DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 03/11/2025 has been received, entered into the record, and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
3. The disclosure (See Paragraphs 04 and 38) is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Double Patenting
4. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public Policy(a Policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
5. A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
6. Claims 1-9, 12-16, and 18-20 are provisionally anticipatorily rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 and 17-20 of copending Application No. 19/075929 (reference application) (herein referred to as Sztyler 929).
7. Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 1 of the instant application substantially recites the limitations of claim 1 of Sztyler 929. Both claims recite substantially similar limitations regarding the determination of patient intervention.
Application Claim 1
U.S. Patent Application 19/075883 Claim 1
1. A computer-implemented method for providing event-specific intervention recommendations, the method comprising:
A) acquiring at least two data streams of a patient by using one or more sensors; B) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
C) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient; and
D) outputting an information of the selected intervention to a user.
1. A computer-implemented method for providing event-specific intervention recommendations, the method comprising:
A) acquiring at least two data streams of a patient by using one or more sensors (Corresponds to Limitation A);
B) wherein at least one the data streams includes images;
C) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient (Corresponds to Limitation B);
D) selecting at least one intervention based on the generated entity-feature-graph and a trained graph classification model (Corresponds to Limitation C); and
E) outputting an information of the selected intervention to a user (Corresponds to Limitation D).
However, the cited patent application of Sztyler 929 also determines patient intervention.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 2 of the instant application substantially recites the limitations of claim 2 of Sztyler 929. Both claims recite substantially similar limitations regarding the training of a classification model.
Application Claim 2
U.S. Patent Application 19/075929 Claim 2
2. The method according to claim 1,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph;
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams.
2. The method according to claim 1,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph (Corresponds to Limitation A);
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams (Corresponds to Limitation B).
However, the cited patent application of Sztyler 929 also trains a classification model.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 3 of the instant application substantially recites the limitations of claim 3 of Sztyler 929. Both claims recite substantially similar limitations regarding the transformation of a feature graph.
Application Claim 3
U.S. Patent Application 19/075929 Claim 3
3. The method according to claim 2,
A) wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same.
3. The method according to claim 2,
A) wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same (Corresponds to Limitation A).
However, the cited patent application of Sztyler 929 also transforms a feature graph.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 4 of the instant application substantially recites the limitations of claim 4 of Sztyler 929. Both claims recite substantially similar limitations regarding the classification of an entity graph.
Application Claim 4
U.S. Patent Application 19/075929 Claim 4
4. The method according to claim 3,
A) wherein the graph classification is performed based on using a graph neural network that is given as input the entity-feature-graph together with a probability information that indicates for each pair of entities of the set of entities a likelihood that both entities of a respective pair are the same.
4. The method according to claim 3, wherein the graph classification is performed based on using a graph neural network that is given as input the entity-feature-graph together with a probability information that indicates for each pair of entities of the set of entities a likelihood that both entities of a respective pair are the same (Corresponds to Limitation A).
However, the cited patent application of Sztyler 929 also classifies an entity graph.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 5 of the instant application substantially recites the limitations of claim 10 of Sztyler 929. Both claims recite substantially similar limitations regarding the defining of streams and sensors.
Application Claim 5
U.S. Patent Application 19/075929 Claim 10
5. The method according to claim 1,
A) wherein the at least two data streams include at least one data stream including images and at least one data stream including text; and
B) wherein the one or more sensors include a camera, a sound recorder, a temperature sensor, a sound-level sensor, a door sensor, and interaction sensors.
10. The method according to claim 1,
A) wherein the at least two data streams include at least one data stream of text and/or at least one data stream of speech that is converted to text (Corresponds to Limitation A); and
B) wherein the one or more sensors include at least one of a camera, sound recorder presence sensor, a temperature sensor, a sound-level sensor, and a door sensor (Corresponds to Limitation B).
However, the cited patent application of Sztyler 929 also defines streams and sensors.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 6 of the instant application substantially recites the limitations of claim 6 of Sztyler 929. Both claims recite substantially similar limitations regarding the telephoning of a contact in response to an accident detection.
Application Claim 6
U.S. Patent Application 19/075929 Claim 6
6. The method according to claim 1, further comprising:
A) determining based on the at least two data streams that the patient has suffered an accident; and
B) executing the selected intervention of establishing a phone connection of an emergency contact associated with the patient.
Claim 5:
5. The method according to claim 1, further comprising:
A) processing image data of the images contained in the at least one of the at least two data streams, which is the basis for decision making; and
B) scanning the images for location information.
Claim 6:
6. The method according to claim 5, further comprising:
A) determining based on the image data that the patient has suffered an accident (Corresponds to Limitation A); and
B) executing the selected intervention of establishing a phone connection of an emergency contact associated with the patient (Corresponds to Limitation B).
However, the cited patent application of Sztyler 929 also telephones a contact in response to an accident detection.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 7 of the instant application substantially recites the limitations of claim 7 of Sztyler 929. Both claims recite substantially similar limitations regarding the modification of a therapy in response to a determination that a post-op patient is underactive.
Application Claim 7
U.S. Patent Application 19/075929 Claim 7
7. The method according to claim 1, further comprising:
A) determining based on the at least two data streams that the patient has undergone a surgery and is underactive; and
B) executing the selected intervention of adapting a therapy associated with the patient.
Claim 5:
5. The method according to claim 1, further comprising:
A) processing image data of the images contained in the at least one of the at least two data streams, which is the basis for decision making; and
B) scanning the images for location information.
Claim 7:
7. The method according to claim 5, further comprising:
A) determining based on the image data of the at least two data streams that the patient has undergone a surgery and is underactive (Corresponds to Limitation A); and
B) executing the selected intervention of adapting a therapy associated with the patient (Corresponds to Limitation B).
However, the cited patent application of Sztyler 929 also modifies a therapy of a post-op patient in response to a determination that that patient is underactive.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 8 of the instant application substantially recites the limitations of claim 8 of Sztyler 929. Both claims recite substantially similar limitations regarding the modification of sports equipment in response to a determination that a post-op patient is watching TV.
Application Claim 8
U.S. Patent Application 19/075929 Claim 8
8. The method according to claim 7,
A) wherein it is determined based on the at least two data streams that the patient is watching television and the selected intervention includes increasing a difficulty of sports equipment of the patient.
Claim 5:
5. The method according to claim 1, further comprising:
A) processing image data of the images contained in the at least one of the at least two data streams, which is the basis for decision making; and
B) scanning the images for location information.
Claim 7:
7. The method according to claim 5, further comprising:
A) determining based on the image data of the at least two data streams that the patient has undergone a surgery and is underactive; and
B) executing the selected intervention of adapting a therapy associated with the patient.
Claim 8:
The method according to claim 7,
A) wherein it is determined based on the at least two data streams that the patient is watching television and the selected intervention includes increasing a difficulty of sports equipment of the patient (Corresponds to Limitation A).
However, the cited patent application of Sztyler 929 also modifies a therapy of a post-op patient in response to a determination that that patient is watching TV.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 9 of the instant application substantially recites the limitations of claim 9 of Sztyler 929. Both claims recite substantially similar limitations regarding the learning of a classification model.
Application Claim 9
U.S. Patent Application 19/075929 Claim 9
9. The method according to claim 1,
A) wherein the graph classification model is learned based on training data extracted from historical data streams and previous intervention selection decisions.
9. The method according to claim 1,
A) wherein the graph classification model is learned based on training data extracted from historical data streams and previous intervention selection decisions (Corresponds to Limitation A).
However, the cited patent application of Sztyler 929 also learns a classification model.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 12 of the instant application substantially recites the limitations of claim 11 of Sztyler 929. Both claims recite substantially similar limitations regarding the determination of patient intervention.
Application Claim 12
U.S. Patent Application 19/075883 Claim 11
12. A computer system for providing event-specific intervention recommendations, the system comprising:
A) one or more processors configured to execute the following steps: acquiring at least two data streams of a patient by using one or more sensors;
B) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
C) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient; and
D) outputting an information of the selected intervention to a user.
11. A computer system for providing event-specific intervention recommendations, the system comprising:
A) one or more processors configured to execute the following steps: acquiring at least two data streams of a patient by using one or more sensors (Corresponds to Limitation A);
B) wherein at least one the data streams includes images;
C) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient (Corresponds to Limitation B);
D) selecting at least one intervention based on the generated entity-feature-graph and a trained graph classification model (Corresponds to Limitation C); and
E) outputting an information of the selected intervention to a user (Corresponds to Limitation D).
However, the cited patent application of Sztyler 929 also determines patient intervention.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 13 of the instant application substantially recites the limitations of claim 12 of Sztyler 929. Both claims recite substantially similar limitations regarding the training of a classification model.
Application Claim 13
U.S. Patent Application 19/075929 Claim 12
13. The system according to claim 12,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph;
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams.
12. The system according to claim 11,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph (Corresponds to Limitation A);
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams (Corresponds to Limitation B).
However, the cited patent application of Sztyler 929 also trains a classification model.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 14 of the instant application substantially recites the limitations of claim 13 of Sztyler 929. Both claims recite substantially similar limitations regarding the transformation of a feature graph.
Application Claim 14
U.S. Patent Application 19/075929 Claim 13
14. The system according to claim 13,
A) wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same.
13. The system according to claim 12,
A) wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same (Corresponds to Limitation A).
However, the cited patent application of Sztyler 929 also transforms a feature graph.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 15 of the instant application substantially recites the limitations of claim 14 of Sztyler 929. Both claims recite substantially similar limitations regarding the classification of an entity graph.
Application Claim 15
U.S. Patent Application 19/075929 Claim 14
15. The system according to claim 14,
A) wherein the graph classification is performed based on using a graph neural network that is given as input the entity-feature-graph together with a probability information that indicates for each pair of entities of the set of entities a likelihood that both entities of a respective pair are the same.
14. The system according to claim 13, wherein the graph classification is performed based on using a graph neural network that is given as input the entity-feature-graph together with a probability information that indicates for each pair of entities of the set of entities a likelihood that both entities of a respective pair are the same (Corresponds to Limitation A).
However, the cited patent application of Sztyler 929 also classifies an entity graph.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 16 of the instant application substantially recites the limitations of claim 18 of Sztyler 929. Both claims recite substantially similar limitations regarding the defining of streams and sensors.
Application Claim 16
U.S. Patent Application 19/075929 Claim 18
16. The system according to claim 12,
A) wherein the at least two data streams include at least one data stream including images and at least one data stream including text; and
B) wherein the one or more sensors include at least one of a camera, a sound recorder, a temperature sensor, a sound-level sensor, a door sensor, and interaction sensors.
18. The system according to claim 11,
A) wherein the at least two data streams include at least one data stream of text and/or at least one data stream of speech that is converted to text; and
B) wherein the one or more sensors include at least one of a camera, sound recorder, presence sensor, a temperature sensor, a sound-level sensor, and a door sensor (Corresponds to Limitation B).
However, the cited patent application of Sztyler 929 also defines streams and sensors.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 18 of the instant application substantially recites the limitations of claim 17 of Sztyler 929. Both claims recite substantially similar limitations regarding the learning of a classification model.
Application Claim 18
U.S. Patent Application 19/075929 Claim 17
18. The system according to claim 12,
A) wherein the graph classification model is learned based on training data extracted from historical data streams and previous intervention selection decisions.
17. The system according to claim 11,
A) wherein the graph classification model is learned based on training data extracted from historical data streams and previous intervention selection decisions (Corresponds to Limitation A).
However, the cited patent application of Sztyler 929 also learns a classification model.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 19 of the instant application substantially recites the limitations of claim 19 of Sztyler 929. Both claims recite substantially similar limitations regarding the determination of patient intervention.
Application Claim 19
U.S. Patent Application 19/075929 Claim 19
19. A tangible, non-transitory computer-readable medium having:
A) instructions thereon which, upon being executed by one or more processors, alone or in combination, provide for execution of a method for providing event-specific intervention recommendations, the method comprising: acquiring at least two data streams of a patient by using one or more sensors;
B) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
C) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient; and
D) outputting an information of the selected intervention to a user.
19. A tangible, non-transitory computer-readable medium having:
A) instructions thereon which, upon being executed by one or more processors, alone or in combination, provide for execution of a method for providing event-specific intervention recommendations, the method comprising: acquiring at least two data streams of a patient by using one or more sensors (Corresponds to Limitation A);
B) wherein at least one the data streams includes images;
C) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient (Corresponds to Limitation B);
D) selecting at least one intervention based on the generated entity-feature-graph and a trained graph classification model (Corresponds to Limitation C); and
E) outputting an information of the selected intervention to a user (Corresponds to Limitation D).
However, the cited patent application of Sztyler 929 also determines patient intervention.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 20 of the instant application substantially recites the limitations of claim 20 of Sztyler 929. Both claims recite substantially similar limitations regarding the training of a classification model.
Application Claim 20
U.S. Patent Application 19/075929 Claim 20
20. The tangible, non-transitory computer-readable medium according to claim 19,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph;
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams.
20. The tangible, non-transitory computer-readable medium according to claim 19,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph (Corresponds to Limitation A);
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams (Corresponds to Limitation B).
However, the cited patent application of Sztyler 929 also trains a classification model.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
8. Claims 1-8, 12-16, and 19-20 are provisionally anticipatorily rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8, 11-15, and 19-20 of copending Application No. 19/076030 (reference application) (herein referred to as Sztyler 030).
9. Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 1 of the instant application substantially recites the limitations of claim 1 of Sztyler 030. Both claims recite substantially similar limitations regarding the determination of patient intervention.
Application Claim 1
U.S. Patent Application 19/076030 Claim 1
1. A computer-implemented method for providing event-specific intervention recommendations, the method comprising:
A) acquiring at least two data streams of a patient by using one or more sensors; B) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
C) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient; and
D) outputting an information of the selected intervention to a user.
1. A computer-implemented method for providing event-specific intervention recommendations, the method comprising:
A) acquiring at least two data streams of a patient by using one or more sensors (Corresponds to Limitation A);
B) determining a location of an event based on the acquired at least two data streams of the patient;
C) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient (Corresponds to Limitation B);
D) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the determined location of the event (Corresponds to Limitation C); and
E) outputting an information of the selected intervention to a user (Corresponds to Limitation D).
However, the cited patent application of Sztyler 030 also determines patient intervention.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 2 of the instant application substantially recites the limitations of claim 2 of Sztyler 030. Both claims recite substantially similar limitations regarding the training of a classification model.
Application Claim 2
U.S. Patent Application 19/076030 Claim 2
2. The method according to claim 1,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph;
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams.
2. The method according to claim 1,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graphs (Corresponds to Limitation A);
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams (Corresponds to Limitation B).
However, the cited patent application of Sztyler 030 also trains a classification model.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 3 of the instant application substantially recites the limitations of claim 3 of Sztyler 030. Both claims recite substantially similar limitations regarding the transformation of a feature graph.
Application Claim 3
U.S. Patent Application 19/076030 Claim 3
3. The method according to claim 2,
A) wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same.
3. The method according to claim 2,
A) wherein the entity-feature-graphs are transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same (Corresponds to Limitation A).
However, the cited patent application of Sztyler 030 also transforms a feature graph.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 4 of the instant application substantially recites the limitations of claim 4 of Sztyler 030. Both claims recite substantially similar limitations regarding the classification of an entity graph.
Application Claim 4
U.S. Patent Application 19/076030 Claim 4
4. The method according to claim 3,
A) wherein the graph classification is performed based on using a graph neural network that is given as input the entity-feature-graph together with a probability information that indicates for each pair of entities of the set of entities a likelihood that both entities of a respective pair are the same.
4. The method according to claim 3,
A) wherein the graph classification is performed based on using a graph neural network that is given as input the entity-feature-graphs together with a probability information that indicates for each pair of entities of the set of entities a likelihood that both entities of a respective pair are the same (Corresponds to Limitation A).
However, the cited patent application of Sztyler 030 also classifies an entity graph.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 5 of the instant application substantially recites the limitations of claim 5 of Sztyler 030. Both claims recite substantially similar limitations regarding the defining of streams and sensors.
Application Claim 5
U.S. Patent Application 19/076030 Claim 5
5. The method according to claim 1,
A) wherein the at least two data streams include at least one data stream including images and at least one data stream including text; and
B) wherein the one or more sensors include a camera, a sound recorder, a temperature sensor, a sound-level sensor, a door sensor, and interaction sensors.
5. The method according to claim 1,
A) wherein the at least two data streams include at least one data stream including images and at least one data stream including text (Corresponds to Limitation A); and
B) wherein the one or more sensors include at least one of a camera, sound recorder, presence sensor, a temperature sensor, a sound-level sensor, and a door sensor (Corresponds to Limitation B).
However, the cited patent application of Sztyler 030 also defines streams and sensors.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 6 of the instant application substantially recites the limitations of claim 6 of Sztyler 030. Both claims recite substantially similar limitations regarding the telephoning of a contact in response to an accident detection.
Application Claim 6
U.S. Patent Application 19/076030 Claim 6
6. The method according to claim 1, further comprising:
A) determining based on the at least two data streams that the patient has suffered an accident; and
B) executing the selected intervention of establishing a phone connection of an emergency contact associated with the patient.
Claim 6:
6. The method according to claim 1, further comprising:
A) determining based on the at least two data streams that the patient has suffered an accident (Corresponds to Limitation A); and
B) executing the selected intervention of establishing a phone connection with an emergency contact associated with the patient (Corresponds to Limitation B).
However, the cited patent application of Sztyler 030 also telephones a contact in response to an accident detection.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 7 of the instant application substantially recites the limitations of claim 7 of Sztyler 030. Both claims recite substantially similar limitations regarding the modification of a therapy in response to a determination that a post-op patient is underactive.
Application Claim 7
U.S. Patent Application 19/076030 Claim 7
7. The method according to claim 1, further comprising:
A) determining based on the at least two data streams that the patient has undergone a surgery and is underactive; and
B) executing the selected intervention of adapting a therapy associated with the patient.
7. The method according to claim 1, further comprising:
A) determining based on the at least two data streams that the patient has undergone a surgery and is underactive (Corresponds to Limitation A); and
B) executing the selected intervention of adapting a therapy associated with the patient (Corresponds to Limitation B).
However, the cited patent application of Sztyler 030 also modifies a therapy of a post-op patient in response to a determination that that patient is underactive.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 8 of the instant application substantially recites the limitations of claim 8 of Sztyler 030. Both claims recite substantially similar limitations regarding the modification of sports equipment in response to a determination that a post-op patient is watching TV.
Application Claim 8
U.S. Patent Application 19/076030 Claim 8
8. The method according to claim 7,
A) wherein it is determined based on the at least two data streams that the patient is watching television and the selected intervention includes increasing a difficulty of sports equipment of the patient.
8. The method according to claim 7, A) wherein it is determined based on the at least two data streams that the patient is watching television and the selected intervention includes increasing a difficulty of sports equipment of the patient (Corresponds to Limitation A).
However, the cited patent application of Sztyler 030 also modifies a therapy of a post-op patient in response to a determination that that patient is watching TV.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 12 of the instant application substantially recites the limitations of claim 11 of Sztyler 030. Both claims recite substantially similar limitations regarding the determination of patient intervention.
Application Claim 12
U.S. Patent Application 19/076030 Claim 11
12. A computer system for providing event-specific intervention recommendations, the system comprising:
A) one or more processors configured to execute the following steps: acquiring at least two data streams of a patient by using one or more sensors;
B) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
C) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient; and
D) outputting an information of the selected intervention to a user.
11. A computer system for providing event-specific intervention recommendations, the system comprising:
A) one or more processors configured to execute the following steps: acquiring at least two data streams of a patient by using one or more sensors (Corresponds to Limitation A);
B) determining a location of an event based on the acquired at least two data streams of the patient;
C) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient (Corresponds to Limitation B);
D) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the determined location of the event (Corresponds to Limitation C); and
E) outputting an information of the selected intervention to a user (Corresponds to Limitation D).
However, the cited patent application of Sztyler 030 also determines patient intervention.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 13 of the instant application substantially recites the limitations of claim 12 of Sztyler 030. Both claims recite substantially similar limitations regarding the training of a classification model.
Application Claim 13
U.S. Patent Application 19/076030 Claim 12
13. The system according to claim 12,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph;
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams.
12. The system according to claim 11,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graphs (Corresponds to Limitation A);
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams (Corresponds to Limitation B).
However, the cited patent application of Sztyler 030 also trains a classification model.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 14 of the instant application substantially recites the limitations of claim 13 of Sztyler 030. Both claims recite substantially similar limitations regarding the transformation of a feature graph.
Application Claim 14
U.S. Patent Application 19/076030 Claim 13
14. The system according to claim 13,
A) wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same.
13. The system according to claim 12,
A) wherein the entity-feature-graphs are transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same (Corresponds to Limitation A).
However, the cited patent application of Sztyler 030 also transforms a feature graph.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 15 of the instant application substantially recites the limitations of claim 14 of Sztyler 030. Both claims recite substantially similar limitations regarding the classification of an entity graph.
Application Claim 15
U.S. Patent Application 19/076030 Claim 14
15. The system according to claim 14,
A) wherein the graph classification is performed based on using a graph neural network that is given as input the entity-feature-graph together with a probability information that indicates for each pair of entities of the set of entities a likelihood that both entities of a respective pair are the same.
14. The system according to claim 13,
A) wherein the graph classification is performed based on using a graph neural network that is given as input the entity-feature-graphs together with a probability information that indicates for each pair of entities of the set of entities a likelihood that both entities of a respective pair are the same (Corresponds to Limitation A).
However, the cited patent application of Sztyler 030 also classifies an entity graph.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 16 of the instant application substantially recites the limitations of claim 15 of Sztyler 030. Both claims recite substantially similar limitations regarding the defining of streams and sensors.
Application Claim 16
U.S. Patent Application 19/076030 Claim 15
16. The system according to claim 12,
A) wherein the at least two data streams include at least one data stream including images and at least one data stream including text; and
B) wherein the one or more sensors include at least one of a camera, a sound recorder, a temperature sensor, a sound-level sensor, a door sensor, and interaction sensors.
15. The system according to claim 11,
A) wherein the at least two data streams include at least one data stream including images and at least one data stream including text (Corresponds to Limitation A); and
B) wherein the one or more sensors include at least one of a camera, sound recorder, presence sensor, a temperature sensor, a sound-level sensor, and a door sensor (Corresponds to Limitation B).
However, the cited patent application of Sztyler 030 also defines streams and sensors.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 19 of the instant application substantially recites the limitations of claim 19 of Sztyler 030. Both claims recite substantially similar limitations regarding the determination of patient intervention.
Application Claim 19
U.S. Patent Application 19/076030 Claim 19
19. A tangible, non-transitory computer-readable medium having:
A) instructions thereon which, upon being executed by one or more processors, alone or in combination, provide for execution of a method for providing event-specific intervention recommendations, the method comprising: acquiring at least two data streams of a patient by using one or more sensors;
B) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
C) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient; and
D) outputting an information of the selected intervention to a user.
19. A tangible, non-transitory computer-readable medium having:
A) instructions thereon which, upon being executed by one or more processors, alone or in combination, provide for execution of a method for providing event-specific intervention recommendations, the method comprising: acquiring at least two data streams of a patient by using one or more sensors (Corresponds to Limitation A);
B) determining a location of an event based on the acquired at least two data streams of the patient;
C) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient (Corresponds to Limitation B);
D) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the determined location of the event (Corresponds to Limitation C); and
E) outputting an information of the selected intervention to a user (Corresponds to Limitation D).
However, the cited patent application of Sztyler 929 also determines patient intervention.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 20 of the instant application substantially recites the limitations of claim 20 of Sztyler 030. Both claims recite substantially similar limitations regarding the training of a classification model.
Application Claim 20
U.S. Patent Application 19/076030 Claim 20
20. The tangible, non-transitory computer-readable medium according to claim 19,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph;
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams.
20. The tangible, non-transitory computer-readable medium according to claim 19,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph (Corresponds to Limitation A);
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams (Corresponds to Limitation B).
However, the cited patent application of Sztyler 030 also trains a classification model.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
10. Claims 1-3, 12-14, and 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 10 of U.S. Patent No. 12,417,232 (herein referred to as Sztyler 232).
11. Although the claims at issue are not identical, they are not patentably distinct from each other because of the following reasons: Claim 1 of the instant application substantially recites the limitations of claim 10 of Sztyler 232. Both claims recite substantially similar limitations regarding the determination of patient intervention.
Application Claim 1
U.S. Patent 12,417,232 Claim 10
1. A computer-implemented method for providing event-specific intervention recommendations, the method comprising:
A) acquiring at least two data streams of a patient by using one or more sensors; B) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
C) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient; and
D) outputting an information of the selected intervention to a user.
Claim 1:
1. A method for providing event-specific intervention recommendations, the method comprising:
A) identifying, based on an event trigger, one or more sensors;
B) determining, by processing data streams provided by the identified sensors, at least one of the data streams including video data, a set of event related entities and constructing, for each entity of the determined set of entities, an associated feature vector of features of the each entity (Corresponds to Limitation A);
C) generating, based on the determined entities and their respective feature vector, an entity-feature-graph (Corresponds to Limitation B);
D) computing, by performing graph classification of a predefined set of allowed interventions using a trained graph classification model that takes as input the entity-feature-graph, a ranked list of interventions, wherein the trained graph classification model was trained by a learning process that considers confidence values assigned to each of the entities of the determined set of entities within the entity-feature-graph, the confidence values indicating confidence levels with which respective ones of the entities have been extracted from available data streams; and
E) wherein the entity-feature-graph has been transformed into an embedding space stored in a vector database by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same; and
F) selecting, according to predefined rules, one or more interventions from the ranked list of interventions (Corresponds to Limitation C); and
G) providing the selected interventions as recommended interventions for execution (Corresponds to Limitation D).
Claim 10:
The method according to claim 1,
A) wherein the selected interventions are for a health care system to support decision making for a medical patient (Corresponds to Limitations A-C).
However, the cited patent of Sztyler 232 also determines patient intervention.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 2 of the instant application substantially recites the limitations of claim 10 of Sztyler 232. Both claims recite substantially similar limitations regarding the training of a classification model.
Application Claim 2
U.S. Patent 12,417,232 Claim 10
2. The method according to claim 1,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph;
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams.
Claim 1:
1. A method for providing event-specific intervention recommendations, the method comprising:
A) identifying, based on an event trigger, one or more sensors;
B) determining, by processing data streams provided by the identified sensors, at least one of the data streams including video data, a set of event related entities and constructing, for each entity of the determined set of entities, an associated feature vector of features of the each entity;
C) generating, based on the determined entities and their respective feature vector, an entity-feature-graph;
D) computing, by performing graph classification of a predefined set of allowed interventions using a trained graph classification model that takes as input the entity-feature-graph, a ranked list of interventions, wherein the trained graph classification model was trained by a learning process that considers confidence values assigned to each of the entities of the determined set of entities within the entity-feature-graph, the confidence values indicating confidence levels with which respective ones of the entities have been extracted from available data streams (Corresponds to Limitations A & B); and
E) wherein the entity-feature-graph has been transformed into an embedding space stored in a vector database by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same; and
F) selecting, according to predefined rules, one or more interventions from the ranked list of interventions; and
G) providing the selected interventions as recommended interventions for execution.
Claim 10:
The method according to claim 1,
A) wherein the selected interventions are for a health care system to support decision making for a medical patient.
However, the cited patent of Sztyler 232 also trains a classification model.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 3 of the instant application substantially recites the limitations of claim 10 of Sztyler 232. Both claims recite substantially similar limitations regarding the transformation of a feature graph.
Application Claim 3
U.S. Patent 12,417,232 Claim 10
3. The method according to claim 2,
A) wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same.
Claim 1:
1. A method for providing event-specific intervention recommendations, the method comprising:
A) identifying, based on an event trigger, one or more sensors;
B) determining, by processing data streams provided by the identified sensors, at least one of the data streams including video data, a set of event related entities and constructing, for each entity of the determined set of entities, an associated feature vector of features of the each entity;
C) generating, based on the determined entities and their respective feature vector, an entity-feature-graph;
D) computing, by performing graph classification of a predefined set of allowed interventions using a trained graph classification model that takes as input the entity-feature-graph, a ranked list of interventions, wherein the trained graph classification model was trained by a learning process that considers confidence values assigned to each of the entities of the determined set of entities within the entity-feature-graph, the confidence values indicating confidence levels with which respective ones of the entities have been extracted from available data streams; and
E) wherein the entity-feature-graph has been transformed into an embedding space stored in a vector database by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same (Corresponds to Limitation A); and
F) selecting, according to predefined rules, one or more interventions from the ranked list of interventions; and
G) providing the selected interventions as recommended interventions for execution.
Claim 10:
The method according to claim 1,
A) wherein the selected interventions are for a health care system to support decision making for a medical patient.
However, the cited patent of Sztyler 232 also transforms a feature graph.
Although the claims at issue are not identical, they are not patentably distinct from each other because of the following reasons: Claim 12 of the instant application substantially recites the limitations of claim 10 of Sztyler 232. Both claims recite substantially similar limitations regarding the determination of patient intervention.
Application Claim 12
U.S. Patent 12,417,232 Claim 10
12. A computer system for providing event-specific intervention recommendations, the system comprising:
A) one or more processors configured to execute the following steps: acquiring at least two data streams of a patient by using one or more sensors;
B) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
C) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient; and
D) outputting an information of the selected intervention to a user.
Claim 1:
1. A method for providing event-specific intervention recommendations, the method comprising:
A) identifying, based on an event trigger, one or more sensors;
B) determining, by processing data streams provided by the identified sensors, at least one of the data streams including video data, a set of event related entities and constructing, for each entity of the determined set of entities, an associated feature vector of features of the each entity (Corresponds to Limitation A);
C) generating, based on the determined entities and their respective feature vector, an entity-feature-graph (Corresponds to Limitation B);
D) computing, by performing graph classification of a predefined set of allowed interventions using a trained graph classification model that takes as input the entity-feature-graph, a ranked list of interventions, wherein the trained graph classification model was trained by a learning process that considers confidence values assigned to each of the entities of the determined set of entities within the entity-feature-graph, the confidence values indicating confidence levels with which respective ones of the entities have been extracted from available data streams; and
E) wherein the entity-feature-graph has been transformed into an embedding space stored in a vector database by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same; and
F) selecting, according to predefined rules, one or more interventions from the ranked list of interventions (Corresponds to Limitation C); and
G) providing the selected interventions as recommended interventions for execution (Corresponds to Limitation D).
Claim 10:
The method according to claim 1,
A) wherein the selected interventions are for a health care system to support decision making for a medical patient (Corresponds to Limitations A-C).
However, the cited patent of Sztyler 232 also determines patient intervention. Moreover, it would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to claim a different statutory class.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 13 of the instant application substantially recites the limitations of claim 10 of Sztyler 232. Both claims recite substantially similar limitations regarding the training of a classification model.
Application Claim 13
U.S. Patent 12,417,232 Claim 10
13. The system according to claim 12,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph;
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams.
Claim 1:
1. A method for providing event-specific intervention recommendations, the method comprising:
A) identifying, based on an event trigger, one or more sensors;
B) determining, by processing data streams provided by the identified sensors, at least one of the data streams including video data, a set of event related entities and constructing, for each entity of the determined set of entities, an associated feature vector of features of the each entity;
C) generating, based on the determined entities and their respective feature vector, an entity-feature-graph;
D) computing, by performing graph classification of a predefined set of allowed interventions using a trained graph classification model that takes as input the entity-feature-graph, a ranked list of interventions, wherein the trained graph classification model was trained by a learning process that considers confidence values assigned to each of the entities of the determined set of entities within the entity-feature-graph, the confidence values indicating confidence levels with which respective ones of the entities have been extracted from available data streams (Corresponds to Limitations A & B); and
E) wherein the entity-feature-graph has been transformed into an embedding space stored in a vector database by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same; and
F) selecting, according to predefined rules, one or more interventions from the ranked list of interventions; and
G) providing the selected interventions as recommended interventions for execution.
Claim 10:
The method according to claim 1,
A) wherein the selected interventions are for a health care system to support decision making for a medical patient.
However, the cited patent of Sztyler 232 also trains a classification model. Moreover, it would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to claim a different statutory class.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 14 of the instant application substantially recites the limitations of claim 10 of Sztyler 232. Both claims recite substantially similar limitations regarding the transformation of a feature graph.
Application Claim 14
U.S. Patent 12,417,232 Claim 10
14. The system according to claim 13,
A) wherein the entity-feature-graph is transformed into an embedding space by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same.
Claim 1:
1. A method for providing event-specific intervention recommendations, the method comprising:
A) identifying, based on an event trigger, one or more sensors;
B) determining, by processing data streams provided by the identified sensors, at least one of the data streams including video data, a set of event related entities and constructing, for each entity of the determined set of entities, an associated feature vector of features of the each entity;
C) generating, based on the determined entities and their respective feature vector, an entity-feature-graph;
D) computing, by performing graph classification of a predefined set of allowed interventions using a trained graph classification model that takes as input the entity-feature-graph, a ranked list of interventions, wherein the trained graph classification model was trained by a learning process that considers confidence values assigned to each of the entities of the determined set of entities within the entity-feature-graph, the confidence values indicating confidence levels with which respective ones of the entities have been extracted from available data streams; and
E) wherein the entity-feature-graph has been transformed into an embedding space stored in a vector database by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same (Corresponds to Limitation A); and
F) selecting, according to predefined rules, one or more interventions from the ranked list of interventions; and
G) providing the selected interventions as recommended interventions for execution.
Claim 10:
The method according to claim 1,
A) wherein the selected interventions are for a health care system to support decision making for a medical patient.
However, the cited patent of Sztyler 232 also transforms a feature graph. Moreover, it would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to claim a different statutory class.
Although the claims at issue are not identical, they are not patentably distinct from each other because of the following reasons: Claim 19 of the instant application substantially recites the limitations of claim 10 of Sztyler 232. Both claims recite substantially similar limitations regarding the determination of patient intervention.
Application Claim 19
U.S. Patent 12,417,232 Claim 10
19. A tangible, non-transitory computer-readable medium having:
A) instructions thereon which, upon being executed by one or more processors, alone or in combination, provide for execution of a method for providing event-specific intervention recommendations, the method comprising: acquiring at least two data streams of a patient by using one or more sensors;
B) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
C) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient; and
D) outputting an information of the selected intervention to a user.
Claim 1:
1. A method for providing event-specific intervention recommendations, the method comprising:
A) identifying, based on an event trigger, one or more sensors;
B) determining, by processing data streams provided by the identified sensors, at least one of the data streams including video data, a set of event related entities and constructing, for each entity of the determined set of entities, an associated feature vector of features of the each entity (Corresponds to Limitation A);
C) generating, based on the determined entities and their respective feature vector, an entity-feature-graph (Corresponds to Limitation B);
D) computing, by performing graph classification of a predefined set of allowed interventions using a trained graph classification model that takes as input the entity-feature-graph, a ranked list of interventions, wherein the trained graph classification model was trained by a learning process that considers confidence values assigned to each of the entities of the determined set of entities within the entity-feature-graph, the confidence values indicating confidence levels with which respective ones of the entities have been extracted from available data streams; and
E) wherein the entity-feature-graph has been transformed into an embedding space stored in a vector database by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same; and
F) selecting, according to predefined rules, one or more interventions from the ranked list of interventions (Corresponds to Limitation C); and
G) providing the selected interventions as recommended interventions for execution (Corresponds to Limitation D).
Claim 10:
The method according to claim 1,
A) wherein the selected interventions are for a health care system to support decision making for a medical patient (Corresponds to Limitations A-C).
However, the cited patent of Sztyler 232 also determines patient intervention. Moreover, it would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to claim a different statutory class.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of the following reasons: Claim 20 of the instant application substantially recites the limitations of claim 10 of Sztyler 232. Both claims recite substantially similar limitations regarding the training of a classification model.
Application Claim 20
U.S. Patent 12,417,232 Claim 10
20. The tangible, non-transitory computer-readable medium according to claim 19,
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph;
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams.
Claim 1:
1. A method for providing event-specific intervention recommendations, the method comprising:
A) identifying, based on an event trigger, one or more sensors;
B) determining, by processing data streams provided by the identified sensors, at least one of the data streams including video data, a set of event related entities and constructing, for each entity of the determined set of entities, an associated feature vector of features of the each entity;
C) generating, based on the determined entities and their respective feature vector, an entity-feature-graph;
D) computing, by performing graph classification of a predefined set of allowed interventions using a trained graph classification model that takes as input the entity-feature-graph, a ranked list of interventions, wherein the trained graph classification model was trained by a learning process that considers confidence values assigned to each of the entities of the determined set of entities within the entity-feature-graph, the confidence values indicating confidence levels with which respective ones of the entities have been extracted from available data streams (Corresponds to Limitations A & B); and
E) wherein the entity-feature-graph has been transformed into an embedding space stored in a vector database by a transformation process that makes use of distances between each pair of entities in the embedding space to encode corresponding probabilities that respective pairs of the entities are the same; and
F) selecting, according to predefined rules, one or more interventions from the ranked list of interventions; and
G) providing the selected interventions as recommended interventions for execution.
Claim 10:
The method according to claim 1,
A) wherein the selected interventions are for a health care system to support decision making for a medical patient.
However, the cited patent of Sztyler 232 also trains a classification model. Moreover, it would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to claim a different statutory class.
Claim Rejections - 35 USC § 101
12. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
13. Claims (1-10), (11-18), and (19-20) are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the 2019 PEG, when considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A prong 1), and if so, it must additionally be determined whether the claim is integrated into a practical application (step 2A prong 2). If an abstract idea is present in the claim without integration into a practical application, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself (step 2B).
In the instant case, claims (1-10), (11-18), and (19-20) are directed to a computer-method, computer system, and tangible, non-transitory computer-readable medium respectively. Thus, each of the claims falls within one of the four statutory categories. However, the claims also fall within the judicial exception of an abstract idea.
Under Step 2A Prong 1, the test is to identify whether the claims are “directed to” a judicial exception. The examiner notes that the claimed invention is directed to an abstract idea in that the instant application is directed to mental processes, specifically recommending patient intervention.
The examiner further notes that claims (1-10), (11-18), and (19-20) recite a computer-method, computer system, and tangible, non-transitory computer-readable medium for recommending patient intervention which is similar to themes defined above of method of mental processes such as performing the recommendation of information, and is similar to the abstract idea identified in the 2019 PEG in grouping “c” in that the claims recite certain methods of mental processes such as performing the recommendation of patient intervention. The limitations, substantially comprising the body of the claim, recite a process of recommending patient intervention. The examiner notes that the claimed invention recommends patient intervention. Because the limitations above closely follow the steps in recommending patient intervention, and the steps of the claims involve mental processes, the claim recites an abstract idea consistent with the “mental processes” grouping set forth in the 2019 PEG.
Claim 1:
A computer-implemented method for providing event-specific intervention recommendations, the method comprising: acquiring at least two data streams of a patient by using one or more sensors;
generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient; and
outputting an information of the selected intervention to a user.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically recommending patient intervention. Recommending patient intervention has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to recommending patient intervention. Additionally, the generation of a graph can be performed by a human via their mind and/or pen & paper. Furthermore, the selection of an intervention based on a generated graph, model, and information related to a patient can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of recommending patient intervention, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
The mere nominal recitation of generic computing components such as one or more sensors does not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application. Specifically, the acquisition of patient data that includes images is simply a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Furthermore, the output of an intervention is simply a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of recommending patient intervention. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that the claim 1 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims do not recite anything that is found to render the abstract idea as being transformed into a patent eligible invention. The dependent claims are merely reciting further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea. Claims 2-11 are directed to further embellishments of the central theme of the abstract idea in that the claims are directed to further embellishments of the recommending patient intervention of the steps of claim 1 and do not amount to significantly more.
Specifically, claim 2 is directed towards the training of a model which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 3 is directed towards the vectorization of probabilities which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 4 is directed towards the classification of a graph which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 5 is directed towards the acquiring of defined data streams that is simply a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Moreover, the mere nominal recitation of generic computing components such as a camera, sound recorder, temperature sensor, sound-level sensor, door sensor, and interaction sensor does not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
Furthermore, claim 6 is directed towards the determination that a patient has suffered an accident which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more. Furthermore, the execution of a determined intervention of contacting an emergency contact via a telephonic connection is not significantly more than the abstract idea.
Moreover, claim 7 is directed towards the determining that a patient has undergone surgery and is underactive and subsequently adapting a patient therapy which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 8 is directed towards the determination that a patient is watching tv which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more. Additionally, the execution of a determined intervention of increasing equipment difficulty is not significantly more than the abstract idea.
Furthermore, claim 9 is directed towards the updating of a model which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 10 is directed towards the identification of a sensor based off of a trigger which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 11 is directed towards the generation of a graph via vectorized features which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Claim 12:
A computer system for providing event-specific intervention recommendations, the system comprising one or more processors configured to execute the following steps: acquiring at least two data streams of a patient by using one or more sensors;
generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient; and
outputting an information of the selected intervention to a user.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically recommending patient intervention. Recommending patient intervention has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to recommending patient intervention. Additionally, the generation of a graph can be performed by a human via their mind and/or pen & paper. Furthermore, the selection of an intervention based on a generated graph, model, and information related to a patient can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of recommending patient intervention, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
The mere nominal recitation of generic computing components such as one or more processors and one or more sensors do not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application. Specifically, the acquisition of patient data that includes images is simply a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Furthermore, the output of an intervention is simply a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of recommending patient intervention. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that the claim 11 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims do not recite anything that is found to render the abstract idea as being transformed into a patent eligible invention. The dependent claims are merely reciting further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea. Claims 13-18 are directed to further embellishments of the central theme of the abstract idea in that the claims are directed to further embellishments of the recommending patient intervention of the steps of claim 12 and do not amount to significantly more.
Specifically, claim 13 is directed towards the training of a model which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 14 is directed towards the vectorization of probabilities which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 15 is directed towards the classification of a graph which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 16 is directed towards the acquiring of defined data streams that is simply a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Moreover, the mere nominal recitation of generic computing components such as a camera, sound recorder, temperature sensor, sound-level sensor, door sensor, and interaction sensor does not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea
Furthermore, claim 17 is directed towards the determination that a patient has suffered an accident which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more. Furthermore, the execution of a determined intervention of contacting an emergency contact via a telephonic connection is not significantly more than the abstract idea.
Furthermore, claim 18 is directed towards the updating of a model which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Claim 19:
A tangible, non-transitory computer-readable medium having instructions thereon which, upon being executed by one or more processors, alone or in combination, provide for execution of a method for providing event-specific intervention recommendations, the method comprising: acquiring at least two data streams of a patient by using one or more sensors;
generating at least one entity-feature-graph based on the acquired at least two data streams of the patient;
selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient; and
outputting an information of the selected intervention to a user.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically recommending patient intervention. Recommending patient intervention has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to recommending patient intervention. Additionally, the generation of a graph can be performed by a human via their mind and/or pen & paper. Furthermore, the selection of an intervention based on a generated graph, model, and information related to a patient can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of recommending patient intervention, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
The mere nominal recitation of generic computing components such as tangible, non-transitory computer-readable medium, one or more processors, and one or more sensors do not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application. Specifically, the acquisition of patient data that includes images is simply a data gathering step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application. Furthermore, the output of an intervention is simply a data output step that is an insignificant extra-solution activity and does not integrate the abstract idea into a practical application
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of recommending patient intervention. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that the claim 19 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims do not recite anything that is found to render the abstract idea as being transformed into a patent eligible invention. The dependent claims are merely reciting further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea. Claim 20 is directed to further embellishments of the central theme of the abstract idea in that the claims are directed to further embellishments of the recommending patient intervention of the steps of claim 19 and do not amount to significantly more.
Specifically, claim 20 is directed towards the training of a model which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Claim Rejections - 35 USC § 102
14. 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.
15. 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.
16. Claims 1, 5, 9-12, 16, and 18-19are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Salazar et al. (U.S. PGPUB 2018/0218126).
17. Regarding claims 1, 12, and 19, Salazar teaches a computer-method, computer system, and tangible, non-transitory computer-readable medium comprising:
A) acquiring at least two data streams of a patient by using one or more sensors (Paragraph 19);
B) generating at least one entity-feature-graph based on the acquired at least two data streams of the patient (Paragraphs 19, 27, 43, and 45);
C) selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient (Paragraphs 19, 40, and 45); and
D) outputting an information of the selected intervention to a user (Paragraphs 19 and 45).
The examiner notes that Salazar teaches “acquiring at least two data streams of a patient by using one or more sensors” as “The patient information database 205 stores information about patients (i.e., users) of the medical triage assistance system 200. Patient information may include identification information, demographics, conversation records, symptoms, medical history, and health insurance claims data. Identification information may be an identifier within the medical triage assistance system 200 associated with the patient, or an identifier from a more ubiquitous entity, like a driver's license or social security number. Conversation records allow the medical triage assistance system 200 access to conversations between the patient and a healthcare professional or the medical triage assistance system 200. These conversations may take place via chat or text messages, or via audio or video calls. For chat or text messages, the conversation record contains the messages and an indication of who sent the message. For an audio or video call, the conversation record is a transcript and may also include who said what. Screenshots (from a video call) or images submitted by the patient may also be included in conversation records. For example, the patient may submit images of a rash. In some embodiments, a conversation between the patient and the healthcare professionals are routed through the medical triage assistance system 200. In this embodiment, the medical triage assistance system 200 is able to record the conversation while it is taking place” (Paragraph 19). The examiner further notes that multiple “streams” of patient data (including transcripts (i.e. text), audio, video, images, etc)) are acquired via the use of one or more “sensors” (such as a camera for a video call for example). The examiner further notes that Salazar teaches “generating at least one entity-feature-graph based on the acquired at least two data streams of the patient” as “The patient information database 205 stores information about patients (i.e., users) of the medical triage assistance system 200. Patient information may include identification information, demographics, conversation records, symptoms, medical history, and health insurance claims data. Identification information may be an identifier within the medical triage assistance system 200 associated with the patient, or an identifier from a more ubiquitous entity, like a driver's license or social security number. Conversation records allow the medical triage assistance system 200 access to conversations between the patient and a healthcare professional or the medical triage assistance system 200. These conversations may take place via chat or text messages, or via audio or video calls. For chat or text messages, the conversation record contains the messages and an indication of who sent the message. For an audio or video call, the conversation record is a transcript and may also include who said what. Screenshots (from a video call) or images submitted by the patient may also be included in conversation records. For example, the patient may submit images of a rash. In some embodiments, a conversation between the patient and the healthcare professionals are routed through the medical triage assistance system 200. In this embodiment, the medical triage assistance system 200 is able to record the conversation while it is taking place” (Paragraph 19), “The training set database 245 stores one or more patient complaint-symptom datasets that are used to generate the knowledge graph 225. These datasets are further described in conjunction with FIG. 6. In some embodiments, the training set database 245 is combined with the patient information database 205” (Paragraph 27), “If the medical triage assistance system 200 receives a correction to the symptoms from the nurse, it can use that feedback to rebalance the connections of the knowledge graph 225 and re-score the multi-class classifier. A nurse can provide a correction by selecting the correct symptom that should have been identified, such as through a multiple-choice interface. The knowledge graph 225 is recomputed based on the correction and the recomputed knowledge graph 225 replaces the current knowledge graph 225 once a threshold improvement in performance is reached. Previous versions of the knowledge graph 225 may be stored to allow for analysis of historical data and models” (Paragraph 43), and “The medical triage assistance system 200 may also select 340 one or more specific medical protocols to recommend based on the patient's symptoms. Each medical protocol is based on one or more symptoms and is made up of a series of questions that are designed to differentiate between life-threatening conditions associated with that symptom and less urgent conditions. The medical triage assistance system 200 maps specific medical protocols to the various medical concepts of the knowledge graph 225. This mapping can be manually created, or learned (i.e., as part of the knowledge graph 225) based on existing patient cases. The medical triage assistance system 200 selects 340 the medical protocols based on confidence scoring. The medical triage assistance system 200 may present the selected 340 protocol(s) to the nurse as a recommendation and wait for approval or correction before proceeding” (Paragraph 45). The examiner further notes that an output recommendation (i.e. intervention) is based on a generated knowledge graph (i.e. the claimed undefined entity-feature graph in the broadest reasonable interpretation) that is generated “based” on received multiple streams of patient data. The examiner further notes that Salazar teaches “selecting at least one intervention based on the generated entity-feature-graph, a trained graph classification model, and an information related to the patient” as “The patient information database 205 stores information about patients (i.e., users) of the medical triage assistance system 200. Patient information may include identification information, demographics, conversation records, symptoms, medical history, and health insurance claims data. Identification information may be an identifier within the medical triage assistance system 200 associated with the patient, or an identifier from a more ubiquitous entity, like a driver's license or social security number. Conversation records allow the medical triage assistance system 200 access to conversations between the patient and a healthcare professional or the medical triage assistance system 200. These conversations may take place via chat or text messages, or via audio or video calls. For chat or text messages, the conversation record contains the messages and an indication of who sent the message. For an audio or video call, the conversation record is a transcript and may also include who said what. Screenshots (from a video call) or images submitted by the patient may also be included in conversation records. For example, the patient may submit images of a rash. In some embodiments, a conversation between the patient and the healthcare professionals are routed through the medical triage assistance system 200. In this embodiment, the medical triage assistance system 200 is able to record the conversation while it is taking place” (Paragraph 19), “The knowledge graph 225 may also be traversed based on probabilistic modeling and detection of anchors and triplets, or deep Kalman filters, including deep learning and probabilistic modeling” (Paragraph 40), “If the medical triage assistance system 200 receives a correction to the symptoms from the nurse, it can use that feedback to rebalance the connections of the knowledge graph 225 and re-score the multi-class classifier. A nurse can provide a correction by selecting the correct symptom that should have been identified, such as through a multiple-choice interface. The knowledge graph 225 is recomputed based on the correction and the recomputed knowledge graph 225 replaces the current knowledge graph 225 once a threshold improvement in performance is reached. Previous versions of the knowledge graph 225 may be stored to allow for analysis of historical data and models” (Paragraph 43), and “The medical triage assistance system 200 may also select 340 one or more specific medical protocols to recommend based on the patient's symptoms. Each medical protocol is based on one or more symptoms and is made up of a series of questions that are designed to differentiate between life-threatening conditions associated with that symptom and less urgent conditions. The medical triage assistance system 200 maps specific medical protocols to the various medical concepts of the knowledge graph 225. This mapping can be manually created, or learned (i.e., as part of the knowledge graph 225) based on existing patient cases. The medical triage assistance system 200 selects 340 the medical protocols based on confidence scoring. The medical triage assistance system 200 may present the selected 340 protocol(s) to the nurse as a recommendation and wait for approval or correction before proceeding” (Paragraph 45). The examiner further notes that a selected intervention is based off of a generated knowledge graph (i.e. the claimed undefined entity feature graph in the broadest reasonable interpretation) that is traversed/mapped via a learned process (i.e. the use of a “trained graph classification model” (which is undefined in the claims) in the broadest reasonable interpretation). Such a generated knowledge graph is also based off of information related to a patient. The examiner further notes that Salazar teaches “outputting an information of the selected intervention to a user” as “The patient information database 205 stores information about patients (i.e., users) of the medical triage assistance system 200. Patient information may include identification information, demographics, conversation records, symptoms, medical history, and health insurance claims data. Identification information may be an identifier within the medical triage assistance system 200 associated with the patient, or an identifier from a more ubiquitous entity, like a driver's license or social security number. Conversation records allow the medical triage assistance system 200 access to conversations between the patient and a healthcare professional or the medical triage assistance system 200. These conversations may take place via chat or text messages, or via audio or video calls. For chat or text messages, the conversation record contains the messages and an indication of who sent the message. For an audio or video call, the conversation record is a transcript and may also include who said what. Screenshots (from a video call) or images submitted by the patient may also be included in conversation records. For example, the patient may submit images of a rash. In some embodiments, a conversation between the patient and the healthcare professionals are routed through the medical triage assistance system 200. In this embodiment, the medical triage assistance system 200 is able to record the conversation while it is taking place” (Paragraph 19) and “The medical triage assistance system 200 may also select 340 one or more specific medical protocols to recommend based on the patient's symptoms. Each medical protocol is based on one or more symptoms and is made up of a series of questions that are designed to differentiate between life-threatening conditions associated with that symptom and less urgent conditions. The medical triage assistance system 200 maps specific medical protocols to the various medical concepts of the knowledge graph 225. This mapping can be manually created, or learned (i.e., as part of the knowledge graph 225) based on existing patient cases. The medical triage assistance system 200 selects 340 the medical protocols based on confidence scoring. The medical triage assistance system 200 may present the selected 340 protocol(s) to the nurse as a recommendation and wait for approval or correction before proceeding” (Paragraph 45). The examiner further notes that a selected intervention is output.
Regarding claims 5 and 16, Salazar further teaches a computer-implemented method and computer system comprising:
A) wherein the at least two data streams include at least one data stream including images and at least one data stream including text (Paragraph 19); and
B) wherein the one or more sensors include at least one of a camera, sound recorder presence sensor, a temperature sensor, a sound-level sensor, and a door sensor (Paragraph 19).
The examiner notes that Salazar teaches “wherein the at least two data streams include at least one data stream including images and at least one data stream including text” as “The patient information database 205 stores information about patients (i.e., users) of the medical triage assistance system 200. Patient information may include identification information, demographics, conversation records, symptoms, medical history, and health insurance claims data. Identification information may be an identifier within the medical triage assistance system 200 associated with the patient, or an identifier from a more ubiquitous entity, like a driver's license or social security number. Conversation records allow the medical triage assistance system 200 access to conversations between the patient and a healthcare professional or the medical triage assistance system 200. These conversations may take place via chat or text messages, or via audio or video calls. For chat or text messages, the conversation record contains the messages and an indication of who sent the message. For an audio or video call, the conversation record is a transcript and may also include who said what. Screenshots (from a video call) or images submitted by the patient may also be included in conversation records. For example, the patient may submit images of a rash. In some embodiments, a conversation between the patient and the healthcare professionals are routed through the medical triage assistance system 200. In this embodiment, the medical triage assistance system 200 is able to record the conversation while it is taking place” (Paragraph 19). The examiner further notes that multiple “streams” of patient data (including images and transcripts (i.e. text)) are acquired via the use of one or more “sensors” (such as a camera for a video call for example). The examiner further notes that Salazar teaches “wherein the one or more sensors include at least one of a camera, sound recorder presence sensor, a temperature sensor, a sound-level sensor, and a door sensor” as “The patient information database 205 stores information about patients (i.e., users) of the medical triage assistance system 200. Patient information may include identification information, demographics, conversation records, symptoms, medical history, and health insurance claims data. Identification information may be an identifier within the medical triage assistance system 200 associated with the patient, or an identifier from a more ubiquitous entity, like a driver's license or social security number. Conversation records allow the medical triage assistance system 200 access to conversations between the patient and a healthcare professional or the medical triage assistance system 200. These conversations may take place via chat or text messages, or via audio or video calls. For chat or text messages, the conversation record contains the messages and an indication of who sent the message. For an audio or video call, the conversation record is a transcript and may also include who said what. Screenshots (from a video call) or images submitted by the patient may also be included in conversation records. For example, the patient may submit images of a rash. In some embodiments, a conversation between the patient and the healthcare professionals are routed through the medical triage assistance system 200. In this embodiment, the medical triage assistance system 200 is able to record the conversation while it is taking place” (Paragraph 19). The examiner further notes that multiple “streams” of patient data (including images and transcripts (i.e. text)) are acquired via the use of one or more “sensors” (such as a camera for a video call for example).
Regarding claims 9 and 18, Salazar further teaches a computer-implemented method and computer system comprising:
A) wherein the graph classification model is learned based on training data extracted from historical data streams and previous intervention selection decisions (Paragraphs 19, 40, and 45).
The examiner notes that Salazar teaches “wherein the graph classification model is learned based on training data extracted from historical data streams and previous intervention selection decisions” as “The patient information database 205 stores information about patients (i.e., users) of the medical triage assistance system 200. Patient information may include identification information, demographics, conversation records, symptoms, medical history, and health insurance claims data. Identification information may be an identifier within the medical triage assistance system 200 associated with the patient, or an identifier from a more ubiquitous entity, like a driver's license or social security number. Conversation records allow the medical triage assistance system 200 access to conversations between the patient and a healthcare professional or the medical triage assistance system 200. These conversations may take place via chat or text messages, or via audio or video calls. For chat or text messages, the conversation record contains the messages and an indication of who sent the message. For an audio or video call, the conversation record is a transcript and may also include who said what. Screenshots (from a video call) or images submitted by the patient may also be included in conversation records. For example, the patient may submit images of a rash. In some embodiments, a conversation between the patient and the healthcare professionals are routed through the medical triage assistance system 200. In this embodiment, the medical triage assistance system 200 is able to record the conversation while it is taking place” (Paragraph 19), “The knowledge graph 225 may also be traversed based on probabilistic modeling and detection of anchors and triplets, or deep Kalman filters, including deep learning and probabilistic modeling” (Paragraph 40), “If the medical triage assistance system 200 receives a correction to the symptoms from the nurse, it can use that feedback to rebalance the connections of the knowledge graph 225 and re-score the multi-class classifier. A nurse can provide a correction by selecting the correct symptom that should have been identified, such as through a multiple-choice interface. The knowledge graph 225 is recomputed based on the correction and the recomputed knowledge graph 225 replaces the current knowledge graph 225 once a threshold improvement in performance is reached. Previous versions of the knowledge graph 225 may be stored to allow for analysis of historical data and models” (Paragraph 43), “The medical triage assistance system 200 may also select 340 one or more specific medical protocols to recommend based on the patient's symptoms. Each medical protocol is based on one or more symptoms and is made up of a series of questions that are designed to differentiate between life-threatening conditions associated with that symptom and less urgent conditions. The medical triage assistance system 200 maps specific medical protocols to the various medical concepts of the knowledge graph 225. This mapping can be manually created, or learned (i.e., as part of the knowledge graph 225) based on existing patient cases. The medical triage assistance system 200 selects 340 the medical protocols based on confidence scoring. The medical triage assistance system 200 may present the selected 340 protocol(s) to the nurse as a recommendation and wait for approval or correction before proceeding” (Paragraph 45), and “FIG. 6 illustrates a training phase 600 of the knowledge graph 225, according to one embodiment. The knowledge graph 225 is generated using a patient complaint-symptom dataset comprised of patient case summaries 640. Patients whose patient case summaries 640 are included in the dataset are those who had both a conversation 610 (e.g., chat-based) with a healthcare professional system 130 and an in-person visit with a medical provider. These patients are chosen because the medical provider is able to verify the patient's symptoms and provide treatment during the in-person visit. Each patient case summary 640 in the patient complaint-symptom dataset includes a record of the patient's conversation 610 with the healthcare professional system 130, one or more triage symptoms 620, and one or more observed symptoms 630 from the in-person visit. The triage symptoms 620 and the observed symptoms 630 are both described in healthcare professional-defined medical language. In some embodiments, this medical language is standardized for better consistency across healthcare professionals. The triage symptoms 620 are determined by the healthcare professional (typically a nurse) operating the healthcare professional system 130 based on their conversation with the patient. The observed symptoms 630 are determined based on the observations of a medical provider who saw the patient during the in-person visit. The observed symptoms 630 are considered to be more accurate than the triage symptoms 620 because they are based on the medical provider's direct observation of the patient's symptoms, rather than the patient's description of them via a remote conversation 610” (Paragraphs 49-50). The examiner further notes that a selected intervention is based off of a generated knowledge graph (i.e. the claimed undefined entity feature graph in the broadest reasonable interpretation) that is traversed/mapped via a learned process (i.e. the use of a “trained graph classification model” (which is undefined in the claims) in the broadest reasonable interpretation). Such a learned process is learned based on historical patient streams and interventions from medical professionals.
Regarding claim 10, Salazar further teaches a computer-implemented method comprising:
A) wherein the one or more sensors are identified based on an event trigger (Paragraph 19).
The examiner notes that Salazar teaches “wherein the one or more sensors are identified based on an event trigger” as “The patient information database 205 stores information about patients (i.e., users) of the medical triage assistance system 200. Patient information may include identification information, demographics, conversation records, symptoms, medical history, and health insurance claims data. Identification information may be an identifier within the medical triage assistance system 200 associated with the patient, or an identifier from a more ubiquitous entity, like a driver's license or social security number. Conversation records allow the medical triage assistance system 200 access to conversations between the patient and a healthcare professional or the medical triage assistance system 200. These conversations may take place via chat or text messages, or via audio or video calls. For chat or text messages, the conversation record contains the messages and an indication of who sent the message. For an audio or video call, the conversation record is a transcript and may also include who said what. Screenshots (from a video call) or images submitted by the patient may also be included in conversation records. For example, the patient may submit images of a rash. In some embodiments, a conversation between the patient and the healthcare professionals are routed through the medical triage assistance system 200. In this embodiment, the medical triage assistance system 200 is able to record the conversation while it is taking place. In other embodiments, the medical triage assistance system 200 may receive conversation records after the fact” (Paragraph 19). The examiner further notes that an audio/video call teaches the claimed event trigger (See also Paragraph 32 of the instant specification giving an example of an event trigger being a telephonic call). Capturing screenshots from a video call entails identifying the sensor camera in the first place.
Regarding claim 11, Salazar further teaches a computer-implemented method comprising:
A) wherein generating the entity-feature-graph based on the acquired at least two data streams comprises: determining, by processing the at least two data streams, a set of event related entities (Paragraphs 19 and 21-23); and
B) constructing, for each entity of the determined set of entities, an associated feature vector of features of the each entity (Paragraphs 19 and 21-23); and
C) generating, based on the determined entities and their respective feature vector, the entity-feature-graph (Paragraphs 19, 21-23, 43, and 52).
The examiner notes that Salazar teaches “wherein generating the entity-feature-graph based on the acquired at least two data streams comprises: determining, by processing the at least two data streams, a set of event related entities” as “The patient information database 205 stores information about patients (i.e., users) of the medical triage assistance system 200. Patient information may include identification information, demographics, conversation records, symptoms, medical history, and health insurance claims data. Identification information may be an identifier within the medical triage assistance system 200 associated with the patient, or an identifier from a more ubiquitous entity, like a driver's license or social security number. Conversation records allow the medical triage assistance system 200 access to conversations between the patient and a healthcare professional or the medical triage assistance system 200. These conversations may take place via chat or text messages, or via audio or video calls. For chat or text messages, the conversation record contains the messages and an indication of who sent the message. For an audio or video call, the conversation record is a transcript and may also include who said what. Screenshots (from a video call) or images submitted by the patient may also be included in conversation records. For example, the patient may submit images of a rash. In some embodiments, a conversation between the patient and the healthcare professionals are routed through the medical triage assistance system 200. In this embodiment, the medical triage assistance system 200 is able to record the conversation while it is taking place. In other embodiments, the medical triage assistance system 200 may receive conversation records after the fact” (Paragraph 19), “The call-response structuring module 210 organizes unstructured conversations (such as conversation records) into call-response units. Call-response units pair questions with corresponding answers to allow the medical triage assistance system 200 to better process the conversation content. For example, a patient's answer alone may omit relevant information that was posed in the preceding question. Call-response units are further described in conjunction with step 320 of FIG. 3 and with FIG. 4” (Paragraph 21), “The medical relevance detection module 215 identifies medically-relevant phrases by tokenizing call-response units (or in some cases, the unstructured conversation), and identifying medically-relevant tokens, such as “pain” and “cough.” The medically-relevant tokens are then mapped back to the call-response units, where they are expanded to medically-relevant phrases” (Paragraph 22), and “The symptom identification module 220 extracts medically-relevant conversation tokens from conversations and uses them to determine medical symptoms by traversing the knowledge graph 225. These tokens are made up of strings (or vectors) explicitly or implicitly derived from the conversation. The tokens may be identified with a type or class of token, such as patient complaints, duration of the complaint, and severity. Patient complaint tokens are words and phrases from mundane language (i.e., from conversations) that directly correspond to symptoms, while duration tokens indicate the duration of a complaint, and severity tokens indicate the severity of a complaint. Tokenization and traversal of the knowledge graph 225 are further discussed in conjunction with FIG. 3” (Paragraph 23). The examiner further notes that ascertaining tokens from multiple streams of a conversation record (which can include images and transcripts) teaches the claimed determination of event-related entities. The examiner further notes that Salazar teaches “constructing, for each entity of the determined set of entities, an associated feature vector of features of the each entity” as “The patient information database 205 stores information about patients (i.e., users) of the medical triage assistance system 200. Patient information may include identification information, demographics, conversation records, symptoms, medical history, and health insurance claims data. Identification information may be an identifier within the medical triage assistance system 200 associated with the patient, or an identifier from a more ubiquitous entity, like a driver's license or social security number. Conversation records allow the medical triage assistance system 200 access to conversations between the patient and a healthcare professional or the medical triage assistance system 200. These conversations may take place via chat or text messages, or via audio or video calls. For chat or text messages, the conversation record contains the messages and an indication of who sent the message. For an audio or video call, the conversation record is a transcript and may also include who said what. Screenshots (from a video call) or images submitted by the patient may also be included in conversation records. For example, the patient may submit images of a rash. In some embodiments, a conversation between the patient and the healthcare professionals are routed through the medical triage assistance system 200. In this embodiment, the medical triage assistance system 200 is able to record the conversation while it is taking place. In other embodiments, the medical triage assistance system 200 may receive conversation records after the fact” (Paragraph 19), “The call-response structuring module 210 organizes unstructured conversations (such as conversation records) into call-response units. Call-response units pair questions with corresponding answers to allow the medical triage assistance system 200 to better process the conversation content. For example, a patient's answer alone may omit relevant information that was posed in the preceding question. Call-response units are further described in conjunction with step 320 of FIG. 3 and with FIG. 4” (Paragraph 21), “The medical relevance detection module 215 identifies medically-relevant phrases by tokenizing call-response units (or in some cases, the unstructured conversation), and identifying medically-relevant tokens, such as “pain” and “cough.” The medically-relevant tokens are then mapped back to the call-response units, where they are expanded to medically-relevant phrases” (Paragraph 22), and “The symptom identification module 220 extracts medically-relevant conversation tokens from conversations and uses them to determine medical symptoms by traversing the knowledge graph 225. These tokens are made up of strings (or vectors) explicitly or implicitly derived from the conversation. The tokens may be identified with a type or class of token, such as patient complaints, duration of the complaint, and severity. Patient complaint tokens are words and phrases from mundane language (i.e., from conversations) that directly correspond to symptoms, while duration tokens indicate the duration of a complaint, and severity tokens indicate the severity of a complaint. Tokenization and traversal of the knowledge graph 225 are further discussed in conjunction with FIG. 3” (Paragraph 23). The examiner further notes that tokens from multiple streams of a conversation record (which can include images and transcripts) are vectors. The examiner further notes that Salazar teaches “generating, based on the determined entities and their respective feature vector, the entity-feature-graph” as “The patient information database 205 stores information about patients (i.e., users) of the medical triage assistance system 200. Patient information may include identification information, demographics, conversation records, symptoms, medical history, and health insurance claims data. Identification information may be an identifier within the medical triage assistance system 200 associated with the patient, or an identifier from a more ubiquitous entity, like a driver's license or social security number. Conversation records allow the medical triage assistance system 200 access to conversations between the patient and a healthcare professional or the medical triage assistance system 200. These conversations may take place via chat or text messages, or via audio or video calls. For chat or text messages, the conversation record contains the messages and an indication of who sent the message. For an audio or video call, the conversation record is a transcript and may also include who said what. Screenshots (from a video call) or images submitted by the patient may also be included in conversation records. For example, the patient may submit images of a rash. In some embodiments, a conversation between the patient and the healthcare professionals are routed through the medical triage assistance system 200. In this embodiment, the medical triage assistance system 200 is able to record the conversation while it is taking place. In other embodiments, the medical triage assistance system 200 may receive conversation records after the fact” (Paragraph 19), “The call-response structuring module 210 organizes unstructured conversations (such as conversation records) into call-response units. Call-response units pair questions with corresponding answers to allow the medical triage assistance system 200 to better process the conversation content. For example, a patient's answer alone may omit relevant information that was posed in the preceding question. Call-response units are further described in conjunction with step 320 of FIG. 3 and with FIG. 4” (Paragraph 21), “The medical relevance detection module 215 identifies medically-relevant phrases by tokenizing call-response units (or in some cases, the unstructured conversation), and identifying medically-relevant tokens, such as “pain” and “cough.” The medically-relevant tokens are then mapped back to the call-response units, where they are expanded to medically-relevant phrases” (Paragraph 22), “The symptom identification module 220 extracts medically-relevant conversation tokens from conversations and uses them to determine medical symptoms by traversing the knowledge graph 225. These tokens are made up of strings (or vectors) explicitly or implicitly derived from the conversation. The tokens may be identified with a type or class of token, such as patient complaints, duration of the complaint, and severity. Patient complaint tokens are words and phrases from mundane language (i.e., from conversations) that directly correspond to symptoms, while duration tokens indicate the duration of a complaint, and severity tokens indicate the severity of a complaint. Tokenization and traversal of the knowledge graph 225 are further discussed in conjunction with FIG. 3” (Paragraph 23), “If the medical triage assistance system 200 receives a correction to the symptoms from the nurse, it can use that feedback to rebalance the connections of the knowledge graph 225 and re-score the multi-class classifier. A nurse can provide a correction by selecting the correct symptom that should have been identified, such as through a multiple-choice interface. The knowledge graph 225 is recomputed based on the correction and the recomputed knowledge graph 225 replaces the current knowledge graph 225 once a threshold improvement in performance is reached. Previous versions of the knowledge graph 225 may be stored to allow for analysis of historical data and models” (Paragraph 43), and “The knowledge graph 225 is generated using machine learning techniques. For each patient case summary 640, the conversation 610 is processed as described above in conjunction with FIG. 3—the conversation 610 is organized into call-response units, and medically-relevant phrases are tokenized into words and phrases. An information metric is applied to the tokens to determine which are the most likely to be medically relevant. For example, term frequency—inverse document frequency (tf-idf) can be applied to determine which tokens are present more frequently in the conversation relative to conversations from other patient case summaries in the dataset. The tokens and the triage symptoms from that conversation 610 are represented as vertices of the knowledge graph, and an edge is created between each token and each of the triage symptoms” (Paragraph 52). The examiner further notes that knowledge graphs can be created based off of tokens (which can be vectors).
Claim Rejections - 35 USC § 103
18. 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.
19. 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.
20. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
21. Claims 2, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Salazar et al. (U.S. PGPUB 2018/0218126) as applied to claims 1, 5, 9-12, 16, and 18-19above, and in view of Badr et al. (U.S. PGPUB 2018/0324117).
22. Regarding claims 2, 12, and 20, Salazar further teaches a computer-method, computer system, and tangible, non-transitory computer-readable medium comprising:
A) wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph (Paragraphs 27, 40, 43, and 45).
The examiner notes that Salazar teaches “wherein the trained graph classification model is trained by a learning process that considers confidence values assigned to each entity of a set of entities within the entity-feature-graph” as “The training set database 245 stores one or more patient complaint-symptom datasets that are used to generate the knowledge graph 225. These datasets are further described in conjunction with FIG. 6. In some embodiments, the training set database 245 is combined with the patient information database 205” (Paragraph 27), “the medical triage assistance system 200 determines 330 the patient's symptoms based on the relevant conversation tokens. The medical triage assistance system 200 traverses the knowledge graph 225 based on the relevant conversation tokens and determines a probability and confidence level that the tokens are associated with specific symptoms. One method for generating the knowledge graph 225 is described in conjunction with FIGS. 7-8. Various complex network metrics, such as adjacency matrices and geodesic paths, may be used to traverse the knowledge graph 225. The knowledge graph 225 may also be traversed based on probabilistic modeling and detection of anchors and triplets, or deep Kalman filters, including deep learning and probabilistic modeling. Multiple symptoms can be presented to the nurse, along with the calculated probabilities and confidence levels” (Paragraph 40), “If the medical triage assistance system 200 receives a correction to the symptoms from the nurse, it can use that feedback to rebalance the connections of the knowledge graph 225 and re-score the multi-class classifier. A nurse can provide a correction by selecting the correct symptom that should have been identified, such as through a multiple-choice interface. The knowledge graph 225 is recomputed based on the correction and the recomputed knowledge graph 225 replaces the current knowledge graph 225 once a threshold improvement in performance is reached. Previous versions of the knowledge graph 225 may be stored to allow for analysis of historical data and models” (Paragraph 43), and “The medical triage assistance system 200 may also select 340 one or more specific medical protocols to recommend based on the patient's symptoms. Each medical protocol is based on one or more symptoms and is made up of a series of questions that are designed to differentiate between life-threatening conditions associated with that symptom and less urgent conditions. The medical triage assistance system 200 maps specific medical protocols to the various medical concepts of the knowledge graph 225. This mapping can be manually created, or learned (i.e., as part of the knowledge graph 225) based on existing patient cases. The medical triage assistance system 200 selects 340 the medical protocols based on confidence scoring. The medical triage assistance system 200 may present the selected 340 protocol(s) to the nurse as a recommendation and wait for approval or correction before proceeding” (Paragraph 45). The examiner further notes that a selected intervention is based off of a generated knowledge graph (i.e. the claimed undefined entity feature graph in the broadest reasonable interpretation) that is traversed/mapped via a learned process (i.e. the use of a “trained graph classification model” (which is undefined in the claims) in the broadest reasonable interpretation). Such a learned process is learned based on probabilities/confidence values of the knowledge graph.
Salazar does not explicitly teach:
B) the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams.
Badr, however, teaches “the confidence values indicating confidence levels with which respective ones of the entities are extracted from the at least two data streams” as “if a digital image received by module 108 includes a particular content item(s) such as the Eiffel tower, and/or a dog standing in front of the Eiffel tower, then module 108 can use logic 204 to extract image features, or pixel data, that correspond to at least one of: a) the Eiffel tower, or b) the dog. Module 108 can then use label generation logic 206 to generate one or more labels (e.g., words, or text phrases) based on extracted features for Eiffel tower and dog” (Paragraph 43) and “labels that are more definitive or descriptive of particular attributes or extracted image features of an item of digital content may be assigned a higher confidence score relative to labels that more generic. For example, referencing the above extracted features for the Eiffel tower and the dog, descriptive labels such as “Eiffel” or “Eiffel tower” may receive higher confidence scores when compared to more generic labels such as “tower” or “Paris.” Likewise, descriptive labels such as “golden retriever” or “cute cocker spaniel” may receive higher confidence scores when compared to more generic labels such as “dog” or “cute dog.” (Paragraph 46)”.
The examiner further notes that although Salazar teaches probabilities/confidence values, there is no explicit teaching that such values are indicative of levels of extracted entities. Nevertheless, Badr teaches the concept of confidence values that are indicative of extracted entities from images. The combination would result in expanding Salazar to also have confidence values that are indicative of levels of extracted entities.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Badr’s would have allowed Salazar’s to provide a method for indicating a relevance level of extracted features, as noted by Badr (Paragraph 45).
23. Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Salazar et al. (U.S. PGPUB 2018/0218126) as applied to claims 1, 5, 9-12, 16, and 18-19above, and in view of Watanabe (U.S. PGPUB 2022/0165092), and further in view of Gabel et al. (U.S. PGPUB 2016/0182707).
24. Regarding claims 6 and 17, Salazar does not explicitly teach a computer-implemented method and computer system comprising:
A) determining based on the at least two data streams that the patient has suffered an accident.
Watanabe, however, teaches “determining based on the at least two data streams that the patient has suffered an accident” as “Accident detection apparatus 20 detects occurrence of an accident such as a fall of a person, based on an image captured by surveillance camera 10 (hereinafter referred to as a “captured image”). The captured image may be either of a still image or a moving image” (Paragraph 22).
The examiner further notes that the secondary reference of Watanabe teaches the concept of detecting accidents from an image. The combination would result in the detection of accidents in the images of Salazar.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Watanabe’s would have allowed Salazar’s and Chen’s to provide a method for reducing detection failures of accidents, as noted by Watanabe (Paragraph 04).
Salazar and Watanabe do not explicitly teach:
B) executing the selected intervention of establishing a phone connection of an emergency contact associated with the patient.
Gabel, however, teaches “executing the selected intervention of establishing a phone connection of an emergency contact associated with the patient” as “The system 104 may also be configured to communicate with one or more devices 120, 122 (e.g., mobile phones or tablet computers) associated with contacts that a given user has indicated should be notified when certain conditions are detected (e.g., the occurrence of an accident or a call to an emergency service provider), as discussed elsewhere herein in great detail” (Paragraph 37), “the application and/or system may be configured to detect an accident event and to notify contacts specified by the user regarding the accident” (Paragraph 48), and “the notifications may be transmitted to the contact(s). The notifications may be provided via a user interface on an instantiation of the application on a device of a notification recipient, via a text message, and/or via an automated voice call to the recipient as similarly described elsewhere herein” (Paragraph 49).
The examiner further notes that the secondary reference of Gabel teaches the concept of automatically notifying (which can include a telephonic call) associated emergency contacts in response to a detected accident. The combination would result in automatically contacting associated emergency contacts after detecting an accident in the image of Watanabe.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Gabel’s would have allowed Salazar’s, and Watanabe’s to provide a method for overwhelming victims of accidents, as noted by Gabel (Paragraph 04).
25. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Salazar et al. (U.S. PGPUB 2018/0218126) as applied to claims 1, 5, 9-12, 16, and 18-19above, and in view of Romeo et al. (U.S. PGPUB 2015/0134088).
26. Regarding claim 7, Salazar does not explicitly teach a computer-implemented method comprising:
A) determining based on the at least two data streams that the patient has undergone a surgery and is underactive; and
B) executing the selected intervention of adapting a therapy associated with the patient.
Romeo, however, teaches “determining based on the at least two data streams that the patient has undergone a surgery and is underactive” as “Once the patient has completed the registration process, the patient meets with the healthcare practitioner to assess the condition and determine a procedure to treat the condition. Upon determining a procedure appropriate for the patient's condition, the physician coordinates the procedure. For example, surgery can be performed and the patient provided with a post-operative brace to be worn during the healing process. The physician can also provide direction to another healthcare practitioner, such as a trainer or physical therapist, to deliver a physical therapy solution for the patient” (Paragraph 24), “The practitioner can then review the data and check on the patient's performance. If the patient is underperforming, the healthcare practitioner can call or schedule an appointment with the patient to review progress, determine if the patient is having difficulties or problems that need to be addressed, or simply to remind the patient to try harder when performing the routines. Where the patient is performing well, the practitioner may reach out and congratulate the patient, and, in some embodiments, the practitioner may have the ability to award the patient with rewards points or additional rewards points for good performance. Importantly, the practitioner uses the information to determine the progress of the patient and to determine whether a change needs to be made in the prescribed physical therapy regimen or if other follow-up treatments are required” (Paragraph 65), and “the doctor or patient can initiate a video or audio phone conference to discuss topics such as the patient's health and condition, the exercise routines, and so on. The doctor or patient can take advantage of a live video conversation to monitor the patient's performance and provide feedback. Likewise, as described in more detail below, photographs and other information can be captured and provided with the data to provide the healthcare practitioner with additional information regarding the patient's performance” (Paragraph 68) and “executing the selected intervention of adapting a therapy associated with the patient” as “Once the patient has completed the registration process, the patient meets with the healthcare practitioner to assess the condition and determine a procedure to treat the condition. Upon determining a procedure appropriate for the patient's condition, the physician coordinates the procedure. For example, surgery can be performed and the patient provided with a post-operative brace to be worn during the healing process. The physician can also provide direction to another healthcare practitioner, such as a trainer or physical therapist, to deliver a physical therapy solution for the patient” (Paragraph 24), “The practitioner can then review the data and check on the patient's performance. If the patient is underperforming, the healthcare practitioner can call or schedule an appointment with the patient to review progress, determine if the patient is having difficulties or problems that need to be addressed, or simply to remind the patient to try harder when performing the routines. Where the patient is performing well, the practitioner may reach out and congratulate the patient, and, in some embodiments, the practitioner may have the ability to award the patient with rewards points or additional rewards points for good performance. Importantly, the practitioner uses the information to determine the progress of the patient and to determine whether a change needs to be made in the prescribed physical therapy regimen or if other follow-up treatments are required” (Paragraph 65), and “the doctor or patient can initiate a video or audio phone conference to discuss topics such as the patient's health and condition, the exercise routines, and so on. The doctor or patient can take advantage of a live video conversation to monitor the patient's performance and provide feedback. Likewise, as described in more detail below, photographs and other information can be captured and provided with the data to provide the healthcare practitioner with additional information regarding the patient's performance” (Paragraph 68).
The examiner further notes that the secondary reference of Romeo teaches the concept of modifying the rehab (i.e. therapy) of a post-op underperforming patient based off of received data. The combination would result in altering interventions based off of underperformance in Salazar.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Romeo’s would have allowed Salazar’s to provide a method for improving the outcomes of surgery, as noted by Romeo (Paragraph 03).
Conclusion
27. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. PGPUB 2019/0251480 issued to Duran et al. on 14 August 2019. The subject matter disclosed therein is pertinent to that of claims 1-20 (e.g., methods to process sensor data).
U.S. PGPUB 2019/0148025 issued to Stone et al. on 16 May 2019. The subject matter disclosed therein is pertinent to that of claims 1-20 (e.g., methods to process sensor data).
Contact Information
28. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mahesh Dwivedi whose telephone number is (571) 272-2731. The examiner can normally be reached on Monday to Friday 8:20 am – 4:40 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached (571) 272-4085. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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Mahesh Dwivedi
Primary Examiner
Art Unit 2168
March 22, 2026
/MAHESH H DWIVEDI/Primary Examiner, Art Unit 2168