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 .
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.
A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Claim 2, 9, and 16 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 5, 10, and 15 of U.S. Patent No. 12306842. Although the claims at issue are not identical, they are not patentably distinct from each other because claim(s) 5, 10, and 15 of patent # 12306842 contain(s) every element of claim(s) 2, 9, and 16 of the instant application and as such anticipate(s) claim(s) 2, 9, and 16 of the instant application.
Instant Application: 19205533
Patent: 12306842
Claim 2
A computer-implemented method comprising:
generating, for a query received from a client device, relevancy scores for a plurality of content items by utilizing a generative model to perform vector similarity matching of a query embedding for the query and content item embeddings for the plurality of content items;
generating, by the generative model, a ranked output comprising the plurality of content items ranked according to the relevancy scores;
receiving, from the client device, a modification to a relevancy score associated with a content item from among the plurality of content items; and
generating, utilizing a retrained generative model instance of the generative model based on the modification to the relevancy score, a modified ranked output comprising the plurality of content items reranked according to the modification to the relevancy score.
Claim 5
A computer-implemented method performed by one or more processors, comprising the operations of:
receiving a natural language-based input associated with a client device of a user;
generating, by the one or more processors, a search criterion for the received natural language-based input;
generating, by the one or more processors, a relevancy-ranked output of content items, wherein generating the relevancy-ranked output comprises:
causing the generated search criterion to be processed, via a generative Artificial Intelligence (AI) search system, comprising one or more machine learning models;
generating by the generative AI search system, a relevancy-ranked output listing content items responsive to the generated search criterion, wherein the content items have a content identifier and a content description; and
causing a portion of the relevancy-ranked output listing to be rendered at the client device of the user;
generating, by the one or more processors, a search summary indicating the content identifiers, the content description and an associated relevancy ranking value of the content items associated with the generated relevancy-ranked output;
receiving, via a user interface, a modified relevancy ranking value for one or more of the content items indicated in the search summary, wherein a relevancy ranking value is based on a scale of multiple numeric relevancy ranking values, wherein one ranking value on the scale indicates that a content item is irrelevant to the generated search criterion, wherein another ranking value on the scale indicates that a content item is exactly relevant to the generated search criterion, and wherein the user interface displays multiple content items and the generated search criterion; and
retraining, by the one or more processors, the one or more machine learning models with the received modified relevancy ranking value, wherein the retrained one or more machine learning models generates a different relevancy-ranked output when the search criterion is applied to the generative AI search system
wherein causing the generated search criterion to be processed, via a search system, comprises the operations of performing, by the one or more processors, a first stage scoring process, to generate a set of content items; and performing, by the one or more processors, a second stage scoring process, by processing the set of content items by an inferencing machine learning model trained to determine item score values; and generating, by the inferencing machine learning model, item score values for the set of content items.
wherein the first stage scoring process comprises:
generating, by the one or more processors, a search vector embedding of the received input;
performing a vector similarity matching of the search vector embedding as to a set of vector embeddings describing the content items; and
generating a listing of those content items where a similarity threshold match value is met or exceeded.
Claim 9
A system comprising:
one or more processors; and
a memory coupled to the one or more processors, wherein the memory includes instructions executable by the one or more processors to:
generate, for a query, relevancy scores for a plurality of content items by utilizing a generative model to perform vector similarity matching of a query embedding for the query and content item embeddings for the plurality of content items;
generate, by the generative model, a ranked output comprising the plurality of content items ranked according to the relevancy scores;
receive, from a client device, a modification to a relevancy score associated with a content item from among the plurality of content items; and
generate, utilizing a retrained generative model instance of the generative model based on the modification to the relevancy score, a modified ranked output comprising the plurality of content items reranked according to the modification to the relevancy score.
Claim 10
A system comprising one or more processors configured to perform the operations of:
receiving a natural language-based input associated with a client device of a user;
generating, by the one or more processors, a search criterion for the received natural language-based input; generating, by the one or more processors, a relevancy-ranked output of content items, wherein generating the relevancy-ranked output comprises:
causing the generated search criterion to be processed, via a generative Artificial Intelligence (AI) search system, comprising one or more machine learning models;
generating by the generative AI search system, a relevancy-ranked output listing content items responsive to the generated search criterion, wherein the content items have a content identifier and a content description; and
causing a portion of the relevancy-ranked output listing to be rendered at the client device of the user;
generating, by the one or more processors, a search summary indicating the content identifiers, the content description and an associated relevancy ranking value of the content items associated with the generated relevancy-ranked output;
receiving, via a user interface, a modified relevancy ranking value for one or more of the content items indicated in the search summary, wherein a relevancy ranking value is based on a scale of multiple numeric relevancy ranking values, wherein one ranking value on the scale indicates that a content item is irrelevant to the generated search criterion, wherein another ranking value on the scale indicates that a content item is exactly relevant to the generated search criterion, and wherein the user interface displays multiple content items and the generated search criterion; and
retraining, by the one or more processors, the one or more machine learning models with the received modified relevancy ranking value, wherein the retrained one or more machine learning models generates a different relevancy-ranked output when the search criterion is applied to the generative AI search system;
wherein causing the generated search criterion to be processed, via a search system, comprises: performing, by the one or more processors, a first stage scoring process, to generate a set of content items; and performing, by the one or more processors, a second stage scoring process, by processing the set of content items by an inferencing machine learning model trained to determine item score values; and generating, by the inferencing machine learning model, item score values for the set of content items;
wherein the first stage scoring process comprises: generating, by the one or more processors, a search vector embedding of the received input; performing a vector similarity matching of the search vector embedding as to a set of vector embeddings describing the content items; and generating a listing of those content items where a similarity threshold match value is met or exceeded.
Claim 16
A non-transitory computer readable medium storing instructions which, when executed by at least one processor, cause the at least one processor to:
generate, for a query received from a client device, relevancy scores for a plurality of content items by utilizing a generative model to perform vector similarity matching of a query embedding for the query and content item embeddings for the plurality of content items;
generate, by the generative model, a ranked output comprising the plurality of content items ranked according to the relevancy scores;
determine a modification to a relevancy score associated with a content item from among the plurality of content items; and
generate, utilizing a retrained generative model instance of the generative model based on the modification to the relevancy score, a modified ranked output comprising the plurality of content items reranked according to the modification to the relevancy score.
Claim 15
A non-transitory computer readable medium storing a software program comprising data and computer implementable instructions that when executed by at least one processor cause the at least one processor to perform operations receiving a natural language-based input associated with a client device of a user;
generating, by the one or more processors, a search criterion for the received natural language-based input;
generating, by the one or more processors, a relevancy-ranked output of content items, wherein generating the relevancy-ranked output comprises:
causing the generated search criterion to be processed, via a generative Artificial Intelligence (AI) search system, comprising one or more machine learning models;
generating by the generative AI search system, a relevancy-ranked output listing content items responsive to the generated search criterion, wherein the content items have a content identifier and a content description; and
causing a portion of the relevancy-ranked output listing to be rendered at the client device of the user; and
generating a search summary indicating the content identifiers, the content description and an associated relevancy ranking value of the content items associated with the generated relevancy-ranked output;
receiving, via a user interface, a modified relevancy ranking value for one or more of the content items indicated in the search summary, wherein a relevancy ranking value is based on a scale of multiple numeric relevancy ranking values, wherein one ranking value on the scale indicates that a content item is irrelevant to the generated search criterion, wherein another ranking value on the scale indicates that a content item is exactly relevant to the generated search criterion, and wherein the user interface displays multiple content items and the generated search criterion; and
retraining, by the one or more processors, the one or more machine learning models with the received modified relevancy ranking value, wherein the retrained one or more machine learning models generates a different relevancy-ranked output when the search criterion is applied to the generative AI search system;
wherein causing the generated search criterion to be processed, via a search system, comprises: performing, by the one or more processors, a first stage scoring process, to generate a set of content items; and performing, by the one or more processors, a second stage scoring process, by processing the set of content items by an inferencing machine learning model trained to determine item score values; and generating, by the inferencing machine learning model, item score values for the set of content items;
wherein the first stage scoring process comprises: generating, by the one or more processors, a search vector embedding of the received input; performing a vector similarity matching of the search vector embedding as to a set of vector embeddings describing the content items; and generating a listing of those content items where a similarity threshold match value is met or exceeded.
Claim Rejections - 35 USC § 101
Claims 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 2
Step 1, This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a method that performs at least one step. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One, This part of the eligibility analysis evaluates whether the claim recites a judicial exception. See MPEP 2106.04 and 2019 PEG.
Step "generating, for a query received from a client device, relevancy scores for a plurality of content items by utilizing a generative model to perform vector similarity matching of a query embedding for the query and content item embeddings for the plurality of content items" (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (e.g., a generative model, a query embedding, content item embeddings). That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking or evaluating about how closely each content item matches the query by comparing the query embedding and content item embedding and assigning each item a relevancy score. For instance, query embedding is [1,1,1,1] and content embedding a [1,1,1,1], b [1,5,6,7]. Content item a is closer to the query and gets a higher score. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment).
Step "generating, by the generative model, a ranked output comprising the plurality of content items ranked according to the relevancy scores" (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (e.g., a generative model). That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user evaluating to order the items by scores. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment).
Step "generating, utilizing a retrained generative model instance of the generative model based on the modification to the relevancy score, a modified ranked output comprising the plurality of content items reranked according to the modification to the relevancy score" (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (e.g., a retrained generative model instance, a generative model). That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user reordering the items based on re-learning in accordance with relevance scores to the items given by the user. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment).
"Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas." MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. "For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record." MPEP 2106.04, subsection II.B. Here, the mentioned steps fall within the mental process grouping of abstract ideas and are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A, Prong Two, this part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. See MPEP 2106.04(d) and 2019 PEG.
The claim recites the additional elements/limitations:
"receiving, from the client device, a modification to a relevancy score associated with a content item from among the plurality of content items" → data receiving;
"a generative model" → generic tool;
"a query embedding" → generic data label;
"content item embeddings" → generic data label;
"a retrained generative model instance" → generic tool
MPEP § 2106.05(a) "Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field."
The additional elements "a generative model", "a query embedding", "content item embeddings", and "a retrained generative model instance" do not improve the functioning of a computer or any other technology. None of these elements makes the computer faster, more accurate, or more efficient.
b) MPEP § 2106.05(b) Particular Machine. The judicial exception does not apply to any particular machine.
The claim is silent regarding specific limitations directed to an improved computer system, processor, memory, network, database, or Internet, nor do applicant direct examiner’s attention to such specific limitations. "[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 573 U.S. at 223; see also Bascom Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1348 (Fed. Cir. 2016) ("An abstract idea on 'an Internet computer network' or on a generic computer is still an abstract idea."). Applying this reasoning here, the claim is not directed to a particular machine, but rather merely implement an abstract idea using generic computer components such as "a generative model" and "a retrained generative model instance" are not particular machines. Thus, the claims fail to satisfy the "tied to a particular machine" prong of the Bilski machine-or-transformation test.
c) MPEP § 2106.05(c) Particular Transformation.
The additional elements "a generative model", "a query embedding", "content item embeddings", and "a retrained generative model instance" do not impose any meaningful limit on practicing the abstract idea. The steps are not a "transformation or reduction of an article into a different state or thing constituting patent-eligible subject matter[.]" See In re Bilski, 545 F.3d 943, 962 (Fed. Cir. 2008) (en bane), aff'd sub nom, Bilski v. Kappas, 561 U.S. 593 (2010); see also CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) ("The mere manipulation or reorganization of data ... does not satisfy the transformation prong."). Applying this guidance here, the claims fail to satisfy the transformation prong of the Bilski machine-or-transformation test.
d) MPEP § 2106.05(e) Other Meaningful Limitations.
This section of the MPEP guides: Diamond v. Diehr provides an example of a claim that recited meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. 450 U.S. 175, ... (1981). In Diehr, the claim was directed to the use of the Arrhenius equation (an abstract idea or law of nature) in an automated process for operating a rubber-molding press. 450 U.S. at 177-78 .... The Court evaluated additional elements such as the steps of installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, and automatically opening the press at the proper time, and found them to be meaningful because they sufficiently limited the use of the mathematical equation to the practical application of molding rubber products. 450 U.S. at 184... In contrast, the claims in Alice Corp. v. CLS Bank International did not meaningfully limit the abstract idea of mitigating settlement risk. 573 U.S._ .... In particular, the Court concluded that the additional elements such as the data processing system and communications controllers recited in the system claims did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers") or were well-understood, routine, conventional activity. MPEP § 2106.05(e). The additional elements "a generative model", "a query embedding", "content item embeddings", and "a retrained generative model instance" do not impose any meaningful limit on practicing the abstract idea.
e) MPEP § 2106.05(g) Insignificant Extra-Solution Activity.
The limitation "receiving, from the client device, a modification to a relevancy score associated with a content item from among the plurality of content items" is pre-solution data gathering.
6) MPEP § 2106.05(h) Field of Use and Technological Environment.
[T]he Supreme Court has stated that, even if a claim does not wholly pre-empt an abstract idea, it still will not be limited meaningfully if it contains only insignificant or token pre- or post-solution activity-such as identifying a relevant audience, a category of use, field of use, or technological environment. Ultramercial, Inc. v. Hulu, LLC, 722 F.3d 1335, 1346 (Fed. Cir. 2013). The additional elements "a generative model", "a query embedding", "content item embeddings", and "a retrained generative model instance" are simply a field of use that attempts to limit the abstract idea to a particular technological environment.
Accordingly, the additional limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations "receiving, from the client device, a modification to a relevancy score associated with a content item from among the plurality of content items" → data receiving, "a generative model" → generic computer component, "a query embedding" → generic data label, "content item embeddings" → generic computer component, and "a retrained generative model instance" → generic computer component do not recite any non-generic arrangement for scoring, ranking, or reranking content items. Taking these limitations as an ordered combination adds nothing beyond what each does alone in implementing the abstract idea. The recited generic components are at a high level of generality. They serve only as generic labels for tools that implement the abstract idea. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 3 recites "wherein generating the relevancy scores for the plurality of content items comprises utilizing the generative model to extract the query embedding from the query and to compare the query embedding with the content item embeddings." (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (e.g., a generative model, a query embedding, content item embeddings). That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user mapping the query to a vector, e.g., query embedding is [1,1,1,1] and evaluating or comparing the query embedding with content embedding a [1,1,1,1], b [1,5,6,7]. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind through observation, evaluation and judgment). The claim does not amount to significantly more than the abstract idea.
Claim 4 recites "wherein receiving the modification to the relevancy score comprises receiving, from the client device, an interaction modifying the relevancy score within a presentation of the ranked output." The claim specifies the user evaluates and make changes to the relevance scores. The claim does not amount to significantly more than the abstract idea.
Claim 5 recites "wherein the retrained generative model instance is a version of the generative model retrained on a modified relevancy score resulting from the modification to the relevancy score." The claim only states that the model is retrained using the user modified score. It does not say how the retraining is done. The claim does not amount to significantly more than the abstract idea.
Claim 6 recites "further comprising generating a response insertion by performing an additional modification to the relevancy score associated with the content item using a blender process." The claim merely recites a functional result, such as making an additional score change using a blender process, rather than a specific technical solution to a technical problem. The claim does not amount to significantly more than the abstract idea.
Claim 7 recites "further comprising providing the response insertion for display on the client device." The claim only adds the generic output step of showing the result on a screen. It does not add any non-generic technical component or improvement. The claim does not amount to significantly more than the abstract idea.
Claim 8 recites "wherein the generative model comprises a primary generative model and one or more domain-specific generative models." The claim does not amount to significantly more than the abstract idea.
Claims 2-21 are similar to claims 2-8. The claims are rejected based on the same reasons.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
U.S. Pub 2024/0419674 – Tepper discloses accessing input vectors and a query vector, the input vectors each having a dimensionality, the query vector associated with a query and having a dimensionality, applying a first vector transformation to the input vectors to generate primary vectors, each of the primary vectors having a dimensionality smaller than the dimensionality associated with the input vectors, applying a second vector transformation to the query vector to generate a modified query vector, the modified query vector having a dimensionality smaller than the dimensionality of the query vector, and conducting a similarity search on the primary vectors based on the modified query vector to generate one or more candidates for the query. In embodiments a first component of the first vector transformation is determined based on an algorithm and a second component of the second vector transformation is determined based on the same algorithm.
U.S Pub 20240403373 – Chao discloses a search engine of a data exchange may receive a query comprising a set of search terms, retrieve a plurality of data listings based on the search terms of the query, compare a first embedding generated by a large language model (LLM) from the search query to second embeddings generated by the LLM for each of the plurality of data listings to determine a respective relevance for each of the plurality of data listings to the search query, and rank the plurality of data listings based on the respective relevance for each of the plurality of data listings to the search query.
U.S. Patent 11803556 – Samdani discloses a system creates and searches knowledge base (KB) articles inside an organization while supporting operation as a SaaS (Software as-a-Service) across a plurality of organizations. The system implements a real-time online learning-to-rank (L2R) algorithm for learning relevance scoring that is customized to each organization. This algorithm incorporates rich lexical features using a query similarity kernel. A scoring function includes a pairwise static module, which may be trained off-line using training data, and a lexical adaptive module, which is trained on-line based on user feedback. The scoring function makes the system easy to deploy, modify, and suitable for handling events that naturally happen over the lifecycle of any KB deployment, without manual training.
U.S Pub 2021/0073215 – Srinivasaraghavan provides a natural language-based content system with corrective feedback and training service. The natural language-based content system with corrective feedback and training service may collect data based on interaction with search results from users. The natural language understanding model may generate feedback data based on the collected data, and use the feedback data to further train the natural language understanding model and update search and discovery logic for searching and discovering contents. The feedback data may categorize errors based on the interaction, and identify differences between search queries received during a search session with a user.
U.S. Pub 2020/0104305 - Wei discloses dynamically generating a data set representative of search results in response to a query and using the data set to accurately rank search results in response to a domain specific search query. Upon receiving the search query, features of the query and features of each search result are extracted. A relevance ranking may be assigned to each search result based on a comparison of the features of the query and each search result. The relevance ranking of each search result may be adjusted based on metrics related to user interactions. A data set may be created which includes the query, search results, extracted features, and metrics. The data set may be used to train a machine learning model to accurately determine a ranking of search results in response to a subsequent search query
U.S. Pub 2021/0406723 – Hintz discloses an interactive search training. A training canvas comprises results associated with a search query. The training canvas may be used as part of a training session that occurs during normal use of a search platform. When the search platform is first used, the results may be provided based on an existing model. An irrelevant result may be removed from the training canvas, such that a replacement result is added in its place. Additionally, results may be reordered, thereby indicating a ranking with which results should be displayed. Such interactions with the training canvas may be used to generate training data, such that a new model is trained accordingly. Thus, interactions with the training canvas yield high-quality training data that is usable to generate a model having equal or greater performance than a model that was trained using an equivalent amount of implicit training data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at 571-270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/Primary Examiner, Art Unit 2154
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