocity. The frequency of recorded normal slow waves was 3.62±0.07 and 3.56±0.03 cpm (p-value = 0.52) over ten consecutive waves in the porcine trials. … The simulated slow waves propagated at the designated velocity of 4.58 mm s−1 in the antegrade direction and 8.51 mm s−1 in the circular direction. The sample of the selected channels of experimentally recorded slow waves [see Fig. 3(a)] showed a more gradual upstroke phase than the simulated slow waves [see Fig. 3(b)]. Both activation plots of normal porcine slow wave events (experimental and simulated) demonstrated that slow waves propagated in the antegrade direction, and in the case of the experimental recording, that they originated from the porcine gastric fundus and propagated in the organoaxial direction towards the gastric antrum.”).
Du teaches generating a characteristics mapping library that includes mappings of the simulated EDGs to one or more characteristic values of the set of characteristic values and the simulated pacing location of the simulation from which the simulated EDGs were generated; (Du disclosed in page 6-7 section B.: “Slow-wave activation maps and gastric electrograms for the simulated and experimental studies, under gastric pacing at 3.53 cpm (period 17 s), are shown in Fig. 4. The pacing frequency of 3.53 cpm was chosen for the purposes of model validation, because pacing at a similar frequency to the native activity allowed a detailed assessment of the interaction between the native and entrained activities in both the experiment and simulation. ... The stimulus effectively produced a secondary pacemaker of slow-wave activity in addition to the native pacemaker. The electrograms shown in Fig. 4(a) demonstrate that the entrained slow-wave events propagated simultaneously in both the antegrade and retrograde directions from the point of stimulus. To simulate the effects of stimulation protocol in the tissue model, a virtual stimulus was placed at a location corresponding to the site of the experimental stimulus, as shown in Fig. 2. The intrinsic frequency of the ICC paced by the stimulus was set to the stimulation frequency (3.53 cpm). … Both the intrinsic and paced activity began at t = 0 s. The origin of the secondary pacemaker corresponded to the location of the pacing needles, which was within 11 mm distal to the fundal line in both experimental recording and simulation. Both experiment and simulation achieved an overall entrainment frequency of 3.53 cpm. The simulation demonstrated that the displacement of entrainment in the retrograde direction was 53 mm from the point of stimulation, and 77 mm in the antegrade direction from the point of stimulation [see Fig. 4(b)].” The disclosure above teaches the limitation “mappings of the simulated EDGs to one or more characteristic values of the set of characteristic values and the simulated pacing location of the simulation from which the simulated EDGs were generated”.
It has been discussed in page 8 and 9 regarding “high-resolution entrainment mapping” revealed gastric dysrhythmias. Further, Fig. 3 (a) shown “activation map of normal porcine gastric slow waves recorded via high-resolution flexible electrode platform”. Therefore these disclosures teach the claim limitation “generating a characteristics mapping library”).
Du teaches for each of a plurality of patient pacing locations, receiving a patient pacing EDG collected cutaneously while pacing at that patient pacing location; (Du disclosed in page 6-7 section B.: “Slow-wave activation maps and gastric electrograms for the simulated and experimental studies, under gastric pacing at 3.53 cpm (period 17 s), are shown in Fig. 4. The pacing frequency of 3.53 cpm was chosen for the purposes of model validation, because pacing at a similar frequency to the native activity allowed a detailed assessment of the interaction between the native and entrained activities in both the experiment and simulation. ... The stimulus effectively produced a secondary pacemaker of slow-wave activity in addition to the native pacemaker. The electrograms shown in Fig. 4(a) demonstrate that the entrained slow-wave events propagated simultaneously in both the antegrade and retrograde directions from the point of stimulus. To simulate the effects of stimulation protocol in the tissue model, a virtual stimulus was placed at a location corresponding to the site of the experimental stimulus, as shown in Fig. 2. The intrinsic frequency of the ICC paced by the stimulus was set to the stimulation frequency (3.53 cpm). … Both the intrinsic and paced activity began at t = 0 s. The origin of the secondary pacemaker corresponded to the location of the pacing needles, which was within 11 mm distal to the fundal line in both experimental recording and simulation. … Over ten consecutive entrained events in the trial demonstrated in Fig. 4(b), the entrained displacement in the retrograde direction was 55±3 mm, and 71±1 mm in antegrade direction (to the extent of the mapped boundary), both measured relative to the location of pacing needles.”).
Du teaches determining that patient pacing location based on a simulated EDG that is similar to the patient pacing EDG based on satisfying a similarity criterion; (Du disclosed in page 6-7 section B.: The pacing frequency of 3.53 cpm was chosen for the purposes of model validation, because pacing at a similar frequency to the native activity allowed a detailed assessment of the interaction between the native and entrained activities in both the experiment and simulation. ... The stimulus effectively produced a secondary pacemaker of slow-wave activity in addition to the native pacemaker. The electrograms shown in Fig. 4(a) demonstrate that the entrained slow-wave events propagated simultaneously in both the antegrade and retrograde directions from the point of stimulus. To simulate the effects of stimulation protocol in the tissue model, a virtual stimulus was placed at a location corresponding to the site of the experimental stimulus, as shown in Fig. 2. … The simulation demonstrated that the displacement of entrainment in the retrograde direction was 53 mm from the point of stimulation, and 77 mm in the antegrade direction from the point of stimulation [see Fig. 4(b)]. The experimental data also demonstrated similar results in term of entrainment displacements. Over ten consecutive entrained events in the trial demonstrated in Fig. 4(b), the entrained displacement in the retrograde direction was 55±3 mm, and 71±1 mm in antegrade direction (to the extent of the mapped boundary), both measured relative to the location of pacing needles.”
The disclosure “The pacing frequency of 3.53 cpm was chosen for the purposes of model validation; electrograms shown in Fig. 4(a) demonstrate that the entrained slow-wave events propagated simultaneously in both the antegrade and retrograde directions from the point of stimulus; the experimental data also demonstrated similar results in term of entrainment displacements;” correspond to claim limitation “determining that patient pacing location based on a simulated EDG that is similar to the patient pacing EDG based on satisfying a similarity criterion”).
and Du teaches outputting the determined pacing location. (Du disclosed in page 7 section B.: “The simulation demonstrated that the displacement of entrainment in the retrograde direction was 53 mm from the point of stimulation, and 77 mm in the antegrade direction from the point of stimulation [see Fig. 4(b)]. The experimental data also demonstrated similar results in term of entrainment displacements. Over ten consecutive entrained events in the trial demonstrated in Fig. 4(b), the entrained displacement in the retrograde direction was 55±3 mm, and 71±1 mm in antegrade direction (to the extent of the mapped boundary), both measured relative to the location of pacing needles.” Fig. 4 (a) shown the “activation map of paced porcine gastric slow waves recorded via high-resolution flexible electrode platform”. The location of pacing needles are marked by “+” and “−”. Fig. 4 (b) Simulated results, where the selected channels, which are highlighted in gray circles, show the electrograms of those channels over a 20 s period.
Sathar and Du are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar and Du, to modify simulating electrical activity of the digestive system of Sathar, to include Du’s teaching related to determining patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Du “This study has presented a multiscale gastric tissue model, containing a biophysically based SM cell model and a cellular automata model of ICCs. We have successfully employed this model to simulate normal slow-wave propagation, and the effects of a typical gastric pacing protocol. Simulated normal slow waves were successfully shown to originate from the pacemaker region and the results showed propagation in the antegrade direction at the designated velocities. Importantly, the simulation results were in good agreement with the experimental data from a small porcine validation study, in terms of the velocities and directions of slow-wave propagation, and in terms of the slow-wave entrainment pattern and area entrained following a single gastric pacing protocol. (Du disclosed in page 7 section IV).
However, Sathar and Du do not explicitly teach the limitation “providing guidance for a catheter within the digestive system of a patient by, for each of a plurality of patient pacing locations”,
and Jackson teaches providing guidance for a catheter within the digestive system of a patient by, (Jackson disclosed in page 7 para [0160]: “direct topical application to skin or any other external tissue, endoscopically, percutaneously, surgically, etc. and with the aid of catheters and/or balloons as necessary.” In page 15-16 para [0366]: “A guiding element 204 such as a guidewire or balloon catheter may be used to guide device 200 to its target position. Guiding element 204 may have at its distal end an anchor 207 which may a balloon, a shaped bend or a kink in guiding element 204, …”).
Sathar, Du and Jackson are analogous art because they are related to work on same field such as effective way of treating gastrointestinal disease. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar, Du and Jackson to include simulating bioelectric pacing activity of Sathar to include the partitioning of the stomach wall localized to the intrinsic gastric pacemaker of Jackson. The suggestion/motivation for doing so would have been obvious by Jackson because “devices may be used for inducing con traction of tissue along the ablated lines, to modify the mechanical behavior of the stomach, such as its distensibility, motility, and capability to propagate gastric contents. Other gastrointestinal related disorders, such as constipation, gastroparesis, irritable bowel syndrome, diabetes, and more, may also be treated using similar approaches. Specific embodiments described herein are related to the field of gastroenterology, and more specifically to the modulation of the activity of gastrointestinal organs using minimally invasive, endoscopic means, to alleviate obesity, constipation, or other gastrointestinal related conditions. (Jackson disclosed in page 3 para [0039 and 0041]).
Regarding claim 6, Sathar, Du and Jackson teach the method of claim 1 however Sathar doesn’t explicitly teach the limitation “the characteristics mapping library includes mappings of clinical EDGs collected from patients to one or more characteristic values representing characteristics of the patients”.
Du teaches the characteristics mapping library includes mappings of clinical EDGs collected from patients to one or more characteristic values representing characteristics of the patients. (Du disclosed in page 6-7 section B.: “Slow-wave activation maps and gastric electrograms for the simulated and experimental studies, under gastric pacing at 3.53 cpm (period 17 s), are shown in Fig. 4. The pacing frequency of 3.53 cpm was chosen for the purposes of model validation, because pacing at a similar frequency to the native activity allowed a detailed assessment of the interaction between the native and entrained activities in both the experiment and simulation. ... The stimulus effectively produced a secondary pacemaker of slow-wave activity in addition to the native pacemaker. The electrograms shown in Fig. 4(a) demonstrate that the entrained slow-wave events propagated simultaneously in both the antegrade and retrograde directions from the point of stimulus. To simulate the effects of stimulation protocol in the tissue model, a virtual stimulus was placed at a location corresponding to the site of the experimental stimulus, as shown in Fig. 2.”).
Sathar and Du are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar and Du, to modify simulating electrical activity of the digestive system of Sathar, to include Du’s teaching related to determining patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Du “This study has presented a multiscale gastric tissue model, containing a biophysically based SM cell model and a cellular automata model of ICCs. We have successfully employed this model to simulate normal slow-wave propagation, and the effects of a typical gastric pacing protocol. Simulated normal slow waves were successfully shown to originate from the pacemaker region and the results showed propagation in the antegrade direction at the designated velocities. Importantly, the simulation results were in good agreement with the experimental data from a small porcine validation study, in terms of the velocities and directions of slow-wave propagation, and in terms of the slow-wave entrainment pattern and area entrained following a single gastric pacing protocol. (Du disclosed in page 7 section IV).
Regarding claim 7, Sathar, Du and Jackson teach the method of claim 1 however Sathar doesn’t explicitly teach the limitation “the characteristics is a source location of abnormal electrical activity”.
wherein Du teaches the characteristics is a source location of abnormal electrical activity. (Du disclosed in page 1 under ‘Abstract’: “Gastric pacing is used to modulate normal or abnormal gastric slow-wave activity for therapeutic purposes. … Concurrent experimental validation was performed via high resolution entrainment mapping in a porcine model … Entrained gastric slow-wave activity was found to be anisotropic (circular direction: 8.51 mm s−1; longitudinal: 4.58 mm s−1), and the simulation velocities were specified accordingly.”).
Sathar and Du are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar and Du, to modify simulating electrical activity of the digestive system of Sathar, to include Du’s teaching related to determining patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Du “This study has presented a multiscale gastric tissue model, containing a biophysically based SM cell model and a cellular automata model of ICCs. We have successfully employed this model to simulate normal slow-wave propagation, and the effects of a typical gastric pacing protocol. Simulated normal slow waves were successfully shown to originate from the pacemaker region and the results showed propagation in the antegrade direction at the designated velocities. Importantly, the simulation results were in good agreement with the experimental data from a small porcine validation study, in terms of the velocities and directions of slow-wave propagation, and in terms of the slow-wave entrainment pattern and area entrained following a single gastric pacing protocol. (Du disclosed in page 7 section IV).
Claims 2-5,10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Sathar, Du and Jackson and further in view of an NPL “A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation from the High Resolution Electrogastrogram” by Anjulie S. Agrusa et al. (hereinafter Agrusa, NPL published on 2019).
Regarding claim 2, Sathar, Du and Jackson teach the method of claim 1 however Sathar, Du and Jackson do not explicitly teach the limitation: “training a machine learning model to output a characteristic value representing a characteristic or a pacing location given an EDG, the machine learning model being trained using the mappings of the characteristics mapping library”.
further Agrusa teaches training a machine learning model to output a characteristic value representing a characteristic or a pacing location given an EDG, the machine learning model being trained using the mappings of the characteristics mapping library. (Agrusa disclosed in page 858-859 section 5: “For each simulation of the slow wave on the serosal surface of the stomach, we generated several independent HR-EGG datasets via manipulation of electrode array placement, abdominal tissue depth, electrode array size, and signal to noise ratio (SNR). We shifted the electrode array horizontally such that the center of the array moved along the abdominal elliptical arc from−12 cm to 12 cm in increments of 3 cm. … We then added white Gaussian noise with these calculated variances to all horizontally, vertically, and laterally shifted permutations of the HR-EGG dataset generated from the particular stomach model. … For example, the original HR-EGG recordings utilized 25 electrode arrays and ambulatory systems capable of recording from 9 electrodes have recently been established. As such, we trained and tested smaller square electrode arrays with 25 and 9 channels and added noise for all training and test datasets of the smaller arrays …”. In page 859 section B.: “We constructed and trained a convolutional neural network (CNN) to classify normal and abnormal HR-EGG electrode data. For comparison, we computed wave propagation spatial features to train a linear discriminant analysis (LDA) classifier.” The disclosure “the generated HR-EGG dataset” corresponds to claim limitation “characteristics mapping library”. Further, the disclosure “the original HR-EGG recordings utilized 25 electrode arrays and ambulatory systems capable of recording from 9 electrodes have recently been established; we trained and tested smaller square electrode arrays with 25 and 9 channels and added noise for all training and test datasets of the smaller arrays” corresponds to claim limitation “the machine learning model being trained using the mappings of the characteristics or a pacing location given an EDG”).
Sathar, Du, Jackson and Agrusa are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar, Du, Jackson and Agrusa, to modify Du’s teaching related to determine patient pacing location based on a simulated electrical activity, to include machine learning model being trained using the electrical mapping in Agrusa’s teaching. The suggestion/motivation for doing so would have been obvious by Agrusa because “the efficacy of using machine learning to classify normal and abnormal slow wave activity from EGG data. This technique is particularly relevant because many foregut GI disorder scan masquerade as one another when relying on symptoms alone. A recent finding indicates that with imaging-guided placement of multi-electrode arrays, slow wave spatial electrical patterns become associated with disease and symptom severity. Altogether, these findings suggest that multi-electrode cutaneous abdominal recordings, combined with modern machine learning techniques, have the potential to address unmet needs and possibly serve as widely deployable screening tools in gastroenterology.” (Agrusa disclosed in page 865 section V).
Regarding claim 3, Sathar, Du, Jackson and Agrusa teach the method of claim 2 however, Sathar, Du and Jackson do not explicitly teach the limitation “the characteristic value that is output by the machine learning model is a value of a discrete domain”.
wherein Agrusa teaches the characteristic value that is output by the machine learning model is a value of a discrete domain. (Agrusa disclosed in page 856 section I (left col., 2nd para): “three-dimensional CNNs are an accepted best-practice in video classification tasks. In a video recognition task, the 3D CNN ‘sees’ the video as an ‘N’ by ‘N’ grid of discrete pixels with varying intensity values over time. The data collected by a square multi-electrode array, as seen in this study, is an ‘N’ by ‘N’ grid of voltage values over time.” In page 857 section 2) (right col.): “We simulated voltage potentials on the full serosal surface of the stomach (Fig. 3). We modeled the normal and abnormal wave initiation and propagation patterns to be consistent with recent findings from invasive human recordings. This was implemented by solving the one dimensional wave equation (4) at discrete points along the Medial Curve via finite difference analysis with a temporal step size … We also imposed trends in wave amplitude consistent with the current literature; amplitudes in the pacemaker, antrum, and corpus regions were 0.57 mV, 0.52 mV, and 0.25 mV, respectively. At the two boundaries, we employed Mur’s boundary condition to prevent waves from reflecting back into the stomach. Finally, we applied each discrete voltage, S(ζ,t), in equipotential rings oriented organoaxially on the stomach associated with points on the Medial Curve …”).
Sathar, Du, Jackson and Agrusa are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar, Du, Jackson and Agrusa, to modify Du’s teaching related to determine patient pacing location based on a simulated electrical activity, to include machine learning model being trained using the electrical mapping in Agrusa’s teaching. The suggestion/motivation for doing so would have been obvious by Agrusa because “the efficacy of using machine learning to classify normal and abnormal slow wave activity from EGG data. This technique is particularly relevant because many foregut GI disorder scan masquerade as one another when relying on symptoms alone. A recent finding indicates that with imaging-guided placement of multi-electrode arrays, slow wave spatial electrical patterns become associated with disease and symptom severity. Altogether, these findings suggest that multi-electrode cutaneous abdominal recordings, combined with modern machine learning techniques, have the potential to address unmet needs and possibly serve as widely deployable screening tools in gastroenterology.” (Agrusa disclosed in page 865 section V).
Regarding claim 4, Sathar, Du, Jackson and Agrusa teach the method of claim 2 however, Sathar, Du and Jackson do not explicitly teach the limitation “the characteristic value that is output by the machine learning model is a value of a continuous domain”.
wherein Agrusa teaches the characteristic value that is output by the machine learning model is a value of a continuous domain. (Agrusa disclosed in page 856-857 section 1): “The voxelized representation of the stomach was iteratively thinned to its ‘geometric skeleton’, which is a set, B = {p1, p2, ..., pM}, since gastric slow wave propagation occurs organoaxially, we developed a method using all points in B to construct a continuous and differentiable function C(ζ) that roughly traces the organoaxis of the stomach. The Medial Curve, C(ζ), was constructed as a linear combi nation of Legendre polynomials … We chose to construct C(ζ) using Legendre polynomials because they each are continuous and differentiable and form an orthogonal basis of functions on the [-1,1] interval. As such, the weighted combination of the Legendre polynomials used to define the Medial Curve is still continuous and differentiable.” In page 857 section 2) (right col.): “We simulated voltage potentials on the full serosal surface of the stomach (Fig. 3). We modeled the normal and abnormal wave initiation and propagation patterns to be consistent with recent findings from invasive human recordings. This was implemented by solving the one dimensional wave equation (4) … In (4), S(ζ,t) is voltage as a function of both time t and position ζ on the Medial Curve. Wave speed, c(ζ), is a function of the Euclidean position C∗(ζ) corresponding to position ζ on the Medial Curve, which is highest in the pacemaker region (6.0 mm/s), second-highest in the antrum (5.9 mm/s), and lowest in the corpus (3.0 mm/s).”).
Sathar, Du, Jackson and Agrusa are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar, Du, Jackson and Agrusa, to modify Du’s teaching related to determine patient pacing location based on a simulated electrical activity, to include machine learning model being trained using the electrical mapping in Agrusa’s teaching. The suggestion/motivation for doing so would have been obvious by Agrusa because “the efficacy of using machine learning to classify normal and abnormal slow wave activity from EGG data. This technique is particularly relevant because many foregut GI disorder scan masquerade as one another when relying on symptoms alone. A recent finding indicates that with imaging-guided placement of multi-electrode arrays, slow wave spatial electrical patterns become associated with disease and symptom severity. Altogether, these findings suggest that multi-electrode cutaneous abdominal recordings, combined with modern machine learning techniques, have the potential to address unmet needs and possibly serve as widely deployable screening tools in gastroenterology.” (Agrusa disclosed in page 865 section V).
Regarding claim 5, Sathar, Du, Jackson and Agrusa teach the method of claim 1 however, Sathar doesn’t explicitly teach the limitation “outputting an indication of the patient pacing location.”
Du teaches outputting an indication of the patient pacing location. (Du disclosed in page 7 section B.: “The simulation demonstrated that the displacement of entrainment in the retrograde direction was 53 mm from the point of stimulation, and 77 mm in the antegrade direction from the point of stimulation [see Fig. 4(b)]. The experimental data also demonstrated similar results in term of entrainment displacements. Over ten consecutive entrained events in the trial demonstrated in Fig. 4(b), the entrained displacement in the retrograde direction was 55±3 mm, and 71±1 mm in antegrade direction (to the extent of the mapped boundary), both measured relative to the location of pacing needles.” Fig. 4 (a) shown the “activation map of paced porcine gastric slow waves recorded via high-resolution flexible electrode platform”. The location of pacing needles are marked by “+” and “−”. Fig. 4 (b) Simulated results, where the selected channels, which are highlighted in gray circles, show the electrograms of those channels over a 20 s period.
Sathar and Du are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar and Du, to modify simulating electrical activity of the digestive system of Sathar, to include Du’s teaching related to determining patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Du “This study has presented a multiscale gastric tissue model, containing a biophysically based SM cell model and a cellular automata model of ICCs. We have successfully employed this model to simulate normal slow-wave propagation, and the effects of a typical gastric pacing protocol. Simulated normal slow waves were successfully shown to originate from the pacemaker region and the results showed propagation in the antegrade direction at the designated velocities. Importantly, the simulation results were in good agreement with the experimental data from a small porcine validation study, in terms of the velocities and directions of slow-wave propagation, and in terms of the slow-wave entrainment pattern and area entrained following a single gastric pacing protocol. (Du disclosed in page 7 section IV).
However, Sathar, Du and Jackson do not explicitly teach the limitation “the determining includes inputting the patient pacing EDG into a machine learning model to generate an output indicating that patient pacing location the machine learning model being trained based on mappings of the characteristics mapping library”;
wherein Agrusa teaches the determining includes: inputting the patient pacing EDG into a machine learning model to generate an output indicating that patient pacing location the machine learning model being trained based on mappings of the characteristics mapping library; ( Agrusa disclosed in page 858-859 section 5: “For each simulation of the slow wave on the serosal surface of the stomach, we generated several independent HR-EGG datasets via manipulation of electrode array placement, abdominal tissue depth, electrode array size, and signal to noise ratio (SNR). We shifted the electrode array horizontally such that the center of the array moved along the abdominal elliptical arc from−12 cm to 12 cm in increments of 3 cm. … We then added white Gaussian noise with these calculated variances to all horizontally, vertically, and laterally shifted permutations of the HR-EGG dataset generated from the particular stomach model. … For example, the original HR-EGG recordings utilized 25 electrode arrays and ambulatory systems capable of recording from 9 electrodes have recently been established. As such, we trained and tested smaller square electrode arrays with 25 and 9 channels and added noise for all training and test datasets of the smaller arrays …”. In page 859 section B.: “We constructed and trained a convolutional neural network (CNN) to classify normal and abnormal HR-EGG electrode data. For comparison, we computed wave propagation spatial features to train a linear discriminant analysis (LDA) classifier.” The disclosure “the generated HR-EGG dataset” corresponds to claim element “characteristics mapping library”).
Sathar, Du, Jackson and Agrusa are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar, Du, Jackson and Agrusa, to modify Du’s teaching related to determine patient pacing location based on a simulated electrical activity, to include machine learning model being trained using the electrical mapping in Agrusa’s teaching. The suggestion/motivation for doing so would have been obvious by Agrusa because “the efficacy of using machine learning to classify normal and abnormal slow wave activity from EGG data. This technique is particularly relevant because many foregut GI disorder scan masquerade as one another when relying on symptoms alone. A recent finding indicates that with imaging-guided placement of multi-electrode arrays, slow wave spatial electrical patterns become associated with disease and symptom severity. Altogether, these findings suggest that multi-electrode cutaneous abdominal recordings, combined with modern machine learning techniques, have the potential to address unmet needs and possibly serve as widely deployable screening tools in gastroenterology.” (Agrusa disclosed in page 865 section V).
Regarding claim 10, the same ground of rejection is made as discussed in claim 1 for substantially similar rationale, therefore claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Sathar, Du, Jackson and Agrusa as discussed above for substantially similar rationale. In addition, claim 10 recites following limitations:
Sathar doesn’t explicitly teach the limitations “for each of a plurality of simulations, generating a simulated digestive electrogram (EDG) representing electrical activity of the digestive system based on the simulated electrical activity of that simulation, the simulated EDG representing an EDG collected cutaneously; generating a characteristics mapping library that includes mappings of the simulated EDGs to one or more characteristic values of the set of characteristic values and the simulated pacing location of the simulation from which the simulated EDGs were generated; and guiding a catheter within the digestive system of a patient by, for each of a plurality of patient pacing locations, receiving a pacing EDG collected cutaneously while pacing at that pacing location, determining a pacing location by inputting the pacing EDG to into a machine learning model that outputs a pacing location, and outputting the pacing location as an indication of that patient pacing location”.
Du teaches for each of a plurality of simulations, generating a simulated digestive electrogram (EDG) representing electrical activity of the digestive system based on the simulated electrical activity of that simulation, the simulated EDG representing an EDG collected cutaneously; (Du disclosed in page 5 section B. “Experimental validation studies were conducted in a porcine model and ethical approval for porcine experiments was obtained from the local institutional committee … Recordings were performed in two female weaner crossbreed pigs …”. In page 6 section III A. “Slow-wave propagation maps and gastric electrograms from the simulated and experimental studies are compared in Fig. 3. Overall, the simulated slow-wave activity produced good agreement with the recording of normal slow waves, in terms of frequency and propagation velocity. The frequency of recorded normal slow waves was 3.62±0.07 and 3.56±0.03 cpm (p-value = 0.52) over ten consecutive waves in the porcine trials. … The simulated slow waves propagated at the designated velocity of 4.58 mm s−1 in the antegrade direction and 8.51 mm s−1 in the circular direction. The sample of the selected channels of experimentally recorded slow waves [see Fig. 3(a)] showed a more gradual upstroke phase than the simulated slow waves [see Fig. 3(b)]. Both activation plots of normal porcine slow wave events (experimental and simulated) demonstrated that slow waves propagated in the antegrade direction, and in the case of the experimental recording, that they originated from the porcine gastric fundus and propagated in the organoaxial direction towards the gastric antrum.”).
Du teaches generating a characteristics mapping library that includes mappings of the simulated EDGs to one or more characteristic values of the set of characteristic values and the simulated pacing location of the simulation from which the simulated EDGs were generated; (Du disclosed in page 6-7 section B.: “Slow-wave activation maps and gastric electrograms for the simulated and experimental studies, under gastric pacing at 3.53 cpm (period 17 s), are shown in Fig. 4. The pacing frequency of 3.53 cpm was chosen for the purposes of model validation, because pacing at a similar frequency to the native activity allowed a detailed assessment of the interaction between the native and entrained activities in both the experiment and simulation. ... The stimulus effectively produced a secondary pacemaker of slow-wave activity in addition to the native pacemaker. The electrograms shown in Fig. 4(a) demonstrate that the entrained slow-wave events propagated simultaneously in both the antegrade and retrograde directions from the point of stimulus. To simulate the effects of stimulation protocol in the tissue model, a virtual stimulus was placed at a location corresponding to the site of the experimental stimulus, as shown in Fig. 2. The intrinsic frequency of the ICC paced by the stimulus was set to the stimulation frequency (3.53 cpm). … Both the intrinsic and paced activity began at t = 0 s. The origin of the secondary pacemaker corresponded to the location of the pacing needles, which was within 11 mm distal to the fundal line in both experimental recording and simulation. Both experiment and simulation achieved an overall entrainment frequency of 3.53 cpm. The simulation demonstrated that the displacement of entrainment in the retrograde direction was 53 mm from the point of stimulation, and 77 mm in the antegrade direction from the point of stimulation [see Fig. 4(b)].” The disclosure above teaches the limitation “mappings of the simulated EDGs to one or more characteristic values of the set of characteristic values and the simulated pacing location of the simulation from which the simulated EDGs were generated”.
It has been discussed in page 8 and 9 regarding “high-resolution entrainment mapping” revealed gastric dysrhythmias. Further, Fig. 3 (a) shown “activation map of normal porcine gastric slow waves recorded via high-resolution flexible electrode platform”. Therefore these disclosures teach the claim limitation “generating a characteristics mapping library”).
Du teaches for each of a plurality of patient pacing locations, receiving a pacing EDG collected cutaneously while pacing at that pacing location, (Du disclosed in page 6-7 section B.: “Slow-wave activation maps and gastric electrograms for the simulated and experimental studies, under gastric pacing at 3.53 cpm (period 17 s), are shown in Fig. 4. The pacing frequency of 3.53 cpm was chosen for the purposes of model validation, because pacing at a similar frequency to the native activity allowed a detailed assessment of the interaction between the native and entrained activities in both the experiment and simulation. ... The stimulus effectively produced a secondary pacemaker of slow-wave activity in addition to the native pacemaker. The electrograms shown in Fig. 4(a) demonstrate that the entrained slow-wave events propagated simultaneously in both the antegrade and retrograde directions from the point of stimulus. To simulate the effects of stimulation protocol in the tissue model, a virtual stimulus was placed at a location corresponding to the site of the experimental stimulus, as shown in Fig. 2. The intrinsic frequency of the ICC paced by the stimulus was set to the stimulation frequency (3.53 cpm). … Both the intrinsic and paced activity began at t = 0 s. The origin of the secondary pacemaker corresponded to the location of the pacing needles, which was within 11 mm distal to the fundal line in both experimental recording and simulation. … Over ten consecutive entrained events in the trial demonstrated in Fig. 4(b), the entrained displacement in the retrograde direction was 55±3 mm, and 71±1 mm in antegrade direction (to the extent of the mapped boundary), both measured relative to the location of pacing needles.”).
and Du teaches outputting the pacing location as an indication of that patient pacing location. (Du disclosed in page 7 section B.: “The simulation demonstrated that the displacement of entrainment in the retrograde direction was 53 mm from the point of stimulation, and 77 mm in the antegrade direction from the point of stimulation [see Fig. 4(b)]. The experimental data also demonstrated similar results in term of entrainment displacements. Over ten consecutive entrained events in the trial demonstrated in Fig. 4(b), the entrained displacement in the retrograde direction was 55±3 mm, and 71±1 mm in antegrade direction (to the extent of the mapped boundary), both measured relative to the location of pacing needles.” Fig. 4 (a) shown the “activation map of paced porcine gastric slow waves recorded via high-resolution flexible electrode platform”. The location of pacing needles are marked by “+” and “−”. Fig. 4 (b) Simulated results, where the selected channels, which are highlighted in gray circles, show the electrograms of those channels over a 20 s period.
Sathar and Du are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar and Du, to modify simulating electrical activity of the digestive system of Sathar, to include Du’s teaching related to determining patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Du “This study has presented a multiscale gastric tissue model, containing a biophysically based SM cell model and a cellular automata model of ICCs. We have successfully employed this model to simulate normal slow-wave propagation, and the effects of a typical gastric pacing protocol. Simulated normal slow waves were successfully shown to originate from the pacemaker region and the results showed propagation in the antegrade direction at the designated velocities. Importantly, the simulation results were in good agreement with the experimental data from a small porcine validation study, in terms of the velocities and directions of slow-wave propagation, and in terms of the slow-wave entrainment pattern and area entrained following a single gastric pacing protocol. (Du disclosed in page 7 section IV).
However, Sathar and Du do not explicitly teach the limitation “providing guidance for a catheter within the digestive system of a patient by”,
and Jackson teaches guiding a catheter within the digestive system of a patient by, (Jackson disclosed in page 7 para [0160]: “direct topical application to skin or any other external tissue, endoscopically, percutaneously, surgically, etc. and with the aid of catheters and/or balloons as necessary.” In page 15-16 para [0366]: “A guiding element 204 such as a guidewire or balloon catheter may be used to guide device 200 to its target position. Guiding element 204 may have at its distal end an anchor 207 which may a balloon, a shaped bend or a kink in guiding element 204, …”).
Sathar, Du and Jackson are analogous art because they are related to working on same field such as effective way of treating gastrointestinal disease. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar, Du and Jackson to include simulating bioelectric pacing activity of Sathar to include the partitioning of the stomach wall localized to the intrinsic gastric pacemaker of Jackson. The suggestion/motivation for doing so would have been obvious by Jackson because “devices may be used for inducing con traction of tissue along the ablated lines, to modify the mechanical behavior of the stomach, such as its distensibility, motility, and capability to propagate gastric contents. Other gastrointestinal related disorders, such as constipation, gastroparesis, irritable bowel syndrome, diabetes, and more, may also be treated using similar approaches. Specific embodiments described herein are related to the field of gastroenterology, and more specifically to the modulation of the activity of gastrointestinal organs using minimally invasive, endoscopic means, to alleviate obesity, constipation, or other gastrointestinal related conditions. (Jackson disclosed in page 3 para [0039 and 0041]).
However, Sathar, Du and Jackson do not explicitly teach the limitation “determining a pacing location by inputting the pacing EDG to into a machine learning model that outputs a pacing location”,
Agrusa teaches determining a pacing location by inputting the pacing EDG to into a machine learning model that outputs a pacing location, (Agrusa disclosed in page 858-859 section 5: “For each simulation of the slow wave on the serosal surface of the stomach, we generated several independent HR-EGG datasets via manipulation of electrode array placement, abdominal tissue depth, electrode array size, and signal to noise ratio (SNR). We shifted the electrode array horizontally such that the center of the array moved along the abdominal elliptical arc from−12 cm to 12 cm in increments of 3 cm. … We then added white Gaussian noise with these calculated variances to all horizontally, vertically, and laterally shifted permutations of the HR-EGG dataset generated from the particular stomach model. … For example, the original HR-EGG recordings utilized 25 electrode arrays and ambulatory systems capable of recording from 9 electrodes have recently been established. As such, we trained and tested smaller square electrode arrays with 25 and 9 channels and added noise for all training and test datasets of the smaller arrays …”. In page 859 section B.: “We constructed and trained a convolutional neural network (CNN) to classify normal and abnormal HR-EGG electrode data.”
The disclosure “We shifted the electrode array horizontally such that the center of the array moved along the abdominal elliptical arc from−12 cm to 12 cm in increments of 3 cm; we trained and tested smaller square electrode arrays with 25 and 9 channels and added noise for all training and test datasets of the smaller arrays” teach the claim limitation “determining a pacing location by inputting the pacing EDG to into a machine learning model”).
Sathar, Du, Jackson and Agrusa are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar, Du, Jackson and Agrusa, to modify Du’s teaching related to determine patient pacing location based on a simulated electrical activity, to include machine learning model being trained using the electrical mapping in Agrusa’s teaching. The suggestion/motivation for doing so would have been obvious by Agrusa because “the efficacy of using machine learning to classify normal and abnormal slow wave activity from EGG data. This technique is particularly relevant because many foregut GI disorder scan masquerade as one another when relying on symptoms alone. A recent finding indicates that with imaging-guided placement of multi-electrode arrays, slow wave spatial electrical patterns become associated with disease and symptom severity. Altogether, these findings suggest that multi-electrode cutaneous abdominal recordings, combined with modern machine learning techniques, have the potential to address unmet needs and possibly serve as widely deployable screening tools in gastroenterology.” (Agrusa disclosed in page 865 section V).
Regarding claim 11, Sathar, Du, Jackson and Agrusa teach the method of claim 10, however, Sathar does not explicitly teach the limitation “displaying an indication of the output pacing location on an image of a digestive system”.
further Du teaches displaying an indication of the output pacing location on an image of a digestive system. (Du disclosed in page 7 section B.: “The simulation demonstrated that the displacement of entrainment in the retrograde direction was 53 mm from the point of stimulation, and 77 mm in the antegrade direction from the point of stimulation [see Fig. 4(b)]. The experimental data also demonstrated similar results in term of entrainment displacements. Over ten consecutive entrained events in the trial demonstrated in Fig. 4(b), the entrained displacement in the retrograde direction was 55±3 mm, and 71±1 mm in antegrade direction (to the extent of the mapped boundary), both measured relative to the location of pacing needles.” Fig. 4 (a) shown the “activation map of paced porcine gastric slow waves recorded via high-resolution flexible electrode platform”. The location of pacing needles are marked by “+” and “−”. Fig. 4 (b) Simulated results, where the selected channels, which are highlighted in gray circles, show the electrograms of those channels over a 20 s period. It has been discussed in page 2 section I that there is an omnipresent gastric electrical activity (GEA) that propagates in the antegrade direction toward the gastric antrum in the normal stomach. The pacemaker potential initiates the peristaltic activity of the stomach, by depolarizing the membrane potential of the SM cells in a rhythmic coordination fashion. Therefore, Fig. 4 (a) and 4(b) show the image of a digestive system with an indication of the output pacing location).
Sathar and Du are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar and Du, to modify simulating electrical activity of the digestive system of Sathar, to include Du’s teaching related to determining patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Du “This study has presented a multiscale gastric tissue model, containing a biophysically based SM cell model and a cellular automata model of ICCs. We have successfully employed this model to simulate normal slow-wave propagation, and the effects of a typical gastric pacing protocol. Simulated normal slow waves were successfully shown to originate from the pacemaker region and the results showed propagation in the antegrade direction at the designated velocities. Importantly, the simulation results were in good agreement with the experimental data from a small porcine validation study, in terms of the velocities and directions of slow-wave propagation, and in terms of the slow-wave entrainment pattern and area entrained following a single gastric pacing protocol. (Du disclosed in page 7 section IV).
Claims 26,28, and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Sathar and in view of Du1.
Sathar teaches one or more computing systems that model electrical activity of a digestive system, the one or more computing systems comprising: one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems to: (Sathar disclosed in page 2685 section I: “The phasic contractions in the gastrointestinal tract are controlled and coordinated by an electrical activity termed slow waves (SW). Interstitial cells of Cajal (ICC) generate and propagate these events, within a syncytial cell network. These signals in turn activate the adjacent smooth muscle cells (SMC). … In this study, we present an efficient anatomically realistic human stomach model for simulating bioelectric pacing activity. Importantly, to simulate the effects of pacing protocols on SW activity, … normal SW activity and pacing induced activity has been efficiently simulated in the context of inter-mingled ICC and SMC cells with anisotropic axes of electrical conduction.”).
Sathar teaches running simulations to simulate electrical activity of the digestive system, each simulation based on a set of characteristic values of characteristics of the digestive system; and one or more processors for controlling the one or more computing systems to execute the one or more computer-executable instructions. (Sathar disclosed in page 2686 section I (left col. last para of section I): “In this study, we present an efficient anatomically realistic human stomach model for simulating bioelectric pacing activity. Importantly, to simulate the effects of pacing protocols on SW activity, we introduce a novel multicell tridomain formulation of the governing equations such that the resulting finite-element discretization is symmetrical, generating positive-definite systems of linear equations that may be efficiently solved. … normal SW activity and pacing induced activity has been efficiently simulated in the context of inter-mingled ICC and SMC cells …”. In page 2689 section E.: “Gastric pacing successfully entrained gastric SW activity in the model as shown in Fig. 5. The pacing activity progressively took over the antegrade activity by inducing a retrograde propagation. As observed in experimental studies, the retrograde propagation incrementally captured greater portions of the stomach. … The rate at which a region was entrained increased with pacing frequency as shown in Fig. 6. The y-axis indicates the increase in the entrained region in terms of a distance for each successive cycle (e.g., C2–C1). The formation of a collision region was defined by both pacing location and relative frequencies. Fig. 6 shows that for constant tissue electrical properties, the cycle-to cycle increase in the entrained region was linearly dependent on the pacing interval.”
The disclosure of using/utilizing “high-performance computers” (in page 2690 section IV (right col.)), in order to generate more efficient solution processes (e.g., human stomach mesh) correspond to claim elements “one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems and one or more processors for controlling the one or more computing systems to execute the one or more computer-executable instructions”. Anyone who has skills in the art would understand that any computing system (or generic computer) always have memory (to store program instructions) and processor to perform/execute any claimed invention (i.e., computer-executable instructions)).
However, Sathar doesn’t explicitly teach the limitations “for each of a plurality of simulations, generate a simulated digestive electrogram (EDG) and simulated physical mesh readings representing electrical activity of the digestive system based on the simulated electrical activity of that simulation, the simulated EDG representing electrical signals received by electrodes placed cutaneously and the simulated physical mesh readings representing electrical signal received by electrodes within the digestive system; and generate a characteristics mapping library that includes mappings of the simulated EDGs and simulated physical mesh readings to one or more characteristic values of the set of characteristic values used in the simulation from which the simulated EDGs and simulated physical mesh readings were generated”;
Du1 teaches for each of a plurality of simulations, generate a simulated digestive electrogram (EDG) and simulated physical mesh readings representing electrical activity of the digestive system based on the simulated electrical activity of that simulation, the simulated EDG representing electrical signals received by electrodes placed cutaneously (Du1 disclosed in page 16 section 3.: “To create an anatomically realistic geometry of the GI organs, finite element fitting techniques have been developed to fit a geometric mesh to a data cloud identifying the organ of interest outline, e.g., the stomach, from a high-resolution image set, such as the visible human data or from clinical imaging systems such as MRI or CT. … The surface mesh of the organ is then created from the data cloud by an iterative linear fitting technique, and a volumetric mesh of the organ wall can be subsequently constructed in order to better represent known anatomical features. … A tube of radius 10 mm was then extruded from the intestinal centerline to form a volumetric mesh.” This disclosure teaches the claim element “physical mesh”.
In page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The major advantage of HR mapping over previous sparse-electrode approaches is that the high spatial density of simultaneous sampling affords a detailed spatiotemporal understanding of the slow wave propagation. … The recent development of a flexible printed circuit board (PCB) electrode now promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, and can be mass produced with high fidelity and low cost (Fig. 7A).”).
and Du1 teaches the simulated physical mesh readings representing electrical signal received by electrodes within the digestive system; (Du1 disclosed in page 16 section 3.: “To create an anatomically realistic geometry of the GI organs, finite element fitting techniques have been developed to fit a geometric mesh to a data cloud identifying the organ of interest outline, e.g., the stomach, from a high-resolution image set, such as the visible human data or from clinical imaging systems … The surface mesh of the organ is then created from the data cloud by an iterative linear fitting technique, and a volumetric mesh of the organ wall can be subsequently constructed in order to better represent known anatomical features. … A tube of radius 10 mm was then extruded from the intestinal centerline to form a volumetric mesh.” This disclosure teaches the claim element “physical mesh”.
In page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The major advantage of HR mapping over previous sparse-electrode approaches is that the high spatial density of simultaneous sampling affords a detailed spatiotemporal understanding of the slow wave propagation. … The recent development of a flexible printed circuit board (PCB) electrode now promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, …”).
and Du1 teaches generate a characteristics mapping library that includes mappings of the simulated EDGs and simulated physical mesh readings to one or more characteristic values of the set of characteristic values used in the simulation from which the simulated EDGs and simulated physical mesh readings were generated; (Du1 disclosed in page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The recent development of a flexible printed circuit board (PCB) electrode now promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, …”. In page 37, Fig. 7 discussed about the he electrode platforms used to record GI slow waves from the serosal surface of the GI tract. (A) PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. The disclosure “data cloud” in page 16 relates to the claim element “mapping library”.
Further, page 35 Fig. 5 (b) discussed about simulated slow waves in an abnormal stomach with a decoupled antral slow wave behavior. The decoupled slow waves in the antrum propagated in the retrograde direction, opposing the normal wave front in the antegrade direction. Up to three wave fronts could be identified at 0 s. This disclosure teaches the claim limitation “one or more characteristic values of the set of characteristic values used in the simulation from which the simulated EDGs”).
Sathar and Du1 are analogous art because they are related to simulate electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar and Du1, to modify identifying patient’s characteristic of a patient digestive system in Sathar’s teaching, to include receiving and identifying patient’s physical mesh readings in Du1’s teaching. The suggestion/motivation for doing so would have been obvious by Du1 because “The advent of appropriate HR mapping tools for the accurate evaluation of human GI tract activity, and the flexible PCB electrodes in particular, will continue to guide more physiologically realistic modeling of GI motility In related work, the flexible PCBs have already been applied to study the entrainment of slow waves following gastric pacing, providing an enhanced understanding of the effects of stimulation on slow wave activity, and enabling the validation of multiscale frameworks for simulating stimulation outcomes. With the advent of anatomically accurate organ models and biophysically based cell models, informed by HR electrical mapping, the path is now clear for substantial upgrades to the current generation of whole-organ models of GI slow wave activity” (Du1 disclosed in page 23 section IV (2nd para)).
Regarding Claim 28, Sathar teaches one or more computing systems for identifying a patient characteristic of a patient digestive system of a patient, (Sathar disclosed in page 2685 under ‘Abstract’ (left col.): “This study presents a novel comprehensive 3-D multiscale modeling frame work of the human stomach, including anisotropic conduction, capable of evaluating pacing strategies. Methods: A high-resolution anatomically realistic mesh was generated from CT images taken from a human stomach. … A continuum-based tridomain formulation was implemented and evaluated for performance and used to model the slow-wave propagation, which takes into account the two main cell types present in gastric musculature.” It has been discussed in page 2689 section D. (left col.) that the simulations performed in SandyBridge architecture CPUs).
Sathar teaches the one or more computing systems comprising: one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems and one or more processors for controlling the one or more computing systems to execute the one or more computer-executable instructions. (Sathar disclosed in page 2690 section IV (right col.): “the solution process was largely stable throughout the entire simulation as shown in Fig. 2(a). The human stomach mesh is relatively coarse and a detailed representation of the internal microstructure could yield a mesh ≈3–4 times larger. This will necessitate more efficient solution processes utilizing available high-performance computers as presented here.” It has been discussed in page 2689 section D. (left col.) that the simulations performed in SandyBridge architecture CPUs.
The disclosure of using/utilizing “high-performance computers” (in page 2690 section IV (right col.)), in order to generate more efficient solution processes (e.g., human stomach mesh) correspond to claim elements “one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems and one or more processors for controlling the one or more computing systems to execute the one or more computer-executable instructions”. Anyone who has skills in the art would understand that any computing system (or generic computer) always have memory (to store program instructions) and processor to perform/execute any claimed invention (i.e., computer-executable instructions)).
However, Sathar doesn’t explicitly teach the limitations “access a characteristics mapping library that includes mappings of library physical mesh readings representing simulated electrical activity of the digestive system to characteristic values of characteristics of the digestive system, the library physical mesh readings representing electrical signals received by electrodes within the digestive system; receive patient physical mesh readings collected from the patient; identify library physical mesh readings based on similarity to the patient physical mesh readings based on a similarity criterion; and output an indication of a characteristic value to which the identified library physical mesh readings are mapped;
Du1 teaches access a characteristics mapping library that includes mappings of library physical mesh readings representing simulated electrical activity of the digestive system to characteristic values of characteristics of the digestive system, (Du1 disclosed in page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The recent development of a flexible printed circuit board (PCB) electrode now promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, …”. In page 37, Fig. 7 discussed about the electrode platforms used to record GI slow waves from the serosal surface of the GI tract. (A) PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. The disclosure “data cloud” in page 16 relates to the claim element “mapping library”.
Further, page 35 Fig. 5 (b) discussed about simulated slow waves in an abnormal stomach with a decoupled antral slow wave behavior. The decoupled slow waves in the antrum propagated in the retrograde direction, opposing the normal wave front in the antegrade direction. Up to three wave fronts could be identified at 0 s. This disclosure teaches the claim limitation “one or more characteristic values of the set of characteristic values used in the simulation from which the simulated EDGs”).
Du1 teaches the library physical mesh readings representing electrical signals received by electrodes within the digestive system; (Du1 disclosed in page 16 section 3.: “To create an anatomically realistic geometry of the GI organs, finite element fitting techniques have been developed to fit a geometric mesh to a data cloud identifying the organ of interest outline, e.g., the stomach, from a high-resolution image set, such as the visible human data or from clinical imaging systems … The surface mesh of the organ is then created from the data cloud by an iterative linear fitting technique, and a volumetric mesh of the organ wall can be subsequently constructed in order to better represent known anatomical features. … A tube of radius 10 mm was then extruded from the intestinal centerline to form a volumetric mesh.” This disclosure teaches the claim element “physical mesh”.
In page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The major advantage of HR mapping over previous sparse-electrode approaches is that the high spatial density of simultaneous sampling affords a detailed spatiotemporal understanding of the slow wave propagation. … The recent development of a flexible printed circuit board (PCB) electrode now promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, …”).
Du1 teaches receive patient physical mesh readings collected from the patient; (Du1 disclosed in page 37, Fig. 7 (A) discussed about the electrode platforms used to record GI slow waves from the serosal surface of the GI tract. PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. The PCB electrodes are used to record GI slow waves at high spatiotemporal resolutions. In same page Fig. 7 (B) discussed about the “Laparoscopic electrodes” (the wand) that can be inserted through a port during keyhole surgery, a number of electrodes were embedded into the shaft of the recording device, which then made contact with the GI serosal surface to record slow wave activity. The wand electrode is minimally invasive and offers an efficient way to take snapshots of slow waves along the GI tract during surgery).
Du1 teaches identify library physical mesh readings based on similarity to the patient physical mesh readings based on a similarity criterion; (Du1 disclosed in page 20: “The major advantage of HR mapping over previous sparse-electrode approaches is that the high spatial density of simultaneous sampling affords a detailed spatiotemporal understanding of the slow wave propagation. … In the second study, HR mapping was used to define the origin and propagation of slow wave activity in the canine stomach, providing new descriptions of a high-amplitude, high-velocity activity in association with the pacemaker site, and the presence of multiple simultaneously propagating wave fronts. promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, and can be mass produced with high fidelity and low cost (Fig. 7A).” Further, in page 37, Fig. 7 (B) discussed about the “Laparoscopic electrodes” (the wand) that can be inserted through a port during keyhole surgery, a number of electrodes were embedded into the shaft of the recording device, which then made contact with the GI serosal surface to record slow wave activity. The wand electrode is minimally invasive and offers an efficient way to take snapshots of slow waves along the GI tract during surgery).
and Du1 teaches output an indication of a characteristic value to which the identified library physical mesh readings are mapped; (Du1 disclosed in page 16 section 3.: “To create an anatomically realistic geometry of the GI organs, finite element fitting techniques have been developed to fit a geometric mesh to a data cloud identifying the organ of interest outline, e.g., the stomach, from a high-resolution image set, such as the visible human data or from clinical imaging systems … The surface mesh of the organ is then created from the data cloud by an iterative linear fitting technique, and a volumetric mesh of the organ wall can be subsequently constructed in order to better represent known anatomical features. … A tube of radius 10 mm was then extruded from the intestinal centerline to form a volumetric mesh.” In page 35 Fig. 5 (b) discussed about simulated slow waves in an abnormal stomach with a decoupled antral slow wave behavior. The decoupled slow waves in the antrum propagated in the retrograde direction, opposing the normal wave front in the antegrade direction. Up to three wave fronts could be identified at 0 s. This disclosure teaches the claim limitation “indication of one or more characteristic values”).
Sathar and Du1 are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar and Du1, to modify identifying patient’s characteristic of a patient digestive system in Sathar’s teaching, to include receiving and identifying patient’s physical mesh readings in Du1’s teaching. The suggestion/motivation for doing so would have been obvious by Du1 because “The advent of appropriate HR mapping tools for the accurate evaluation of human GI tract activity, and the flexible PCB electrodes in particular, will continue to guide more physiologically realistic modeling of GI motility In related work, the flexible PCBs have already been applied to study the entrainment of slow waves following gastric pacing, providing an enhanced understanding of the effects of stimulation on slow wave activity, and enabling the validation of multiscale frameworks for simulating stimulation outcomes. With the advent of anatomically accurate organ models and biophysically based cell models, informed by HR electrical mapping, the path is now clear for substantial upgrades to the current generation of whole-organ models of GI slow wave activity” (Du1 disclosed in page 23 section IV (2nd para)).
Regarding claim 38, Sathar teaches the one or more computing systems comprising: one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems and one or more processors for controlling the one or more computing systems to execute the one or more computer-executable instructions. (Sathar disclosed in page 2690 section IV (right col.): “the solution process was largely stable throughout the entire simulation as shown in Fig. 2(a). The human stomach mesh is relatively coarse and a detailed representation of the internal microstructure could yield a mesh ≈3–4 times larger. This will necessitate more efficient solution processes utilizing available high-performance computers as presented here.” It has been discussed in page 2689 section D. (left col.) that the simulations performed in SandyBridge architecture CPUs.
The disclosure of using/utilizing “high-performance computers” (in page 2690 section IV (right col.)), in order to generate more efficient solution processes (e.g., human stomach mesh) correspond to claim elements “one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems and one or more processors for controlling the one or more computing systems to execute the one or more computer-executable instructions”. Anyone who has skills in the art would understand that any computing system (or generic computer) always have memory (to store program instructions) and processor to perform/execute any claimed invention (i.e., computer-executable instructions)).
However, Sathar doesn’t explicitly teach the limitations “one or more computing systems for determining a pacing electrode location of a pacing electrode within a digestive system of a patient, receive a patient digestive electrogram (EDG) that was collected cutaneously while a pacing electrode within the digestive system of the patient stimulates electrical activity of the digestive system; determine the pacing electrode location of the pacing electrode based on mappings of mapping EDGs representing cutaneously collected EDGs to mapping electrode locations, the determination based on a similarity of the patient EDG to the mapping EDGs; and output an indication of the determined pacing electrode location;
Du1 teaches one or more computing systems for determining a pacing electrode location of a pacing electrode within a digestive system of a patient, (Du1 disclosed in page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The major advantage of HR mapping over previous sparse-electrode approaches is that the high spatial density of simultaneous sampling affords a detailed spatiotemporal understanding of the slow wave propagation”. In page 21 section C.1.: “EGG involves the placement of electrodes on the body surface to record the distant electrical signals, … HR mapping information will also help to inform multiscale models that seek to solve the physical dispersion of slow wave currents over anatomically accurate torso geometries in order to better understand EGG sources and predict idealized EGG electrode placements.”).
Du1 teaches receive a patient digestive electrogram (EDG) that was collected cutaneously while a pacing electrode within the digestive system of the patient stimulates electrical activity of the digestive system; (Du1 disclosed in page 37, Fig. 7 (A) discussed about the electrode platforms used to record GI slow waves from the serosal surface of the GI tract. PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. The PCB electrodes are used to record GI slow waves at high spatiotemporal resolutions. In same page Fig. 7 (B) discussed about the “Laparoscopic electrodes” (the wand) that can be inserted through a port during keyhole surgery, a number of electrodes were embedded into the shaft of the recording device, which then made contact with the GI serosal surface to record slow wave activity. The wand electrode is minimally invasive and offers an efficient way to take snapshots of slow waves along the GI tract during surgery. It has been discussed in page 14 section C. (last para of this section) that for simulations of gastric electrical stimulation where an extraneous current is applied to the cell, the stimulus terms are required to represent extracellular stimulus current.).
Du1 teaches determine the pacing electrode location of the pacing electrode based on mappings of mapping EDGs representing cutaneously collected EDGs to mapping electrode locations, (Du1 disclosed in page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The recent development of a flexible printed circuit board (PCB) electrode now promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, …”. In page 37, Fig. 7 discussed about the electrode platforms used to record GI slow waves from the serosal surface of the GI tract. (A) PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. The disclosure “data cloud” in page 16 relates to the claim element “mapping library”. Further, page 35 Fig. 5 (b) discussed about simulated slow waves in an abnormal stomach with a decoupled antral slow wave behavior. The decoupled slow waves in the antrum propagated in the retrograde direction, opposing the normal wave front in the antegrade direction. Up to three wave fronts could be identified at 0 s.
Du1 teaches the determination based on a similarity of the patient EDG to the mapping EDGs; (Du1 disclosed in page 20: “The major advantage of HR mapping over previous sparse-electrode approaches is that the high spatial density of simultaneous sampling affords a detailed spatiotemporal understanding of the slow wave propagation. … In the second study, HR mapping was used to define the origin and propagation of slow wave activity in the canine stomach, providing new descriptions of a high-amplitude, high-velocity activity in association with the pacemaker site, and the presence of multiple simultaneously propagating wave fronts. promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, and can be mass produced with high fidelity and low cost (Fig. 7A).” Further, in page 37, Fig. 7 (B) discussed about the “Laparoscopic electrodes” (the wand) that can be inserted through a port during keyhole surgery, a number of electrodes were embedded into the shaft of the recording device, which then made contact with the GI serosal surface to record slow wave activity. The wand electrode is minimally invasive and offers an efficient way to take snapshots of slow waves along the GI tract during surgery).
and Du1 teaches output an indication of the determined pacing electrode location; (Du1 disclosed in page 37, Fig. 7 (A) discussed about the electrode platforms used to record GI slow waves from the serosal surface of the GI tract. PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. The PCB electrodes are used to record GI slow waves at high spatiotemporal resolutions. In same page Fig. 7 (B) discussed about the “Laparoscopic electrodes” (the wand) that can be inserted through a port during keyhole surgery, a number of electrodes were embedded into the shaft of the recording device, which then made contact with the GI serosal surface to record slow wave activity. The wand electrode is minimally invasive and offers an efficient way to take snapshots of slow waves along the GI tract during surgery).
Sathar and Du1 are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar and Du1, to modify identifying patient’s characteristic of a patient digestive system in Sathar’s teaching, to include receiving and identifying patient’s physical mesh readings in Du1’s teaching. The suggestion/motivation for doing so would have been obvious by Du1 because “The advent of appropriate HR mapping tools for the accurate evaluation of human GI tract activity, and the flexible PCB electrodes in particular, will continue to guide more physiologically realistic modeling of GI motility In related work, the flexible PCBs have already been applied to study the entrainment of slow waves following gastric pacing, providing an enhanced understanding of the effects of stimulation on slow wave activity, and enabling the validation of multiscale frameworks for simulating stimulation outcomes. With the advent of anatomically accurate organ models and biophysically based cell models, informed by HR electrical mapping, the path is now clear for substantial upgrades to the current generation of whole-organ models of GI slow wave activity” (Du1 disclosed in page 23 section IV (2nd para)).
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Sathar and Du1 and in view of Agrusa.
Regarding claim 27, Sathar and Du1 teach the one or more computing systems of claim 26 however, Sathar and Du1 do not explicitly teach the limitations “the computer-executable instructions further include instructions to train a machine learning model to output a characteristic value representing a characteristic given a patient EDG or patient physical mesh readings, the machine learning model being trained using the mappings of the characteristics mapping library”.
wherein Agrusa teaches the computer-executable instructions further include instructions to train a machine learning model to output a characteristic value representing a characteristic given a patient EDG or patient physical mesh readings, the machine learning model being trained using the mappings of the characteristics mapping library. (Examiner notes that the claim language includes two optional embodiments, a first embodiment “a patient EDG” “or” a second embodiment “patient mesh readings”. Since "and/or" is interpreted as at least one of, only one of the two embodiments need to be taught by the reference.
Agrusa disclosed in page 858-859 section 5: “For each simulation of the slow wave on the serosal surface of the stomach, we generated several independent HR-EGG datasets via manipulation of electrode array placement, abdominal tissue depth, electrode array size, and signal to noise ratio (SNR). We shifted the electrode array horizontally such that the center of the array moved along the abdominal elliptical arc from−12 cm to 12 cm in increments of 3 cm. … We then added white Gaussian noise with these calculated variances to all horizontally, vertically, and laterally shifted permutations of the HR-EGG dataset generated from the particular stomach model. … For example, the original HR-EGG recordings utilized 25 electrode arrays and ambulatory systems capable of recording from 9 electrodes have recently been established. As such, we trained and tested smaller square electrode arrays with 25 and 9 channels and added noise for all training and test datasets of the smaller arrays …”. In page 859 section B.: “We constructed and trained a convolutional neural network (CNN) to classify normal and abnormal HR-EGG electrode data. For comparison, we computed wave propagation spatial features to train a linear discriminant analysis (LDA) classifier.” The disclosure “the generated HR-EGG dataset” corresponds to claim element “characteristics mapping library”).
Sathar, Du1 and Agrusa are analogous art because they are related to simulate electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar, Du1 and Agrusa to modify Sathar and Du1’s teaching related to determine patient pacing location based on a simulated electrical activity, to include machine learning model being trained using the electrical mapping of Agrusa. The suggestion/motivation for doing so would have been obvious by Agrusa because “the efficacy of using machine learning to classify normal and abnormal slow wave activity from EGG data. This technique is particularly relevant because many foregut GI disorder scan masquerade as one another when relying on symptoms alone. A recent finding indicates that with imaging-guided placement of multi-electrode arrays, slow wave spatial electrical patterns become associated with disease and symptom severity. Altogether, these findings suggest that multi-electrode cutaneous abdominal recordings, combined with modern machine learning techniques, have the potential to address unmet needs and possibly serve as widely deployable screening tools in gastroenterology.” (Agrusa disclosed in page 865 section V).
Claims 29-32 are rejected under 35 U.S.C. 103 as being unpatentable over Sathar, Du1 and further in view of Jackson.
Regarding claim 29, Sathar and Du1 teach the one or more computing systems of claim 28, however, Sathar and Du1 do not explicitly teach the limitation “computing systems is a cloud-based computing system that executes the instructions.”
wherein Jackson teaches at least one of computing systems is a cloud-based computing system that executes the instructions. (Jackson disclosed in page 16 para [0394-0395]: “Any measurements of gastrointestinal organ dimensions, compliance, impedance, activity, motility, or other parameters, obtained using the devices and methods disclosed herein, as well as patient's clinical, demographic, procedural, and follow-up data, may optionally be used in various ways. Typically, with the consent of the patients, such data may be gathered in a database, either locally, e.g. on a computer or local network, or on a distant or cloud based storage means.”).
Sathar, Du1 and Jackson are analogous art because they are related to work in the same field such as effective way of treating gastrointestinal disease. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar, Du1 and Jackson, to modify simulating bioelectric pacing activity of Sathar to include the partitioning of the stomach wall localized to the intrinsic gastric pacemaker of Jackson. The suggestion/motivation for doing so would have been obvious by Jackson because “devices may be used for inducing con traction of tissue along the ablated lines, to modify the mechanical behavior of the stomach, such as its distensibility, motility, and capability to propagate gastric contents. Other gastrointestinal related disorders, such as constipation, gastroparesis, irritable bowel syndrome, diabetes, and more, may also be treated using similar approaches. Specific embodiments described herein are related to the field of gastroenterology, and more specifically to the modulation of the activity of gastrointestinal organs using minimally invasive, endoscopic means, to alleviate obesity, constipation, or other gastrointestinal related conditions. (Jackson disclosed in page 3 para [0039 and 0041]).
Regarding claim 30, Sathar, Du1 and Jackson teaches the one or more computing systems of claim 29, however, Sathar and Du1 do not explicitly teach the limitation “the patient physical mesh readings are received from a computing system”.
wherein Jackson teaches the patient physical mesh readings are received from a computing system. (Jackson disclosed in page 13 para [0318]: “In some embodiments, the other electrodes may be bipolar, coupled either with a distant patient electrode, or with other bipolar electrodes on the same device. … A specific embodiment of such a device incorporating spiral bipolar electrodes together with parallel near-field bipolar electrodes is described in FIGS. 18A-C. The benefit of using this combination of electrodes is that the parallel electrodes lend themselves to manufacturing as part of a PCB (Printed Circuit Board) …”. The disclosure “spiral bipolar electrodes combination of electrodes that is parallel electrodes to manufacture as part of a PCB (Printed Circuit Board)” teaches the claim element “physical mesh”
In page 16 para [0394-0395]: “Any measurements of gastrointestinal organ dimensions, compliance, impedance, activity, motility, or other parameters, obtained using the devices and methods disclosed herein, as well as patient's clinical, demographic, procedural, and follow-up data, may optionally be used in various ways. Typically, with the consent of the patients, such data may be gathered in a database, either locally, e.g. on a computer or local network, or on a distant or cloud based storage means.” The disclosure “Any measurements of gastrointestinal organ dimensions, compliance, as well as patient's clinical, demographic, procedural, and follow-up data, may optionally be used in various ways, such data may be gathered in a database, either locally, e.g. on a computer or local network, or on a distant” corresponds to claim limitation “the patient readings are received from a client computing system”).
Sathar, Du1 and Jackson are analogous art because they are related to work in the same field such as effective way of treating gastrointestinal disease. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar, Du1 and Jackson, to modify simulating bioelectric pacing activity of Sathar to include the partitioning of the stomach wall localized to the intrinsic gastric pacemaker of Jackson. The suggestion/motivation for doing so would have been obvious by Jackson because “devices may be used for inducing con traction of tissue along the ablated lines, to modify the mechanical behavior of the stomach, such as its distensibility, motility, and capability to propagate gastric contents. Other gastrointestinal related disorders, such as constipation, gastroparesis, irritable bowel syndrome, diabetes, and more, may also be treated using similar approaches. Specific embodiments described herein are related to the field of gastroenterology, and more specifically to the modulation of the activity of gastrointestinal organs using minimally invasive, endoscopic means, to alleviate obesity, constipation, or other gastrointestinal related conditions. (Jackson disclosed in page 3 para [0039 and 0041]).
Regarding claim 31, Sathar, Du1 and Jackson teaches the one or more computing systems of claim 28, however, Sathar doesn’t explicitly teach the limitation “the characteristics mapping library includes mappings based on simulated electrical activity of the digestive system”.
wherein Du1 teaches the characteristics mapping library includes mappings based on simulated electrical activity of the digestive system. (Du1 disclosed in page 16 section 3.: “To create an anatomically realistic geometry of the GI organs, finite element fitting techniques have been developed to fit a geometric mesh to a data cloud identifying the organ of interest outline, e.g., the stomach, from a high-resolution image set, such as the visible human data or from clinical imaging systems such as MRI or CT.” In page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The major advantage of HR mapping over previous sparse-electrode approaches is that the high spatial density of simultaneous sampling affords a detailed spatiotemporal understanding of the slow wave propagation.”).
Sathar and Du1 are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar and Du1, to modify identifying patient’s characteristic of a patient digestive system in Sathar’s teaching, to include receiving and identifying patient’s physical mesh readings in Du1’s teaching. The suggestion/motivation for doing so would have been obvious by Du1 because “The advent of appropriate HR mapping tools for the accurate evaluation of human GI tract activity, and the flexible PCB electrodes in particular, will continue to guide more physiologically realistic modeling of GI motility In related work, the flexible PCBs have already been applied to study the entrainment of slow waves following gastric pacing, providing an enhanced understanding of the effects of stimulation on slow wave activity, and enabling the validation of multiscale frameworks for simulating stimulation outcomes. With the advent of anatomically accurate organ models and biophysically based cell models, informed by HR electrical mapping, the path is now clear for substantial upgrades to the current generation of whole-organ models of GI slow wave activity” (Du1 disclosed in page 23 section IV (2nd para)).
Regarding claim 32, Sathar, Du1 and Jackson teaches the one or more computing systems of claim 28, however, Sathar doesn’t explicitly teach the limitation “the characteristics mapping library includes mappings based on clinical physical mesh readings collected from patients”.
wherein Du1 teaches the characteristics mapping library includes mappings based on clinical physical mesh readings collected from patients. (Du1 disclosed in page 20 section III B.: “The recent advent of high-resolution (HR) electrical mapping has been an important advance. This technique involves the placement of spatially dense arrays of many electrodes over the serosal surface along the GI tract in order to simultaneously record extracellular potentials from up to hundreds of electrodes as the slow waves propagate through the tissue beneath. … The recent development of a flexible printed circuit board (PCB) electrode now promises to facilitate the HR mapping of the human GI tract. Flexible PCB electrodes are constructed of gold or silver contacts and copper wires in a polyimide ribbon base, …”. In page 37, Fig. 7 discussed about the he electrode platforms used to record GI slow waves from the serosal surface of the GI tract. (A) PCB electrodes are manufactured on flexible base material (Polymide), with an array of 32 electrodes in a 4 × 8 configuration printed onto each PCB array. The disclosure “data cloud” in page 16 relates to the claim element “mapping library”).
Sathar and Du1 are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sathar and Du1, to modify identifying patient’s characteristic of a patient digestive system in Sathar’s teaching, to include receiving and identifying patient’s physical mesh readings in Du1’s teaching. The suggestion/motivation for doing so would have been obvious by Du1 because “The advent of appropriate HR mapping tools for the accurate evaluation of human GI tract activity, and the flexible PCB electrodes in particular, will continue to guide more physiologically realistic modeling of GI motility In related work, the flexible PCBs have already been applied to study the entrainment of slow waves following gastric pacing, providing an enhanced understanding of the effects of stimulation on slow wave activity, and enabling the validation of multiscale frameworks for simulating stimulation outcomes. With the advent of anatomically accurate organ models and biophysically based cell models, informed by HR electrical mapping, the path is now clear for substantial upgrades to the current generation of whole-organ models of GI slow wave activity” (Du1 disclosed in page 23 section IV (2nd para)).
Claims 33-35 and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Jackson and further in view of Du.
Regarding claim 33, Jackson teaches a method for guiding a catheter within the digestive system of a patient, (Jackson disclosed in page 7 para [0160]: “direct topical application to skin or any other external tissue, endoscopically, percutaneously, surgically, etc. and with the aid of catheters and/or balloons as necessary.” In page 15-16 para [0366]: “A guiding element 204 such as a guidewire or balloon catheter may be used to guide device 200 to its target position. Guiding element 204 may have at its distal end an anchor 207 which may a balloon, a shaped bend or a kink in guiding element 204, …”).
Jackson teaches the method comprising: inserting the catheter into the digestive system of the patient, the catheter having a pacing electrode for stimulating electrical activity; (Jackson disclosed in page 7 para [0160]: “direct topical application to skin or any other external tissue, endoscopically, percutaneously, surgically, etc. and with the aid of catheters and/or balloons as necessary.” In page 16 para [0369]: “guiding element 204 may be inserted through the working channel of the colonoscope … In some embodiments, guiding element 204 may be a balloon catheter and anchor 207 may be a balloon, which can be inflated to anchor it in place at the cecum or inside the distal ileum”.
In page 11 para [0274]: “In one example, an electrode array may deliver stimulation to the distal esophagus, to tighten the esophageal sphincter and increase its tone, to treat gastroesophageal reflux. … In another example, such an electrode array may be inserted through the anus, expanded to contact the rectal wall and it then may be used to stimulate the rectum to increase rectal tone, …”.).
and Jackson teaches for each of a plurality of locations within the digestive system, placing the pacing electrode in contact with the mucosa of the digestive system; (Jackson disclosed in page 10 para [0247-0248]: “The lesions making up the above described ablation patterns may be of various depths and may include several of the gastric wall layers shown in FIG. 8B. Typically, lesions may extend through the mucosa MCS, submucosa SMC, and at least part of the muscularis MUS. In page 13 para [0318]: “A specific embodiment of such a device incorporating spiral bipolar electrodes together with parallel near-field bipolar electrodes is described in FIGS. 18A-C. The benefit of using this combination of electrodes is that the parallel electrodes lend themselves to manufacturing as part of a PCB (Printed Circuit Board) … while the flexible spiral bipolar electrodes may be easily folded in any direction, thus enabling creation of lesions perpendicular to the longitudinal ones, which may be desirable in various situations.”).
Jackson teaches directing the pacing electrode to stimulate electrical activity of the digestive system; (Jackson disclosed in page 14-15 para [0341-0342]: “In some embodiments , an ablation device of the “one-size-fits-all” type may comprise a compliant balloon and sliding electrodes, which may be pulled in by the inflating balloon until the balloon fills the organ snugly, and the electrodes may then be in good contact with the organ wall. Determination of proper contact between conductors 161 and the gastric wall may be done using any of measurement of impedance through conductors … In some embodiments, stimulation of the gastric wall by delivering a current through conductors 161 may be done, …”).
and Jackson teaches directing movement of the electrode to another location. (Jackson disclosed in page 14 para [0338-339]: “In some embodiments, ablation devices of several sizes may be available, and the appropriate size may be chosen by the user. … Once inflated, verification of fit may be done using imaging, endoscopy, measurement of impedances between electrodes on the surface of such a device, … Measurement of impedances between the electrodes and/or between the electrodes and a reference electrode inside or outside the patient's body, may be performed. In some embodiments, such measurements may be done simultaneously during inflation of the sizing device balloon. By plotting impedances against inflated volume and/or device diameter at certain locations along the device, …”).
However, Jackson doesn’t explicitly teach the limitation “collecting a digestive electrogram (EDG) cutaneously from the patient based on the stimulated electrical activity; receiving an indication of the location of the pacing electrode, the location being determined based on mappings of mapping EDGs representing cutaneously collected EDGs to mapping locations;
Du teaches collecting a digestive electrogram (EDG) cutaneously from the patient based on the stimulated electrical activity; (Du disclosed in page 5 section B. “Experimental validation studies were conducted in a porcine model and ethical approval for porcine experiments was obtained from the local institutional committee … Recordings were performed in two female weaner crossbreed pigs …”. In page 6 section III A. “Slow-wave propagation maps and gastric electrograms from the simulated and experimental studies are compared in Fig. 3. Overall, the simulated slow-wave activity produced good agreement with the recording of normal slow waves, in terms of frequency and propagation velocity. The frequency of recorded normal slow waves was 3.62±0.07 and 3.56±0.03 cpm (p-value = 0.52) over ten consecutive waves in the porcine trials. … The simulated slow waves propagated at the designated velocity of 4.58 mm s−1 in the antegrade direction and 8.51 mm s−1 in the circular direction. The sample of the selected channels of experimentally recorded slow waves [see Fig. 3(a)] showed a more gradual upstroke phase than the simulated slow waves [see Fig. 3(b)]”).
Du teaches receiving an indication of the location of the pacing electrode, the location being determined based on mappings of mapping EDGs representing cutaneously collected EDGs to mapping locations; (Du disclosed in page 6-7 section B.: “Slow-wave activation maps and gastric electrograms for the simulated and experimental studies, under gastric pacing at 3.53 cpm (period 17 s), are shown in Fig. 4. The pacing frequency of 3.53 cpm was chosen for the purposes of model validation,... The stimulus effectively produced a secondary pacemaker of slow-wave activity in addition to the native pacemaker. The electrograms shown in Fig. 4(a) demonstrate that the entrained slow-wave events propagated simultaneously in both the antegrade and retrograde directions from the point of stimulus. … The intrinsic frequency of the ICC paced by the stimulus was set to the stimulation frequency (3.53 cpm). … Both the intrinsic and paced activity began at t = 0 s. The origin of the secondary pacemaker corresponded to the location of the pacing needles, which was within 11 mm distal to the fundal line in both experimental recording and simulation. … Over ten consecutive entrained events in the trial demonstrated in Fig. 4(b), the entrained displacement in the retrograde direction was 55±3 mm, and 71±1 mm in antegrade direction (to the extent of the mapped boundary), both measured relative to the location of pacing needles.”).
Jackson and Du are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Jackson and Du, to modify directing electrode pacing to stimulate electrical activity of the digestive system in Jackson’s teaching, to include Du’s teaching related to determining patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Du “This study has presented a multiscale gastric tissue model, containing a biophysically based SM cell model and a cellular automata model of ICCs. We have successfully employed this model to simulate normal slow-wave propagation, and the effects of a typical gastric pacing protocol. Simulated normal slow waves were successfully shown to originate from the pacemaker region and the results showed propagation in the antegrade direction at the designated velocities. Importantly, the simulation results were in good agreement with the experimental data from a small porcine validation study, in terms of the velocities and directions of slow-wave propagation, and in terms of the slow-wave entrainment pattern and area entrained following a single gastric pacing protocol. (Du disclosed in page 7 section IV).
Regarding claim 34, Jackson and Du teach the method of claim 33 wherein Jackson teaches the catheter is guided to a target location. (Jackson disclosed in page 7 para [0160]: “direct topical application to skin or any other external tissue, endoscopically, percutaneously, surgically, etc. and with the aid of catheters and/or balloons as necessary.” In page 15-16 para [0366]: “A guiding element 204 such as a guidewire or balloon catheter may be used to guide device 200 to its target position. Guiding element 204 may have at its distal end an anchor 207 which may a balloon, a shaped bend or a kink in guiding element 204, …”).
Regarding claim 35, Jackson and Du teach the method of claim 33 however, Jackson doesn’t explicitly teach the limitation “the EDG is input to a device that outputs the location of the electrode”.
wherein Du teaches the EDG is input to a device that outputs the location of the electrode. (Du disclosed in page 6-7 section B.: “The electrograms shown in Fig. 4(a) demonstrate that the entrained slow-wave events propagated simultaneously in both the antegrade and retrograde directions from the point of stimulus. … The intrinsic frequency of the ICC paced by the stimulus was set to the stimulation frequency (3.53 cpm). … Both the intrinsic and paced activity began at t = 0 s. The origin of the secondary pacemaker corresponded to the location of the pacing needles, which was within 11 mm distal to the fundal line in both experimental recording and simulation. … Over ten consecutive entrained events in the trial demonstrated in Fig. 4(b), the entrained displacement in the retrograde direction was 55±3 mm, and 71±1 mm in antegrade direction (to the extent of the mapped boundary), both measured relative to the location of pacing needles.” Further, in page 15 Fig. 4 disclosed “(a) Activation map of paced porcine gastric slow waves recorded via high-resolution flexible electrode platform. The location of pacing needles are marked by “+” and “−”. (b) Simulated results. The selected channels, which are highlighted in gray circles, show the electrograms of those channels over a 20 s period”).
Jackson and Du are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Jackson and Du, to modify directing electrode pacing to stimulate electrical activity of the digestive system in Jackson’s teaching, to include Du’s teaching related to determining patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Du “This study has presented a multiscale gastric tissue model, containing a biophysically based SM cell model and a cellular automata model of ICCs. We have successfully employed this model to simulate normal slow-wave propagation, and the effects of a typical gastric pacing protocol. Simulated normal slow waves were successfully shown to originate from the pacemaker region and the results showed propagation in the antegrade direction at the designated velocities. Importantly, the simulation results were in good agreement with the experimental data from a small porcine validation study, in terms of the velocities and directions of slow-wave propagation, and in terms of the slow-wave entrainment pattern and area entrained following a single gastric pacing protocol. (Du disclosed in page 7 section IV).
Regarding claim 37, Jackson and Du teach the method of claim 33, however Jackson doesn’t explicitly teach the limitation “indication is displayed on a digestive system graphic at the location”.
wherein Du teaches indication is displayed on a digestive system graphic at the location. (Du disclosed in page 7 section B.: “Over ten consecutive entrained events in the trial demonstrated in Fig. 4(b), the entrained displacement in the retrograde direction was 55±3 mm, and 71±1 mm in antegrade direction (to the extent of the mapped boundary), both measured relative to the location of pacing needles.” Fig. 4 (a) shown the “activation map of paced porcine gastric slow waves recorded via high-resolution flexible electrode platform”. The location of pacing needles are marked by “+” and “−”. Fig. 4 (b) Simulated results, where the selected channels, which are highlighted in gray circles, show the electrograms of those channels over a 20 s period).
Jackson and Du are analogous art because they are related to simulating electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Jackson and Du, to modify directing electrode pacing to stimulate electrical activity of the digestive system in Jackson’s teaching, to include Du’s teaching related to determining patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Du “This study has presented a multiscale gastric tissue model, containing a biophysically based SM cell model and a cellular automata model of ICCs. We have successfully employed this model to simulate normal slow-wave propagation, and the effects of a typical gastric pacing protocol. Simulated normal slow waves were successfully shown to originate from the pacemaker region and the results showed propagation in the antegrade direction at the designated velocities. Importantly, the simulation results were in good agreement with the experimental data from a small porcine validation study, in terms of the velocities and directions of slow-wave propagation, and in terms of the slow-wave entrainment pattern and area entrained following a single gastric pacing protocol. (Du disclosed in page 7 section IV).
Claim 36 is rejected under 35 U.S.C. 103 as being unpatentable over Jackson and Du, and further in view of Agrusa.
Regarding claim 36, Jackson and Du teach the method of claim 33, however Jackson and Du do not explicitly teach the limitation “the location is determined by a computing system that inputs the EDG to a machine learning (ML) model that outputs the location, the ML model trained with training data derived from the mappings.”
wherein Agrusa teaches the location is determined by a computing system that inputs the EDG to a machine learning (ML) model that outputs the location, the ML model trained with training data derived from the mappings. (Agrusa disclosed in page 858-859 section 5: “For each simulation of the slow wave on the serosal surface of the stomach, we generated several independent HR-EGG datasets via manipulation of electrode array placement, abdominal tissue depth, electrode array size, and signal to noise ratio (SNR). We shifted the electrode array horizontally such that the center of the array moved along the abdominal elliptical arc from−12 cm to 12 cm in increments of 3 cm. … We then added white Gaussian noise with these calculated variances to all horizontally, vertically, and laterally shifted permutations of the HR-EGG dataset generated from the particular stomach model. … For example, the original HR-EGG recordings utilized 25 electrode arrays and ambulatory systems capable of recording from 9 electrodes have recently been established. As such, we trained and tested smaller square electrode arrays with 25 and 9 channels and added noise for all training and test datasets of the smaller arrays …”. In page 859 section B.: “We constructed and trained a convolutional neural network (CNN) to classify normal and abnormal HR-EGG electrode data. For comparison, we computed wave propagation spatial features to train a linear discriminant analysis (LDA) classifier.”).
Jackson, Du and Agrusa are analogous art because they are related to simulate electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Jackson, Du and Agrusa, to modify Du’s teaching related to determine patient pacing location based on a simulated electrical activity, to include machine learning model being trained using the electrical mapping of Agrusa. The suggestion/motivation for doing so would have been obvious by Agrusa because “the efficacy of using machine learning to classify normal and abnormal slow wave activity from EGG data. This technique is particularly relevant because many foregut GI disorder scan masquerade as one another when relying on symptoms alone. A recent finding indicates that with imaging-guided placement of multi-electrode arrays, slow wave spatial electrical patterns become associated with disease and symptom severity. Altogether, these findings suggest that multi-electrode cutaneous abdominal recordings, combined with modern machine learning techniques, have the potential to address unmet needs and possibly serve as widely deployable screening tools in gastroenterology.” (Agrusa disclosed in page 865 section V).
Claim 39 is rejected under 35 U.S.C. 103 as being unpatentable over Sathar and Du1 and further in view of Agrusa.
Regarding claim 39, Sathar and Du1 teach the one or more computing systems of claim 38, however Sathar and Du1 do not explicitly teach the limitation “the location is determined by a computing system that inputs the patient EDG to a machine learning (ML) model that outputs the pacing electrode location, the ML model trained with training data derived from the mappings”.
wherein Agrusa teaches the location is determined by a computing system that inputs the patient EDG to a machine learning (ML) model that outputs the pacing electrode location, the ML model trained with training data derived from the mappings. (Agrusa disclosed in page 858-859 section 5: “For each simulation of the slow wave on the serosal surface of the stomach, we generated several independent HR-EGG datasets via manipulation of electrode array placement, abdominal tissue depth, electrode array size, and signal to noise ratio (SNR). We shifted the electrode array horizontally such that the center of the array moved along the abdominal elliptical arc from−12 cm to 12 cm in increments of 3 cm. … We then added white Gaussian noise with these calculated variances to all horizontally, vertically, and laterally shifted permutations of the HR-EGG dataset generated from the particular stomach model. … For example, the original HR-EGG recordings utilized 25 electrode arrays and ambulatory systems capable of recording from 9 electrodes have recently been established. As such, we trained and tested smaller square electrode arrays with 25 and 9 channels and added noise for all training and test datasets of the smaller arrays …”. In page 859 section B.: “We constructed and trained a convolutional neural network (CNN) to classify normal and abnormal HR-EGG electrode data. For comparison, we computed wave propagation spatial features to train a linear discriminant analysis (LDA) classifier.”).
Sathar, Du1 and Agrusa are analogous art because they are related to simulate electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Jackson, Du and Agrusa, to modify Du’s teaching related to determine patient pacing location based on a simulated electrical activity, to include machine learning model being trained using the electrical mapping of Agrusa. The suggestion/motivation for doing so would have been obvious by Agrusa because “the efficacy of using machine learning to classify normal and abnormal slow wave activity from EGG data. This technique is particularly relevant because many foregut GI disorder scan masquerade as one another when relying on symptoms alone. A recent finding indicates that with imaging-guided placement of multi-electrode arrays, slow wave spatial electrical patterns become associated with disease and symptom severity. Altogether, these findings suggest that multi-electrode cutaneous abdominal recordings, combined with modern machine learning techniques, have the potential to address unmet needs and possibly serve as widely deployable screening tools in gastroenterology.” (Agrusa disclosed in page 865 section V).
Claims 44 and 45 are rejected under 35 U.S.C. 103 as being unpatentable over Du1 and in view Navalgund et al. (Pub. No. US2020/0107781A1).
Regarding Claim 44, Du1 teaches the method of claim 43, however Du1 does not explicitly teach the limitation “training a machine learning model with training data that includes library EDGs labeled with library physical mesh readings”.
further Navalgund teaches training a machine learning model with training data that includes library EDGs labeled with library mesh readings. (Navalgund disclosed in page 13 para [0177]: “in FIG. 46 , a method 4600 of extracting valid gastrointesatinal tract EMG data from a raw time series data set acquired from skin-surface mounted electrode patch includes: obtaining data from a skin-surface mounted electrode patch configured to sense and acquire EMG voltage signals at block S4610; identifying one or more artifacts within a raw time series training data set derived from the EMG voltage signals at block S4620; training a machine learning model on the identified artifacts in the raw time series training data set at block S4630; and applying the machine learning model to a raw time series test data set at block S4640. The machine learning model is configured to: identify the one or more artifacts in the raw time series test data set and classify each of the one or more artifacts based on one or more characteristics of the one or more artifacts, for example an amplitude, a periodicity of variations, a frequency of variations …”. The disclosure above “electrode patch” corresponds to claim element “physical mesh”).
Du1 and Navalgund are analogous art because they are related to simulate electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Du1 and Navalgund, to modify simulating electrical activity of the digestive system of Du1, to include Navalgund’s teaching related to training a machine learning model with training data with mesh or electrode readings in GI. The suggestion/motivation for doing so would have been obvious by Navalgund because “the machine learning model is configured to: identify the one or more artifacts in the raw time series test data set and classify each of the one or more artifacts based on one or more characteristics of the one or more artifacts; eliminate the one or more identified artifacts from the raw time series test data set by tracking them down to any of a zero - crossing or a midpoint - crossing point on either side of a high amplitude artifact; and replace the one or more identified artifacts with any of interpolated points or constant value points that span a gap across the eliminated artifacts to create a clean time series data set comprising the In valid gastrointestinal tract EMG signals”. (Navalgund disclosed in page 3 para [0032]).
Regarding Claim 45, Du1 teaches the method of claim 43, however Du1 does not explicitly teach the limitation “the identifying includes applying a machine learning model to the patient EDG wherein the machine learning model outputs the identified mesh readings”.
wherein Navalgund teaches the identifying includes applying a machine learning model to the patient EDG wherein the machine learning model outputs the identified mesh readings. (Navalgund disclosed in page 13 para [0177]: “in FIG. 46 , a method 4600 of extracting valid gastrointesatinal tract EMG data from a raw time series data set acquired from skin-surface mounted electrode patch includes: obtaining data from a skin-surface mounted electrode patch configured to sense and acquire EMG voltage signals at block S4610; identifying one or more artifacts within a raw time series training data set derived from the EMG voltage signals at block S4620; training a machine learning model on the identified artifacts in the raw time series training data set at block S4630; and applying the machine learning model to a raw time series test data set at block S4640. The machine learning model is configured to: identify the one or more artifacts in the raw time series test data set and classify each of the one or more artifacts based on one or more characteristics of the one or more artifacts, for example an amplitude, a periodicity of variations, a frequency of variations …”. The disclosure above “electrode patch” corresponds to claim element “physical mesh”).
Du1 and Navalgund are analogous art because they are related to simulate electrical mapping in the Gastrointestinal Tract. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Du1 and Navalgund, to modify simulating electrical activity of the digestive system of Du1, to include Navalgund’s teaching related to training a machine learning model with training data with mesh or electrode readings in GI. The suggestion/motivation for doing so would have been obvious by Navalgund because “the machine learning model is configured to: identify the one or more artifacts in the raw time series test data set and classify each of the one or more artifacts based on one or more characteristics of the one or more artifacts; eliminate the one or more identified artifacts from the raw time series test data set by tracking them down to any of a zero - crossing or a midpoint - crossing point on either side of a high amplitude artifact; and replace the one or more identified artifacts with any of interpolated points or constant value points that span a gap across the eliminated artifacts to create a clean time series data set comprising the In valid gastrointestinal tract EMG signals”. (Navalgund disclosed in page 3 para [0032]).
Conclusion
9. 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.
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. Evans (Pub. No. US2005/0251219A1) disclosed the device and method for placement of an instrument, specifically electrodes, in the GI tract allows for placement of electrodes for gastric electrical Stimulation into the gastric wall using endoscopic techniques. Once the device is placed, the first end of the device body is removed to expose the wires, allowing electrical connection to an external electrical signal generator to provide electrical stimulus. Gastric electrical stimulation (GES) can be used in the treatment of gastroparesis, a disorder in which food moves through the Stomach more slowly than normal, among other GI disorders. Electrical impulses can also be used to decrease appetite. The precise effect of GES appears dependent on the location of electrodes used to provide electrical stimulus, as well as the amplitude and frequency of the applied electrical impulse.
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/NUPUR DEBNATH/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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