DETAILED ACTION
This Office Action is in response to Arguments submitted on April 23, 2026 for Application # 19/016,017 filed on January 10, 2025 in which claims 1-27 are presented for examination.
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 .
Status of claims
Claims 1-29 are pending, of which claims 1-29 are rejected under 35 U.S.C. 103 and also claims 1-29 are rejected under 35 U.S.C. 101.
No claims are amended.
No claims are canceled.
Claims 28 and 29 are newly added.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-29 are rejected under 35 U.S.C. 101. because the claims are directed to an abstract idea; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—Claims 1-29 recite a method, device and readable medium respectively.
The analysis of claims 1, 10 and 19 is as follows:
Step 2A, prong one: Does claims 1, 10 and 19 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “receiving a query that includes at least one question to which an answer is being sought;
identifying one or more digital assets that are relevant to the query;
providing, to at least one machine learning model, (1) the query, and (2) the one or more digital assets, to cause the at least one machine learning model to generate the answer to the at least one question; and
displaying respective affordances for the answer and at least one of the one or more digital assets” as drafted, are mental steps based on various processes can be performed in a human mind of retrieving answer to a question from digital assets (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “method”, “device” and “readable medium”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “receiving a query that includes at least one question to which an answer is being sought;
identifying one or more digital assets that are relevant to the query;
providing, to at least one machine learning model, (1) the query, and (2) the one or more digital assets, to cause the at least one machine learning model to generate the answer to the at least one question; and
displaying respective affordances for the answer and at least one of the one or more digital assets” are mere gathering data and applying process steps (i.e., identifying answer); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “displaying respective affordances for the answer and at least one of the one or more digital assets“, the gathering and determining are also recited at a high level of generality and merely generally link to respective technological environments (e.g., obtain operation data) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on similarity and relevance scores of the anchor and candidate pages is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the gathering and determining are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 1, 10 and 19 are rejected as being directed to non-patentable subject matter under §101.
The analysis of claims 2-9, 11-18 and 20-29 are as follows:
Step 2A, prong one: Does claims 2-9, 11-18 and 20-29 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “
claims 2, 11 and 20 wherein identifying the one or more digital assets that are relevant to the query comprises:
generating a query vector based at least in part on the query, wherein the query is associated with a user account, and the user account is associated with a user account vector;
generating an output vector based at least in part on the query vector and the user account vector;
obtaining, based at least in part on the query, a plurality of digital asset vectors, wherein each digital asset vector of the plurality of digital asset vectors corresponds to a respective digital asset;
comparing the output vector to the plurality of digital asset vectors to generate respective similarity scores for the plurality of digital asset vectors;
filtering the plurality of digital asset vectors in accordance with the similarity scores to establish a filtered plurality of digital asset vectors, wherein the one or more digital assets correspond to the filtered plurality of digital asset vectors.
Claims 3, 12 and 21 wherein filtering the plurality of digital asset vectors in accordance with the similarity scores to establish the filtered plurality of digital asset vectors comprises:
excluding, from the filtered plurality of digital asset vectors, digital asset vectors having respective similarity scores that do not satisfy a threshold similarity score.
Claims 4, 13 and 22 wherein the query vector is generated based at least in part on the query using a transformer-based large language model (LLM).
Claims 5, 14 and 23 wherein the user account vector is generated based at least in part on a first set digital asset vectors that correspond to digital assets marked as favorites in association with the user account;
a second set of digital asset vectors that correspond to digital assets that are frequently accessed in association with the user account; and
a third set of query history vectors that correspond to queries provided in association with the user account within a threshold period of time.
Claims 6, 15 and 24 prior to generating the output vector based at least in part on the query vector and the user account vector:
concatenating the query vector to the user account vector, or vice-versa.
Claims 7, 16 and 25 wherein: the output vector is generated based at least in part on the query vector and the user account vector using a transformer-based large language model (LLM), and the transformer-based LLM implements a set of fully connected layers and a set of input normalization layers.
Claims 8, 17 and 26 wherein a given digital asset vector of the plurality of digital asset vectors is generated by:
obtaining, from a transformer-based LLM, a first digital asset vector based at least in part on metadata associated with the corresponding respective digital asset;
obtaining, from a machine learning model, a second digital asset vector based at least in part on data content of the corresponding respective digital asset; and
generating the digital asset vector based at least in part on combining the first and second digital asset vectors.
Claims 9, 18 and 27 wherein the query comprises text content, image content, audio content, video content, or some combination thereof,
Claim 28 wherein the one or more digital assets are used to supplement the query when the at least one machine learning model generates the answer to the at least one question.
Claim 29 after displaying the respective affordances for the answer and the at least one of the one or more digital assets, detecting an input corresponding to an affordance of the at least one of the one or more digital assets; and in response to detecting the input corresponding to the affordance of the at least one of the one or more digital assets, performing a function” as drafted, are mental steps based on various processes can be performed in a human mind of modifying page with adding links to other similar pages that meets the certain score and applying certain models (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “method”, “device” and “readable medium”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites“
claims 2, 11 and 20 wherein identifying the one or more digital assets that are relevant to the query comprises:
generating a query vector based at least in part on the query, wherein the query is associated with a user account, and the user account is associated with a user account vector;
generating an output vector based at least in part on the query vector and the user account vector;
obtaining, based at least in part on the query, a plurality of digital asset vectors, wherein each digital asset vector of the plurality of digital asset vectors corresponds to a respective digital asset;
comparing the output vector to the plurality of digital asset vectors to generate respective similarity scores for the plurality of digital asset vectors;
filtering the plurality of digital asset vectors in accordance with the similarity scores to establish a filtered plurality of digital asset vectors, wherein the one or more digital assets correspond to the filtered plurality of digital asset vectors.
Claims 3, 12 and 21 wherein filtering the plurality of digital asset vectors in accordance with the similarity scores to establish the filtered plurality of digital asset vectors comprises:
excluding, from the filtered plurality of digital asset vectors, digital asset vectors having respective similarity scores that do not satisfy a threshold similarity score.
Claims 4, 13 and 22 wherein the query vector is generated based at least in part on the query using a transformer-based large language model (LLM).
Claims 5, 14 and 23 wherein the user account vector is generated based at least in part on a first set digital asset vectors that correspond to digital assets marked as favorites in association with the user account;
a second set of digital asset vectors that correspond to digital assets that are frequently accessed in association with the user account; and
a third set of query history vectors that correspond to queries provided in association with the user account within a threshold period of time.
Claims 6, 15 and 24 prior to generating the output vector based at least in part on the query vector and the user account vector:
concatenating the query vector to the user account vector, or vice-versa.
Claims 7, 16 and 25 wherein:
the output vector is generated based at least in part on the query vector and the user account vector using a transformer-based large language model (LLM), and
the transformer-based LLM implements a set of fully connected layers and a set of input normalization layers.
Claims 8, 17 and 26 wherein a given digital asset vector of the plurality of digital asset vectors is generated by:
obtaining, from a transformer-based LLM, a first digital asset vector based at least in part on metadata associated with the corresponding respective digital asset;
obtaining, from a machine learning model, a second digital asset vector based at least in part on data content of the corresponding respective digital asset; and
generating the digital asset vector based at least in part on combining the first and second digital asset vectors.
Claims 9, 18 and 27 wherein the query comprises text content, image content, audio content, video content, or some combination thereof.
Claim 28 wherein the one or more digital assets are used to supplement the query when the at least one machine learning model generates the answer to the at least one question.
Claim 29 after displaying the respective affordances for the answer and the at least one of the one or more digital assets, detecting an input corresponding to an affordance of the at least one of the one or more digital assets; and in response to detecting the input corresponding to the affordance of the at least one of the one or more digital assets, performing a function” are mere gathering data and applying process steps (i.e., modifying anchor page); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “generate an embedding for the anchor page .. by applying page embedding “, the gathering and determining are also recited at a high level of generality and merely generally link to respective technological environments (e.g., obtain operation data) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on similarity and relevance scores of the anchor and candidate pages is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the gathering and determining are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 2-9, 11-18 and 20-29 are rejected as being directed to non-patentable subject matter under §101.
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 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.
Claims 1-29 are rejected under 35 U.S.C. 103 as being unpatentable over LaRhette et al. US 2024/0281472 A1 (hereinafter ‘LaRhette’) in view of Liu et al. US 2018/0052908 A1 (hereinafter ‘Liu’) (IDS 10/02/2025).
As per claim 1, LaRhette disclose, A method for providing answers to questions included in search queries (LaRhette: paragraph 0033: disclose user query results (e.g., final summarized answer to a user query), and interactive clarifying questions to refine search intent, all within the browser-based interface), the method comprising, by a client computing device (LaRhette: paragraph 0040: disclose computing device):
receiving a query that includes at least one question to which an answer is being sought (LaRhette: paragraph 0067: disclose dialogues based search interface that accepts a question and answering the query);
identifying one or more digital assets that are relevant to the query (LaRhette: paragraph 0035: disclose search engine presents the retrieved results to the user through a user interface, typically a web page that displays a list of search results. Examiner equates results to digital assets. However, examiner would discuss about digital assets in secondary art below as intermediatory results);
providing, to at least one machine learning model (LaRhette: paragraph 0035: disclose machine learning algorithms), (1) the query (LaRhette: paragraph 0035: search query input), and (2) the one or more digital assets (LaRhette: paragraph 0035: disclose search engine presents the retrieved results to the user through a user interface, typically a web page that displays a list of search results), to cause the at least one machine learning model to generate the answer to the at least one question (LaRhette: paragraph 0035: disclose machine learning algorithms that analyze user interaction with search results ‘digital assets’ (e.g., click-through rates, time spent on a page, etc.) to continuously improve the relevance and accuracy of the search results ‘answer to the question’); and
displaying respective affordances for the answer and at least one of the one or more digital assets (LaRhette: paragraph 0070: disclose Natural Language Generation (NLG) for automated content creation and dialogue systems; machine translation for multilingual communication; abstractive and extractive summarization of extensive documents; sophisticated question answering systems).
It is noted, however, LaRhette did not specifically detail the aspects of
identifying one or more digital assets;
displaying respective affordances as recited in claim 1.
On the other hand, Liu achieved the aforementioned limitations by providing mechanisms of
identifying one or more digital assets (Liu: paragraph 0022: disclose identify a plurality of candidate publications ‘digital assets’ in the publication corpus);
displaying respective affordances (Liu: paragraph 0034: disclose generate relevant results and Fig. 2 Element 222: display respective results ‘answer’).
LaRhette and Liu are analogous art because they are from the “same field of endeavor” and both from the same “problem-solving area”. Namely, they are both from the field of “Relevant Search Systems”.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the systems of LaRhette and Liu because they are both directed to relevant search systems and both are from the same field of endeavor. The skilled person would therefore regard it as a normal option to include the restriction features of Liu with the method described by LaRhette in order to solve the problem posed.
The motivation for doing so would have been to improve search results of a large corpus (Liu: paragraph 0002).
Therefore, it would have been obvious to combine Liu with LaRhette to obtain the invention as specified in instant claim 1.
As per claim 2, most of the limitations of this claim have been noted in the rejection of claim 1 above.
It is noted, however, LaRhette did not specifically detail the aspects of
wherein identifying the one or more digital assets that are relevant to the query comprises:
generating a query vector based at least in part on the query, wherein the query is associated with a user account, and the user account is associated with a user account vector;
generating an output vector based at least in part on the query vector and the user account vector;
obtaining, based at least in part on the query, a plurality of digital asset vectors, wherein each digital asset vector of the plurality of digital asset vectors corresponds to a respective digital asset;
comparing the output vector to the plurality of digital asset vectors to generate respective similarity scores for the plurality of digital asset vectors;
filtering the plurality of digital asset vectors in accordance with the similarity scores to establish a filtered plurality of digital asset vectors, wherein the one or more digital assets correspond to the filtered plurality of digital asset vectors as recited in claim 2.
On the other hand, Liu achieved the aforementioned limitations by providing mechanisms of
wherein identifying the one or more digital assets that are relevant to the query comprises:
generating a query vector based at least in part on the query, wherein the query is associated with a user account (Liu: paragraph 0026: disclose user provides input and paragraph 0025: disclose to authenticate user, which examiner equates to user account), and the user account is associated with a user account vector (Liu: paragraph 0022: disclose search query encoded by a semantic vector in a semantic vector space);
generating an output vector based at least in part on the query vector and the user account vector (Liu: paragraph 0078: disclose a semantic search vector is accessed that encodes the search query, such that the semantic search vector represents the semantic meaning of the search query. The semantic search vector is pre-generated and just accessed in one embodiment. In another embodiment, the semantic search vector is generated and then accessed. The semantic search vector is based on the entire search query, multiple terms of the search query, or a single term in the event of short search queries);
obtaining, based at least in part on the query, a plurality of digital asset vectors, wherein each digital asset vector of the plurality of digital asset vectors corresponds to a respective digital asset (Liu: paragraph 0130: disclose publication corpus ‘digital asset’, in response to a search query transmitted from a device operated by a user, is generally covered. At, a search query Y is received from a user device. At sequence semantic embedding (SSE) query elements into semantic vector);
comparing the output vector to the plurality of digital asset vectors to generate respective similarity scores for the plurality of digital asset vectors (Liu: paragraph 0132: disclose measure ‘comparison’ the similarity between the semantic vectors representations of the search query (Y) and the semantic vector representations of publications (X));
filtering the plurality of digital asset vectors in accordance with the similarity scores to establish a filtered plurality of digital asset vectors, wherein the one or more digital assets correspond to the filtered plurality of digital asset vectors (Liu: paragraph 0132: disclose filtering is performed using both candidate publications X from and on search query).
As per claim 3, most of the limitations of this claim have been noted in the rejection of claims 1 and 2 above.
It is noted, however, LaRhette did not specifically detail the aspects of
wherein filtering the plurality of digital asset vectors in accordance with the similarity scores to establish the filtered plurality of digital asset vectors comprises: excluding, from the filtered plurality of digital asset vectors, digital asset vectors having respective similarity scores that do not satisfy a threshold similarity score as recited in claim 3.
On the other hand, Liu achieved the aforementioned limitations by providing mechanisms of
wherein filtering the plurality of digital asset vectors in accordance with the similarity scores to establish the filtered plurality of digital asset vectors comprises: excluding, from the filtered plurality of digital asset vectors, digital asset vectors having respective similarity scores that do not satisfy a threshold similarity score (Liu: paragraph 0132: disclose filtering is performed using both candidate publications X from and on search query Y. L0 filtering is shown in more detail at FIG. 13. At 1236, semantic vector distance between X and Y is used to measure the similarity between the semantic vectors representations of the search query (Y) and the semantic vector representations of publications (X)).
As per claim 4, most of the limitations of this claim have been noted in the rejection of claims 1 and 2 above. In addition, LaRhette disclose, wherein the query vector is generated based at least in part on the query using a transformer-based large language model (LLM) (LaRhette: paragraph 0074: disclose he block diagram 300 comprises a large language model and task-specific generative model 302 paradigm including a large language models 304 listing and a task-specific generative models 318 listing. The large language models 304 listing includes examples of LLMs, such as a Generative Pre-trained Transformer 3 308 (GPT-3)).
As per claim 5, most of the limitations of this claim have been noted in the rejection of claims 1 and 2 above. In addition, LaRhette disclose, wherein the user account vector is generated based at least in part on a first set digital asset vectors that correspond to digital assets marked as favorites in association with the user account (LaRhette: paragraph 0051: disclose similarity in search terminology and/or search logic, similarity in search responses such as the type of sources trusted, language in responses that the user tends to favor as responsive, etc.); a second set of digital asset vectors that correspond to digital assets that are frequently accessed in association with the user account (LaRhette: paragraph 0052: disclose parse potentially responsive documents may include providing a vector description of elements of the potentially responsive documents); and a third set of query history vectors that correspond to queries provided in association with the user account within a threshold period of time (LaRhette: paragraph 0051: disclose The user type may be a classification related to the user that can be utilized to inform responsive search results, and may be based upon characteristics of the user (e.g., user education level, user history favoring certain document types such as news, academic papers, articles from particular industry, etc.) and/or the context of the user search operations (e.g., professional user, academic user, casual user, traveling user, etc.), including aspects such as the device being used by the user, the time of day, the geographic location of the user, etc).
As per claim 6, most of the limitations of this claim have been noted in the rejection of claims 1 and 2 above. In addition, LaRhette disclose, prior to generating the output vector based at least in part on the query vector and the user account vector: concatenating the query vector to the user account vector, or vice-versa (LaRhette: paragraph 0051: disclose similarity in search terminology and/or search logic, similarity in search responses such as the type of sources trusted, language in responses that the user tends to favor as responsive, etc.).
As per claim 7, most of the limitations of this claim have been noted in the rejection of claims 1 and 2 above. In addition, LaRhette disclose, wherein: the output vector is generated based at least in part on the query vector and the user account vector using a transformer-based large language model (LLM), and the transformer-based LLM implements a set of fully connected layers and a set of input normalization layers (LaRhette: paragraph 0074: disclose he block diagram 300 comprises a large language model and task-specific generative model 302 paradigm including a large language models 304 listing and a task-specific generative models 318 listing. The large language models 304 listing includes examples of LLMs, such as a Generative Pre-trained Transformer 3 308 (GPT-3)).
As per claim 8, most of the limitations of this claim have been noted in the rejection of claims 1 and 2 above. In addition, LaRhette disclose, wherein a given digital asset vector of the plurality of digital asset vectors is generated by: obtaining, from a transformer-based LLM, a first digital asset vector based at least in part on metadata associated with the corresponding respective digital asset; obtaining, from a machine learning model, a second digital asset vector based at least in part on data content of the corresponding respective digital asset; and generating the digital asset vector based at least in part on combining the first and second digital asset vectors (LaRhette: paragraph 0074: disclose he block diagram 300 comprises a large language model and task-specific generative model 302 paradigm including a large language models 304 listing and a task-specific generative models 318 listing. The large language models 304 listing includes examples of LLMs, such as a Generative Pre-trained Transformer 3 308 (GPT-3)).
As per claim 9, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, LaRhette disclose, wherein the query comprises text content, image content, audio content, video content, or some combination thereof (LaRhette: paragraph 0034: disclose search engine processes and analyzes the content of the page to understand its subject matter. Key elements such as text, images, and video content).
As per claim 10, LaRhette disclose, A non-transitory computer readable storage medium configured to store instructions that, when executed by at least one processor included in a computing device (LaRhette: paragraph 0247: disclose a tangible machine-readable storage medium (e.g., a non-transitory storage medium) includes instructions that, when executed by a machine, cause the machine to perform operations): remaining limitations in this claim 10 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1.
As per claim 11, limitations of this claim are similar to claim 2. Therefore, examiner rejects claim 9 limitations under the same rationale as claim 2.
As per claim 12, limitations of this claim are similar to claim 3. Therefore, examiner rejects claim 12 limitations under the same rationale as claim 3.
As per claim 13, limitations of this claim are similar to claim 4. Therefore, examiner rejects claim 13 limitations under the same rationale as claim 4.
As per claim 14, limitations of this claim are similar to claim 5. Therefore, examiner rejects claim 14 limitations under the same rationale as claim 5.
As per claim 15, limitations of this claim are similar to claim 6. Therefore, examiner rejects claim 15 limitations under the same rationale as claim 6.
As per claim 16, limitations of this claim are similar to claim 7. Therefore, examiner rejects claim 16 limitations under the same rationale as claim 7.
As per claim 17, limitations of this claim are similar to claim 8. Therefore, examiner rejects claim 17 limitations under the same rationale as claim 8.
As per claim 18, limitations of this claim are similar to claim 9. Therefore, examiner rejects claim 18 limitations under the same rationale as claim 9.
As per claim 19, LaRhette disclose, A computing device configured to provide answers to questions included in search queries, the computing device comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the computing device to carry out steps that include at least one processor configured to cause the computing device to carry out steps that include (LaRhette: paragraph 0041: disclose instructions stored on a computer readable medium and configured to cause a processor to perform recited operations): remaining limitations in this claim 19 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1.
As per claim 20, limitations of this claim are similar to claim 2. Therefore, examiner rejects claim 20 limitations under the same rationale as claim 2.
As per claim 21, limitations of this claim are similar to claim 3. Therefore, examiner rejects claim 21 limitations under the same rationale as claim 3.
As per claim 22, limitations of this claim are similar to claim 4. Therefore, examiner rejects claim 22 limitations under the same rationale as claim 4.
As per claim 23, limitations of this claim are similar to claim 5. Therefore, examiner rejects claim 23 limitations under the same rationale as claim 5.
As per claim 24, limitations of this claim are similar to claim 6. Therefore, examiner rejects claim 24 limitations under the same rationale as claim 6.
As per claim 25, limitations of this claim are similar to claim 7. Therefore, examiner rejects claim 25 limitations under the same rationale as claim 7.
As per claim 26, limitations of this claim are similar to claim 8. Therefore, examiner rejects claim 26 limitations under the same rationale as claim 8.
As per claim 27, limitations of this claim are similar to claim 9. Therefore, examiner rejects claim 27 limitations under the same rationale as claim 9.
As per claim 28, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, LaRhette disclose, wherein the one or more digital assets are used to supplement the query (LaRhette: paragraph 0146: disclose updated search query with adjusted ‘supplement’ search parameters ‘digital assets’) when the at least one machine learning model (LaRhette: paragraph 0035: disclose machine learning algorithms) generates the answer to the at least one question (LaRhette: paragraph 0067: disclose dialogues based search interface that accepts a question and answering the query).
As per claim 29, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, LaRhette disclose, after displaying the respective affordances for the answer and the at least one of the one or more digital assets (LaRhette: paragraph 0070: disclose Natural Language Generation (NLG) for automated content creation and dialogue systems; machine translation for multilingual communication; abstractive and extractive summarization of extensive documents; sophisticated question answering systems), detecting an input corresponding to an affordance of the at least one of the one or more digital assets (LaRhette: paragraph 0035: disclose the interaction ‘detect input’ with search results ); and in response to detecting the input corresponding to the affordance of the at least one of the one or more digital assets, performing a function (LaRhette: paragraph 0035: disclose continuously improve the relevance and accuracy ‘function’ of the search results).
It is noted, however, LaRhette did not specifically detail the aspects of
identifying one or more digital assets;
displaying respective affordances as recited in claim 29.
On the other hand, Liu achieved the aforementioned limitations by providing mechanisms of
identifying one or more digital assets (Liu: paragraph 0022: disclose identify a plurality of candidate publications ‘digital assets’ in the publication corpus);
displaying respective affordances (Liu: paragraph 0034: disclose generate relevant results and Fig. 2 Element 222: display respective results ‘answer’).
Response to Arguments
Applicant's arguments filed on April 23, 2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Claims 1, 10 and 19 are directed to the abstract idea of receiving a query that includes at least one question to which an answer is being sought;
identifying one or more digital assets that are relevant to the query;
providing, to at least one machine learning model, (1) the query, and (2) the one or more digital assets, to cause the at least one machine learning model to generate the answer to the at least one question; and
displaying respective affordances for the answer and at least one of the one or more digital assets.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements such as providing answers to questions via search queries and displaying the answers based on digital assets, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Claims 1, 10 and 19 are directed to an abstract recited in the form of a generalized invention which can be performed in a human mind with a pencil and paper. The particular claimed elements which constitute the abstract idea include
receiving a query that includes at least one question to which an answer is being sought;
identifying one or more digital assets that are relevant to the query;
providing, to at least one machine learning model, (1) the query, and (2) the one or more digital assets, to cause the at least one machine learning model to generate the answer to the at least one question; and
displaying respective affordances for the answer and at least one of the one or more digital assets.
Displaying answers based on affordances to the digital assets trained by machine learning model, based on the broadest reasonable interpretation in view of the specification.
Mathematical relationships and algorithms have been found by the courts (e.g. Benson, Flook, Diehr, Grams) to be abstract ideas. For example, in Benson, a mathematical procedure for converting one form of numerical representation to another was found to be an exception, as was an algorithm for calculating parameters indicating an abnormal condition in Grams. The concept described in claims 1, 10 and 19 does not meaningfully differ from those found by the courts to constitute mathematical algorithms.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Additional elements recited in the claim include the limitations: a computer-readable medium storing computer-executable instructions that when executed by a computer cause the computer to perform the method; training machine learning model on the digital assets for answers, can also be interpreted as algorithmic logic. These limitations are directed to realizing the mathematical algorithm in a computer system. Executing the using a model to generate answers to question than a broad recitation of generic use of a computer (i.e., executing). Providing the machine learning model is at most insignificant post solution activity of training on digital assets. The preamble's recitation of a "computer-readable medium" and a "computer" are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Further, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because merely providing a result and executing the algorithm by a computer is akin to adding the words “apply it” with a computer in conjunction with the abstract idea. Such limitations are not enough to add significantly more to the method of business rules of constructing file system view, which represent mathematical relationships and algorithms.
Considering all the limitations in combination, the claimed additional computer elements do not show any inventive concept in applying the mathematical operations, such as improving the performance of a computer or any other technology. The steps describe nothing more than a computer’s basic function of answers to questions based on digital assets generated by machine learning model, and do not meaningfully limit the performance of the calculation. Therefore, the claim does not amount to significantly more than the abstract idea itself.
Applicant's arguments filed on April 23, 2026 have been fully considered but they are not persuasive.
Examiner’s response to applicant’s argument related to claims 1, 10 and 19.
Applicant argues neither LaRhette nor Liu teach ‘providing, to at least one machine learning model, (1) the query, and (2) the one or more digital assets, to cause the at least one machine learning model to generate the answer to the at least one question’. Examiner respectfully disagrees with applicant’s argument. LaRhette disclose in paragraph 0035 of machine learning algorithms ‘model’ of where providing, to at least one machine learning model and LaRhette disclose in paragraph 0035 of a search query input to the machine learning algorithm where (1) the query.
Examiner cites addition paragraphs in LaRhette to enhance the argument the prior art teaches the following limitation of (2) the one or more digital assets in paragraph 0031 where pre-trained models are providing an initial corpus of training data, which examiner equates to digital assets and LaRhette teaches in paragraph 0035 of machine learning algorithms that analyze user interaction with search results ‘digital assets’ (e.g., click-through rates, time spent on a page, etc.) to continuously improve the relevance and accuracy of the search results ‘answer to the question’ of limitation to cause the at least one machine learning model to generate the answer to the at least one question. Examiner believes the applicant argument is related to generate the answer ‘result’ to the at least on question ‘query’. Since examiner can’t import limitations unnecessarily into the claim language under BRI guidelines. Examiner interprets these claim limitations under BRI (Broadest Reasonable Interpretation) guidelines where examiner without importing limitations from the specification into the claims unnecessarily.
MPEP 2106.II.C
USPTO personnel are to give claims their broadest reasonable interpretation in light of the supporting disclosure. In re Morris, 127 F.3d 1048, 1054-55, 44 USPQ2d 1023, 1027-28 (Fed. Cir. 1997). Limitations appearing in the specification but not recited in the claim should not be read into the claim. E-Pass Techs., Inc. v. 3Com Corp., 343 F.3d 1364, 1369, 67 USPQ2d 1947, 1950 (Fed. Cir. 2003) (claims must be interpreted “in view of the specification” without importing limitations from the specification into the claims unnecessarily). In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969). See also In re Zletz, 893 F.2d 319, 321-22, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) (“During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow.... The reason is simply that during patent prosecution when claims can be amended, ambiguities should be recognized, scope and breadth of language explored, and clarification imposed.... An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. US 2019/0294690 A1 disclose “MEDIA CONTENT ITEM RECOMMENDATION SYSTEM”
US Pub. US 2019/0361946 A1 disclose “METHODS, DEVICES AND SYSTEMS FOR IMPROVED SEARCH OF PERSONAL AND ENTERPRISE DIGITAL ASSETS”
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PAVAN MAMILLAPALLI/
Primary Examiner, Art Unit 2159
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