DETAILED ACTION
This Office Action is in response to the Preliminary Amendment submitted on 02/12/2024. Claims 1-29 are canceled. Claims 30-49 are newly added. Claims 30-49 are pending.
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Claim Objections
Claims 30 and 40 are objected to because of the following informalities:
Claims 30 and 40 recite the limitation: “receiving text from a consumer, wherein the consumer is one of a human user and a cognitive agent”, and this limitation should end with a semicolon (“;”).
Claim 30 recites the limitation: “analyzing the synthesized media for voids based on the collated content elements and semantic network;”, and this limitation should end with a final period (“.”).
Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 30-49 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-24 of U.S. Patent No. 11,868,903 in view of Kreulen et al (US Pub. No. 2002/0169783). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application claims are similar in scope to the claims that appear in the Patent, as they are both directed to a system that synthesizes a semantic network using a knowledge representation based on a user’s input, wherein the system finds related data entities and relationships in the domain and dynamically generates a semantic network around the user’s input. The instant application recites the limitation “analyzing the synthesized media for voids based on the collated content elements and semantic network”, which is not taught by the Patent, however, prior art Kreulen teaches this limitation, as it can be seen for example at [0009] (see rejection below for more detailed explanation). It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teachings of Sweeney with the above teachings of Kreulen in order to discover knowledge gaps/voids more rapidly and accurately to better apply engineering or other resources to write new solutions that will have the most beneficial impact.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 30-33, 37-43, 47-49 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ershov (US Patent No. 7,437,370- hereinafter Ershov), in view of Kreulen et al (US Pub. No. 2002/0169783 - hereinafter Kreulen).
Referring to Claim 30, Ershov teaches a computer-implemented method for analyzing synthesized media using consumer-directed semantic synthesis, the method comprising:
using at least one processor configured to execute computer-readable instructions stored on a computer readable storage medium (see Ershov at Claim 20: “the system comprising a processor and memory containing computer executable code”) to perform:
synthesizing media using a knowledge representation derived from a domain of information, wherein the domain of information includes data entities, and wherein the synthesized media includes content elements (see Ershov at Fig. 1, Column 3: line 54- Column 4: line 24; Ershov teaches displaying search results to a user from a user’s query, in which the display consists of a webpage displaying a map generated by a neural network with webpages and images related to what the user asked for. Moreover, Ershov at Claim 1 teaches the map showing search query terms and other terms derived from the search query terms and from the search results, wherein a location of all the displayed terms relative to each other corresponds to contextual relationship between all the displayed terms, the contextual relationship determined by a relevance of one term to a conceptual environment of the other terms, wherein only a single query is displayed on the map. Therefore, this corresponds to the claimed synthesized media using a knowledge representation and including entities and content elements);
receiving text from a consumer, wherein the consumer is one of a human user and a cognitive agent (see Ershov at Fig. 1, Column 3: line 54- Column 4: line 24; Ershov teaches displaying search results to a user from a user’s query, in which the display consists of a webpage displaying a map generated by a neural network with webpages and images related to what the user asked for. Therefore, this user query corresponds to the claimed text from a consumer)
synthesizing a semantic network based on the text from the consumer, the synthesizing comprising:
translating the text into an active concept (see Ershov at Fig. 1, Column 3: line 54- Column 4: line 24; Ershov teaches displaying search results to a user from a user’s query, in which the display consists of a webpage displaying a map generated by a neural network with webpages and images related to what the user asked for. Moreover, Ershov at Fig. 1 shows the active concept being Jaguar Car company. Therefore, this corresponds to the claimed translation of the text);
including the active concept as a node in the semantic network (see Ershov at Fig. 1, Column 3: line 54- Column 4: line 24; Ershov teaches displaying search results to a user from a user’s query, in which the display consists of a webpage displaying a map generated by a neural network with webpages and images related to what the user asked for. Moreover, Ershov at Claim 1 teaches the map showing search query terms and other terms derived from the search query terms and from the search results, wherein a location of all the displayed terms relative to each other corresponds to contextual relationship between all the displayed terms, the contextual relationship determined by a relevance of one term to a conceptual environment of the other terms, wherein only a single query is displayed on the map. Therefore, this map with all the terms derived from the user query shown as nodes corresponds to the claimed active concept as a node);
deriving relationships between the active concept and data entities from the domain of information (see Ershov at Fig. 1, Column 3: line 54- Column 4: line 24; Ershov teaches displaying search results to a user from a user’s query, in which the display consists of a webpage displaying a map generated by a neural network with webpages and images related to what the user asked for. Moreover, Ershov at Claim 1 teaches the map showing search query terms and other terms derived from the search query terms and from the search results, wherein a location of all the displayed terms relative to each other corresponds to contextual relationship between all the displayed terms, the contextual relationship determined by a relevance of one term to a conceptual environment of the other terms, wherein only a single query is displayed on the map. Therefore, this contextual relationship corresponds to the claimed active concept and data entities);
collating the content elements derived from the domain of information within the structure of the semantic network (see Ershov at Fig. 1, Column 3: line 54- Column 4: line 24; Ershov teaches displaying search results to a user from a user’s query, in which the display consists of a webpage displaying a map generated by a neural network with webpages and images related to what the user asked for. As it can be seen at Fig. 1, terms are shown generated by a neural network in which the terms are the nodes, and they are connected according to their contextual relationship. Therefore, the webpage and images displayed in the map connected with all their contextual relationships corresponds to the claimed content elements collated).
However, Ershov fails to teach analyzing the synthesized media for voids based on the collated content elements and semantic network.
Kreulen teaches, in an analogous system, analyzing the synthesized media for voids based on the collated content elements and semantic network (see Kreulen at [0009]: “The problem that this invention addresses is that of rapidly discovering the areas or categories of problems in the help desk logs that are not well represented in the Solutions Knowledge Base. In the present application, such areas of poor representation are referred to as "knowledge gaps". The more rapidly and accurately these knowledge gaps are discovered, the better that engineering or other resources can be applied to write new solutions that will have the most beneficial impact”. Further at [0011]: “In view of the foregoing and other problems, it is, therefore, an object of the present invention to provide a structure and method for discovering and isolating knowledge gaps between two databases”. Therefore, this discovery of knowledge gaps is interpreted as the analysis to determine voids).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teachings of Ershov with the above teachings of Kreuler by synthesizing media using a knowledge representation derived from a domain of information, as taught by Ershov, and identifying voids in the synthesized media, as taught by Kreuler. The modification would have been obvious because one of ordinary skill in the art would be motivated combine these teachings as the quicker and more accurately these knowledge gaps are discovered, the better that engineering or other resources can be applied to write new solutions that will have the most beneficial impact (as suggested by Kreuler at 0009).
Referring to Claim 31, the combination of Ershov and Kreulen teaches the method of claim 30 wherein synthesizing a semantic network based on the text from the consumer further includes synthesizing the semantic network based on synthesis parameters (see Ershov at Claim 1: “a map displayed to a user on a screen, the map showing search query terms and other terms derived from the search query terms and from the search results, wherein a location of all the displayed terms relative to each other corresponds to contextual relationship between all the displayed terms, the contextual relationship determined by a relevance of one term to a conceptual environment of the other terms”. Therefore, this contextual relationship between the terms and the conceptual environment corresponds to the claimed ‘synthesis parameters’).
Referring to Claim 32, the combination of Ershov and Kreulen teaches the method of claim 30 wherein the text is received as a direct input of text from the consumer (see Ershov at Col. 3: lines 45-48: “In particular, the present invention is directed to the use of a search results map, as illustrated in FIG. 1, which is shown to the user in response to the user's query”. Therefore, the user’s query corresponds to the direct input text form the consumer).
Referring to Claim 33, the combination of Ershov and Kreulen teaches the method of claim 30 wherein receiving the text includes receiving an input of the consumer and deriving the text from the input (see Ershov at Col. 3: lines 45-48: “In particular, the present invention is directed to the use of a search results map, as illustrated in FIG. 1, which is shown to the user in response to the user's query”. Further at Claim 1: “a map displayed to a user on a screen, the map showing search query terms and other terms derived from the search query terms and from the search results”. Therefore, the map in response to the user’s query corresponds to deriving the text from the input).
Referring to Claim 37, the combination of Ershov, Kreulen and Estes teaches the method of claim 36 further comprising synthesizing second/other media and analyzing the synthesized media and derived consumer data (see Ershov at Col. 4: lines 46-53: “As yet another option, the map can be a three-dimensional "topographic" map, displayed on a screen as if viewed at an angle downward, such that the different search terms and graphical elements can be located at various "coordinates" in three degrees of freedom (X, Y, Z), with the user being able to drag-and-rotate the map using a mouse, and with the relative distance between the elements and their height corresponding to relevance and interrelationships to each other”. Therefore, this alternative map is interpreted as a second/other media synthesized).
Referring to Claim 38, the combination of Ershov and Kreulen teaches the method of claim 30 wherein collating the content elements within the semantic network includes categorizing semantically-annotated content elements within the semantic network (see Ershov at Fig. 1, Column 3: line 54- Column 4: line 24; Ershov teaches displaying search results to a user from a user’s query, in which the display consists of a webpage displaying a map generated by a neural network with webpages and images related to what the user asked for. As it can be seen at Figs. 1 and 2, terms are shown generated by a neural network in which the terms are the nodes, and they are connected according to their contextual relationship and category (for example the automobile category showing terms like car, jaguar, dealership, among others)).
Referring to Claim 39, the combination of Ershov and Kreulen teaches the method of claim 30 wherein collating the content elements within the semantic network includes third-party information retrieval to build associations within the semantic network (see Ershov at Fig. 1, Column 4: lines 10-23: “Note the small image next to the word "jaguar," which is the favorite icon for the website for Jaguar (the car company), and is "pulled off" the HTML script for the page at http***//www***jaguar***com/global/default***htm (i.e., one of the pages identified by the search engine as highly relevant to the query). By clicking on the graphical image next to the word "jaguar" in the map, the user is automatically taken to the website at http***//www***jaguar***com/global/default***htm, same as he would have been, were he to click to the search result on the right, where the search results are displayed in hyperlink form. Thus, this simplifies, for the user, the process of actually reaching the website that he is seeking”. Therefore, this data pulled from the html script corresponds to the use of third party information retrieval to build associations).
Referring to independent Claim 40, it is rejected on the same basis as independent claim 30, mutatis mutandis, since both are analogous claims.
Referring to dependent Claim 41, it is rejected on the same basis as dependent claim 31, mutatis mutandis, since both are analogous claims.
Referring to dependent Claim 42, it is rejected on the same basis as dependent claim 32, mutatis mutandis, since both are analogous claims.
Referring to dependent Claim 43, it is rejected on the same basis as dependent claim 33, mutatis mutandis, since both are analogous claims.
Referring to dependent Claim 47, it is rejected on the same basis as dependent claim 37, mutatis mutandis, since both are analogous claims.
Referring to dependent Claim 48, it is rejected on the same basis as dependent claim 38, mutatis mutandis, since both are analogous claims.
Referring to dependent Claim 49, it is rejected on the same basis as dependent claim 39, mutatis mutandis, since both are analogous claims.
Claims 34 and 44 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ershov (US Patent No. 7,437,370- hereinafter Ershov), in view of Kreulen et al (US Pub. No. 2002/0169783 - hereinafter Kreulen) and further in view of Kondo et al (US Patent No. 6,154,761- hereinafter Kondo).
Referring to Claim 34, the combination of Ershov and Kreulen teaches the method of claim 33, however, fails to teach further comprising pre-processing the consumer input using an adaptive classification scheme generator.
Kondo teaches, in an analogous system, further comprising pre-processing the consumer input using an adaptive classification scheme generator (see Kondo at Col. 7: lines 46-53: “an adaptive class tap structure is used to determine the ADRC class ID of the target data. An adaptive class tap structure is a class tap structure used in a multiple classification scheme. An adaptive class tap structure is used to more accurately represent the class tap structure of the area containing the target data since it describes more than one characteristic of the target data”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the combination of Ershov and Kreuler with the above teachings of Kondo by receiving a user input for synthesizing media using a knowledge representation derived from a domain of information and identifying voids in the synthesized media, as taught by Ershov and Kreuler, and using an adaptive classification scheme generator to analyze the user input, as taught by Kondo. The modification would have been obvious because one of ordinary skill in the art would be motivated to combine these teachings in order to more accurately represent the class structure of the consumer’s text (as suggested by Kondo at Col. 7).
Referring to dependent Claim 44, it is rejected on the same basis as dependent claim 34, mutatis mutandis, since both are analogous claims.
Claims 35-36 and 45-46 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ershov (US Patent No. 7,437,370- hereinafter Ershov), in view of Kreulen et al (US Pub. No. 2002/0169783 - hereinafter Kreulen) and further in view of Estes (US Patent No. 7,249,117- hereinafter Estes).
Referring to Claim 35, the combination of Ershov and Kreulen teaches the method of claim 33, however, fails to teach further comprising deriving consumer data from the input and storing the consumer data derived from the input of the consumer.
Estes teaches, in an analogous system, further comprising deriving consumer data from the input and storing the consumer data derived from the input of the consumer (see Estes at Col. 8: lines 35-47: “Personal Search Agent System Overview. One exemplary implementation of the concepts discussed above and further disclosed in Appendix A and the enclosed CD-ROM is a Personal Search Agent (PSA) which is a multi-phase effort designed to develop a set of search tools using Digital Reasoning and Data Bonding technologies developed by Unetworks. These new search tools learn the users preferences of a user and can infer his or her intent based on environmental factors and learned behaviors. This enhanced insight into the user's preferences, environment, and behavior, provide the user with a concise and better-fitted listing of responses to a given query”. Therefore, this learning of the user preferences, environment and behavior corresponds to the claimed storing consumer data).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the combination of Ershov and Kreuler with the above teachings of Estes by receiving a user input for synthesizing media using a knowledge representation derived from a domain of information and identifying voids in the synthesized media, as taught by Ershov and Kreuler, and using storing consumer data, as taught by Estes. The modification would have been obvious because one of ordinary skill in the art would be motivated to combine these teachings in order provide the user with a concise and better-fitted listing of responses to a given query (as suggested by Estes at Col. 8).
Referring to Claim 36, the combination of Ershov and Kreulen teaches the method of claim 30 further comprising deriving consumer data from the synthesis of media and the synthesis of the semantic network (see Ershov at Col. 4: lines 6-24: “consider the display of the search results in FIG. 1, where the search terms are "jaguar" and "car", resulting in the identified search results shown on the right of the page, and the map shown on the left. Note the small image next to the word "jaguar," which is the favorite icon for the website for Jaguar (the car company), and is "pulled off" the HTML script for the page at http***//www***jaguar***com/global/default***htm (i.e., one of the pages identified by the search engine as highly relevant to the query). By clicking on the graphical image next to the word "jaguar" in the map, the user is automatically taken to the website at http***//www***jaguar***com/global/default***htm, same as he would have been, were he to click to the search result on the right, where the search results are displayed in hyperlink form. Thus, this simplifies, for the user, the process of actually reaching the website that he is seeking (assuming, of course, that, in this case, Jaguar the car company is what he was looking for)”. Therefore, consumer data is derived as it is inferred that the user is looking information about a Jaguar brand automobile).
However, it fails to teach storing the consumer data.
Estes teaches, in an analogous system, storing the consumer data (see Estes at Col. 8: lines 35-47: “Personal Search Agent System Overview. One exemplary implementation of the concepts discussed above and further disclosed in Appendix A and the enclosed CD-ROM is a Personal Search Agent (PSA) which is a multi-phase effort designed to develop a set of search tools using Digital Reasoning and Data Bonding technologies developed by Unetworks. These new search tools learn the users preferences of a user and can infer his or her intent based on environmental factors and learned behaviors. This enhanced insight into the user's preferences, environment, and behavior, provide the user with a concise and better-fitted listing of responses to a given query”. Therefore, this learning of the user preferences, environment and behavior corresponds to the claimed storing consumer data).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the combination of Ershov and Kreuler with the above teachings of Estes by receiving a user input for synthesizing media using a knowledge representation derived from a domain of information and identifying voids in the synthesized media, as taught by Ershov and Kreuler, and storing consumer data, as taught by Estes. The modification would have been obvious because one of ordinary skill in the art would be motivated to combine these teachings in order provide the user with a concise and better-fitted listing of responses to a given query (as suggested by Estes at Col. 8).
Referring to dependent Claim 45, it is rejected on the same basis as dependent claim 35, mutatis mutandis, since both are analogous claims.
Referring to dependent Claim 46, it is rejected on the same basis as dependent claim 36, mutatis mutandis, since both are analogous claims.
Conclusion
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/LUIS A SITIRICHE/ Primary Examiner, Art Unit 2126
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