DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
STATUS OF CLAIMS
Claims 1-3 have been amended.
Claims 6-21 are newly presented.
Claims 1-3 and 5-21 as presented March 30, 2026 are currently pending and considered below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
CLAIMS 1-3, 6-12 and 14-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Nuthi (US 2020/0210966 A1) in further view of Dreystadt (US 2006/0156220 A1).
CLAIM 1 –
Nuthi discloses a method having the limitations of:
A learning method comprising: implementing machine learning using a data structure to build a first database for selecting one of a plurality of items of reference information related to a medical device, wherein the data structure is stored in a storage unit provided in a computer, (identifying manual sections, replacement parts, and/or tools (i.e. reference information) for a particular problem of the machine, e.g. imaging machines, that a user is experiencing, e.g. see [0042], [0023], [0002]; a trained neural network based on machine information that provides database information corresponding to a universe of machines (i.e. first database), e.g. see [0054])
wherein the data structure for machine learning comprises a plurality of items of training data that each include evaluation target information including incident information and image data, and at least one meta-ID stored in association with the incident information and image data in the first database, each of the at least one meta-ID in each of the plurality of items of training data including an incident meta-ID relating to the incident information, wherein the image data includes an image that shows the medical device and an identification label for identifying the medical device, (the neural network may be trained using machine information that provides database information corresponding to a universe of machines, where machines are identified based on contextual information including photo of the machine, characteristics of the image, location based on the photo, to determine the make, model, modality and/or other information of the identified machine (i.e. image data) and a determined identifier of the machine (i.e. meta-ID), e.g. see [0054], [0036], [0049]; “The problem solution database 220…stores data corresponding to prior problem and solutions correlations based on survey data from prior completed service requests.”, e.g. see [0042]; “information is stored for any duration (e.g., for extended time periods, permanently…), e.g. see [0097]; the neural network may be trained on previous service request data, including transforming error codes and/or issues (i.e. incident information) to predict solutions (the error code or identified issue are used to predict solutions and are analogous to an incident meta-ID that represents a classified problem state), e.g. see [0049], [0037], [0003])
wherein each of the at least one meta-ID is smaller in volume than the image data, and the content ID is smaller in volume than the reference information, and (identifying the machine by, e.g. a scan or barcode (i.e. meta-ID, is understood to be smaller than), the photo of the machine (i.e. an image), e.g. see [0054], [0076]; the error codes, symptoms/problems and solutions (i.e. content ID, such as a numerical code) of the machine (is understood to be smaller than) the information corresponding to manual/tutorial information, machine information, necessary tools, and replacement parts, e.g. see [0023], [0025])
Nuthi teaches the reference information, evaluation target information, link between the meta-ID and the content ID and a relationship between the evaluation target information and the meta-ID as described above but does not explicitly disclose the below limitations.
However, Dreystadt in the analogous art of managing and updating stored information (e.g. see [0003], [0005]) discloses a system having the limitations of:
wherein each of the at least one meta-ID is linked with a content ID across the first database and a second database, the second database storing the content ID in correspondence with the reference information (managing database derived content, e.g. see [0060]; “The links table 70, contains all links in the system. Each link includes one or more titles…and may have metadata properties associated with it.” (the entry in the links table corresponds to the meta-ID), e.g. see [0081]; “The resources table 82 contains a listing of resources within a repository. Each addressable object (documents, element, etc.) has a resource definition specified in this table. Resources may be addressed by URI” (the entry in the resource table corresponds to the content-ID and the “URI” or “addressable object” corresponds to the reference information); “The resource_pairs table 92 contains a listing of all resource pairs in the system. Resource pairs are components of a link that comprise a starting and ending resource (both of which are relationships to the resource table)…Resource pairs are bound to links” (linking the meta-ID (“Resource pairs are bound to links”) with a content-ID (“relationships to the resource table”)), e.g. see [0082])
wherein when the reference information is updated anew, a link between the at least one meta-ID and the content ID corresponding to the updated reference information is updated or a correspondence between the updated reference information and the content ID is updated, and a relationship between the evaluation target information and the at least one meta-ID is not updated. (“The DCA server is a system designed to manage linking information externally from documents, while still maintaining actual links as if they solely exist within documents. The DCA server provides late-binding linking” (The source document (evaluation target information), and its embedded link ID (meta-ID) remain static (“not updated”) because the linking logic is handled externally.), e.g. see [0075]; “FIG. 10 illustrates a screen shot of a Modify Link Properties dialog 210. This dialog allows the user to modify any of the properties associated with the link. The Source Label field 212 allows the user to assign a label to the source resource. The Target Name field 214 displays the name of the target resource. The Target Locator field 216 displays the locator of the target resource.” (When a user modifies the “target resource” or “Target Locator” (content-ID) associated with the link (meta-ID) (“the reference information is updated anew”), the database link is updated to point to the new reference information (“a link between the meta-ID and the content ID corresponding to the updated reference information is updated”).), e.g. see [0092])
It would have been obvious to one of ordinary skill in the art, at the time of the effective filing date, to expand the system of Nuthi in view of Dreystadt to include the meta-ID is linked with a content ID across the first database and a second database, the second database storing the content ID in correspondence with the reference information, when the reference information is updated anew, a link between the meta-ID and the content ID corresponding to the updated reference information is updated or a correspondence between the updated reference information and the content ID is updated, and a relationship between the evaluation target information and the meta-ID is not updated with the motivation of managing the “linking” and “assembly” of content to avoid “laborious” updates (see Dreystadt [0003], [0005]).
CLAIM 2 –
Nuthi discloses a system having the limitations of:
An information providing system for selecting one of a plurality of items of reference information related to a medical device, the information providing system comprising: a first database that is built on machine learning, using a data structure for machine learning, (identifying manual sections, replacement parts, and/or tools (i.e. reference information) for a particular problem of the machine, e.g. imaging machines, that a user is experiencing, e.g. see [0042], [0023], [0002]; a trained neural network based on machine information that provides database information corresponding to a universe of machines (i.e. first database), e.g. see [0054])
wherein the data structure for machine learning comprises a plurality of items of training data that each include evaluation target information including incident information and image data, and at least one meta-ID stored in association with the incident information and the image data in the first database, each of the at least one meta-ID in each of the plurality of items of training data including an incident meta-ID relating to the incident information, wherein the image data includes an image that shows the medical device and an identification label for identifying the medical device, (the neural network may be trained using machine information that provides database information corresponding to a universe of machines, where machines are identified based on contextual information including photo of the machine, characteristics of the image, location based on the photo, to determine the make, model, modality and/or other information of the identified machine (i.e. image data) and a determined identifier of the machine (i.e. meta-ID), e.g. see [0054], [0036], [0049]; “The problem solution database 220…stores data corresponding to prior problem and solutions correlations based on survey data from prior completed service requests.”, e.g. see [0042]; “information is stored for any duration (e.g., for extended time periods, permanently…), e.g. see [0097]; the neural network may be trained on previous service request data, including transforming error codes and/or issues (i.e. incident information) to predict solutions (the error code or identified issue are used to predict solutions and are analogous to an incident meta-ID that represents a classified problem state), e.g. see [0049], [0037], [0003])
wherein the information providing system further comprises a hardware processor that is configured to select the reference information using the first database, (generating the smart care package (i.e. select the reference information) that provides customized, relevant, actionable information corresponds to relevant manual/tutorial information, machine information, necessary tools, replacement parts, artificial intelligence model information, predicted problems and/or solutions for the servicing, etc., based on database information corresponding to previous servicing of the machine and/or other machines, e.g. see [0023])
wherein the at least one meta-ID is smaller in volume than the image data, and the content ID is smaller in volume than the reference information, and (identifying the machine by, e.g. a scan or barcode (i.e. meta-ID, is understood to be smaller than), the photo of the machine (i.e. an image), e.g. see [0054], [0076]; the error codes, symptoms/problems and solutions (i.e. content ID, such as a numerical code) of the machine (is understood to be smaller than) the information corresponding to manual/tutorial information, machine information, necessary tools, and replacement parts, e.g. see [0023], [0025])
Nuthi teaches the reference information, evaluation target information, the plurality of items of training data, link between the meta-ID and the content ID and a relationship between the evaluation target information and the meta-ID as described above but does not explicitly disclose the below limitations.
However, Dreystadt discloses a system having the limitations of:
wherein each of the at least one meta-ID is linked with a content ID across the first database and a second database, the second database storing the content ID in correspondence with the reference information (managing database derived content, e.g. see [0060]; “The links table 70, contains all links in the system. Each link includes one or more titles…and may have metadata properties associated with it.” (the entry in the links table corresponds to the meta-ID), e.g. see [0081]; “The resources table 82 contains a listing of resources within a repository. Each addressable object (documents, element, etc.) has a resource definition specified in this table. Resources may be addressed by URI” (the entry in the resource table corresponds to the content-ID and the “URI” or “addressable object” corresponds to the reference information); “The resource_pairs table 92 contains a listing of all resource pairs in the system. Resource pairs are components of a link that comprise a starting and ending resource (both of which are relationships to the resource table)…Resource pairs are bound to links” (linking the meta-ID (“Resource pairs are bound to links”) with a content-ID (“relationships to the resource table”)), e.g. see [0082])
wherein when the reference information is updated anew, the hardware processor is configured to update a link between the at least one meta-ID and the content ID corresponding to the updated reference information or a correspondence between the updated reference information and the content ID, without updating a relationship between the evaluation target information and the at least one meta-ID. (“The DCA server is a system designed to manage linking information externally from documents, while still maintaining actual links as if they solely exist within documents. The DCA server provides late-binding linking” (The source document (evaluation target information), and its embedded link ID (meta-ID) remain static (“without updating”) because the linking logic is handled externally.), e.g. see [0075]; “FIG. 10 illustrates a screen shot of a Modify Link Properties dialog 210. This dialog allows the user to modify any of the properties associated with the link. The Source Label field 212 allows the user to assign a label to the source resource. The Target Name field 214 displays the name of the target resource. The Target Locator field 216 displays the locator of the target resource.” (When a user modifies the “target resource” or “Target Locator” (content-ID) associated with the link (meta-ID) (“the reference information is updated anew”), the database link is updated to point to the new reference information (“update a link between the meta-ID and the content ID corresponding to the updated reference information”).), e.g. see [0092])
It would have been obvious to one of ordinary skill in the art, at the time of the effective filing date, to expand the system of Nuthi in view of Dreystadt to include the meta-ID is linked with a content ID across the first database and a second database, the second database storing the content ID in correspondence with the reference information, when the reference information is updated anew and the hardware processor is configured to update a link between the meta-ID and the content ID corresponding to the updated reference information or a correspondence between the updated reference information and the content ID, without updating a relationship between the evaluation target information and the meta-ID with the motivation of managing the “linking” and “assembly” of content to avoid “laborious” updates (see Dreystadt [0003], [0005]).
CLAIM 3 –
Nuthi discloses a system having the limitations of:
An information providing system for selecting one of a plurality of items of reference information related to a medical device works on the task, the information providing system comprising: (identifying manual sections, replacement parts, and/or tools (i.e. reference information) for a particular problem of the machine, e.g. imaging machines, that a user is experiencing, e.g. see [0042], [0023], [0002]; a trained neural network based on machine information that provides database information corresponding to a universe of machines (i.e. first database), e.g. see [0054])
a hardware processor that is configured to acquire acquired data including first image data, in which a specific medical device and a specific identification label for identifying the specific medical device are photographed, and first incident information; and (a device that scans its field of view, e.g., scans, barcodes, radio frequency identifiers (RFIDs), visual profile/characteristics, etc., to provide identification, photograph, video feed, etc. of the machine (i.e. specific medical device), e.g. see [0075]-[0076]; receiving a service request when a problem with the machine occurs (i.e. first incident information), e.g. see [0023])
a first database that is built on machine learning, using a data structure for machine learning, which comprises a plurality of items of training data that each include evaluation target information including incident information and image data, and at least one meta-ID linked with the incident information and image data and including an incident meta-ID relating to the incident information, wherein the at least one meta-ID stored in association with the incident information and image data in the first database, and wherein the image data included in the evaluation target information includes an image showing (i) the medical device and (ii) an identification label for identifying the medical device; (the neural network may be trained using machine information that provides database information corresponding to a universe of machines, where machines are identified based on contextual information including photo of the machine, characteristics of the image, location based on the photo, to determine the make, model, modality and/or other information of the identified machine (i.e. image data) and an identifier of the machine (i.e. meta-ID) is determined, e.g. see [0054], [0036], [0049]; “The problem solution database 220…stores data corresponding to prior problem and solutions correlations based on survey data from prior completed service requests.”, e.g. see [0042]; “information is stored for any duration (e.g., for extended time periods, permanently…), e.g. see [0097]; the neural network may be trained on previous service request data, including transforming error codes and/or issues (i.e. incident information) to predict solutions (the error code or identified issue are used to predict solutions and are analogous to an incident meta-ID that represents a classified problem state), e.g. see [0049], [0037], [0003]; the neural network includes weighted connections between the nodes, e.g. see [0092]-[0094])
wherein the hardware processor is further configured to look up the first database and select a first meta-ID, among the plurality of meta-IDs, based on the acquired data; (identifying the machine identifier (i.e. first meta-ID) using processing techniques based on inputs related to photos and video, e.g. see [0054], [0075])
wherein the hardware processor is further configured to: look up the second database and select a first content ID, among the plurality of content IDs, based on the first meta-ID; and look up the second database and select first reference information, among the plurality of items of reference information, based on the first content ID, and (using the identified machine to identify error codes, symptoms/problems and solutions (i.e. select a first content ID); generating the smart care package which provides customized, relevant, actionable information corresponding to relevant manual/tutorial information, machine information, necessary tools, replacement parts; more detailed and/or relevant information provided based on one or more identified error codes or one or more solutions (i.e. select first reference information, among the plurality of items of reference information), e.g. see [0023], [0025], [0048])
wherein the meta-IDs are smaller in volume than the image data, and the content IDs are smaller in volume than the respective corresponding items of reference information (the machine identifier, e.g. an imaging device identifier (i.e. meta-ID, which is known to be a unique string of letters and numbers is understood to be smaller than), the photo of the machine (i.e. an image), e.g. see [0054], [0036]; the error codes, symptoms/problems and solutions (i.e. content ID, which can be as small as a numerical code) of the machine (is understood to be smaller than) the information corresponding to manual/tutorial information, machine information, necessary tools, and replacement parts, e.g. see [0023], [0025])
wherein the hardware processor is configured to generate a meta-ID list with the plurality of meta-IDs, […] and select, as the first meta-ID, a meta-ID (identifying potential solutions (i.e. generate a meta-ID list), e.g. see [0049])
Nuthi teaches the meta-ID and meta-ID list as described above but does not explicitly disclose the below limitations.
However, Dreystadt discloses a system having the limitations of:
wherein the information providing system further comprises a second database that stores a plurality of content IDs and the plurality of items of reference information each corresponding to a respective one of the content IDs, wherein the plurality of content IDs are linked with the meta-IDs across the first database in which the meta-IDs are stored and the second database in which the content IDs are stored; (managing database derived content, e.g. see [0060]; “The links table 70, contains all links in the system. Each link includes one or more titles…and may have metadata properties associated with it.” (the entry in the links table corresponds to the meta-ID), e.g. see [0081]; “The resources table 82 contains a listing of resources within a repository. Each addressable object (documents, element, etc.) has a resource definition specified in this table. Resources may be addressed by URI” (the entry in the resource table corresponds to the content-ID and the “URI” or “addressable object” corresponds to the reference information); “The resource_pairs table 92 contains a listing of all resource pairs in the system. Resource pairs are components of a link that comprise a starting and ending resource (both of which are relationships to the resource table)…Resource pairs are bound to links” (linking the meta-ID (“Resource pairs are bound to links”) with a content-ID (“relationships to the resource table”)), e.g. see [0082])
generate a reference summary list, select a summary of reference information from the reference summary list, and select, as the first meta-ID, a meta-ID from the selected summary from the reference summary list, (the links table contains all the links in the system; the link_title table contains one or more names for each link (the link name serves as the reference summary), e.g. see [0081]; “The DCAM Targets table 144 is a tree control of the available targets in the DCAM Repository”; “The Link Name field 124 allows the user to assign a human readable name to the link.” (the user selects the link name (reference summary) displayed in the table (reference summary list), e.g. see [0090]; “Resource pairs are bound to links”, e.g. see [0082]; “The Insert button 134 allows the user to insert a link reference”, e.g. see [0089] (By selecting the reference summary (the link name in the table), the processor selects the “first meta-ID” (the underlying link ID database key) associated with that summary.”)
wherein when the reference information is updated anew, the hardware processor is configured to update a link between the meta-ID and the content ID corresponding to the updated reference information or a correspondence between the updated reference information and the content ID, without updating a relationship between the evaluation target information and the meta-ID. (“The DCA server is a system designed to manage linking information externally from documents, while still maintaining actual links as if they solely exist within documents. The DCA server provides late-binding linking” (The source document (evaluation target information), and its embedded link ID (meta-ID) remain static (“without updating”) because the linking logic is handled externally.), e.g. see [0075]; “FIG. 10 illustrates a screen shot of a Modify Link Properties dialog 210. This dialog allows the user to modify any of the properties associated with the link. The Source Label field 212 allows the user to assign a label to the source resource. The Target Name field 214 displays the name of the target resource. The Target Locator field 216 displays the locator of the target resource.” (When a user modifies the “target resource” or “Target Locator” (content-ID) associated with the link (meta-ID) (“the reference information is updated anew”), the database link is updated to point to the new reference information (“update a link between the meta-ID and the content ID corresponding to the updated reference information”).), e.g. see [0092])
It would have been obvious to one of ordinary skill in the art, at the time of the effective filing date, to expand the system of Nuthi in view of Dreystadt to include a second database that stores a plurality of content IDs, and a plurality of items of reference information corresponding to the content IDs, wherein the plurality of content IDs are linked with the meta-IDs across the first database in which the meta-IDs are stored and the second database in which the content IDs are stored, generate a reference summary list, select a summary of reference information from the reference summary list, and select, as the first meta-ID, a meta-ID from the selected summary from the reference summary list, and when the reference information is updated anew and the hardware processor is configured to update a link between the meta-ID and the content ID corresponding to the updated reference information or a correspondence between the updated reference information and the content ID, without updating a relationship between the evaluation target information and the meta-ID with the motivation of managing the “linking” and “assembly” of content to avoid “laborious” updates (see Dreystadt [0003], [0005]).
CLAIMS 6 and 14 –
Nuthi and Dreystadt disclose the learning method according to claim 1.
Nuthi further discloses the limitation of:
wherein each of the least one meta-ID in each of the plurality of items of training data includes an apparatus meta-ID that classifies the medical device shown in the image data, and a task procedure meta-ID that relates to task procedures for the medical device shown in the image data (the database of historical service requests/training data stores identifiers that classify the make, model and modality of the identified machine (i.e. apparatus meta-ID) and further classify the fault or required maintenance solution (i.e. task procedure meta-ID), e.g. see [0054], [0036], [0049])
CLAIMS 7 and 15 –
Nuthi and Dreystadt disclose the learning method according to claim 1.
Nuthi further discloses the limitation of:
wherein the at least one meta-ID in each of the plurality of items of training data comprises a plurality of meta-IDs such that each of the plurality of items of training data includes a plurality of meta-IDs (a single historical service record (i.e. item of training data) is stored in the database with multiple distinct classification codes, such as both a machine model code and a fault classification code (i.e. a plurality of meta-IDs), e.g. see [0054])
CLAIMS 8 and 16 –
Nuthi and Dreystadt disclose the learning method according to claim 7.
Nuthi further discloses the limitation of:
wherein each of the plurality of items of training data includes at least one meta-ID that is also included in at least one other of the plurality of items of training data such that at least one meta-ID is present in two or more of the plurality of items of training data (training the neural network using a database of historical service requests to determine the machine identifier or fault code (the same meta-ID is present in two or more items of training data), e.g. see [0036], [0054])
CLAIMS 9 and 17 –
Nuthi and Dreystadt disclose the learning method according to claim 7.
Dreystadt further discloses the limitation of:
wherein each of the plurality of meta-IDs is linked with a plurality of content IDs across the first database and the second database (“The links table 70, contains all links in the system. Each link includes one or more titles…and may have metadata properties associated with it.” (the entry in the links table corresponds to the meta-ID), e.g. see [0081]; “The resources table 82 contains a listing of resources within a repository. Each addressable object (documents, element, etc.) has a resource definition specified in this table. Resources may be addressed by URI” (the entry in the resource table corresponds to the content-ID and the “URI” or “addressable object” corresponds to the reference information); the distinct tables are linked together via a “resource_pairs table 92” that “contains a listing of all resource pairs in the system…Resource pairs are bound to links” (By binding multiple resource relationships (content IDs from table 82) to a single metadata link (the metadata from table 70) via the pairs table 92, a meta-ID is linked to a plurality of content-IDs across the database architecture.), e.g. see [0082])
It would have been obvious to one of ordinary skill in the art, at the time of the effective filing date, to expand the system of Nuthi in view of Dreystadt to include each of the plurality of meta-IDs is linked with a plurality of content IDs across the first database and the second database with the motivation of managing the “linking” and “assembly” of content to avoid “laborious” updates (see Dreystadt [0003], [0005]).
CLAIMS 10 and 18 –
Nuthi and Dreystadt disclose the learning method according to claim 9.
Nuthi teaches the plurality of meta-IDs in each of a plurality of items of training data.
Dreystadt further discloses the limitation of:
wherein at least one of the plurality of meta-IDs is linked with a common one of a plurality of content IDs across the first database and the second database (the “resource_pairs table 92” acts as a cross-reference junction table that binds entries from the “link table 70” (i.e. the plurality of meta-IDs) to relationships in the “resource table 82” (i.e. the content IDs) (This allows multiple distinct metadata links in table 70 (the meta-IDs) to be bound to the exact same resource definition entry in table 82 (a common content-ID).), e.g. see [0081]-[0082]) )
It would have been obvious to one of ordinary skill in the art, at the time of the effective filing date, to expand the system of Nuthi in view of Dreystadt to include at least one of the plurality of meta-IDs is linked with a common one of a plurality of content IDs across the first database and the second database with the motivation of managing the “linking” and “assembly” of content to avoid “laborious” updates (see Dreystadt [0003], [0005]).
CLAIMS 11 and 19 –
Nuthi and Dreystadt disclose the learning method according to claim 9.
Dreystadt further discloses the limitation of:
wherein the second database stores each of the plurality of content IDs in correspondence with a respective one of the plurality of items of reference information (each entry in the “resources table 82” specifies a resource definition (i.e. the content ID) for an “addressable object (documents, element, etc.)” and these resources are “addressed by URI” (i.e. the reference information), e.g. see [0082])
It would have been obvious to one of ordinary skill in the art, at the time of the effective filing date, to expand the system of Nuthi in view of Dreystadt to include the second database stores each of the plurality of content IDs in correspondence with a respective one of the plurality of items of reference information with the motivation of managing the “linking” and “assembly” of content to avoid “laborious” updates (see Dreystadt [0003], [0005]).
CLAIMS 12 and 20 –
Nuthi and Dreystadt disclose the learning method according to claim 1.
Nuthi further discloses the limitation of:
wherein degrees of meta association between the evaluation target information and the meta-IDs are stored in the first database, the degree of meta association indicating relative strength of a link between the evaluation target information and the at least one meta-ID in each of the items of training data (training a deep learning neural network on a “set of expert classified data” (i.e. the items of training data) to build the initial parameters for the network, e.g. see [0088]; during this process, the backpropagation algorithms are used to “alter internal parameters (e.g., node weights) of the deep learning machine” (i.e. the degree of meta association), e.g. see [0081]; the “neural connections” 530, 550 and 570 bridge the input layers to the output layers, e.g. see [0092]; certain connections “can be given added weight while other example connections…can be given less weight in the neural network 500”, e.g. see [0094])
CLAIM 5 is rejected as under 35 U.S.C. 103(a) as being unpatentable over Nuthi and Dreystadt in further view of Agosta (US 2014/0355879 A1).
CLAIM 5 –
Nuthi and Dreystadt disclose the system having the limitations of claim 3.
Nuthi further discloses the limitation of:
The information providing system according to claim 3:wherein the hardware processor is further configured to acquire first video information (a device that scans its field of view, e.g., scans, barcodes, radio frequency identifiers (RFIDs), visual profile/characteristics, etc., to provide identification, photograph, video feed, etc. of the machine (i.e. first video information), e.g. see [0075]-[0076])
Nuthi and Dreystadt do not explicitly disclose the below limitations.
However, Agosta discloses a system having the limitations of:
wherein the information providing system further comprises a scene model database that stores past first video information, which is acquired in advance, scene information, which includes a scene ID linked with the past first video information, and three or more degrees of scene association between the past first video information and the scene information (trained classifiers which create classification models that associate dimensions and characteristics and corresponding labels that makes up a particular scene within the video sequences (i.e. a scene model database) and training such models to output a set of scene variable nodes associated with the labels, e.g. see [0012], [0086], [0118])
wherein the hardware processor is further configured to: look up the scene model database and acquire a scene ID list, which includes a first degree of scene association between the first video information and the scene information; generate a scene name list corresponding to the scene ID list; acquire the acquired data, which includes, as one pair, the first image data, and a first scene ID corresponding to a scene name selected from the scene name list. (the scene classifiers output a set of scene variable nodes associated with the labels for the video, where the resulting classification for the scene is the scene name selected from a plurality of possible scenes for the classifier for the particular image/frame from the video, e.g. see Fig. 3 – items 306-310, Figs. 4A – 4B items 410-420, [0012], [0086], [0118])
It would have been obvious to one of ordinary skill in the art, at the time of the effective filing date, to expand the system of Nuthi and Dreystadt in view of Agosta, to include an information providing system further comprising a scene model database that stores past first video information, which is acquired in advance, scene information, which includes a scene ID linked with the past first video information, and three or more degrees of scene association between the past first video information and the scene information, with the motivation of improving the speed and/or efficiency in which images can be classified (see Agosta [0052]).
CLAIMS 13 and 21 are rejected as under 35 U.S.C. 103(a) as being unpatentable over Nuthi and Dreystadt in further view of Huang (US 2017/0169015 A1).
CLAIMS 13 and 21 –
Nuthi and Dreystadt disclose the learning method according to claim 12.
Nuthi and Dreystadt do not explicitly disclose the below limitations.
However, Huang in the analogous art discloses a system having the limitations of:
wherein the degrees of meta association are expressed in percentage (the machine learning evaluates the quality of an input and computes “confidence scores on a 1-100 scale” (the machine learning system expresses its relational confidence scores as a percentage (degree of meta association), e.g. see [0046])
It would have been obvious to one of ordinary skill in the art, at the time of the effective filing date, to expand the system of Nuthi and Dreystadt in view of Huang, to include the degrees of meta association are expressed in percentage with the motivation of dictating automated actions (see Huang [0048]).
Response to Arguments
Regarding the rejection under 35 U.S.C. § 112(a) of Claims 1-3 and 5, the Applicant has amended the claims to overcome the bases of rejection.
Regarding the rejection under 35 U.S.C. § 112(b) of Claims 1-3 and 5, the Applicant has amended the claims to overcome the bases of rejection.
Regarding the rejection under 35 U.S.C. § 103 of Claims 1-3 and 5, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive.
Applicant argues that Nuthi fails to disclose “image data” including "an image that shows the medical device and an identification label for identifying the medical device".
The Examiner respectfully disagrees. Under the broadest reasonable interpretation, the term “image data” encompasses a set of data comprising both an image of the device and an identification label that identifies the device (e.g., text metadata). Nuthi teaches a training database that stores a photo of the machine (the image) along with determined make, model and modality of the machine ([0054]).
Applicant argues that Nuthi merely creates an algorithm and does not maintain the actual image data in a database.
The Examiner respectfully disagrees. Nuthi teaches that system data can be stored for any duration including permanently ([0097]). Therefore, the Applicant’s assertion that the images no longer exist once the neural network is trained is inaccurate. In addition, even notwithstanding the explicit disclosure of permanent storage, the claims recite a first database comprising a plurality of items of training data, but they do not require the system to permanently maintain these images. The training database of Nuthi, upon which the neural network is trained, comprises a historical corpus storing previously received photos of the machines and their corresponding identifiers, constitutes the claimed “first database” of training data.
Applicant argues Nuthi does not teach the four-part organized data structure shown in Figure 4.
The Examiner respectfully disagrees. The exact tabular arrangement and “degree of meta association” depicted in the drawings are not explicitly recited in the independent claims. The claims require that the training data associates evaluation target information (incident information and image data) with at least one meta-ID, which includes an incident meta-ID. Nuthi teaches a training database that associates previous service requests including photos of machines (image data), error codes or reported issues (incident information/incident meta-ID) and determined identifiers such as machine identity (meta-ID) ([0036], [0049], [0054]). Because this training database of the neural network associates these historical inputs and outputs to train the model, Nuthi discloses the claimed structural relationship.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.A./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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