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 .
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. The claim recites a computer-implemented method that performs at least one step. Thus, the claim is a process/method, which is one of the statutory categories of invention under 35 U.S.C. § 101.
Step 2A Prong 1, This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Under MPEP guidance on the non-mutual exclusivity of classification, a single claim limitation may recite more than one type of abstract idea grouping simultaneously. The purpose of this step is to identify all judicial exceptions present in the claim, regardless of how many groupings they fall into.
Limitation: “generating, for a query received from a client device, relevancy scores for a plurality of content items by utilizing a generative model to judge relevance of the plurality of content items relative to a query context corresponding to the query” This limitation recites a mental process abstract idea. The core concept described is judging relevance of content items relative to a query context. A user could mentally compare different pieces of information and determine which items are more relevant. While this limitation involves utilizing a generative model as the tool to perform the judgment, the core concept being claimed is a cognitive function that does not require specialized hardware or mathematical calculations.
Limitation: “by performing vector similarity matching of a query embedding for the query and content item embeddings for the plurality of content items according to the query context.”
This limitation recites both a mental process abstract idea AND a mathematical concept abstract idea under the non-mutual exclusivity principle recognized in MPEP.
As a mental process, this limitation describes an evaluation or judgment activity that can be performed in the human mind. A user could mentally compare different pieces of information (query and content items) against a stated context and determine which items are more relevant. The core concept is performing similarity matching-comparing two things to determine how closely they relate-which humans do regularly.
As a mathematical concept, this limitation recites a mathematical concept abstract idea. The core concept described is performing vector similarity matching, which describes applying specific mathematical operations such as dot products, cosine similarity calculations to compare numerical representations (embeddings) of data. This limitation describes the performance of a specific mathematical operation-vector similarity matching-to derive results by comparing numerical values representing vectors.
Limitation: “generating, by the generative model, a ranked output comprising the plurality of content items ranked according to the relevancy scores” This limitation recites a mental process abstract idea. The core concept described is ordering information based on assigned values-a process that can be performed mentally. A human could take a list of items with associated scores and arrange them in order from highest to lowest score, creating a ranked output through simple comparison and organization. While this limitation involves utilizing a generative model as the tool to perform the judgment, the core concept being claimed is a cognitive function that does not require specialized hardware or mathematical calculations.
Limitation: “generating, utilizing a retrained generative model instance of the generative model to judge relevance to the query context 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” This limitation recites a mental process abstract idea. The core concept described is re-evaluating or re-judging relevance based on new information-a cognitive activity that can be performed mentally. A human could take previously assigned scores, receive feedback about adjusting them, and then reassess which items should be ranked higher based on the updated criteria. This involves observation, evaluation, and opinion formation. While this limitation mentions utilizing a retrained generative model instance, the core abstract idea being claimed is the act of judging relevance based on modifications. While this limitation involves utilizing a retrained model as the tool to perform the judgment, the core concept being claimed is a cognitive function that does not require specialized hardware or mathematical calculations.
Limitation 6: “determining, using an allocation system to process the modified ranked output, a content item allocation indicating display positions for one or more content items from the plurality of content items” This limitation recites a mental process abstract idea. The core concept described is deciding about which items should be displayed and in what order. A human could look at a list of ranked content items and decide “this item goes first, this one goes second” based on their judgment of importance, or relevance. While the implementation uses an allocation system to process output and determine display positions, the underlying concept is one of making placement decisions based on ranked input.
“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. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). The limitations mentioned are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A Prong 2: 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 or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
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
a client device, a generative model, a retrained generative model instance, an allocation system as generic computer components.
MPEP 2106.05(a) Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field
The additional elements, receiving modifications from client devices and using allocation systems, describe data transmission and display positions. The claim covers the general idea of adjusting relevance scores and determining display positions.The claim merely applies abstract ideas on generic technological infrastructure without demonstrating a technological solution to a technological problem.
MPEP 2106.05(b) Particular Machine
The additional elements, client device, generative model, retrained generative model instance, and allocation system, describe types of computing components used for data processing and display positions. These elements do not integrate the exception into a practical application because the additional elements are recited at a high level of generality without describing any particular machine that performs the claimed functions in an unconventional way.
MPEP 2106.05(c) Particular Transformation
The additional elements, receiving modifications to relevancy scores and determining content item allocations, describe data manipulation. These elements do not integrate the exception into a practical application because there is no particular transformation of a physical article or tangible medium described in the claim.
MPEP 2106.05(e) Other Meaningful Limitations
The additional elements, receiving modifications from client devices and using allocation systems to determine display positions, describe interactions involving network communication and interface management. However, these elements do not integrate the exception into a practical application because they are conventional features in the field of content ranking and display systems. The claim does not recite any unconventional or non-conventional implementation details that would meaningfully limit the abstract idea.
MPEP 2106.05(f) Mere Instructions to Apply an Exception
The additional elements, client device for receiving modifications and allocation system for determining display positions, when evaluated individually and in combination with the abstract idea limitations, these elements do not integrate the exception into a practical application because they amount to mere instructions to implement an abstract idea on a computer. The claim essentially recites the words "apply it" through generic references to client devices and allocation systems. The additional elements do not improve computer capabilities.
MPEP 2106.05(g) Insignificant Extra-Solution Activity
The additional element recited in the claim—receiving modifications from a client device—is beyond the judicial exception as it describes an external data input activity that occurs outside the mental process of judging relevance. However, when evaluated individually and in combination with the abstract idea limitations, this element does not integrate the exception into a practical application because it constitutes insignificant extra-solution activity. Receiving electronic data from another device is a routine computer function that does not add any meaningful limitation to the claim; it merely provides input for the abstract idea to operate upon without effecting any transformation or improvement beyond the mental process itself. The receiving step is conventional in the field of networked computing and does not meaningfully limit how the abstract idea is applied or integrated into a practical technological solution.
MPEP 2106.05(h) Field of Use and Technological Environment
The additional elements, client device, generative model, retrained generative instance, and allocation system, are components operating within a content ranking and display environment. These elements do not integrate the exception into a practical application because they are claimed at a generic field-of-use level without specifying any particular technological improvements or constraints. The claim does not limit the abstract ideas to a specific technological environment that would meaningfully narrow their scope.
Step 2B, Examine the additional elements individually and as an ordered combination to see if they provide an inventive concept that adds "significantly more" than the exception itself. The additional elements, receiving modifications from client devices, utilizing retrained generative model instances, and using allocation systems to determine display positions, are conventional practices in the field of content ranking and recommendation systems. Receiving user input to adjust relevance scores is a well-established feature in search engines. Using machine learning models (including retrained instances) for content ranking is standard practice in modern AI applications (learn to rank). Determining display positions based on ranked outputs is a conventional function of web browsers. None of these elements represent unconventional steps. The claim limitations do not describe a specific, non-generic solution to a specific technical problem. While the claim recites receiving modifications and using retrained generative models, it does not specify how these features solve any particular technical challenge. Under Berkheimer v. HP Inc., factual inquiries regarding whether these limitations describe unconventional solutions are not supported by the claim language as currently drafted because no specific technical improvements or non-conventional implementations are disclosed. The ordered combination of additional elements does not add "significantly more" than the judicial exception itself. When evaluated individually and in combination, the additional elements are well-understood, routine, and conventional features in the field of content ranking and display technologies. The claim as a whole is directed to judicial exceptions (mental processes and mathematical concepts) integrated into a practical application only insofar as it applies these abstractions on conventional technological tools, which does not satisfy Step 2B requirements for an inventive concept.
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” Extracting a query embedding and comparing it with content item embeddings describes conventional data processing operations that are well-understood, routine practices in machine learning and natural language processing systems. This additional limitation does not integrate the abstract idea into a practical application because it does not provide any unconventional solution to a specific technical problem or improving computer functioning.
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” Receiving an interaction (such as clicking, tapping, or selecting) to modify a relevancy score is a well-understood, routine practice in interactive computing systems where users provide input through graphical interfaces. This additional element constitutes insignificant extra-solution activity because it involves standard human-computer interaction mechanisms (clicking buttons, selecting items in an interface) that are generic to all interactive applications.
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” Retraining an AI model based on new data or feedback is standard practice in machine learning systems. This limitation merely specifies how the generative model is updated without describing any unconventional algorithm. The retraining step does not provide an inventive concept beyond automating conventional model optimization processes.
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 term "blender process" is ambiguous and does not clearly specify what unconventional or non-conventional technique is being employed. The claim does not clearly distinguish a particular technical solution from conventional blending techniques used in content ranking and recommendation algorithms, leaving open whether this additional element provides significantly more than the abstract ideas of judging relevance and modifying scores.
Claim 7 recites “further comprising providing the response insertion for display on the client device” Providing content for display is insignificant extra-solution activity. It describes a routine output function that does not meaningfully limit how the abstract ideas are implemented. The claim does not have any additional limitations that 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” This limitation does not integrate the abstract idea into a practical application because it merely specifies how multiple AI components are organized within a system without describing any unconventional architecture or specific technical improvement. The claim does not have any additional limitations that amount to significantly more than the abstract idea.
Claims 9-21 are similar to claims 2-8. The claims are rejected based on the same reasons.
Response to Arguments
Section - Rejections Under 35 U.S.C 101
Pg. 11, Applicant argues that “ As one of ordinary skill in the art would understand, "generating, for a query received from a client device, relevancy scores for a plurality of content items by utilizing a generative model to judge relevance of the plurality of content items," "generating, utilizing a retrained generative model instance of the generative model to judge relevance to the query context based on the modification to the relevancy score, a modified ranked output," and "determining, using an allocation system to process the modified ranked output, a content item allocation indicating display positions for one or more content items from the plurality of content items," are steps that specifically utilize a retrained generative model and an allocation system to generate a modified ranked output and determine content item display positions. No human mind is equipped to perform the recited operations that generate and process machine learning outputs to determine content item allocations within a computer system, particularly using generative models as understood by one skilled in the art. Accordingly, the claims are not directed to a mental process…”
The Applicant argues that because the claimed steps use complex machine learning (ML) and computer systems, they cannot be considered "Mental Processes."
The guidance defines a Mental Process as an idea that can be performed in the human mind, including observations, evaluations, judgments, and opinions. The fact that using advanced technology to implement these ideas does not change the fundamental nature of the idea itself.
Regarding “generating... relevancy scores for a plurality of content items by utilizing a generative model to judge relevance…" The Applicant argues that because this uses a generative model, it is not a mental process. However, the core concept here is judging relevance. Judging whether one piece of information is more relevant than another is fundamentally a human judgment. This type of evaluation is exactly what the guidance defines as a Mental Process. The generative model is simply the tool used by the system to perform this human-like act of judgment.
Regarding “generating, utilizing a retrained generative model instance... to judge relevance... a modified ranked output...” The Applicant argues that re-judging relevance using a retrained ML model is too complex for a human mind. The idea being claimed is re-evaluation based on new information. If a person receives feedback (a modification to a score) and then mentally reassesses their initial judgment, the act of re-judging is a Mental Process. The retrained model merely automates the process of human reconsideration or adjustment of an opinion based on new information.
Regarding “determining, using an allocation system to process the modified ranked output, a content item allocation indicating display positions...” The Applicant argues that determining display positions requires technology and is not mental. The examiner respectfully disagrees because the underlying concept is making a placement decision. When a human decides which slides to put first in a presentation, they are making an allocation or arrangement judgment. The “allocation system” is just the technical way of executing that human-like decision-making process.
Because all three limitations describe core functions—judging, re-evaluating, and arranging based on judgment—that are fundamentally acts of observation, evaluation, and opinion formation, they fall within the grouping of Mental Processes as defined by the guidance documents. The use of advanced ML models or allocation systems only describes the technical means to perform these mental processes.
Pg. 11-13, Applicant argues that “… The currently amended claims integrate any alleged abstract idea into a practical application by reciting elements that improve the functioning of a computer, or an improvement to another technology or technical field. For instance, the currently amended claims provide an improvement to a technical problem specific to relevance ranker systems by at least eliminating the need to convert relevance rankings into a form that is usable by composite allocation systems. As an example, the Specification describes how conventional relevance ranker systems generate "relative" relevance ranking scores that are only meaningful relative to other search results and must be converted into "absolute" relevance scores before they can be used in composite allocation systems. Specification at [0037]. The Specification further explains that there is a need to convert relative similarity score rankings into judgments such as an "expected relevance label for this item in this context," and that this system does that. Id…
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as recited by currently amended independent claim 2 and as similarly recited by currently amended independent claims 9 and 16. Indeed, as discussed above, the claimed invention determines, "using an allocation system to process the modified ranked output, a content item allocation indicating display positions for one or more content items," where the generated relevance information is directly usable by a composite allocation system without requiring a separate conversion of relevance rankings before processing.
Accordingly, the claims recite a specific technological improvement to relevance ranker systems by generating relevance information that is directly usable by a composite allocation system without requiring the intermediate conversion process used by conventional systems. Thus, the claimed invention improves the operation of computer-based relevance ranking systems.
Therefore, Applicant respectfully submits that the recited currently amended claims provide a practical application and that the rejection of the claims pursuant to 35 U.S.C. § 101 be withdrawn…”
The Applicant points to paragraph [0037] that supports the idea “eliminating the need to convert relative scores into absolute scores” as technological improvement.
The fact that a system is described as more efficient because it uses modern technology (like generative models) does not automatically make the claim eligible under the law. The claim is analyzed if the steps in the claim are abstract concepts, even when they run on a computer.
The Applicant argues that the claims improve computer function by eliminating the need to convert relative rankings into absolute scores. However, claim 2 merely says: “determining, using an allocation system to process the modified ranked output, a content item allocation...” The claim does not say how the output data is formatted, how it avoids a translation step. Because the claim language does not include the specific technical solution described in your remarks, the claim does not include a specific technical improvement.
Further, the steps are fundamentally abstract:
Judging Relevance: The claim recites utilizing a model to “judge relevance.” Evaluating or judging importance is a mental process. Using a computer tool to make this judgment does not change its abstract nature.
Vector Similarity Matching: The claim recites performing “vector similarity matching” of query and content item embeddings. This limitation is classified as both mental process and math concept in previous section.
Determining Display Positions: Deciding where to place items on a screen based on a score is an act of making a decision, which is a mental process.
Simply using software tools to perform basic human tasks (like judging relevance or making a placement decision) does not change the fact that the underlying steps are abstract.
The claims are still primarily directed to Mental Processes (judging relevance, deciding placement, vector similarity matching) and Mathematical Concepts (calculating vector similarity matching). The tools (e.g., generative models, allocation systems) are simply used to execute these abstract ideas.
Applicant also argues that the currently amended claims recite a specific technological improvement by generating relevance information that is directly usable by a composite allocation system without requiring an intermediate conversion process. Specifically, Applicant asserts that the claimed invention determines a content item allocation "where the generated relevance information is directly usable by a composite allocation system without requiring a separate conversion of relevance rankings before processing."
However, features or advantages discussed in the remarks but not explicitly recited in the claims cannot be given patentable weight. The claim does not recite a “composite” allocation system, nor does it contain any structural or functional limitations requiring the system to bypass, eliminate, or operate “without” an intermediate conversion process. The claim as written fails to recite a specific technological improvement. Accordingly, the rejection of independent claim 2 (and independent claims 9 and 16) under 35 U.S.C. § 101 is maintained.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
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
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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/ Primary Examiner, Art Unit 2154
Prosecution Insights