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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/26/25 has been entered.
STATUS OF CLAIMS
Claims 1-3 have been amended.
Claims 1-3 and 5 as presented September 26, 2025 are currently pending and considered below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 1, 2025 is being considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
Claim Objections
Claim 3 is objected to because of the following informalities: “wherein information providing system further comprises” should read “wherein the information providing system further comprises”. Appropriate correction is required. For the purposes of compact prosecution, claim 3 will be interpreted as reading “wherein the information providing system further comprises”.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3 and 5 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. This is a new matter rejection.
As to claims 1-3, the claims recite the “meta-ID persistently stored in association with the evaluation target information in the first database”. A review of the specification reveals “a meta-ID estimation processing database (first database)…in which a plurality of items of training data, including evaluation target…and meta-IDs, are stored” ([0038]). The specification further specifies this includes the storing of “the degrees of meta association between evaluation target information and meta-IDs” ([0027]). However, the specification fails to disclose that the meta-ID in association with the evaluation target information are stored “persistently” in the first database. The term “persistently” does not appear anywhere in the specification and constitutes new matter. For the purposes of compact prosecution, the claim will be interpreted in a manner as best understood by the examiner and in light of the specification as the “meta-ID stored in association with the evaluation target information in the first database”.
Claims 5 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), second paragraph due to its dependence on claim 3.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention
Claims 1-3 and 5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claims 1-3, the claims recite the “meta-ID persistently stored in association with the evaluation target information in the first database”. The term “persistently” in claims 1-3 is a relative term which renders the claims indefinite. The term “persistently” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The storing of the meta-ID in association with the evaluation target information in the first database has been rendered indefinite by the use of the term “persistently”. For purposes of examination, the term “persistently” is interpreted as any storing of the meta-ID in association with the evaluation target information in the first database.
Claims 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph due to its dependence on claim 3.
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 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 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 a meta-ID persistently stored in association with the evaluation target information in the first database, the meta-ID 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 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 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 (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 links 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 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. (“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 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 a meta-ID persistently stored in association with the evaluation target information in the first database, the meta-ID 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 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 discloses a system having the limitations of:
wherein 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 (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 links 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 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 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 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 a meta-ID linked with the evaluation target information including an incident meta-ID relating to the incident information, wherein the meta-ID persistently stored in association with the evaluation target information 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 information providing system further comprises 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; (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 links 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]).
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 discloses a system having the limitations of claim 3.
Nuthi further discloses a system having 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]).
Response to Arguments
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 “Nuthi does not fairly teach meta-IDs as persistent meta-IDs in a machine learning data structure, where the meta-IDs are persistently stored in association with evaluation target information including incident information and image data”.
The Examiner respectfully disagrees. Nuthi discloses a “problem solution database” that “stores data corresponding to prior problem and solutions” ([0042]) and that “information is stored for any duration” ([0097]. The distinction made by Applicant between “derived” and “stored” is unpersuasive because Nuthi’s system builds a knowledge base from historical service requests ([0042]). The issue IDs or “error codes” associated with these past requests represent the claimed “meta-IDs” and are stored in the database to enable future predictions.
Applicant argues that “Nuthi does not describe any link between a meta-ID and a content ID, which is in turn stored in correspondence with reference information”.
These arguments are moot given the new grounds of rejection as necessitated by amendment and/or afforded by the present RCE. See details above.
Applicant argues that Fujimoto’s “link” is different from the “link” in the present application. Applicant further argues that Nuthi and Fujimoto fail to teach updating the reference information without updating the relationship between the evaluation target information and meta-ID.
These arguments are moot given the new grounds of rejection as necessitated by amendment and/or afforded by the present RCE. See details above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference Shelton (US 2019/0206556 A1) discloses real-time analysis of comprehensive cost of all instrumentation used in surgery utilizing data fluidity to track instruments through stocking and in-house processes. Reference Bharat (US 2005/0131762 A1) discloses generating user information for use in targeted advertising. Reference Zhou (US 2019/0205606 A1) artificial intelligence based medical image segmentation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Aaisha Abdullah whose telephone number is (571)272-5668. The examiner can normally be reached Monday through Friday 8:00 am - 5:00 pm.
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/A.A./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681