DETAILED ACTION
Claim Status
This action is in response to the application filed on February 23, 2026. Claims 1-2, 4-5, and 7-11 are amended and claim 12 has been cancelled. Thus, claims 1-11 and 13-14 are pending for examination in this application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged that application is a National Stage application of PCT/IB2021/059800. Priority to PCT/IB2021/059800 with a priority date of October 24, 2021 is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Receipt is acknowledged that application claims priority to foreign application with application number FR2010970 dated October 26, 2020. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Response to Amendments
Applicant’s remarks and amendments filed February 23, 2026, have been entered.
Applicant’s arguments regarding the 35 U.S.C. 112(b) rejection previously set forth in the previously set forth in the Non-Final Office Action mailed October 21, 2025, are persuasive. Accordingly, the 35 U.S.C. 112(b) rejection is withdrawn in response.
Applicant’s arguments regarding the 35 U.S.C. 101 rejections previously set forth in the Non-Final Office Action mailed October 21, 2025, are persuasive. Accordingly, the 35 U.S.C. 101 rejections are withdrawn in response.
Response to Arguments
Applicant’s arguments filed February 23, 2026, have been fully considered but they are not persuasive.
Argument: On page 13, the applicant alleges, that the combination of elements set forth in claim 1 is not disclosed or suggested by the references relied on by the Examiner.
The examiner respectfully disagrees. The examiner asserts that Karargyris in view of Kajinaga in further view of Yazdani teach the amended claims 1-2, 4-5, and 7-11 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.
Claim(s) 1-5, 9-10, and 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karargyris et al, US 20200321101 in view of Kajinaga et al, US 20170255603 in further view of Yazdani et al, US 20190205445.
Regarding claim 1, Karargyris teaches
A method of annotating training data by a processing unit of a computer for an artificial intelligence module comprising the following steps (see Karargyris, Abstract, “the electronic processor is also configured to generate an annotation for each of the one or more ambiguous reports in the set of reports, and train the artificial intelligence engine using a training set including the annotation,”):
storing, by the processing unit, in a memory of a database, a set of data to be annotated (see Karargyris, Fig. 1 and Paragraph [0023], “a medical image database 110”);
receiving, by the processing unit, through a communication module, data representing at least a first annotation of said first filtered data (see Karargyris, Fig. 2 210, and Paragraph [0043]-[0044], “an annotation (non-ambiguous label) is generated for each ambiguous report identified in the set of reports (at block 210) … After applying the annotations to the ambiguous reports, each report in the dataset now has a non-ambiguous label for a finding of interest. This full set of reports can be referred to as a “training set,” and can be used to train an AI engine, such as the learning engine 145 (at block 212),”);
wherein said data representing said first annotation is stored in said memory of the database not in association with the first filtered data and wherein said first filtered data is not altered in said memory of the database (see Karargyris Paragraph [0032], “n some embodiments, the medical image database 110 stores additional data associated with the medical images 165, such as a classification associated with each of the medical images 165 and/or clinician notes associated with one or more of the medical images 165 as described below in more detail. Accordingly, in some embodiments, the medical image database 110 stores the training information used to train the classification models stored in the classification model database 150. In other embodiments, this information (along with the associated image data) may be stored separate from the medical image database 110. The medical image database 110 may also store acquired or captured medical images that are not part of a training dataset”).
Karargyris does not expressively teach
storing, by the processing unit, in said memory of said database, at least a first description of a first facet defining filtering and selection of data in said set of data, said first description being associated with a first task to be performed by said artificial intelligence, said task comprising automatically predicting an annotation of data in said set of data;
and storing, by the processing unit, said data representing said first annotation in the memory of the database in association with the data coding for said description of said first facet.
However, Kajinaga in a similar invention in the same field of endeavor teaches
storing, by the processing unit, in said memory of said database, at least a first description of a first facet defining filtering and selection of data in said set of data, said first description being associated with a first task to be performed by said artificial intelligence, said task comprising automatically predicting an annotation of data in said set of data (see Kajinaga, Paragraph [0043], “In the example of the apparatus 100 shown in FIG. 1, the data input section 110 obtains the document(s) from an external document storage 101, but the document(s) may be stored and/or created within the apparatus 100 and/or can be obtained from or found on other computer(s) or server(s) through a network such as the Internet, WAN, and/or LAN,” and Paragraph [0053], “The annotator generating section 150 generates an annotator based on the facet tree stored in the facet tree storage 140,” the document(s) may be stored or created within the apparatus 100 and facet tree storage 140 within apparatus 100 therefore is considered to be said database storing a first facet and data to be annotated);
and storing, by the processing unit, said data representing said first annotation in the memory of the database in association with the data coding for said description of said first facet (see Kajinaga, Paragraph [0055], “the outputs may be stored, uploaded to a server, printed, or otherwise made available for viewing or analysis, or may be displayed on a screen in relation to a user query as an intermediate step in a process performed by the apparatus 100. … The various outputs of the apparatus 100 output by the output section 170 may include, for example, … the annotator generated by the annotator generating section 150 based on the facet tree stored in the facet tree storage 140, an annotated document produced by the annotating section 160”).
The combination of Karargyris and Kajinaga are analogous art because they are both in the field of endeavor of object detection. Therefore, it would’ve been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for outputs to be stored and various outputs to include the annotator generated by the annotator generating section; for the facet tree to define a hierarchical parent-child relationship; based on the relationship in the facet tree the annotator generating section generates an annotator; for the annotator to include one or more branches defined by the plurality of subgroups; for the annotator generating section to generate an annotator if the input document belongs to the subgroup; for the document classifying section to assign one more of documents to a plurality of subgroups according to a category or classification value which is considered to be a filtering condition applied to the annotations associated with regions as well as second facet and based on the prevalence of keywords associated is considered to be for which filtering condition is verified; as taught in the method for dynamic facet tree creation of Kajinaga in the system directed to training artificial intelligence engine of Karargyris so that document analysis can be performed more quickly and easily than by using conventional methods (see Kajinaga, Paragraph [0041)).
Karargyris in view of Kajinaga does not expressively teach
selecting, by the processing unit, said data coding for the first facet in said database;
applying, by the processing unit, said first facet, using said data coding for said first facet, to data in said set of data to obtain first filtered data;
However, Yazdani in a similar invention in the same field of endeavor teaches
selecting, by the processing unit, said data coding for the first facet in said database (see Yazdani, Paragraph [0016], “The server is then programmed to convert the search query by identifying a first set of facets from the search query, such as by extracting words or phrases from the search query, comparing the words or phrases with the data in the core databases or dictionaries, or running the words or phrases through a linguistic model”);
applying, by the processing unit, said first facet, using said data coding for said first facet, to data in said set of data to obtain first filtered data (see Yazdani, Paragraph [0016], “In the example, the first set of facets may contain a first facet of “company” with an associated value of “deep”,” the first set of facets contain first facet which is considered to be applying said facet to obtain first filtered data);
The combination of Karargyris, Kajinaga, and Yazdani are analogous art because they are all in the field of endeavor of object detection. Therefore, it would’ve been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify a first set of facets from the search query with data in core databases; for the first set of facets to contain a first facet value which is considered to be applying said facet to obtain first filtered data; as taught in the method of Yazdani in the system directed to training artificial intelligence engine of Karargyris in view of Kajinaga to further reduce the amount of user effort required in such a search while increasing the accuracy of the search result (see Yazdani, Paragraph [0003])).
Regarding claim 2, Karargyris in view of Kajinaga in further view of Yazdani teaches the method of claim 1,
wherein said database stores in said memory data coding for a plurality of descriptions of a plurality of facets defining filtering and selection of data in said set of data and wherein: said data coding for said first description includes a hierarchical link to data stored in said memory of said database coding for a second description of a second facet defining filtering and selection of data in the memory of the database (see Kajinaga, Paragraph [0053], “the facet tree may define a hierarchical parent-child relationship between the words “President” and “Lincoln” that indicates that the word “President” (e.g. “the President”) is used to refer to President Lincoln, at least in certain documents”);
and said first facet is applied, by the processing unit, to second filtered data obtained by applying said second facet to data of said set of data (see Kajinaga, Paragraph [0053], “On the basis of this relationship in the facet tree, the annotator generating section 150 may generate an annotator that adds the annotation “Lincoln” to every occurrence of the word “President” in an input document. As another example, the facet tree may define a hierarchical parent-child relationship between the words “President” and “Lincoln” that indicates that “Lincoln” is an example of a “President.””).
The rationale of claim 1 has been applied herein.
Regarding claim 3, Karargyris in view of Kajinaga in further view of Yazdani teaches the method of claim 2,
wherein: said second facet covers a plurality of regions in said set of data (see Kajinaga, Paragraph [0067], “the annotator may include one or more branches defined by the plurality of subgroups”);
the first facet is applied on each region on which the second facet is applied (see Kajinaga, Paragraph [0067], “In this situation, the annotator generating section 150 may generate an annotator that adds the annotation “Lincoln” to every occurrence of the word “President” if the input document belongs to the subgroup “Subject: U.S. Civil War.””).
The rationale of claim 2 has been applied herein.
Regarding claim 4, Karargyris in view of Kajinaga in further view of Yazdani teaches the method of claim 3,
wherein: data representing annotations are associated with some of said regions covered by said second facet and are associated with said second facet (see Kajinaga, Paragraph [0067], “the annotator may include one or more branches defined by the plurality of subgroups”);
and the first facet is applied on each region carrying an annotation associated with the second facet (see Kajinaga, Paragraph [0067], “In this situation, the annotator generating section 150 may generate an annotator that adds the annotation “Lincoln” to every occurrence of the word “President” if the input document belongs to the subgroup “Subject: U.S. Civil War.””).
The rationale of claim 3 has been applied herein.
Regarding claim 5, Karargyris in view of Kajinaga in further view of Yazdani teaches the method of claim 2,
wherein the data coding for the description of the first facet comprises a filtering condition applied to the data representing the annotations associated with said regions as well as to said data coding for the second facet (see Kajinaga, Paragraph [0044], “The document classifying section 111 assigns each of the one or more documents obtained by the data input section 110 to one or more of a plurality of subgroups according to a category or classification value of the document,” assigning each of the one or more documents to one or more of a plurality of subgroups according to a category or classification value is considered to be a filtering condition applied),
and wherein the first facet is applied only for those regions for which said filtering condition is verified (see Kajinaga, Paragraph [0044], “The document classifying section 111 may determine that a document has a certain subject based on the prevalence of keywords associated with that subject in the document,” the prevalence of keywords associated is considered to be for which said filtering conditions is verified).
The rationale of claim 2 has been applied herein.
Regarding claim 9, Karargyris in view of Kajinaga in further view of Yazdani further teaches the method according to claim 1,
further comprising a step of displaying, by a graphical user interface unit, said first filtered data to a user, said annotation being user (see Paragraph [0030], “The user device 115 may also include a human-machine interface 140. The human-machine interface 140 may include one or more input devices, one or more output devices, or a combination thereof. Accordingly, in some embodiments, the human-machine interface 140 allows a user to interact with (for example, provide input to and receive output from) the user device 115. For example, the human-machine interface 140 may include a keyboard, a cursor-control device (for example, a mouse), a touch screen, a scroll ball, a mechanical button, a display device (for example, a liquid crystal display (LCD)), a printer, a speaker, a microphone, or a combination thereof. As illustrated in FIG. 1, in some embodiments, the human-machine interface 140 includes a display device 160”).
The rationale of claim 1 has been applied herein.
Regarding claim 10, Karargyris in view of Kajinaga in further view of Yazdani teaches the method according to claim 1,
wherein said first filtered data is provided as input to an artificial intelligence module implementing said task, said data representing said annotation being received from said module (see Karargyris, Fig. 2 and Paragraph [0037], “FIG. 2 shows an example method 200 for training an artificial intelligence engine (the learning engine 145) to generate one or more models, such as a classification model. Generally, the method 200 is directed to training an artificial intelligence engine to generate one or more models for identifying one or more labels for a finding of interest in an image, such as a CXR”).
The rationale of claim 1 has been applied herein.
Regarding claim 11, Karargyris teaches a computer-implemented machine learning method
for performing a task by an artificial intelligence module, comprising the following steps (see Karargyris, Abstract, “see Karargyris, Abstract, “the electronic processor is also configured to generate an annotation for each of the one or more ambiguous reports in the set of reports, and train the artificial intelligence engine using a training set including the annotation,” and Paragraph [0034], “the learning engine 145 applies machine learning (artificial intelligence) to mimic cognitive functions, including but not limited to learning and problem solving. Machine learning generally refers to the ability of a computer program to learn without being explicitly programmed. In some embodiments, a computer program (sometimes referred to as a learning engine) is configured to construct a model (for example, one or more algorithms) based on example inputs”):
associating, by said processing unit, said first filtered data with annotations (see Karargyris, Fig. 2 210, and Paragraph [0043]-[0044], “an annotation (non-ambiguous label) is generated for each ambiguous report identified in the set of reports (at block 210) … After applying the annotations to the ambiguous reports, each report in the dataset now has a non-ambiguous label for a finding of interest. This full set of reports can be referred to as a “training set,” and can be used to train an AI engine, such as the learning engine 145 (at block 212),” applying annotations for each ambiguous report identified is considered to be associating first filtered data with annotations);
performing said task by said artificial intelligence module, wherein said annotation is generated according to a method according to claim 1 (see Karargyris, Paragraph [0034], “The learning engine 145 applies machine learning (artificial intelligence) to mimic cognitive functions, including but not limited to learning and problem solving.”).
Karargyris does not expressively teach
accessing, by a processing unit, a memory of a database comprising a set of data and data coding for at least one definition of at least one facet filtering and selection of data in said set of data, said data coding for said definition further comprising data representing at least one annotation associated with said facet,
storing, by said processing unit, said first filtered data in an annotated training data memory of the database,
However, Kajinaga in a similar invention in the same field of endeavor teaches
accessing, by a processing unit, a memory of a database comprising a set of data and data coding for at least one definition of at least one facet filtering and selection of data in said set of data, said data coding for said definition further comprising data representing at least one annotation associated with said facet (see Kajinaga, Paragraph [0043], “In the example of the apparatus 100 shown in FIG. 1, the data input section 110 obtains the document(s) from an external document storage 101, but the document(s) may be stored and/or created within the apparatus 100 and/or can be obtained from or found on other computer(s) or server(s) through a network such as the Internet, WAN, and/or LAN,” and Paragraph [0053], “The annotator generating section 150 generates an annotator based on the facet tree stored in the facet tree storage 140,” the document(s) may be stored or created within the apparatus 100 and facet tree storage 140 within apparatus 100 therefore is considered to be accessing a database comprising a set of data of at least one facet for data selection further comprising at least one annotation),
storing, by said processing unit, said first filtered data in an annotated training data memory of the database (see Kajinaga, Paragraph [0055], “the outputs may be stored, uploaded to a server, printed, or otherwise made available for viewing or analysis, or may be displayed on a screen in relation to a user query as an intermediate step in a process performed by the apparatus 100. … The various outputs of the apparatus 100 output by the output section 170 may include, for example, … the annotator generated by the annotator generating section 150 based on the facet tree stored in the facet tree storage 140, an annotated document produced by the annotating section 160,” the output is considered to be first filtered data and it may be stored in face tree storage which is considered to be an annotated training data memory),
The combination of Karargyris and Kajinaga are analogous art because they are both in the field of endeavor of object detection. Therefore, it would’ve been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for documents to be stored and/or created within the apparatus and for the annotator generating section to generate an annotator based on the facet tree stored in the facet tree storage; for the outputs to be stored, uploaded to a server, etc. which is considered to be storing in an annotated training data memory; as taught in the method for dynamic facet tree creation of Kajinaga in the system directed to training artificial intelligence engine of Karargyris so that document analysis can be performed more quickly and easily than by using conventional methods (see Kajinaga, Paragraph [0041)).
Karargyris in view of Kajinaga does not expressively teach
applying, by said processing unit, said filtering data selection facet to said set of data to obtain first filtered data;
However, Yazdani in a similar invention in the same field of endeavor teaches
applying, by said processing unit, said filtering data selection facet to said set of data to obtain first filtered data (see Yazdani, Paragraph [0016], “In the example, the first set of facets may contain a first facet of “company” with an associated value of “deep”,” the first set of facets contain first facet which is considered to be applying said facet to obtain first filtered data);
The combination of Karargyris, Kajinaga, and Yazdani are analogous art because they are all in the field of endeavor of object detection. Therefore, it would’ve been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify a first set of facets from the search query with data in core databases; for the first set of facets to contain a first facet value which is considered to be applying said facet to obtain first filtered data; as taught in the method of Yazdani in the system directed to training artificial intelligence engine of Karargyris in view of Kajinaga to further reduce the amount of user effort required in such a search while increasing the accuracy of the search result (see Yazdani, Paragraph [0003])).
Regarding claim 13, Karargyris in view of Kajinaga in further view of Yazdani teaches
A device comprising a processing unit configured to implement steps according to the method according to claim 1 (see Paragraph [0025], “As illustrated in FIG. 1, the server 105 includes an electronic processor 125, a memory 130, and a communication interface 135.”).
As per claim 14, Claim 14 claims the same limitation as Claim 13 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale is analogous to that made in Claim 13.
Claim(s) 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karargyris et al, US 20200321101 in view of Kajinaga et al, US 20170255603 in view of Yazdani et al, US 20190205445 in further view of Alcantara et al, WO 2019178676.
Regarding claim 6, Karargyris in view of Kajinaga in further view of Yazdani does not teach the method according to claim 5,
wherein said filtering condition is associated with the regions annotated by said second facet,
and wherein the first facet is applied only to data from a cropping by these regions and wherein the first facet is applied only to data from a cropping by these regions and for which the condition is verified.
However, Alcantara in a similar invention in the same field of endeavor teaches the method according to claim 5,
wherein said filtering condition is associated with the regions annotated by said second facet (see Alcantara, Paragraph [0120], “The object-of-interest search described immediately above is done after one or more facet searches. In at least some example embodiments, the object-of-interest search may be done before a facet search is done. … In at least some example embodiments, the system 108 identifies facets appearing in those image search results, and displays, on the display, a list of those facets,” facets are applied to the object of interest which is considered to be regions annotated by said second facet),
and wherein the first facet is applied only to data from a cropping by these regions and wherein the first facet is applied only to data from a cropping by these regions and for which the condition is verified (see Alcantara, Paragraph [0126], “The training images may comprise image chips derived from images captured by one of the cameras 169, where a “chip” is a region corresponding to portion of a frame of a selected video recording, such as that portion within a bounding box 310. [0127] Once the facet image training set is generated, it is used to train the artificial neural network to classify the type of facet depicted in the training images comprising the set when a sample image comprising that type of facet is input to the network,” a “chip” is a region that is considered to be a cropped region wherein the first facet is applied).
The combination of Karargyris, Kajinaga, Yazdani, and Alcock are analogous art because they are all in the field of endeavor of object detection. Therefore, it would’ve been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for object-of-interest- search to be done as well as facet searches; for a region within a bounding box and to classify the type of facet depicted; to save the facet data structure; as taught in the method of Alcantara in the system directed to training artificial intelligence engine of Karargyris in view of Kajinaga in further view of Yazdani to increase the efficiency with which an object-of- interest can be identified using a video surveillance system (see Alcantara, Paragraph [0003])).
Regarding claim 7, Karargyris in view of Kajinaga in view of Yazdani in further view of Alcantara teaches the method according to claim 6,
further comprising based on the data representing said annotation, generates generating, by said processing unit, data coding for the definition of a region in said set of data, storing, by said processing unit, data representing said region in a-the memory of the database in relation to the region used to crop the annotated data (see Alcantara, Paragraph [0126]-[0127], “The training images may comprise image chips derived from images captured by one of the cameras 169, where a “chip” is a region corresponding to portion of a frame of a selected video recording, such as that portion within a bounding box 310. Once the facet image training set is generated, it is used to train the artificial neural network to classify the type of facet depicted in the training images comprising the set when a sample image comprising that type of facet is input to the network.”),
wherein said data representing the annotation is stored, by said processing unit, in said memory of said database in relation to the data coding for said first facet and the data representing said region (see Alcantara, Paragraph [0105], “The system 108 in at least some example embodiments saves the facet data structure in storage 190 as comprising a “descriptor” and a “tag”.”).
The rationale of claim 6 has been applied herein.
Regarding claim 8, Karargyris in view of Kajinaga in view of Yazdani in further view of Alcantara teaches the method according to claim 6,
wherein based on the data representing said annotation, the processing unit does not generate data coding for a new region and wherein the processing unit stores the data representing said annotation in the memory of said database in relation to the data coding for said first facet as well as the data representing the region used to crop the annotated data (see Alcantara, Paragraph [0013], “At least one of the training images may comprise an image chip derived from an image captured by a camera,” the image chip is a region corresponding to a bounding box 310 derived from the input image and is not a new region ).
The rationale of claim 6 has been applied herein.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOMINIQUE JAMES whose telephone number is (703)756-1655. The examiner can normally be reached 9:00 am - 6:00 pm EST.
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/DOMINIQUE JAMES/Examiner, Art Unit 2666 /MING Y HON/Primary Examiner, Art Unit 2666
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