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
Claims 1-7,10,11 and 19-52 are pending.
Drawings
The drawings filed on 06/28/2022 are accepted.
Oath/Declaration
Applicants didn’t submit oath/declaration for this application. Applicants are required to submit oath/declaration as per MPEP 602.01(a).
Preliminary Amendment
Applicants filed Preliminary Amendment dated 5/9/2023. Applicants have amended claims 1,2, 5, 10-11, 25-28, 30, 32, 33 and 40, claims 8-9 and 12-18 have been canceled and further claims 41-52 have been newly added. Claims 1-7,10-11 and 19-52 are pending with claims 1,10,19,26,28,33,38,40, and 43 being independent in the instant application.
Examiner Notes
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 Objections
Claims 10, 28, 38 and 43 are objected to because of the following informalities:
The last limitation of claim 10 recites: “guiding a catheter within the digestive system of a patient by, for each of a plurality of patient pacing locations, receiving a pacing ECG …”. Examiner presumes an error or typo being occurred in this scenario, since the term “ECG” has not been mentioned in Specification of current application or anywhere else in the claim set. Therefore, appropriate correction is required. For purposes of applying prior art and to facilitate compact prosecution, Examiner would construe this ECG as EDG (simulated digestive electrogram).
The 3rd limitation of Claim 38 recites: “and output an indication of the determined location.” This limitation started with “and” and ended with a period (.), whereas there is one more limitation after this 3rd limitation (one or more processors for controlling the one or more computing systems …). Examiner assumes a minor typo have been occurred in 3rd limitation of claim 38. Appropriate correction is required (e.g., ; and one or more processors for controlling the one or more computing systems …).
The 4th limitation of Claim 43 recites: “and outputting an indication of the identified mesh readings”. This limitation is missing with any punctuation sign at the end (e.g., a semicolon (;) is missing. Appropriate correction is required (e.g., ; and wherein an EDG represents electrical activity collected via electrodes placed cutaneously.).
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.
Claims 28,31,43 and 46-52 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sathar, an NPL “A Multiscale Tridomain Model for Simulating Bioelectric Gastric Pacing” by Shameer Sathar et al., (this paper is available online on 2015).
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).
Sathar teaches the one or more computing systems to: access a characteristics mapping library that includes mappings of library mesh readings representing simulated electrical activity of the digestive system to characteristic values of characteristics of the digestive system, (Sathar disclosed in page 2687 section II B. (left col.): “two separate tests were performed on extreme cases: one for a slower conduction velocity of ≈ 3 mm·s−1 and another for conduction velocity of ≈ 13 mm·s−1. Approximate conductivity parameters were initially estimated for a 0.1-mm resolution mesh and later assigned to a coarser mesh to identify a suitable mesh resolution. The conduction velocities were noted for both simulations at different spatial resolutions.” In same page 2687 section II C. (left col.): “An anatomically realistic stomach geometry was obtained from CT images taken from a human patient ... A cubic Hermite finite-element mesh was fitted to the digitized 3-D outline of the stomach using an iterative fitting method. The surface of the generated surface mesh was projected normally to obtain a realistic muscle layer thickness of 2.66 mm.”
In page 2685 section I (left col.): “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.” This disclosure corresponds to claim limitation “access a characteristics mapping library that includes mappings of library mesh readings”).
Sathar teaches the library mesh readings representing electrical signal received by electrodes within the digestive system; receive patient mesh readings collected from the patient; (Sathar disclosed in page 2689-2690 section III D. to E.: “Normal SW propagation was simulated with activity starting at the pacemaker region just below the fundus. The activity propagated in the antegrade direction with a velocity of 3 and 8 mm·s−1 along the circumferential direction. The transmembrane potential pattern is shown in Fig. 5(a). A maximum of three waves were present in the stomach at any point of time. … 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 site of collision formed due to the pacing induced activity and the normal antegrade SW activity gradually moved proximally, as shown in Fig. 5(a). For each successive cycle, the corresponding collision regions are marked as C1, C2, and C3.” In Fig. 5 (a), it has been shown the “Normal SW activity was entrained by extracellular pacing electrodes (marked in red dots) with pulses of duration 200 ms and amplitude 5 mA”).
Sathar teaches identify library mesh readings based on similarity to the patient mesh readings based on a similarity criterion; (Sathar disclosed in page 2687 section E. (right col., 2nd para): “The pacemaker region was localized to the greater curvature region just below the fundus, with an intrinsic pacing frequency of 3cycles·min−1 initiated by setting start parameter of the FSM-ICC CB model ... The ODE solver (for the FSM ICC-CB model) time-step was set to 0.1 ms and PDE solver time-step was set to 1 ms, a selection based on a convergence analysis to obtain a stable solution.” In page 2688 section F: “Gastric pacing was initiated at the antrum region (indicated in Fig. 5). The pacing was induced extracellularly … pulse width 200 ms and frequency of 4 cycles· min−1, consistent with established human pacing protocols.” In same page 2688 section III A. and B.: “Convergence analysis showed that a more refined mesh was required to capture the slower conduction velocity compared to the higher conduction velocity. Fig. 1 shows that a minimum resolution of ≈0.4–0.5 mm was required to accurately represent the slow longitudinal component of the gastric SW activity. ... A realistic 3-D stomach tetrahedral mesh was generated from an initial surface mesh [see Fig. 3(a)] … based on the mesh resolution requirement informed from the convergence analysis. … The boxplot analysis of internodal spacing for each edges in the generated mesh is shown in Fig. 3(c). The mesh had a mean edge of 0.32 mm, which was accurate enough to capture the numerical solution process as per the convergence analysis.”
The disclosure above “gastric SW activity generated in mesh resolution (in Fig. 1) corresponds to claim element “patient mesh readings”. Further, the disclosure “a realistic 3-D stomach tetrahedral mesh was generated from an initial surface mesh [see Fig. 3(a)], which is based on the mesh resolution requirement informed from the convergence analysis” corresponds to claim limitation “identify library mesh readings based on similarity to the patient mesh readings” and the last disclosure “the generated mesh is shown in Fig. 3(c), which was accurate enough to capture the numerical solution process as per the convergence analysis” teaches the limitation “identify library mesh readings based on a similarity criterion”).
and Sathar teaches output an indication of a characteristic value to which the identified library mesh readings are mapped (Sathar disclosed in page 2689-2690 section III 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 site of collision formed due to the pacing induced activity and the normal antegrade SW activity gradually moved proximally, as shown in Fig. 5(a). For each successive cycle, the corresponding collision regions are marked as C1, C2, and C3.” In Fig. 5 (a), it has been shown the “Normal SW activity was entrained by extracellular pacing electrodes (marked in red dots) with pulses of duration 200 ms and amplitude 5 mA”).
Regarding Claim 31, Sathar teaches the one or more computing systems of claim 28 wherein the characteristics mapping library includes mappings based on simulated electrical activity of 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 …”).
Regarding Claim 43, Sathar teaches a method performed by one or more computing systems for identifying mesh readings of electrical activity of a digestive system a patient, (Sathar disclosed in page 2687 section C. (left col.): “An anatomically realistic stomach geometry was obtained from CT images taken from a human patient … A cubic Hermite finite-element mesh was fitted to the digitized 3-D outline of the stomach using an iterative fitting method. … The stomach geometry volume was discretized into tetrahedral elements delimited by tetrahedral volume constraint … to obtain a suitable computational mesh.”).
Sathar teaches the method comprising: accessing a mesh mapping library that maps library digestive electrogram (EDG) to associated library mesh readings of electrodes of an electrode mesh within a digestive system, each library EDG and its associated library mesh readings correspond the same electrical activity of the digestive system; (Sathar disclosed in page 2689-2690 section III D. to E.: “Normal SW propagation was simulated with activity starting at the pacemaker region just below the fundus. The activity propagated in the antegrade direction with a velocity of 3 and 8 mm·s−1 along the circumferential direction. The transmembrane potential pattern is shown in Fig. 5(a). A maximum of three waves were present in the stomach at any point of time. … 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 site of collision formed due to the pacing induced activity and the normal antegrade SW activity gradually moved proximally, as shown in Fig. 5(a). For each successive cycle, the corresponding collision regions are marked as C1, C2, and C3.” In Fig. 5 (a), it has been shown the “Normal SW activity was entrained by extracellular pacing electrodes (marked in red dots) with pulses of duration 200 ms and amplitude 5 mA”.
In page 2685 section I (left col.): “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.” This disclosure corresponds to claim limitation “access a characteristics mapping library that includes mappings of library mesh readings”).
Sathar teaches receiving a patient EDG of the patient; (Sathar disclosed in page 2686 section I (left col., last para): “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.”).
Sathar teaches identifying mesh readings derived from library EDGs based on similarity to the patient EDG to one or more library EDGs; (Sathar disclosed in page 2686 section I (left col., last para): “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.” In page 2690 section IV (right col.): “This study resolves all of these issues and therefore represents a significant advance in physiologically accurate gastric modeling, with diverse potential applications in integrated electrophysiology, investigating pathophysiology, and therapeutic assessments for electroceuticals and drug design. The tridomain formulation resulted in a symmetric positive definite matrix, improving the simulation efficiency by a factor of 2 compared to an alternative formulation. Additionally, the solution process was largely stable throughout the entire simulation as shown in Fig. 2(a).”).
and Sathar teaches outputting an indication of the identified mesh readings (Sathar disclosed in page 2689-2690 section III 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 site of collision formed due to the pacing induced activity and the normal antegrade SW activity gradually moved proximally, as shown in Fig. 5(a). For each successive cycle, the corresponding collision regions are marked as C1, C2, and C3.” In Fig. 5 (a), it has been shown the “Normal SW activity was entrained by extracellular pacing electrodes (marked in red dots) with pulses of duration 200 ms and amplitude 5 mA”).
wherein Sathar teaches an EDG represents electrical activity collected via electrodes placed cutaneously. (Sathar disclosed in page 2689-2690 section D. (left col.): “Normal SW propagation was simulated with activity starting at the pacemaker region just below the fundus. The activity propagated in the antegrade direction with a velocity of 3 and 8mm·s−1 along the circumferential direction. The transmembrane potential pattern is shown in Fig. 5(a). A maximum of three waves were present in the stomach at any point of time. Because of a higher resting membrane potential in the fundus and the preferential spread along the circular direction, the activity initiated from the pacemaker region quickly formed a ring which propagated in the antegrade direction, in accordance with experimental observations.” In Fig. 5 (a), it has been shown the “Normal SW activity was entrained by extracellular pacing electrodes (marked in red dots) with pulses of duration 200 ms and amplitude 5 mA”).
Regarding Claim 46, Sathar teaches the method of claim 43 wherein the mesh mapping library includes simulated EDGs and simulated mesh readings generated by running simulations of electrical activity of digestive system and generating simulated mesh readings and simulated EDGs based on the simulated electrical activity. (Sathar disclosed in page 2686 section I (left col., last para): “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.” In page 2689 section III D. to E.: “Normal SW propagation was simulated with activity starting at the pacemaker region just below the fundus. The activity propagated in the antegrade direction with a velocity of 3 and 8 mm·s−1 along the circumferential direction. The transmembrane potential pattern is shown in Fig. 5(a). A maximum of three waves were present in the stomach at any point of time. … 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.”.
Further in page 2689-2690 section IV: “This study presents a multiscale 3-D stomach model with a realistic biophysically based FSM-CB ICC model and anisotropic conduction for analyzing the effects of gastric pacing and its interaction with normal SW activity. A key development was to introduce a numerically efficient and stable tridomain formulation. A high-resolution mesh was generated with anatomically realistic geometrical properties to accurately solve the numerical problem. … This study resolves all of these issues and therefore represents a significant advance in physiologically accurate gastric modeling, with diverse potential applications in integrated electrophysiology, investigating pathophysiology, and therapeutic assessments for electroceuticals and drug design. The tridomain formulation resulted in a symmetric positive definite matrix, improving the simulation efficiency by a factor of 2 compared to an alternative formulation. Additionally, 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.”).
Regarding Claim 47, Sathar teaches the method of claim 43 wherein the mesh mapping library includes EDGs and mesh readings collected from patients. (Sathar disclosed in page 2687 section II C. (left col.): “An anatomically realistic stomach geometry was obtained from CT images taken from a human patient ... A cubic Hermite finite-element mesh was fitted to the digitized 3-D outline of the stomach using an iterative fitting method. The surface of the generated surface mesh was projected normally to obtain a realistic muscle layer thickness of 2.66 mm.”
In page 2685 section I (left col.): “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.” This disclosure corresponds to claim element “mesh mapping library”).
Regarding Claim 48, Sathar teaches the method of claim 43 wherein the mesh mapping library maps library EDGs or library mesh readings to characteristics of the digestive system from which the library EDGs were generated. (Sathar disclosed in page 2685 heading ‘Abstract’: “Gastric motility disorders have been associated with abnormal slow wave electrical activity (gastric dysrhythmias). Gastric pacing is a potential therapy for gastric dysrhythmias; however, new pacing protocols are required that can effectively modulate motility patterns, …”. In page 2687 section II C. (left col.): “An anatomically realistic stomach geometry was obtained from CT images taken from a human patient ... A cubic Hermite finite-element mesh was fitted to the digitized 3-D outline of the stomach using an iterative fitting method.”).
Regarding Claim 49, Sathar teaches the method of claim 48 wherein a characteristic is a type of digestive disorder. (Sathar disclosed in page 2685 heading ‘Abstract’: “Gastric motility disorders have been associated with abnormal slow wave electrical activity (gastric dysrhythmias). Gastric pacing is a potential therapy for gastric dysrhythmias; however, new pacing protocols are required that can effectively modulate motility patterns, …”).
Regarding Claim 50, Sathar teaches the method of claim 43 wherein a characteristic is location of a digestive disorder. (Sathar disclosed in page 2685 heading ‘Abstract’: “Gastric motility disorders have been associated with abnormal slow wave electrical activity (gastric dysrhythmias).” In page 2686 section I (left col. last para): “In this study, we present an efficient anatomically realistic human stomach model for simulating bioelectric pacing activity.”).
Regarding Claim 51, Sathar teaches the method of claim 48 wherein a characteristic is an indication of a treatment. (Sathar disclosed in page 2691 section IV (2nd para): “The model will be used to optimize pacing protocols for functional motility disorders, including gastroparesis, where therapeutic pacing has already shown promise. Interestingly, gastric pacing is also being investigated for the treatment of obesity, as it attempts to disrupt the normal antegrade activity and induce retrograde events thereby slowing the motility and inducing satiety”).
Regarding Claim 52, Sathar teaches the method of claim 43 wherein the mesh mapping library maps library EDGs or library mesh readings to pacing locations. (Sathar disclosed in page 2689-2690 section III D. to E.: “Normal SW propagation was simulated with activity starting at the pacemaker region just below the fundus. The activity propagated in the antegrade direction with a velocity of 3 and 8 mm·s−1 along the circumferential direction. The transmembrane potential pattern is shown in Fig. 5(a). A maximum of three waves were present in the stomach at any point of time. … 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 site of collision formed due to the pacing induced activity and the normal antegrade SW activity gradually moved proximally, as shown in Fig. 5(a). For each successive cycle, the corresponding collision regions are marked as C1, C2, and C3.” In Fig. 5 (a), it has been shown the “Normal SW activity was entrained by extracellular pacing electrodes (marked in red dots) with pulses of duration 200 ms and amplitude 5 mA”).
Claim 38 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by an NPL “Methods for High-Resolution Electrical Mapping in the Gastrointestinal Tract” by Gregory O’Grady et al. (hereinafter Grady, NPL published on 2018).
Regarding Claim 38, Grady teaches one or more computing systems for determining a location of an electrode with a digestive system of a patient, 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; one or more processors for controlling the one or more computing systems to execute the one or more computer-executable instructions. (Grady disclosed in page 293 section F. (left col.): “In historical studies, GI electrical recordings were performed by intubation of the human GI lumen dating to the 1940s, when single electrodes or bipolar pairs were placed on the surface of a balloon introduced to the intestine. Once positioned in the intestine, the balloons were inflated to achieve contact between the electrode and mucosa … Recently, attempts have been made to extend gastric mucosal recordings by adopting HR strategies. In particular, Angeli et al. demonstrated the feasibility for this approach using an inflatable balloon device adapted from a cardiac map ping catheter [see Fig. 3(E)]. The prototype employed an 8 × 8 spherical array of electrodes of 75 mm diameter, …”. In page 291 section B. (left col.): “Acquisition hardware requires software. These interfaces, of ten coded in LabView (National Instruments, Texas), enable control of data collection as well as visualization of the electro gram potentials in real time.”).
Grady teaches the one or more computing systems to: receive a digestive electrogram (EDG) that was collected while an electrode within the digestive system of the patient stimulates electrical activity of the digestive system; (Grady disclosed in page 288-289 section III A.: “A quantitative biophysical model of this relationship has been demonstrated, showing a close concordance between the experimentally recorded configuration and the theoretical principles of syncytial current flow … the principle of detecting extracellular “activation times” (ATs) at the point of steepest negative descent of the biphasic activation phase, as this moment of rapid gradient corresponds to the wavefront passing directly under the electrode (see Figs. 1 and 2). … The morphology of the recovery potential recorded by extracellular electrodes has been shown to be dependent on the presence of other nearby wave fronts, as well as by the membrane potential gradient across the gut wall underlying the electrode. … Further validation for the bioelectrical origins of extracellular recordings and their standard configuration comes from suction recordings (see Fig. 2). Suction electrode recordings adhere to the same extracellular field principles detailed above; … In studies validating GI extracellular recordings, concordance is observed between conventional extracellular potentials and suction electrodes, with the timing of the biphasic potential coinciding with the monophasic activation (upstroke) potential.”).
Grady teaches determine the location of the electrode based on mappings of EDGs to location; (Grady disclosed in page 291 section IV C. (right col.): “Another consideration is the density of the electrode array. This choice again depends on the application, with the most detailed studies to-date utilizing high-density grids of electrodes spaced 1–4 mm apart, whereas lower density arrays have been employed in many other studies. Putney et al. have evaluated an efficient interelectrode spacing for achieving accurate data capture for wavefront tracking, by employing mathematical models of human gastric dysrhythmia, and considering the spatial Nyquist criterion that the interelectrode distance be less than half the smallest relevant spatial wavelength. Higher density electrodes are clearly necessary when the spatial scales of interest are small. In practice, Putney et al. found that under most circumstances in the human stomach, a 10 mm separation is sufficient, but a higher density is desirable when dealing with complex and heterogeneous activation wavefronts as seen during re-entry or more chaotic events.”).
and Grady teaches output an indication of the determined location. (Grady disclosed in page 295-296 section IV: “Key elements in the signal processing pipeline for GI HR mapping include filtering, event detection, clustering of events into propagating sequences, generation of quantitative and qualitative maps (e.g., AT, velocity, and amplitude field maps), and intuitive visualization techniques (e.g., anatomical registration, animation) (see Fig. 4).” It is shown in Fig. 4, the “velocity field map” displays the magnitude as a color gradient and the direction at each electrode as an arrow. A single frame of the “propagation animation” shows multiple waves in the mapped area at one time, represented in different colors and corresponding to wave 4 and wave 5 in the electrogram mentioned above. In page 297 section E.: “AT mapping can be achieved either with a series of static images or with animated sequences. The former are typically shown as pseudo-colored contour plots indicating the area of propagation of a wavefront per unit time; there is one isochronal AT map for each cycle. ... Animated “flashlight” sequences can also be helpful to visualize activation timing across the electrode array. Each pixel representing an electrode site illuminates to the maximum intensity when at the time of activation and progressively fades back to zero over a user-defined interval, … A recently introduced wavefront-orientation interpolation technique may be applied to increase the resolution of the animations, making them more visually appealing, accurate, and readily interpretable. Furthermore, other metrics such as velocity field and the type of underlying conduction (e.g., pacemaker, colliding wavefronts, and conduction block) can be represented as an animated sequence”).
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.
9. Claims 1,6,7,26 and 32 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) and in view of Grady.
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; (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.”).
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; 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 providing guidance for a catheter within the digestive system of a patient by, for each of a plurality of patient pacing locations, receiving a patient pacing EDG while pacing at that patient pacing location; determining that patient pacing location based on a simulated EDG that is similar the patient EDG based on satisfying a similarity criterion; and outputting the determined pacing location.
Grady 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; (Grady disclosed in page 295 section H.: “EGG involves the placement of electrodes on the skin surface and measuring the resultant far-field measurements of the electrical activity produced by the stomach. This is a historic concept, with Alvarez having reported the first successful human EGG recordings … EGG has been used for assessing a variety of functional disorders including gastroparesis and functional dyspepsia … Recently, there has been renewed interest in the use of a larger number of electrodes (i.e., greater than 16 electrodes) to map the potentials on the body surface (high-resolution electrogastrography, HR-EGG) and noninvasively detect signatures of gastric dysrhythmias. The multichannel amplifiers described above for invasive measurements can also be applied for noninvasive HR-EGG recordings. … Mathematical simulation studies using “inverse imaging” methods have also shown the ability to both extract multiple simultaneous wavefronts and distinguish between normal and abnormal patterns from HR-EGG signals. … Every electrical source is associated with both a resultant electrical field and magnetic field, and the magnetogastrogram (MGG) records the magnetic fields due to the electrical activity in the stomach. … Similar to recent EGG studies, MGG studies have shown the ability to extract parameters such as propagation direction and speed experimentally.”).
and Grady 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; (Grady disclosed in page 298 section F.: “Determining the velocity, amplitude, and frequency profiles of slow waves propagation has become a key component of GI electrophysiological mapping, in-part because substantial changes in these metrics indicate the presence of dysrhythmic activity. … Amplitude and frequency are two key metrics that were utilized with sparse GI recordings. With HR mapping, these metrics can be computed for propagating wavefronts and can be correlated to velocity estimates. The amplitudes of slow waves are calculated as the difference between the maximum and minimum signal value around the activation point of the slow wave. … To date, these metrics have been extensively used in mapping gastric and intestinal slow waves in animals and humans. … In human stomach, the average slow wave frequency is around three cycles per minute, with an observed range during HR mapping from around 2 to 5 cycles per minute. Slow wave velocity is approximately 8 mm/s in the gastric pacemaker region, slowing to 3 mm/s when ring contractions are formed in the corpus, and accelerating to approximately 8 mm/s at the terminal antrum.” It has been disclosed in page 295 section H 3rd para: “Mathematical simulation studies using “inverse imaging” methods have also shown the ability to both extract multiple simultaneous wavefronts and distinguish between normal and abnormal patterns from HR-EGG signals.”).
and Grady teaches providing guidance for a catheter within the digestive system of a patient by, for each of a plurality of patient pacing locations, (Grady disclosed in page 291-292 section D.: “adapted FPC electrodes to facilitate human GI mapping [see Fig. 3(B)] … FPCs also have the advantage of being able to conform to the curved surfaces of the gut, including around the circumference of the small intestine [49], [85]. In addition, multiple FPCs can also be joined to map a larger surface area, for which we use a sterile adhesive such as Tegaderm to temporarily bind the arrays together.” In page 295 section H: “EGG involves the placement of electrodes on the skin surface and measuring the resultant far-field measurements of the electrical activity produced by the stomach. … Recently, there has been renewed interest in the use of a larger number of electrodes (i.e., greater than 16 electrodes) to map the potentials on the body surface (high-resolution electrogastrography, HR-EGG) and noninvasively detect signatures of gastric dysrhythmias. … Advantages of reliable noninvasive measurements include the ability to assess postprandial activity in awake subjects, when many symptoms occur, as well as repeating measures to guide and track the effect of interventions (e.g., medication, neuromodulation, and pacing). Further, in page 295-296 section IV: “Key elements in the signal processing pipeline for GI HR mapping include filtering, event detection, clustering of events into propagating sequences, generation of quantitative and qualitative maps (e.g., AT, velocity, and amplitude field maps), and intuitive visualization techniques (e.g., anatomical registration, animation) (see Fig. 4).” It is shown in Fig. 4, the “velocity field map” displays the magnitude as a color gradient and the direction at each electrode as an arrow. A single frame of the “propagation animation” shows multiple waves in the mapped area at one time, represented in different colors and corresponding to wave 4 and wave 5 in the electrogram mentioned above.”).
Grady teaches receiving a patient pacing EDG while pacing at that patient pacing location; (Grady disclosed in page 298 section F.: “Determining the velocity, amplitude, and frequency profiles of slow waves propagation has become a key component of GI electrophysiological mapping, in-part because substantial changes in these metrics indicate the presence of dysrhythmic activity. … Amplitude and frequency are two key metrics that were utilized with sparse GI recordings. With HR mapping, these metrics can be computed for propagating wavefronts and can be correlated to velocity estimates. The amplitudes of slow waves are calculated as the difference between the maximum and minimum signal value around the activation point of the slow wave. … To date, these metrics have been extensively used in mapping gastric and intestinal slow waves in animals and humans. … In human stomach, the average slow wave frequency is around three cycles per minute, with an observed range during HR mapping from around 2 to 5 cycles per minute. Slow wave velocity is approximately 8 mm/s in the gastric pacemaker region, slowing to 3 mm/s when ring contractions are formed in the corpus, and accelerating to approximately 8 mm/s at the terminal antrum.”).
Grady teaches determining that patient pacing location based on a simulated EDG that is similar the patient EDG based on satisfying a similarity criterion; (Grady disclosed in page 290 section C.: “Several reproducible variations of the typical extracellular slow wave waveform have been observed. … In addition, human gastric dysrhythmias are routinely accompanied by a similar 2.5-fold increase in velocity and therefore amplitude near to sources of the dysrhythmia. We have hypothesized that this velocity shift occurs because the leading ICC-network switches to the circular ICC-IM during dysrhythmia due to aberrant slow wave initiation or conduction interrupting the normal ring wavefronts. The ICC-IM layer enables rapid slow wave conduction, and it has been proposed that this acts to restore the ring wavefronts that pattern peristaltic contractions. … Further waveform variability arises during gastric dysrhythmias. “Double potentials” are an example in which two activation deflections are detected in rapid sequence. These are typically observed at the boundaries of functional blocks including in the middle of re-entrant circuits, where two wavefronts propagate in different directions or collide in close spatial proximity. Multiple competing wavefronts are also seen to propagate in some complex states of gastric dysrhythmia, leading to the breakdown of orderly waveforms, particularly in areas where wavefronts collide and merge.”).
and Grady teaches outputting the determined pacing location. (Grady disclosed in page 295-296 section IV: “Key elements in the signal processing pipeline for GI HR mapping include filtering, event detection, clustering of events into propagating sequences, generation of quantitative and qualitative maps (e.g., AT, velocity, and amplitude field maps), and intuitive visualization techniques (e.g., anatomical registration, animation) (see Fig. 4).” It is shown in Fig. 4, the “velocity field map” displays the magnitude as a color gradient and the direction at each electrode as an arrow. A single frame of the “propagation animation” shows multiple waves in the mapped area at one time, represented in different colors and corresponding to wave 4 and wave 5 in the electrogram mentioned above. In page 297 section E.: “AT mapping can be achieved either with a series of static images or with animated sequences. The former are typically shown as pseudo-colored contour plots indicating the area of propagation of a wavefront per unit time; there is one isochronal AT map for each cycle. ... Animated “flashlight” sequences can also be helpful to visualize activation timing across the electrode array. Each pixel representing an electrode site illuminates to the maximum intensity when at the time of activation and progressively fades back to zero over a user-defined interval, … A recently introduced wavefront-orientation interpolation technique may be applied to increase the resolution of the animations, making them more visually appealing, accurate, and readily interpretable. Furthermore, other metrics such as velocity field and the type of underlying conduction (e.g., pacemaker, colliding wavefronts, and conduction block) can be represented as an animated sequence”).
Sathar and Grady 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 Grady, to modify simulating electrical activity of the digestive system of Sathar, to include Grady’s teaching related to determine patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Grady because “The field of GI electrophysiology is now at an exciting juncture, with the potential to resolve several longstanding questions of physiological and clinical importance. These include the opportunity to definitively evaluate the prevalence and significance of electrophysiological abnormalities in multiple functional and postoperative motility disorders, and to define whether various types of gastric dysrhythmias are a causative pathology in themselves or a biomarker of interest. In summary, HR mapping strategies for the GI tract have expanded and proven their validity and value over the recent two decades for use in GI electrophysiology and pathophysiology.” (Grady disclosed in page 299 section VIII).
Regarding claim 6, Sathar and Grady 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”.
wherein Grady teaches the characteristics mapping library includes mappings of clinical EDGs collected from patients to one or more characteristic values representing characteristics of the patients. (Grady disclosed in page 295 section H.: “EGG involves the placement of electrodes on the skin surface and measuring the resultant far-field measurements of the electrical activity produced by the stomach. … Since these initial recordings, EGG has been used for assessing a variety of functional disorders including gastroparesis and functional dyspepsia. … Recently, there has been renewed interest in the use of a larger number of electrodes (i.e., greater than 16 electrodes) to map the potentials on the body surface (high-resolution electrogastrography, HR-EGG) and noninvasively detect signatures of gastric dysrhythmias. … a small number of research groups have used noncontact magnetic field measurements to determine the properties of underlying GI electrical activity. Every electrical source is associated with both a resultant electrical field and magnetic field, and the magnetogastrogram (MGG) records the magnetic fields due to the electrical activity in the stomach.”).
Sathar and Grady 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 Grady, to modify simulating electrical activity of the digestive system of Sathar, to include Grady’s teaching related to determine patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Grady because “The field of GI electrophysiology is now at an exciting juncture, with the potential to resolve several longstanding questions of physiological and clinical importance. These include the opportunity to definitively evaluate the prevalence and significance of electrophysiological abnormalities in multiple functional and postoperative motility disorders, and to define whether various types of gastric dysrhythmias are a causative pathology in themselves or a biomarker of interest. In summary, HR mapping strategies for the GI tract have expanded and proven their validity and value over the recent two decades for use in GI electrophysiology and pathophysiology.” (Grady disclosed in page 299 section VIII).
Regarding claim 7, Sathar and Grady 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 Grady teaches the characteristics is a source location of abnormal electrical activity. (Grady disclosed in page 295 section H.: “EGG involves the placement of electrodes on the skin surface and measuring the resultant far-field measurements of the electrical activity produced by the stomach. … Since these initial recordings, EGG has been used for assessing a variety of functional disorders including gastroparesis and functional dyspepsia … While preliminary studies used conventional ECG electrodes, electrode arrays can also be developed for HR-EGG to simplify data acquisition and further increase array density and the number of channels … Mathematical simulation studies using “inverse imaging” methods have also shown the ability to both extract multiple simultaneous wavefronts and distinguish between normal and abnormal patterns from HR-EGG signals.”).
Sathar and Grady 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 Grady, to modify simulating electrical activity of the digestive system of Sathar, to include Grady’s teaching related to determine patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Grady because “The field of GI electrophysiology is now at an exciting juncture, with the potential to resolve several longstanding questions of physiological and clinical importance. These include the opportunity to definitively evaluate the prevalence and significance of electrophysiological abnormalities in multiple functional and postoperative motility disorders, and to define whether various types of gastric dysrhythmias are a causative pathology in themselves or a biomarker of interest. In summary, HR mapping strategies for the GI tract have expanded and proven their validity and value over the recent two decades for use in GI electrophysiology and pathophysiology.” (Grady disclosed in page 299 section VIII).
Regarding claim 26, the same ground of rejection is made as discussed in claim 1 for substantially similar rationale, therefore claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Sathar and Grady as discussed above for substantially similar rationale. In addition, claim 26 recites following limitations:
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 to: for each of a plurality of simulations, generate a simulated digestive electrogram (EDG) and simulated mesh readings representing electrical activity of the digestive system based on the simulated electrical activity of that simulation, 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 2687 section II B. (left col.): “two separate tests were performed on extreme cases: one for a slower conduction velocity of ≈ 3 mm·s−1 and another for conduction velocity of ≈ 13 mm·s−1. Approximate conductivity parameters were initially estimated for a 0.1-mm resolution mesh and later assigned to a coarser mesh to identify a suitable mesh resolution. The conduction velocities were noted for both simulations at different spatial resolutions.” In same page 2687 section II C. (left col.): “An anatomically realistic stomach geometry was obtained from CT images taken from a human patient ... A cubic Hermite finite-element mesh was fitted to the digitized 3-D outline of the stomach using an iterative fitting method. … The stomach geometry volume was discretized into tetrahedral elements delimited by tetrahedral volume constraint and quality constraint … to obtain a suitable computational mesh.” Further 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.”
The disclosure of using/utilizing “high-performance computers” 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 having 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)).
and Sathar teaches the simulated mesh readings representing electrodes within the digestive system; (Sathar disclosed in page 2689-2690 section III D. to E.: “Normal SW propagation was simulated with activity starting at the pacemaker region just below the fundus. The activity propagated in the antegrade direction with a velocity of 3 and 8 mm·s−1 along the circumferential direction. The transmembrane potential pattern is shown in Fig. 5(a). A maximum of three waves were present in the stomach at any point of time. … 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 site of collision formed due to the pacing induced activity and the normal antegrade SW activity gradually moved proximally, as shown in Fig. 5(a). For each successive cycle, the corresponding collision regions are marked as C1, C2, and C3.” In Fig. 5 (a), it has been shown the “Normal SW activity was entrained by extracellular pacing electrodes (marked in red dots) with pulses of duration 200 ms and amplitude 5 mA”).
and Sathar teaches generate a characteristics mapping that includes simulated mesh readings and the set of characteristic values used in the simulation from which the simulated mesh readings were generated; (Sathar disclosed in page 2687 section II B. (left col.): “two separate tests were performed on extreme cases: one for a slower conduction velocity of ≈ 3 mm·s−1 and another for conduction velocity of ≈ 13 mm·s−1. Approximate conductivity parameters were initially estimated for a 0.1-mm resolution mesh and later assigned to a coarser mesh to identify a suitable mesh resolution. The conduction velocities were noted for both simulations at different spatial resolutions.” In same page 2687 section II C. (left col.): “An anatomically realistic stomach geometry was obtained from CT images taken from a human patient ... A cubic Hermite finite-element mesh was fitted to the digitized 3-D outline of the stomach using an iterative fitting method. The surface of the generated surface mesh was projected normally to obtain a realistic muscle layer thickness of 2.66 mm.”).
However, Sathar doesn’t explicitly teach the limitations “the simulated EDG representing electrical signals received by electrodes placed cutaneously, generate a characteristics mapping library that includes mappings of the simulated EDGs to one or more characteristic values the set of characteristic values used in the simulation from which the simulated EDGs.
Grady teaches the simulated EDG representing electrical signals received by electrodes placed cutaneously (Grady disclosed in page 295 section H. (left col.): “EGG involves the placement of electrodes on the skin surface and measuring the resultant far-field measurements of the electrical activity produced by the stomach. … Since these initial recordings, EGG has been used for assessing a variety of functional disorders including gastroparesis and functional dyspepsia … there has been renewed interest in the use of a larger number of electrodes (i.e., greater than 16 electrodes) to map the potentials on the body surface (high-resolution electrogastrography, HR-EGG) and noninvasively detect signatures of gastric dysrhythmias. … There is ongoing work to determine how spatial dysrhythmias present in upper GI disorders (e.g., conduction block and abnormal initiation) manifest on cutaneous HR-EGG measurements. Mathematical simulation studies using “inverse imaging” methods have also shown the ability to both extract multiple simultaneous wavefronts and distinguish between normal and abnormal patterns from HR-EGG signals.”).
and Grady teaches generate a characteristics mapping library that includes mappings of the simulated EDGs to one or more characteristic values the set of characteristic values used in the simulation from which the simulated EDGs (Grady disclosed in page 298 section F.: “Determining the velocity, amplitude, and frequency profiles of slow waves propagation has become a key component of GI electrophysiological mapping, in-part because substantial changes in these metrics indicate the presence of dysrhythmic activity. … Amplitude and frequency are two key metrics that were utilized with sparse GI recordings. With HR mapping, these metrics can be computed for propagating wavefronts and can be correlated to velocity estimates. The amplitudes of slow waves are calculated as the difference between the maximum and minimum signal value around the activation point of the slow wave. … To date, these metrics have been extensively used in mapping gastric and intestinal slow waves in animals and humans. … In human stomach, the average slow wave frequency is around three cycles per minute, with an observed range during HR mapping from around 2 to 5 cycles per minute. Slow wave velocity is approximately 8 mm/s in the gastric pacemaker region, slowing to 3 mm/s when ring contractions are formed in the corpus, and accelerating to approximately 8 mm/s at the terminal antrum.” It has been disclosed in page 295 section H 3rd para: “Mathematical simulation studies using “inverse imaging” methods have also shown the ability to both extract multiple simultaneous wavefronts and distinguish between normal and abnormal patterns from HR-EGG signals.”).
Sathar and Grady 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 Grady, to modify simulating electrical activity of the digestive system of Sathar, to include Grady’s teaching related to determine patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Grady because “The field of GI electrophysiology is now at an exciting juncture, with the potential to resolve several longstanding questions of physiological and clinical importance. These include the opportunity to definitively evaluate the prevalence and significance of electrophysiological abnormalities in multiple functional and postoperative motility disorders, and to define whether various types of gastric dysrhythmias are a causative pathology in themselves or a biomarker of interest. In summary, HR mapping strategies for the GI tract have expanded and proven their validity and value over the recent two decades for use in GI electrophysiology and pathophysiology.” (Grady disclosed in page 299 section VIII).
Regarding Claim 32, Sathar teaches the one or more computing systems of claim 28 wherein Sathar teaches mappings based on mesh readings collected from patients. (Sathar disclosed in page 2687 section C. (left col.): “An anatomically realistic stomach geometry was obtained from CT images taken from a human patient … A cubic Hermite finite-element mesh was fitted to the digitized 3-D outline of the stomach using an iterative fitting method. The surface of the generated surface mesh was projected normally to obtain a realistic muscle layer thickness of 2.66 mm. The stomach geometry volume was discretized into tetrahedral elements delimited by tetrahedral volume constraint … to obtain a suitable computational mesh.”).
However, Sathar doesn’t explicitly teach the limitation “characteristics mapping library includes clinical readings collected from patients”.
Grady teaches characteristics mapping library includes clinical readings collected from patients. (Grady disclosed in page 294 section F. (right col.): “In recent years, serosal HR mapping has been applied to define spatially complex slow wave initiation and conduction disorders in gastroparesis, as well as in “chronic unexplained nausea and vomiting” —an associated condition showing overlapping epidemiology and symptoms with gastroparesis but without delayed gastric emptying … Mapping in this situation has revealed compelling evidence for the clinical significance of gastric dysrhythmias, with chronic postoperative ectopic pacemaking showing clinicopathological correlation with severe gastric dysfunction … Our group has had significant success with FPC arrays for translational purposes, which are easily sterilized, biocompatible, and achieve an adequate (if not optimal) SNR for most clinical applications at the human GI tract serosa.”).
Sathar and Grady 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 Grady, to modify simulating electrical activity of the digestive system of Sathar, to include Grady’s teaching related to determine patient pacing location based on a simulated electrical activity. The suggestion/motivation for doing so would have been obvious by Grady because “The field of GI electrophysiology is now at an exciting juncture, with the potential to resolve several longstanding questions of physiological and clinical importance. These include the opportunity to definitively evaluate the prevalence and significance of electrophysiological abnormalities in multiple functional and postoperative motility disorders, and to define whether various types of gastric dysrhythmias are a causative pathology in themselves or a biomarker of interest. In summary, HR mapping strategies for the GI tract have expanded and proven their validity and value over the recent two decades for use in GI electrophysiology and pathophysiology.” (Grady disclosed in page 299 section VIII).
Claims 2-5,10,11 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Sathar and Grady 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 and Grady teach the method of claim 1, however Sathar and Grady 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 element “characteristics mapping library”).
Sathar, Grady 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, Grady and Agrusa, to modify Sathar and Grady’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).
Regarding claim 3, Sathar, Grady and Agrusa teach the method of claim 2, however, Sathar, and Grady 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, Grady 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, Grady and Agrusa, to modify Sathar and Grady’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).
Regarding claim 4, Sathar, Grady and Agrusa teach the method of claim 2 however, Sathar and Grady 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, Grady 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, Grady and Agrusa, to modify Sathar and Grady’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).
Regarding claim 5, Sathar and Grady teach the method of claim 1 however, Sathar doesn’t explicitly teach the limitation “outputting an indication of the indicated patient pacing location.”
and Grady teaches outputting an indication of the indicated patient pacing location. (Grady disclosed in page 295-296 section IV: “Key elements in the signal processing pipeline for GI HR mapping include filtering, event detection, clustering of events into propagating sequences, generation of quantitative and qualitative maps (e.g., AT, velocity, and amplitude field maps), and intuitive visualization techniques (e.g., anatomical registration, animation) (see Fig. 4).” It is shown in Fig. 4, the “velocity field map” displays the magnitude as a color gradient and the direction at each electrode as an arrow. A single frame of the “propagation animation” shows multiple waves in the mapped area at one time, represented in different colors and corresponding to wave 4 and wave 5 in the electrogram mentioned above. In page 297 section E.: “AT mapping can be achieved either with a series of static images or with animated sequences. The former are typically shown as pseudo-colored contour plots indicating the area of propagation of a wavefront per unit time; there is one isochronal AT map for each cycle. ... Animated “flashlight” sequences can also be helpful to visualize activation timing across the electrode array. Each pixel representing an electrode site illuminates to the maximum intensity when at the time of activation and progressively fades back to zero over a user-defined interval, … A recently introduced wavefront-orientation interpolation technique may be applied to increase the resolution of the animations, making them more visually appealing, accurate, and readily interpretable. Furthermore, other metrics such as velocity field and the type of underlying conduction (e.g., pacemaker, colliding wavefronts, and conduction block) can be represented as an animated sequence”).
However, Sathar and Grady do not explicitly teach the limitation “the determining includes inputting the patient 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 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, Grady 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, Grady and Agrusa, to modify Sathar and Grady’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).
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 and Grady and further in view of Agrusa as discussed above for substantially similar rationale. In addition, claim 10 recites following limitations:
Sathar doesn’t explicitly teach the limitation “outputting the output pacing location as an indication of that patient pacing location”.
and Grady teaches outputting the output pacing location as an indication of that patient pacing location. (Grady disclosed in page 295-296 section IV: “Key elements in the signal processing pipeline for GI HR mapping include filtering, event detection, clustering of events into propagating sequences, generation of quantitative and qualitative maps (e.g., AT, velocity, and amplitude field maps), and intuitive visualization techniques (e.g., anatomical registration, animation) (see Fig. 4).” It is shown in Fig. 4, the “velocity field map” displays the magnitude as a color gradient and the direction at each electrode as an arrow. A single frame of the “propagation animation” shows multiple waves in the mapped area at one time, represented in different colors and corresponding to wave 4 and wave 5 in the electrogram mentioned above. In page 297 section E.: “AT mapping can be achieved either with a series of static images or with animated sequences. The former are typically shown as pseudo-colored contour plots indicating the area of propagation of a wavefront per unit time; there is one isochronal AT map for each cycle. ... Animated “flashlight” sequences can also be helpful to visualize activation timing across the electrode array. Each pixel representing an electrode site illuminates to the maximum intensity when at the time of activation and progressively fades back to zero over a user-defined interval, … A recently introduced wavefront-orientation interpolation technique may be applied to increase the resolution of the animations, making them more visually appealing, accurate, and readily interpretable. Furthermore, other metrics such as velocity field and the type of underlying conduction (e.g., pacemaker, colliding wavefronts, and conduction block) can be represented as an animated sequence”).
However, Sathar and Grady do not explicitly teach the limitation “inputting the pacing EDG to a machine learning model that outputs a pacing location,”
Agrusa teaches inputting the pacing EDG to 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.”).
Sathar, Grady 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, Grady and Agrusa, to modify Sathar and Grady’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).
Regarding claim 11, Sathar, Grady and Agrusa teach the method of claim 10, however, Sathar and Agrusa do not explicitly teach the limitation “displaying an indication of the output pacing location on an image of a digestive system.”
further Grady teaches displaying an indication of the output pacing location on an image of a digestive system. (Grady disclosed in page 295-296 section IV: “Key elements in the signal processing pipeline for GI HR mapping include filtering, event detection, clustering of events into propagating sequences, generation of quantitative and qualitative maps (e.g., AT, velocity, and amplitude field maps), and intuitive visualization techniques (e.g., anatomical registration, animation) (see Fig. 4).” It is shown in Fig. 4, the “velocity field map” displays the magnitude as a color gradient and the direction at each electrode as an arrow. A single frame of the “propagation animation” shows multiple waves in the mapped area at one time, represented in different colors and corresponding to wave 4 and wave 5 in the electrogram mentioned above. In page 297 section E.: “AT mapping can be achieved either with a series of static images or with animated sequences. The former are typically shown as pseudo-colored contour plots indicating the area of propagation of a wavefront per unit time; there is one isochronal AT map for each cycle. ... Animated “flashlight” sequences can also be helpful to visualize activation timing across the electrode array. Each pixel representing an electrode site illuminates to the maximum intensity when at the time of activation and progressively fades back to zero over a user-defined interval, … A recently introduced wavefront-orientation interpolation technique may be applied to increase the resolution of the animations, making them more visually appealing, accurate, and readily interpretable. Furthermore, other metrics such as velocity field and the type of underlying conduction (e.g., pacemaker, colliding wavefronts, and conduction block) can be represented as an animated sequence”).
Sathar, Grady 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, Grady and Agrusa, to modify Sathar and Grady’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).
Regarding claim 27, Sathar and Grady teach the one or more computing systems of claim 26 however, Sathar and Grady 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 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 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, Grady 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, Grady and Agrusa, to modify Sathar and Grady’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 19-25, 40 and 41 are rejected under 35 U.S.C. 103 as being unpatentable over an NPL “A Novel Gastric Pacing Device to Modulate Slow Waves and Assessment by High-Resolution Mapping” by Saeed Alighaleh et al. (hereinafter Alighaleh, date of publication 2019) and in view of Dong et al. (Pub. No. US20190110836A1).
Regarding claim 19, Alighaleh teaches a method for stimulating electrical activity of a patient digestive system of a patient, (Alighaleh disclosed in page 2823 section I (right col.): “The motility, or the rhythmic contractions of the gastrointestinal (GI) tract, is coordinated in part by underlying electrical events known as slow waves. … Motility disorders, such as gastroparesis and functional dyspepsia, are associated with disordered electrical activity. The use of external electrical pacemakers in cardiac electrophysiology to correct disordered rhythms is well established. … High frequency stimulation is believed to affect the neural pathways while low frequency pacing affects the ICC to modulate slow wave activation. A clinical device is currently available for gastric stimulation that uses high frequency neuromodulation protocols (Enterra, Medtronic, Minneapolis, MN), …”).
Alighaleh teaches the method comprising: inserting an electrode into the patient digestive system, so that the electrodes contact the inner lining of the patient digestive system; (Alighaleh disclosed in page 2826 section B. 3) (right col.): “For pacing, bipolar temporary cardiac pacing electrodes separated by 1 cm (TME 64 S, OSCOR Inc.) were used as the cathode and anode electrodes. These electrodes were inserted into the stomach wall through a small hole with the diameter of 1.5 mm located in the center of the HR mapping electrode array (Fig. 5).”
and Alighaleh teaches directing electrical signals be sent to the electrodes in a designated pattern to stimulate electrical activity of the patient digestive system. (Alighaleh disclosed in page 2827 section III A.: “With pacing onset, HR mapping showed a consistent shift of the wavefront origin to the pacing electrode site, with radial outward propagation, as demonstrated in Fig. 5(b). In this figure, from a representative experiment, the illustrated signals were from five recording channels oriented along the axis of the stomach and close to the pacing leads. Before pacing, the intrinsic slow wave period was 20 ± 0.2 s and propagated in a normal antegrade fashion towards the pylorus. Pacing was applied to the stomach with a period of 18 ± 0.1 s (10 percent less than intrinsic period of slow wave) amplitude of 4 mA, and pulse-width of 400 ms. After the onset of pacing, the slow wave period decreased immediately to 18 s. The pacing initiated the new origin of the slow wave activity close to the location of the pacing leads.” In Fig. 5 (b) shown “Five gastric signals recorded (as marked by green rectangle in panel a) during pacing from a column in the centre of the array, where leads were inserted. Before pacing, the intrinsic slow wave propagated toward the distal stomach (blue arrows). Once pacing was applied, the slow wave configuration changed in spatial pattern (green arrows) …”).
However, Alighaleh doesn’t explicitly teach the limitation “inserting an expandable electrode mesh into the patient system, the expandable electrode mesh having electrodes; expanding the expandable electrode mesh so that the electrodes contact the patient system”;
Dong teaches inserting an expandable electrode mesh into the patient system, the expandable electrode mesh having electrodes; (Dong disclosed in page 1 para [0007]: “In the radiofrequency ablation catheter, the radiofrequency electrodes are mounted on a carrier, such as a stent, at the front end of the radiofrequency ablation catheter. The stent is used for carrying the radiofrequency electrodes, expanding and contacting the wall before the radiofrequency energy begins to be released, …”. In page 5 para [0057]: “In some embodiments the carrier 6 has elastic or super-elastic shape memory properties such that when force is removed, the carrier 6 elastically returns to a relaxed state or a natural state as shown in FIG. 2. Force may be applied by the control wire 19 to deform the carrier 6 into one state, and when force is removed, the mesh carrier 6 returns to its relaxed state … Alternatively, a relaxed state of the carrier 6 may be a collapsed or compressed configuration and the control wire 19 may be pulled (tension applied) to shorten the carrier 6 and increase its diameter, placing it in an expanded configuration. In some embodiments, the control wire 19 may be a solid or stranded wire or cable made from a metal or polymer. In other embodiments, the control wire 19 may be a hollow tube that can be passed over a guide wire to facilitate insertion through an intravascular path to a targeted site …”. It has been disclosed in page 3 para [0044]: “Referring to FIGS. 1 and 2, the carrier 6 may carry two or more therapeutic members 7 for intravascular treatment. The therapeutic member 7 may be for example an electrode …”.
From the disclosure above, it is understood that the carrier 6 (e.g., in Fig. 1, 2 and 4) carries two or more therapeutic members 7 as RF electrode, further carries 6 has mesh structure, which is expandable or stretchable. Therefore, the disclosure above teaches the whole limitation).
Dong teaches expanding the expandable electrode mesh so that the electrodes contact the patient system. (Dong disclosed in page 5 para [0057]: “In some embodiments the carrier 6 has elastic or super-elastic shape memory properties such that when force is removed, the carrier 6 elastically returns to a relaxed state or a natural state as shown in FIG. 2. Force may be applied by the control wire 19 to deform the carrier 6 into one state, and when force is removed, the mesh carrier 6 returns to its relaxed state … Alternatively, a relaxed state of the carrier 6 may be a collapsed or compressed configuration and the control wire 19 may be pulled (tension applied) to shorten the carrier 6 and increase its diameter, placing it in an expanded configuration. In some embodiments, the control wire 19 may be a solid or stranded wire or cable made from a metal or polymer. In other embodiments, the control wire 19 may be a hollow tube that can be passed over a guide wire to facilitate insertion through an intravascular path to a targeted site …”. It has been disclosed in page 3 para [0044]: “Referring to FIGS. 1 and 2, the carrier 6 may carry two or more therapeutic members 7 for intravascular treatment. The therapeutic member 7 may be for example an electrode …”. It is understood from the disclosure above that the carrier 6 (e.g., in Fig. 1, 2 and 4) carries two or more therapeutic members 7 as RF electrode, further carries 6 has mesh structure, which is expandable or stretchable).
Alighaleh and Dong are analogous art because they are related to perform electrical activity in patient’s body using electrode as a part of apparatus. 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 Alighaleh and Dong, to modify inserting an electrode into the patient digestive system of Alighaleh, to include Dong’s teaching related to insert an expandable electrode mesh into the patient system. The suggestion/motivation for doing so would have been obvious by Dong because “Radiofrequency electrodes are commonly used for converting the radio frequency signal into heat, and for treating human tissues through thermal effects. During the surgery, whether the radiofrequency electrodes effectively contact a wall such as a blood vessel internal wall has a decisive effect for the radiofrequency ablation treatment. A new radiofrequency ablation catheter can also improve the coverage for the curved blood vessels, and maintain or stabilize its original stent configuration, the application scope of the radiofrequency ablation will be greatly broadened, the effect of the radiofrequency ablation will be improved at the same time, and there will be a positive effect on the promotion of radiofrequency ablation usage.” (Dong disclosed in page 1 para [0006 and 0009]).
Regarding claim 20, Alighaleh and Dong teach the method of claim 19 wherein Alighaleh teaches the patient is under anesthesia. (Alighaleh disclosed in page 2826 section B. 1) (left col.): “the pigs were anesthetized, after which a mid-line laparotomy was performed to gain access to the stomach, … The animal was euthanized at the end of the study while under anesthesia.”).
Regarding claim 21, Alighaleh and Dong teach the method of claim 19, however, Alighaleh doesn’t explicitly teach the limitation “the expandable electrode mesh has a tubular shape prior to being expanded into a three-dimensional mesh”.
wherein Dong teaches the expandable electrode mesh has a tubular shape prior to being expanded into a three-dimensional mesh. (Dong disclosed in page 5 para [0057]: “the carrier 6 elastically returns to a relaxed state or a natural state as shown in FIG. 2. Force may be applied by the control wire 19 to deform the carrier 6 into one state, and when force is removed, the mesh carrier 6 returns to its relaxed state … Alternatively, a relaxed state of the carrier 6 may be a collapsed or compressed configuration and the control wire 19 may be pulled (tension applied) to shorten the carrier 6 and increase its diameter, placing it in an expanded configuration. In some embodiments, the control wire 19 may be a solid or stranded wire or cable made from a metal or polymer. In other embodiments, the control wire 19 may be a hollow tube that can be passed over a guide wire to facilitate insertion through an intravascular path to a targeted site …”. In page 8 para [0080]: “As shown in FIG. 8, at least one therapeutic assembly 70 is configured to wrap around at least one of said four wire helix segments AB, BC, CD and DA … the carrier 6 carries six therapeutic assemblies 70a - 70f as shown in FIG. 6, each of which includes an electrode 7 as the therapeutic member 7, providing six electrodes in total.” Further, in page 9 para [0084], it has been disclosed: “As shown in FIG. 9B, at least one of (preferably all) the two terminal bodies 702/703 if any and the main body 710 may include one, two or more protrusions 788 … guiding one or more wire helixes Ly or Ly + 1 that slide (s) along different directions (represented as the dotted lines Ly) over the wire segment AB around which the therapeutic assembly 70 wraps. When there are three or more protrusions 788 …”. It has been discussed in para [0077] that the carrier 6 comprises m right-handed wire helixes and n left-handed wire helixes.
Therefore, it can be concluded from above disclosures that the carrier 6 carries six therapeutic assemblies as shown in FIG. 6, each of which includes an electrode 7 as the therapeutic member 7, providing six electrodes. The carrier 6 comprises right and left-handed wire helixes and one or more wire helixes being guided along different directions around which the therapeutic assembly 70 wraps with 3 or more protrusions (as shown in FIG. 9B), this scenario corresponds to claim element “electrode expanded into 3D mesh”. The mesh carrier 6 has expandable or stretchable structure in control wire 19 (shown in FIG. 2) and control wire 19 is a hollow tube, i.e., electrode mesh has a tubular shape).
Alighaleh and Dong are analogous art because they are related to perform electrical activity in patient’s body using electrode as a part of apparatus. 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 Alighaleh and Dong, to modify inserting an electrode into the patient digestive system of Alighaleh, to include Dong’s teaching related to insert an expandable electrode mesh into the patient system. The suggestion/motivation for doing so would have been obvious by Dong because “Radiofrequency electrodes are commonly used for converting the radio frequency signal into heat, and for treating human tissues through thermal effects. During the surgery, whether the radiofrequency electrodes effectively contact a wall such as a blood vessel internal wall has a decisive effect for the radiofrequency ablation treatment. A new radiofrequency ablation catheter can also improve the coverage for the curved blood vessels, and maintain or stabilize its original stent configuration, the application scope of the radiofrequency ablation will be greatly broadened, the effect of the radiofrequency ablation will be improved at the same time, and there will be a positive effect on the promotion of radiofrequency ablation usage.” (Dong disclosed in page 1 para [0006 and 0009]).
Regarding claim 22, Alighaleh and Dong teach the method of claim 19 however, Alighaleh doesn’t explicitly teach the limitation “the expandable electrode mesh is expanded by pulling a cable that is inside a catheter to which the expandable electrode mesh is attached”.
wherein Dong teaches the expandable electrode mesh is expanded by pulling a cable that is inside a catheter to which the expandable electrode mesh is attached. (Dong disclosed in page 5 para [0054]: “When catheter apparatus 1 is being used, the distal portion 5 of the elongated shaft 2 as well as the carrier 6 may be moved through an intravascular path by following a path defined by a guide catheter, …”. In para [0057]: “The elongated shaft 2, the coupling 18, the carrier 6, and the end piece 16 may include passages sized and shaped to accommodate a control wire or pull/push wire 19 that is fixed to the distal end of the carrier 6 … In some embodiments the carrier 6 has elastic or super - elastic shape memory proper ties such that when force is removed, the carrier 6 elastically returns to a relaxed state or a natural state as shown in FIG. 2. Force may be applied by the control wire 19 to deform the carrier 6 into one state, and when force is removed, the mesh carrier 6 returns to its relaxed state. … Alternatively, a relaxed state of the carrier 6 may be a collapsed or compressed configuration and the control wire 19 may be pulled (tension applied) to shorten the carrier 6 and increase its diameter, placing it in an expanded configuration. In some embodiments, the control wire 19 may be a solid or stranded wire or cable made from a metal or polymer.”).
Alighaleh and Dong are analogous art because they are related to perform electrical activity in patient’s body using electrode as a part of apparatus. 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 Alighaleh and Dong, to modify inserting an electrode into the patient digestive system of Alighaleh, to include Dong’s teaching related to insert an expandable electrode mesh into the patient system. The suggestion/motivation for doing so would have been obvious by Dong because “Radiofrequency electrodes are commonly used for converting the radio frequency signal into heat, and for treating human tissues through thermal effects. During the surgery, whether the radiofrequency electrodes effectively contact a wall such as a blood vessel internal wall has a decisive effect for the radiofrequency ablation treatment. A new radiofrequency ablation catheter can also improve the coverage for the curved blood vessels, and maintain or stabilize its original stent configuration, the application scope of the radiofrequency ablation will be greatly broadened, the effect of the radiofrequency ablation will be improved at the same time, and there will be a positive effect on the promotion of radiofrequency ablation usage.” (Dong disclosed in page 1 para [0006 and 0009]).
Regarding claim 23, Alighaleh and Dong teach the method of claim 19 further Alighaleh teaches analyzing a patient digestive electrogram (EDG) collected during the stimulated electrical activity. (Alighaleh disclosed in page 2828 section B. (left col.): “Representative experimental data in Fig. 8 showed that the slow wave period was successfully modulated through pacing periods between 14s and 30s in our study. In this figure, before pacing, the intrinsic slow wave activity was dysrhythmic with bradygastric period (40–100 s). Once pacing was applied, the slow wave period immediately matched the pacing period, normalizing the dysrhythmia.” In same page 2828 section IV: “Gastric pacing has been proven to be a potential treatment to regulate dysrhythmic slow waves. Current clinical stimulation devices, such as Enterra, rely instead on high-frequency neuromodulation protocols which might improve symptoms, … this paper represents a critical stepping stone to translational studies, demonstrating its validity in pigs, which are a well-established model of gastric slow wave activity. This device can now be used in human trials.”).
Regarding claim 24, Alighaleh and Dong teach the method of claim 19 wherein Alighaleh teaches the analyzing includes reviewing patient characteristic values of characteristics retrieved from a mapping library that maps EDGs to characteristic values, the patient characteristic values being mapped to a EDG of the mapping library that is similar to the patient EDG. (Alighaleh disclosed in page 2828 section B. (left col.): “Representative experimental data in Fig. 8 showed that the slow wave period was successfully modulated through pacing periods between 14s and 30s in our study. In this figure, before pacing, the intrinsic slow wave activity was dysrhythmic with bradygastric period (40–100 s). Once pacing was applied, the slow wave period immediately matched the pacing period, normalizing the dysrhythmia. … In Fig. 8, it can also be seen that the slow wave period returned to the initial bradygastric frequency when the pacing was stopped for 100 s (between 780 s and 880 s in the recorded duration), and followed again the period of pacing (22 s) when pacing was reapplied.” In same page 2828 section IV: “Recent HR electrical mapping studies have shown that dysrhythmic slow wave activity is associated with functional motility disorders, and post-surgical dysmotility Gastric pacing has been proven to be a potential treatment to regulate dysrhythmic slow waves. Current clinical stimulation devices, such as Enterra, rely instead on high-frequency neuromodulation protocols which might improve symptoms, … This study presented a newly designed pacing device with the aim of modulating gastric slow wave propagation pattern and period. … This study employed HR mapping to assess the effect of pacing on the slow waves, enabling a substantially improved understanding of the effects on propagation pattern and velocity.”).
Regarding claim 25, Alighaleh and Dong teach the method of claim 19 wherein Alighaleh teaches at least one of the electrodes receives a signal generated by the digestive system in response to the stimulated electrical activity. (Alighaleh disclosed in page 2827 section III A.: “The first aim of our validation study was to initiate a pacemaker. With pacing onset, HR mapping showed a consistent shift of the wavefront origin to the pacing electrode site, with radial outward propagation, as demonstrated in Fig. 5(b). In this figure, from a representative experiment, the illustrated signals were from five recording channels oriented along the axis of the stomach and close to the pacing leads. … The pacing initiated the new origin of the slow wave activity close to the location of the pacing leads. … HR mapping allowed spatiotemporal quantification of the spread of electrical activity. Fig. 6 shows the spatiotemporal quantification of the data presented in Fig. 5. Isochronal activation and velocity maps of selected propagation sequences before and during pacing are shown. Prior to pacing, the slow waves propagated onto the top left corner of the array and propagated circumferentially and longitudinally to the bottom edge of the recording area. After the onset of pacing, the pulses changed the origin of the slow waves to where the pacing electrodes were located (near the middle of the mapped field). The new pacemaker region remained consistent during the entire pacing protocol in all animals.”).
Regarding claim 40, Alighaleh teaches a method for stimulating electrical activity of a patient digestive system of a patient under anesthesia, (Alighaleh disclosed in page 2823 section I (right col.): “The motility, or the rhythmic contractions of the gastrointestinal (GI) tract, is coordinated in part by underlying electrical events known as slow waves. … Motility disorders, such as gastroparesis and functional dyspepsia, are associated with disordered electrical activity. The use of external electrical pacemakers in cardiac electrophysiology to correct disordered rhythms is well established. … High frequency stimulation is believed to affect the neural pathways while low frequency pacing affects the ICC to modulate slow wave activation. A clinical device is currently available for gastric stimulation that uses high frequency neuromodulation protocols (Enterra, Medtronic, Minneapolis, MN), …”. In page 2826 section B. 1) (left col.): “the pigs were anesthetized, after which a mid-line laparotomy was performed to gain access to the stomach, … The animal was euthanized at the end of the study while under anesthesia.”).
Alighaleh teaches the method comprising: inserting an electrode into the patient digestive system, so that the electrodes contact the inner lining of the patient digestive system; (Alighaleh disclosed in page 2826 section B. 3) (right col.): “For pacing, bipolar temporary cardiac pacing electrodes separated by 1 cm (TME 64 S, OSCOR Inc.) were used as the cathode and anode electrodes. These electrodes were inserted into the stomach wall through a small hole with the diameter of 1.5 mm located in the center of the HR mapping electrode array (Fig. 5).”).
Alighaleh teaches directing electrical signals be sent to the electrodes in a designated pattern to stimulate electrical activity of the patient digestive system, (Alighaleh disclosed in page 2827 section III A.: “With pacing onset, HR mapping showed a consistent shift of the wavefront origin to the pacing electrode site, with radial outward propagation, as demonstrated in Fig. 5(b). In this figure, from a representative experiment, the illustrated signals were from five recording channels oriented along the axis of the stomach and close to the pacing leads. Before pacing, the intrinsic slow wave period was 20 ± 0.2 s and propagated in a normal antegrade fashion towards the pylorus. Pacing was applied to the stomach with a period of 18 ± 0.1 s (10 percent less than intrinsic period of slow wave) amplitude of 4 mA, and pulse-width of 400 ms. After the onset of pacing, the slow wave period decreased immediately to 18 s. The pacing initiated the new origin of the slow wave activity close to the location of the pacing leads.” In Fig. 5 (b) shown “Five gastric signals recorded (as marked by green rectangle in panel a) during pacing from a column in the centre of the array, where leads were inserted. Before pacing, the intrinsic slow wave propagated toward the distal stomach (blue arrows). Once pacing was applied, the slow wave configuration changed in spatial pattern (green arrows) …”).
Alighaleh teaches the designated pattern represents sending electrical signals to electrodes in sequence; (Alighaleh disclosed in page 2827 section B. 5) (left col.): “the raw signals were filtered, and then the slow wave activation times were detected and clustered into propagating wavefronts. Isochronal activation maps were used to visualize the spatial pattern, after which velocity and amplitude maps were calculated. The onset of entrainment was identified by evaluating the temporal relationship between pacing artifacts and slow wave events, and by spatial shifting of the wavefront origin to the region of the pacing electrodes. Furthermore, slow wave periods between consecutive waves were calculated to verify the modulation of the slow wave period.” In page 2827 section III A.: “With pacing onset, HR mapping showed a consistent shift of the wavefront origin to the pacing electrode site, with radial outward propagation, as demonstrated in Fig. 5(b). In this figure, from a representative experiment, the illustrated signals were from five recording channels oriented along the axis of the stomach and close to the pacing leads. Before pacing, the intrinsic slow wave period was 20 ± 0.2 s and propagated in a normal antegrade fashion towards the pylorus. Pacing was applied to the stomach with a period of 18 ± 0.1 s (10 percent less than intrinsic period of slow wave) amplitude of 4 mA, and pulse-width of 400 ms. After the onset of pacing, the slow wave period decreased immediately to 18 s. The pacing initiated the new origin of the slow wave activity close to the location of the pacing leads.”).
and Alighaleh teaches analyzing a patient digestive electrogram (EDG) collected during the stimulated electrical activity. (Alighaleh disclosed in page 2823-2824 section I: “In clinical studies, gastric pacing has been shown to improve the gastric emptying rate in patients with gastroparesis using a pacing frequency of 10 percent more than the intrinsic gastric frequency (IGF) with the pacing leads located along the greater curvature about 15 cm proximal to the pylorus. The effect of gastric electrical stimulation/pacing on morbid obesity has also been investigated. Induction of retrograde gastric pacing resulted in about 15% reduction in consumption of food and water during a three day trial in healthy volunteers, and was achieved with a frequency of 9 cpm through a pair of electrodes implanted 5 cm proximal to the pylorus … The novel device was applied in conjunction with high-resolution (HR) electrical mapping to evaluate the efficacy of the pacing device to modulate slow wave activation. Recently, HR mapping techniques have been applied in the GI field in in vivo animal and human studies and have provided significant improvements in the clinical and experimental understanding of the GI electrical activity. HR slow wave mapping allows for accurate quantification and visualization of individual wavefronts.”).
However, Alighaleh doesn’t explicitly teach the limitation “inserting an expandable electrode mesh into the patient system, the expandable electrode mesh having electrodes; expanding the expandable electrode mesh so that the electrodes contact the patient system;”
Dong teaches inserting an expandable electrode mesh into the patient system, the expandable electrode mesh having electrodes; (Dong disclosed in page 1 para [0007]: “In the radiofrequency ablation catheter, the radiofrequency electrodes are mounted on a carrier, such as a stent, at the front end of the radiofrequency ablation catheter. The stent is used for carrying the radiofrequency electrodes, expanding and contacting the wall before the radiofrequency energy begins to be released, …”. In page 5 para [0057]: “In some embodiments the carrier 6 has elastic or super-elastic shape memory properties such that when force is removed, the carrier 6 elastically returns to a relaxed state or a natural state as shown in FIG. 2. Force may be applied by the control wire 19 to deform the carrier 6 into one state, and when force is removed, the mesh carrier 6 returns to its relaxed state … Alternatively, a relaxed state of the carrier 6 may be a collapsed or compressed configuration and the control wire 19 may be pulled (tension applied) to shorten the carrier 6 and increase its diameter, placing it in an expanded configuration. In some embodiments, the control wire 19 may be a solid or stranded wire or cable made from a metal or polymer. In other embodiments, the control wire 19 may be a hollow tube that can be passed over a guide wire to facilitate insertion through an intravascular path to a targeted site …”. It has been disclosed in page 3 para [0044]: “Referring to FIGS. 1 and 2, the carrier 6 may carry two or more therapeutic members 7 for intravascular treatment. The therapeutic member 7 may be for example an electrode …”.
From the disclosure above, it is understood that the carrier 6 (e.g., in Fig. 1, 2 and 4) carries two or more therapeutic members 7 as RF electrode, further carries 6 has mesh structure, which is expandable or stretchable. Therefore, the disclosure above teaches the whole limitation).
Dong teaches expanding the expandable electrode mesh so that the electrodes contact the patient system; (Dong disclosed in page 5 para [0057]: “In some embodiments the carrier 6 has elastic or super-elastic shape memory properties such that when force is removed, the carrier 6 elastically returns to a relaxed state or a natural state as shown in FIG. 2. Force may be applied by the control wire 19 to deform the carrier 6 into one state, and when force is removed, the mesh carrier 6 returns to its relaxed state … Alternatively, a relaxed state of the carrier 6 may be a collapsed or compressed configuration and the control wire 19 may be pulled (tension applied) to shorten the carrier 6 and increase its diameter, placing it in an expanded configuration. In some embodiments, the control wire 19 may be a solid or stranded wire or cable made from a metal or polymer. In other embodiments, the control wire 19 may be a hollow tube that can be passed over a guide wire to facilitate insertion through an intravascular path to a targeted site …”. It has been disclosed in page 3 para [0044]: “Referring to FIGS. 1 and 2, the carrier 6 may carry two or more therapeutic members 7 for intravascular treatment. The therapeutic member 7 may be for example an electrode …”. It is understood from the disclosure above that the carrier 6 (e.g., in Fig. 1, 2 and 4) carries two or more therapeutic members 7 as RF electrode, further carries 6 has mesh structure, which is expandable or stretchable).
Alighaleh and Dong are analogous art because they are related to perform electrical activity in patient’s body using electrode as a part of apparatus. 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 Alighaleh and Dong, to modify inserting an electrode into the patient digestive system of Alighaleh, to include Dong’s teaching related to insert an expandable electrode mesh into the patient system. The suggestion/motivation for doing so would have been obvious by Dong because “Radiofrequency electrodes are commonly used for converting the radio frequency signal into heat, and for treating human tissues through thermal effects. During the surgery, whether the radiofrequency electrodes effectively contact a wall such as a blood vessel internal wall has a decisive effect for the radiofrequency ablation treatment. A new radiofrequency ablation catheter can also improve the coverage for the curved blood vessels, and maintain or stabilize its original stent configuration, the application scope of the radiofrequency ablation will be greatly broadened, the effect of the radiofrequency ablation will be improved at the same time, and there will be a positive effect on the promotion of radiofrequency ablation usage.” (Dong disclosed in page 1 para [0006 and 0009]).
Regarding claim 41, Alighaleh and Dong teach the method of claim 19 wherein Alighaleh teaches designated pattern includes sending electrical signals in sequence to electrodes at various locations within the digestive system. (Alighaleh disclosed in page 2827 section III A.: “With pacing onset, HR mapping showed a consistent shift of the wavefront origin to the pacing electrode site, with radial outward propagation, as demonstrated in Fig. 5(b). In this figure, from a representative experiment, the illustrated signals were from five recording channels oriented along the axis of the stomach and close to the pacing leads. Before pacing, the intrinsic slow wave period was 20 ± 0.2 s and propagated in a normal antegrade fashion towards the pylorus. Pacing was applied to the stomach with a period of 18 ± 0.1 s (10 percent less than intrinsic period of slow wave) amplitude of 4 mA, and pulse-width of 400 ms. After the onset of pacing, the slow wave period decreased immediately to 18 s. The pacing initiated the new origin of the slow wave activity close to the location of the pacing leads.” In Fig. 5 (b) shown “Five gastric signals recorded (as marked by green rectangle in panel a) during pacing from a column in the centre of the array, where leads were inserted. Before pacing, the intrinsic slow wave propagated toward the distal stomach (blue arrows). Once pacing was applied, the slow wave configuration changed in spatial pattern (green arrows) …”).
Claims 29 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Sathar and further in view of Jackson et al. (Pub. No. US2021/0186605A1).
Regarding claim 29, Sathar teaches the one or more computing systems of claim 28, however, Sathar doesn’t explicitly teach the limitation “at least one of 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 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 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 30, Sathar teaches the one or more computing systems of claim 29, wherein the patient mesh readings are received from a computing system. (Sathar disclosed in page 2687 section II C. (left col.): “An anatomically realistic stomach geometry was obtained from CT images taken from a human patient ... A cubic Hermite finite-element mesh was fitted to the digitized 3-D outline of the stomach using an iterative fitting method. … The stomach geometry volume was discretized into tetrahedral elements delimited by tetrahedral volume constraint and quality constraint … to obtain a suitable computational mesh.”). It has been discussed in page 2689 section D. (left col.) that the simulations performed in SandyBridge architecture CPUs).
However, Sathar doesn’t explicitly teach the limitation “the patient readings are received from a client computing system”.
Jackson teaches the patient readings are received from a client computing system. (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.” 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 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 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]).
Claims 33-35 and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Hovda et al. (Patent No. US6363937B1) and further in view of Grady.
Regarding claim 33, Hovda teaches a method for guiding a catheter within the digestive system of a patient, (Hovda disclosed in col. 2 lines 42-65: “The present invention provides systems, methods and apparatus for applying high frequency electrical energy to treat tissue in regions of the digestive system. The present invention is particularly Suited for applying electrical energy to the lower esophageal Sphincter to treat gastroesophageal reflux. … The electrosurgical instrument may comprise a catheter that is advanced transluminally or through the patient's mouth and esophagus down to the lower sphincter, or a more rigid probe that is introduced through a percutaneous or open penetration in the patient.”).
Hovda teaches the method comprising: inserting the catheter into the digestive system of the patient, the catheter having an electrode for stimulating electrical activity; (Hovda disclosed in col. 4 lines 39-48: “The catheter shaft will include an electrically insulating electrode support member having a tissue treatment surface at the distal end of the shaft. One or more electrode terminal(s) are coupled to, or integral with, the electrode support member such that the electrode terminal(s) are spaced from the return electrode. In one embodiment, the catheter includes an electrode array having a plurality of electrically isolated electrode terminals embedded into the electrode support member such that the electrode terminals extend about 0.0 mm to about 10 mm.” In col. 6 lines 4-7: “In the present invention, high frequency (RF) electrical energy is applied to one or more electrode terminals in the presence of electrically conductive fluid to remove and/or modify the structure of tissue structures.”).
However, Hovda doesn’t explicitly teach the limitations for each of a plurality of locations within the digestive system, placing an electrode in contact with the mucosa of the digestive system; directing the electrode to stimulate electrical activity of the digestive system; collecting a digestive electrogram (EDG) from the patient based on the stimulated electrical activity; receiving an indication of the location of the electrode, the location determined based on mappings of EDGs to locations; directing movement of the electrode to another location.
and Grady teaches for each of a plurality of locations within the digestive system, placing an electrode in contact with the mucosa of the digestive system; (Grady disclosed in page 293 section F. (left col.): “In historical studies, GI electrical recordings were performed by intubation of the human GI lumen dating to the 1940s, when single electrodes or bipolar pairs were placed on the surface of a balloon introduced to the intestine. Once positioned in the intestine, the balloons were inflated to achieve contact between the electrode and mucosa … Recently, attempts have been made to extend gastric mucosal recordings by adopting HR strategies. In particular, Angeli et al. demonstrated the feasibility for this approach using an inflatable balloon device adapted from a cardiac map ping catheter [see Fig. 3(E)]. The prototype employed an 8 × 8 spherical array of electrodes of 75 mm diameter, …”. Fig. 3(E) shown spiral electrode arrays designed for less-invasive intra-operative deployment and Fig. 3(F) Prototype spherical electrode array designed for minimally invasive endoscopic deployment to the gastric mucosa.”).
Grady teaches directing the electrode to stimulate electrical activity of the digestive system; collecting a digestive electrogram (EDG) from the patient based on the stimulated electrical activity; (Grady disclosed in page 288-289 section III A.: “A quantitative biophysical model of this relationship has been demonstrated, showing a close concordance between the experimentally recorded configuration and the theoretical principles of syncytial current flow … the principle of detecting extracellular “activation times” (ATs) at the point of steepest negative descent of the biphasic activation phase, as this moment of rapid gradient corresponds to the wavefront passing directly under the electrode (see Figs. 1 and 2). … The morphology of the recovery potential recorded by extracellular electrodes has been shown to be dependent on the presence of other nearby wave fronts, as well as by the membrane potential gradient across the gut wall underlying the electrode. … Further validation for the bioelectrical origins of extracellular recordings and their standard configuration comes from suction recordings (see Fig. 2). Suction electrode recordings adhere to the same extracellular field principles detailed above; … In studies validating GI extracellular recordings, concordance is observed between conventional extracellular potentials and suction electrodes, with the timing of the biphasic potential coinciding with the monophasic activation (upstroke) potential.”).
Grady teaches receiving an indication of the location of the electrode, the location determined based on mappings of EDGs to locations; (Grady disclosed in page 291 section IV C. (right col.): “Another consideration is the density of the electrode array. This choice again depends on the application, with the most detailed studies to-date utilizing high-density grids of electrodes spaced 1–4 mm apart, whereas lower density arrays have been employed in many other studies. Putney et al. have evaluated an efficient interelectrode spacing for achieving accurate data capture for wavefront tracking, by employing mathematical models of human gastric dysrhythmia, and considering the spatial Nyquist criterion that the interelectrode distance be less than half the smallest relevant spatial wavelength. Higher density electrodes are clearly necessary when the spatial scales of interest are small. In practice, Putney et al. found that under most circumstances in the human stomach, a 10 mm separation is sufficient, but a higher density is desirable when dealing with complex and heterogeneous activation wavefronts as seen during re-entry or more chaotic events.”
and Grady teaches directing movement of the electrode to another location. (Grady disclosed in page 297 section C. (left col.): “Identifying the AT of each individual slow wave event is a prerequisite to creating isochronal activation maps that visualize and quantify slow wave propagation. The rapid, large-amplitude, negative deflection (∼–1mV/s) in the serosally recorded slow wave waveform corresponds to the time at which a propagating wavefront arrives at an electrode site; the AT is identified as the point of steepest descent along this component. … The output is a set of all ATs marked at each electrode site, of which there are typically ∼50 000 ATs array-wide during a 1 h study (3 events/min/channel × 256 channels × 60 min).”).
Hovda and Grady are analogous art because they are related to apply electrical energy to a target location within 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 Hovda and Grady, to modify applying catheter having an electrode for stimulating electrical activity of Hovda, to include Grady’s teaching related to receiving and directing the location of the electrode to stimulate electrical activity of the digestive system. The suggestion/motivation for doing so would have been obvious by Grady because “The field of GI electrophysiology is now at an exciting juncture, with the potential to resolve several longstanding questions of physiological and clinical importance. These include the opportunity to definitively evaluate the prevalence and significance of electrophysiological abnormalities in multiple functional and postoperative motility disorders, and to define whether various types of gastric dysrhythmias are a causative pathology in themselves or a biomarker of interest. In summary, HR mapping strategies for the GI tract have expanded and proven their validity and value over the recent two decades for use in GI electrophysiology and pathophysiology.” (Grady disclosed in page 299 section VIII).
Regarding claim 34, Hovda and Grady teach the method of claim 33 wherein Hovda teaches the catheter is guided to a target location. (Hovda disclosed in col. 14 lines 17-23: “Referring now to FIG. 1, a catheter system 50 for treating the digestive system is illustrated according to the present invention. Catheter System 50 generally comprises an electrosurgical catheter 60 connected to a power supply 80 by an interconnecting cable 86 for providing high frequency voltage to a target tissue and an irrigant reservoir or source 100 for providing electrically conducting fluid to the target site.”).
Regarding claim 35, Hovda and Grady teach the method of claim 33 however, Hovda doesn’t explicitly teach the limitation “the EDG is input to a device that outputs the location of the electrode”.
wherein Grady teaches the EDG is input to a device that outputs the location of the electrode. (Grady disclosed in page 297 section C. (left col.): “Identifying the AT of each individual slow wave event is a prerequisite to creating isochronal activation maps that visualize and quantify slow wave propagation. The rapid, large-amplitude, negative deflection (∼–1mV/s) in the serosally recorded slow wave waveform corresponds to the time at which a propagating wavefront arrives at an electrode site; the AT is identified as the point of steepest descent along this component. Automated methods, such as the falling edge variable threshold (FEVT) algorithm, can be used to mark slow wave ATs recorded from both the stomach and small intestine with appropriately tuned parameters. The core idea behind the FEVT algorithm is to compute a signal transform that strongly emphasizes high-energy components of the recorded slow wave, convolved with an edge detector to identify where large negative deflections occurred in the electrogram. The output is a set of all ATs marked at each electrode site, of which there are typically ∼50 000 ATs array-wide during a 1 h study (3 events/min/channel × 256 channels × 60 min).” It has been discussed in page 291 section B. (left col.) that LabView software enable control of data collection as well as visualization of the electrogram potentials in real time. These signals are generally viewed as events corresponding to selected rows or columns of the electrode array, so that the lag between slow wave events can be monitored, …”. Therefore, electrogram or EDG is an input to a device (e.g., computing device having Acquisition hardware with LabView software) that outputs the location of the electrode e.g., signals from electrogram are viewed as events corresponding to selected rows or columns of the electrode array, as discussed above).
Hovda and Grady are analogous art because they are related to apply electrical energy to a target location within 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 Hovda and Grady, to modify applying catheter having an electrode for stimulating electrical activity of Hovda, to include Grady’s teaching related to receiving and directing the location of the electrode to stimulate electrical activity of the digestive system. The suggestion/motivation for doing so would have been obvious by Grady because “The field of GI electrophysiology is now at an exciting juncture, with the potential to resolve several longstanding questions of physiological and clinical importance. These include the opportunity to definitively evaluate the prevalence and significance of electrophysiological abnormalities in multiple functional and postoperative motility disorders, and to define whether various types of gastric dysrhythmias are a causative pathology in themselves or a biomarker of interest. In summary, HR mapping strategies for the GI tract have expanded and proven their validity and value over the recent two decades for use in GI electrophysiology and pathophysiology.” (Grady disclosed in page 299 section VIII).
Regarding claim 37, Hovda and Grady teach the method of claim 33 Hovda doesn’t explicitly teach the limitation “indication is displayed on a digestive system graphic at the location”.
wherein Grady teaches indication is displayed on a digestive system graphic at the location. (Grady disclosed in page 295-296 section IV: “Key elements in the signal processing pipeline for GI HR mapping include filtering, event detection, clustering of events into propagating sequences, generation of quantitative and qualitative maps (e.g., AT, velocity, and amplitude field maps), and intuitive visualization techniques (e.g., anatomical registration, animation) (see Fig. 4).” It is shown in Fig. 4, the “velocity field map” displays the magnitude as a color gradient and the direction at each electrode as an arrow. A single frame of the “propagation animation” shows multiple waves in the mapped area at one time, represented in different colors and corresponding to wave 4 and wave 5 in the electrogram mentioned above. In page 297 section E.: “AT mapping can be achieved either with a series of static images or with animated sequences. The former are typically shown as pseudo-colored contour plots indicating the area of propagation of a wavefront per unit time; there is one isochronal AT map for each cycle. ... Animated “flashlight” sequences can also be helpful to visualize activation timing across the electrode array. Each pixel representing an electrode site illuminates to the maximum intensity when at the time of activation and progressively fades back to zero over a user-defined interval, … A recently introduced wavefront-orientation interpolation technique may be applied to increase the resolution of the animations, making them more visually appealing, accurate, and readily interpretable. Furthermore, other metrics such as velocity field and the type of underlying conduction (e.g., pacemaker, colliding wavefronts, and conduction block) can be represented as an animated sequence”).
Hovda and Grady are analogous art because they are related to apply electrical energy to a target location within 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 Hovda and Grady, to modify applying catheter having an electrode for stimulating electrical activity of Hovda, to include Grady’s teaching related to receiving and directing the location of the electrode to stimulate electrical activity of the digestive system. The suggestion/motivation for doing so would have been obvious by Grady because “The field of GI electrophysiology is now at an exciting juncture, with the potential to resolve several longstanding questions of physiological and clinical importance. These include the opportunity to definitively evaluate the prevalence and significance of electrophysiological abnormalities in multiple functional and postoperative motility disorders, and to define whether various types of gastric dysrhythmias are a causative pathology in themselves or a biomarker of interest. In summary, HR mapping strategies for the GI tract have expanded and proven their validity and value over the recent two decades for use in GI electrophysiology and pathophysiology.” (Grady disclosed in page 299 section VIII).
Claims 36 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Hovda and Grady, and further in view of Agrusa.
Regarding claim 36, Hovda and Grady teach the method of claim 33, however Hovda and Grady 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.”).
Hovda, Grady 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 Hovda, Grady and Agrusa, to modify Hovda and Grady’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).
Regarding claim 39, Grady teaches the one or more computing systems of claim 38, is incorporating the rejections of claim 36, because claim 39 has substantially similar claim language as claim 36, therefore claim 39 is rejected under 35 U.S.C. 103 as being unpatentable over Hovda and Grady and further in view of Agrusa as discussed above for substantially similar rationale.
Claim 42 is rejected under 35 U.S.C. 103 as being unpatentable over Alighaleh and Dong and further in view of Jackson.
Regarding claim 42, Alighaleh and Dong teach the method of claim 41, however Alighaleh and Dong do not explicitly teach the limitation “the locations include the proximal end of the ascending colon and the distal end of the ascending colon.
wherein Jackson teaches the locations include the proximal end of the ascending colon and the distal end of the ascending colon. (Jackson disclosed in page 15 para [0351-0353]: “the inventors believe that the dilatation and relaxation of the colon may, at least in part, be mediated by a rapid arrhythmia pattern of conduction within the colon … Partitioning of the colon (as described above for the stomach) may prevent such fibrillations of the colon and may allow normal colon function. Typical ablation patterns may create partitions of the colon, … More particularly, starting from the left lower quadrant and moving clockwise are seen cecum 181, ascending (right) colon 182, transverse colon 183, descending (left) colon 184 … Dashed line 188 marks a possible contour of a dilated cecum and ascending colon.”).
Alighaleh, Dong 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 Alighaleh, Dong and Jackson, to modify Alighaleh and Dong’s teaching to send electrical signals to electrodes at various locations within the digestive system, 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]).
Claims 44 and 45 are rejected under 35 U.S.C. 103 as being unpatentable over Sathar and in view of an NPL “Transfer learning from simulations improves the classification of OCT images of glandular epithelia” Sassan Ostvar et al. (hereinafter Ostvar, NPL published on 2020).
Regarding Claim 44, Sathar teaches the method of claim 43, however Sathar does not explicitly teach the limitation “training a machine learning model with training data that includes library EDGs labeled with library mesh readings”.
further Ostvar teaches training a machine learning model with training data that includes library EDGs labeled with library mesh readings. (Ostvar disclosed in 1st page under ‘Abstract’ lines 18-21: “As a proof of concept, we studied the binary classification of esophageal OCT into normal squamous and glandular mucosae, typical of BE. We found that deep convolutional networks trained on synthetic data had improved performance over models trained on clinical datasets with uncertain labels.” In page 3 lines 144-149: “Subsurface imaging for EAC surveillance targets the esophageal mucosa (EM). … Clinical OCT frames are constructed out of groups of adjacent axial optical reflectivity profiles (A-lines).” In page 4 lines 169-172: “To estimate the A-lines, we adopted a mesh-based Monte Carlo (MC) algorithm to simulate subsurface scattering in model tissue geometries … The MC method provides an estimate of the spatial distribution of the energy of back-scattered radiation via sampling a set of possible trajectories of individual ‘photon packets’ as they interact with mesh elements.” Further, in page 7 section IV lines 263-269: “CAD systems based on models of computer vision employing deep learning rely on sizable and precisely annotated clinical datasets. … We found that fine-tuning a pre-trained deep convolutional architecture on synthetic data derived from simulations of light scattering provided a performance advantage to fine-tuning on a (larger) clinical data set with noisy labels.” In page 7-8 lines 286-293: “signal localization in frequency-domain OCT in the Monte Carlo method may further improve the fidelity of the estimated OCT signal. … as is the case in glandular mucosa. Finally, material inhomogeneities inside each tissue layer can be accounted for using models of epithelial morphogenesis … The mesh-based Monte Carlo method facilitates the integration of biomechanical modeling in the existing data generation pipeline.”).
Sathar and Ostvar 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 Ostvar, to modify simulating electrical activity of the digestive system of Sathar, to include Ostvar’s teaching related to train a machine learning model with training data with mesh readings in GI or digestive system. The suggestion/motivation for doing so would have been obvious by Ostvar because “Effective adoption of subsurface imaging in Esophageal adenocarcinoma (EAC) care can be facilitated by computer-aided detection (CAD) systems based on deep learning. Here we explore the use of synthetic datasets–specifically data derived from simulations of optical back-scattering during imaging– in the development of CAD systems based on deep learning. As a proof of concept, we studied the binary classification of esophageal OCT into normal squamous and glandular mucosae, typical of Barrett’s esophagus (BE). We found that deep convolutional networks trained on synthetic data had improved performance over models trained on clinical datasets with uncertain labels. Model performance also improved with dataset size during training on synthetic data. Our findings demonstrate the utility of transfer from simulations to real data in the context of medical imaging, especially in the severely data-poor regime and when significant uncertainty in labels are present, and motivate further development of transfer learning from simulations to aid the development of CAD for rare malignancies.” (Ostvar disclosed in 1st page under ‘Abstract’).
Regarding Claim 45, Sathar and Ostvar teach the method of claim 43 however, Sathar doesn’t 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 Ostvar teaches the identifying includes applying a machine learning model to the patient EDG wherein the machine learning model outputs the identified mesh readings. (Ostvar disclosed in page 3 lines 144-149: “Subsurface imaging for EAC surveillance targets the esophageal mucosa (EM). … Clinical OCT frames are constructed out of groups of adjacent axial optical reflectivity profiles (A-lines).” In page 4 lines 169-172: “To estimate the A-lines, we adopted a mesh-based Monte Carlo (MC) algorithm to simulate subsurface scattering in model tissue geometries … The MC method provides an estimate of the spatial distribution of the energy of back-scattered radiation via sampling a set of possible trajectories of individual ‘photon packets’ as they interact with mesh elements.” Further, in page 7 section IV lines 263-269: “CAD systems based on models of computer vision employing deep learning rely on sizable and precisely annotated clinical datasets. … We found that fine-tuning a pre-trained deep convolutional architecture on synthetic data derived from simulations of light scattering provided a performance advantage to fine-tuning on a (larger) clinical data set with noisy labels.” In page 7-8 lines 286-293: “signal localization in frequency-domain OCT in the Monte Carlo method may further improve the fidelity of the estimated OCT signal. … as is the case in glandular mucosa. Finally, material inhomogeneities inside each tissue layer can be accounted for using models of epithelial morphogenesis … The mesh-based Monte Carlo method facilitates the integration of biomechanical modeling in the existing data generation pipeline.”).
Sathar and Ostvar 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 Ostvar, to modify simulating electrical activity of the digestive system of Sathar, to include Ostvar’s teaching related to train a machine learning model with training data with mesh readings in GI or digestive system. The suggestion/motivation for doing so would have been obvious by Ostvar because “Effective adoption of subsurface imaging in Esophageal adenocarcinoma (EAC) care can be facilitated by computer-aided detection (CAD) systems based on deep learning. Here we explore the use of synthetic datasets–specifically data derived from simulations of optical back-scattering during imaging– in the development of CAD systems based on deep learning. As a proof of concept, we studied the binary classification of esophageal OCT into normal squamous and glandular mucosae, typical of Barrett’s esophagus (BE). We found that deep convolutional networks trained on synthetic data had improved performance over models trained on clinical datasets with uncertain labels. Model performance also improved with dataset size during training on synthetic data. Our findings demonstrate the utility of transfer from simulations to real data in the context of medical imaging, especially in the severely data-poor regime and when significant uncertainty in labels are present, and motivate further development of transfer learning from simulations to aid the development of CAD for rare malignancies.” (Ostvar disclosed in 1st page under ‘Abstract’).
Conclusion
10. 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