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 .
Detailed Action
Office Action is in response to the instant Application 18/815,182 filed on 8/26/2024. Claims 1-20 are pending. This Office Action is Non-Final.
Information Disclosure Statement
The information disclosure statement (IDS), submitted on 8/26/2024 and 4/23/2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
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Claims 1, 8 and 15 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8 and 15 of U.S. Patent No. 12,074,895. Although the claims at issue are not identical, they are not patentably distinct from each other because all the limitations of claims 1, 8 and 15, with regards to artificial intelligence (ai) driven classifier using defined taxonomy framework are anticipated by the limitations recited in 1, 8 and 15 of U.S. Patent No. 12,074,895. See table Below:
Instant Application
U.S. Patent No. 12,074,895
1. A method comprising:
generating, by a processing device, a first query for a classification model, the first query comprising a first set of options for classification of an entity at a first classification granularity level of a taxonomy framework;
providing the first query comprising the first set of options for classification to the classification model;
receiving, from the classification model, a selection of one or more options of the first set of options for classification; and determining a classification of the entity based at least in part on the selection of the one or more options of the first set of options.
8. A system comprising: a memory; and a processing device, operatively coupled to the memory, to: generate a first query for a classification model, the first query comprising a first set of options for classification of an entity at a first classification granularity level of a taxonomy framework;
provide the first query comprising the first set of options for classification to the classification model;
receive, from the classification model, a selection of one or more options of the first set of options for classification; and determine a classification of the entity based at least in part on the selection of the one or more options of the first set of options.
15. A non-transitory computer readable storage medium including instructions that, when executed by a processing device, cause the processing device to: generate, by the processing device, a first query for a classification model, the first query comprising a first set of options for classification of an entity at a first classification granularity level of a taxonomy framework;
provide the first query comprising the first set of options for classification to the classification model;
receive, from the classification model, a selection of one or more options of the first set of options for classification; and determine a classification of the entity based at least in part on the selection of the one or more options of the first set of options.
1. A method comprising: identifying information associated with an entity;
generating, by a processing device, a first query comprising the information associated with the entity and a first set of options for classification of the entity at a first classification granularity level of a taxonomy framework, wherein the taxonomy framework comprises a plurality of classification granularity levels, each granularity level comprising a set of options for classification at the corresponding granularity level;
providing the first query comprising the information associated with the entity and the first set of options for classification to a classification model;
receiving, from the classification model, a selection of a first option of the first set of options for classification; and determining a classification of the entity based at least in part on the selection of the first option of the first set of options.
8. A system comprising: a memory; and a processing device, operatively coupled to the memory, to: identify information associated with an entity; generate a first query comprising the information associated with the entity and a first set of options for classification of the entity at a first level of classification granularity of a taxonomy framework, wherein the taxonomy framework comprises a plurality of classification granularity levels, each granularity level comprising a set of options for classification at the corresponding granularity level;
provide the first query comprising the information associated with the entity and the first set of options for classification to a classification model;
receive, from the classification model, a selection of a first option of the first set of options for classification; and determine a classification of the entity based at least in part on the selection of the first option of the first set of options.
15. A non-transitory computer readable storage medium including instructions that, when executed by a processing device, cause the processing device to: identify information associated with an entity; generate, by the processing device, a first query comprising the information associated with the entity and a first set of options for classification of the entity at a first level of classification granularity of a taxonomy framework, wherein the taxonomy framework comprises a plurality of classification granularity levels, each granularity level comprising a set of options for classification at the corresponding granularity level;
provide the first query comprising the information associated with the entity and the first set of options for classification to a classification model;
receive, from the classification model, a selection of a first option of the first set of options for classification; and determine a classification of the entity based at least in part on the selection of the first option of the first set of options.
Regarding claims 2-7, 9-14 and 16-20; claims 2-7, 9-14 and 16-20 are also rejected under Double Patenting for similar reasons respectively and are dependent on claims 1, 8 and 14 and therefore inherit the rejection from issues of the independent claims.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Alfred et al. (US 2023/0214677) in view of Xu et al. (US 9,009,134).
As per claim 1, Alfred teaches a method comprising: generating, by a processing device, a first query for a classification model, the first query comprising a first set of options for classification of an entity at a first classification granularity level of a taxonomy framework; providing the first query comprising the first set of options for classification to the classification model (Alfred, Paragraph 0063 recites “At block 152, the process 150 involves receiving a model classification query for a model from a remote computing device for one or more target entities. The remote computing device can be a client computing system 117 that provides one or more services the one or more target entities, which comprise one or more user computing systems 115. The model classification query can also be received by the auditing system 110 from a remote computing device associated with an entity authorized to request model classification for the model. In some instances, the model classification query is for a first model that includes one or more modifications to a second model that the client computing system 117 already uses. The one or more modifications could include a modification to the platform upon which the model is to be executed (e.g., transitioning from platform 113 to platform 113-1), a modification to one or more parameters of the model, and/or other modifications.”).
But fails to teach receiving, from the classification model, a selection of one or more options of the first set of options for classification; and determining a classification of the entity based at least in part on the selection of the one or more options of the first set of options.
However, in an analogous art Xu teaches receiving, from the classification model, a selection of one or more options of the first set of options for classification (Xu, Col. 14 Lines 25-28 recites “At block 602, an input query 118 is received from a user 102. The query 118 may be received directly from the user 102 through an input device, such as a keyboard and/or a mouse at a terminal. For example, the query 118 may be received at the device 104. In an alternate embodiment, the input query 118 may be received indirectly, such as via web input, data transmission over a communications network, data transfer from a storage media device, or the like.”);
and determining a classification of the entity based at least in part on the selection of the one or more options of the first set of options (Xu, Col. 14 Lines 43-51 “At block 606, one or more classifications 122 are predicted for the named entity 120. In one embodiment, the classifications 122 that are predicted for the named entity 120 are based on the context of the named entity 120 in the query 118. For example, if a query 118 "harry potter walkthrough" is received at block 602, then the named entity 120 "harry potter" may be detected in the query 118 at block 604, and a likely classification 122 "Game" may be predicted at block 606 based on the context "walkthrough."”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Xu’s Named Entity Recognition In Query with Korst’s Method and apparatus for automatic generation of recommendations Alfred’s techniques for evaluating an effect of changes to machine learning models because the use of automating the classification of data provides the advantage of making data processing more efficient.
Regarding claims 8 and 15, claims 8 and 15 are directed to a system and a non-transitory readable medium associated with the method of claim 1. Claims 8 and 15 are of similar scope to claim 1, and are therefore rejected under similar rationale.
Claim(s) 2-6, 9-13 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Alfred et al. (US 2023/0214677) and Xu et al. (US 9,009,134) and in further view of Korst et al. (US 2014/0223488).
As per claim 2, Alfred in combination with Xu teaches the method of claim 1, but fails to teach further iteratively generating additional queries to the classification model comprising additional levels of classification granularity levels of the taxonomy framework until a leaf node of the taxonomy framework is reached; and determining the classification of the entity based on the classification granularity levels determined by the classification model.
However, in an analogous art Korst teaches further iteratively generating additional queries to the classification model comprising additional levels of classification granularity levels of the taxonomy framework until a leaf node of the taxonomy framework is reached; and determining the classification of the entity based on the classification granularity levels determined by the classification model (Korst, Paragraphs 0026-0028 recites “In another embodiment, the profile generation unit comprises a classification database, which allocates a respective class of entities according to at least one entity classification criterion to a respective set of at least one keyword to be included in a query. wherein the profile generation unit is configured to allocate at least one class to the extracted identification data identifying a respective entity of interest in accordance with the classification database, and wherein the query generation unit is configured to generate the queries using the respective identification data and at least one of the keywords allocated to the respective class of the identification data in accordance with the classification database.”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Korst’s Method and apparatus for automatic generation of recommendations with Alfred’s techniques for evaluating an effect of changes to machine learning models because the use of automating the classification of data provides the advantage of automated evaluation of imported pre-profile data and an interleaving of recommendations from different content hit lists for a given user, the recommendation engine of the present invention achieves a close adherence of the recommendations to actual interests and expectations of the user.
As per claim 3, Alfred in combination with Xu teaches the method of claim 1, but fails to teach generating a second query comprising a second set of options for a second level of classification granularity of the taxonomy framework; receiving, from the classification model, a selection of one or more options of the second set of options for classification; and determining a classification of the entity based at least in part on the selection of the one or more options of the first set of options and the one or more options of the second set of options.
However, in an analogous art Korst teaches generating a second query comprising a second set of options for a second level of classification granularity of the taxonomy framework; receiving, from the classification model, a selection of one or more options of the second set of options for classification; and determining a classification of the entity based at least in part on the selection of the one or more options of the first set of options and the one or more options of the second set of options (Korst, Paragraphs 0026-0028 recites “In another embodiment, the profile generation unit comprises a classification database, which allocates a respective class of entities according to at least one entity classification criterion to a respective set of at least one keyword to be included in a query. wherein the profile generation unit is configured to allocate at least one class to the extracted identification data identifying a respective entity of interest in accordance with the classification database, and wherein the query generation unit is configured to generate the queries using the respective identification data and at least one of the keywords allocated to the respective class of the identification data in accordance with the classification database.”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Korst’s Method and apparatus for automatic generation of recommendations with Alfred’s techniques for evaluating an effect of changes to machine learning models because the use of automating the classification of data provides the advantage of automated evaluation of imported pre-profile data and an interleaving of recommendations from different content hit lists for a given user, the recommendation engine of the present invention achieves a close adherence of the recommendations to actual interests and expectations of the user.
As per claim 4, Alfred in combination with Xu teaches the method of claim 1, but fails to teach wherein the first query further comprises information associated with the entity.
However, in an analogous art Korst teaches wherein the first query further comprises information associated with the entity (Korst, Paragraph 0089 recites “Step S4: generating, using the extracted identification data from the initial user profile data set, at least two queries semantically different from each other to be directed to at least one content repository;”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Korst’s Method and apparatus for automatic generation of recommendations with Alfred’s techniques for evaluating an effect of changes to machine learning models because the use of automating the classification of data provides the advantage of automated evaluation of imported pre-profile data and an interleaving of recommendations from different content hit lists for a given user, the recommendation engine of the present invention achieves a close adherence of the recommendations to actual interests and expectations of the user.
As per claim 5, Alfred in combination with Xu and Korst teaches the method of claim 4, Korst further teaches wherein the information associated with the entity comprises services associated with the entity (Korst, Paragraphs 0010-0014 recites “According to a first aspect of the present invention, a recommender engine for recommending content items to a user, comprises a profile generation unit having a pre-profile input, which is configured to receive from a data base, which is external to the recommender engine, pre-profile data comprising pre-profile text data suitable for identifying entities of interest to a given user, and having a pre-profile analysis unit, which is connected with the pre-profile input and configured to extract from the pre-profile data identification data identifying the entities of interest and to generate an initial user profile data set for the given user from the extracted identification data; a query generation unit, which is connected with the profile generation unit and configured to generate, using the extracted identification data from the initial user profile data set, at least two queries semantically different from each other to be directed to at least one content repository; a content retrieval unit, which is connected with the query generation unit and configured to issue the generated queries to the at least one content repository and which is configured to receive from the at least one content repository, in response to the query, content-related response data comprising respective hit lists having at least one respective content-location identifier indicative of a storage location of a respective content item; and an interleaver unit, which is connected with the content retrieval unit and which is configured to generate from the different hit lists a single recommendation list by interleaving the content-location identifiers comprised in different ones of the hit lists with each other.”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Korst’s Method and apparatus for automatic generation of recommendations with Alfred’s techniques for evaluating an effect of changes to machine learning models because the use of automating the classification of data provides the advantage of automated evaluation of imported pre-profile data and an interleaving of recommendations from different content hit lists for a given user, the recommendation engine of the present invention achieves a close adherence of the recommendations to actual interests and expectations of the user.
As per claim 6, Alfred in combination with Xu and Korst teaches the method of claim 4, Korst further teaches wherein the information associated with the entity comprises one or more known properties of the entity or at least a partial description of the entity (Korst, Paragraphs 0010-0014 recites “According to a first aspect of the present invention, a recommender engine for recommending content items to a user, comprises a profile generation unit having a pre-profile input, which is configured to receive from a data base, which is external to the recommender engine, pre-profile data comprising pre-profile text data suitable for identifying entities of interest to a given user, and having a pre-profile analysis unit, which is connected with the pre-profile input and configured to extract from the pre-profile data identification data identifying the entities of interest and to generate an initial user profile data set for the given user from the extracted identification data; a query generation unit, which is connected with the profile generation unit and configured to generate, using the extracted identification data from the initial user profile data set, at least two queries semantically different from each other to be directed to at least one content repository; a content retrieval unit, which is connected with the query generation unit and configured to issue the generated queries to the at least one content repository and which is configured to receive from the at least one content repository, in response to the query, content-related response data comprising respective hit lists having at least one respective content-location identifier indicative of a storage location of a respective content item; and an interleaver unit, which is connected with the content retrieval unit and which is configured to generate from the different hit lists a single recommendation list by interleaving the content-location identifiers comprised in different ones of the hit lists with each other.”).
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Korst’s Method and apparatus for automatic generation of recommendations with Alfred’s techniques for evaluating an effect of changes to machine learning models because the use of automating the classification of data provides the advantage of automated evaluation of imported pre-profile data and an interleaving of recommendations from different content hit lists for a given user, the recommendation engine of the present invention achieves a close adherence of the recommendations to actual interests and expectations of the user.
Regarding claims 9 and 16, claims 9 and 16 are directed to a system and a non-transitory readable medium associated with the method of claim 2. Claims 9 and 16 are of similar scope to claim 2, and are therefore rejected under similar rationale.
Regarding claims 10 and 17, claims 10 and 17 are directed to a system and a non-transitory readable medium associated with the method of claim 3. Claims 10 and 17 are of similar scope to claim 3, and are therefore rejected under similar rationale.
Regarding claims 11 and 18, claims 11 and 18 are directed to a system and a non-transitory readable medium associated with the method of claim 4. Claims 11 and 18 are of similar scope to claim 4, and are therefore rejected under similar rationale.
Regarding claims 12 and 19, claims 12 and 19 are directed to a system and a non-transitory readable medium associated with the method of claim 5. Claims 12 and 19 are of similar scope to claim 5, and are therefore rejected under similar rationale.
Regarding claims 13 and 20, claims 13 and 20 are directed to a system and a non-transitory readable medium associated with the method of claim 6. Claims 13 and 20 are of similar scope to claim 6, and are therefore rejected under similar rationale.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Alfred et al. (US 2023/0214677), Xu et al. (US 9,009,134) and Korst et al. (US 2014/0223488) and in further view of Wu et al. (US 2021/0312260).
As per claim 7, Alfred in combination with Xu and Korst teaches the method of claim 4, but fails to teach wherein the one or more options comprise a plurality of possible classifications for the entity at the first classification granularity level and a confidence score associated with each of the possible classifications based on the information associated with the entity.
However, in an analogous art Wu teaches wherein the one or more options comprise a plurality of possible classifications for the entity at the first classification granularity level and a confidence score associated with each of the possible classifications based on the information associated with the entity (Wu, Paragraph 0051 recites “The output layer 410 interfaces with the full-connection layer 408 to output a ranking for query-response pairs. For pairwise ranking models, the output layer provides a ranking score for each query-response pair. For classification models, by contrast, the output layer provides confidence scores of the query-response pair as conversationally relevance one or not. The output layer 410 represents class scores for ranked query response pairs. In examples, processing operations may be executed to interface with one or more applications/services to output the ranked query-response pairs, for example, using a distributed network. In one instance, a top ranked query-response pair may be transmitted to an application/service. However, any number of ranked query-response pairs may be output to applications/services.”)
It would have been obvious to a person of ordinary skill in the art, at the earliest effective filing date to use Wu’s Conversational relevance modeling using convolutional neural network with Alfred’s techniques for evaluating an effect of changes to machine learning models because the use of a confident score may be generated to evaluate a sentence pair matching between a query and a candidate response.
Regarding claim 14, claim 14 is directed to a system associated with the method of claim 7. Claim 14 is of similar scope to claim 7, and are therefore rejected under similar rationale.
Conclusion
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RODERICK . TOLENTINO
Examiner
Art Unit 2439
/RODERICK TOLENTINO/ Primary Examiner, Art Unit 2439