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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 17-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 17, the limitation of “the labeled text component” has unclear antecedent basis because the claim previously recites “a plurality of labeled text components” rather than a single “labeled text component.” Thus, the recitation of “the labeled text component” is unclear as to whether it refers to each of the plurality of labeled text components or at least one of the plurality of labeled text components. For purposes of examination, the above limitation has been interpreted to be “at least one of the plurality of labeled text components.”
Claims 18-20 are also rejected for the same grounds because they incorporate the limitations of claim 17 due to their dependencies.
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 1-16 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, independent claim 1, for example, recites an abstract idea in the form of mental processes. A mental process is a process that “can be performed in the human mind, or by a human using a pen and paper” (MPEP § 2106.04(a)(2)(III), paragraph 1). Examples of mental processes include “observations, evaluations, judgments, and opinions” (MPEP § 2106.04(a)(2)(III), paragraph 2).
Claim 1 recites the following limitations of that are mental processes:
“determining a user interest parameter for a content component of content output by a user interface, the content component being of a content type for a content class” [This is a mental process that can be performed by observation, evaluation, judgment and opinion because it only recites an act of determining defined at a high degree of generality without any specific procedures that require a level of complexity or accuracy that distinguishes over what a human is capable of. Therefore, this step can be performed by a human.]
“determine, based on the interest score and a threshold interest score, a different content type for the content class;” [This is a mental process that can be performed by observation, evaluation, judgment and opinion because it only recites an act of determining defined at a high degree of generality without any specific procedures that require a level of complexity or accuracy that distinguishes over what a human is capable of. Therefore, this step can be performed by a human.]
“selecting a layer for a subsequent content component of the content having the different content type.” [This is a mental process that can be performed by observation, evaluation, judgment and opinion because it only recites an act of selection defined at a high degree of generality without any specific procedures that require a level of complexity or accuracy that distinguishes over what a human is capable of. Therefore, this step can be performed by a human.]
The above analysis is also applied to the other independent claim 9 which recite features that are the same or analogous to those discussed above.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No. The judicial exception recited in the above discussed claims is not integrated into a practical application.
Independent claims 1 and 17 recite the following additional elements, but these additional elements are not sufficient to integrate the judicial exception into a practical application:
“receiving an interest score for the content component from an interest model in response to providing the user interest parameter as input to the interest model” (claims 1 and 17) [This element constitutes “adding insignificant extra-solution activity to the judicial exception” (MPEP § 2106.05(g)) since it merely amounts to necessary data gathering or outputting, which identifies is identified in MPEP § 2106.05(g) as a form of extra-solution activity.]
“A system for generating content for user interfaces, comprising: a memory having executable instructions stored thereon; one or more processors configured to execute the executable instructions to cause the system to perform a method” (claim 9) [These elements constitute no more than mere instructions to apply the judicial exception using generic computer components (MPEP § 2106.04(d)(I)). These additional elements merely invoke the use of generic computer components, namely a generic computer, as tools to perform the abstract idea, and do not place any limitations on the abstract idea other than the use of such generic computer components. Therefore, these additional elements do not integrate the judicial exception into a practical application.]
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No. The claims do not include additional elements that are sufficient for the claims to amount to significantly more than the judicial exception the Step 2B analysis.
Additional elements that are mere instructions to apply an exception do not constitute significantly more than a judicial exception under MPEP § 2106.05(I)(A). Therefore, those additional elements identified above in the Prong One analysis as mere instructions to apply an exception do not constitute significantly more.
Additional elements that are considered to be extra-solution activity do not amount to significantly more if, upon their reevaluation in Step 2B, they are also merely appending “well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception” (MPEP § 2106.05(I)(A)). Here, the additional elements that were previously identified as extra-solution activity are reevaluated as follows:
“receiving an interest score for the content component from an interest model in response to providing the user interest parameter as input to the interest model” (claims 1 and 17) [This element is well-understood, routine, conventional activity because it is merely a limitation “storing and retrieving information in memory,” which MPEP § 2106.05(d)(II) identifies as an example of well‐understood, routine, and conventional computer functions.]
Dependent Claims
The remaining dependent claims being rejected do not recite additional elements, whether considered individually or in combination, that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 2:
“wherein the interest model comprises a machine learning model trained to generate an interest score for a content component of a user interface based on user interest parameters.” [These elements are additional elements besides the abstract idea, but they constitute no more than mere instructions to apply the judicial exception using generic computer functions (MPEP § 2106.04(d)(I)), namely the generic computer function of machine learning or a machine learning model. These additional elements merely invoke the use of generic machine learning as a tool to apply an abstract idea.] (Note: the same analysis also applies to claim 10, which recites an “inference model” rather than a machine learning model, since an inference model is similarly a generic machine learning model.)
Claim 3:
“wherein the user interest parameter comprises a scroll rate, a click bar position, or a reading speed.” [This limitation merely further defines the mental process recited in the parent claim and is therefore considered to be part of the mental process of the parent claim. This claim does not recite any non-abstract additional elements for purposes of Step 2A Prong Two and Step 2B analysis.]
Claim 4:
“wherein the content comprises a plurality of content components having a plurality of layers generated by a generative model, the plurality of layers corresponding to content types provided to the generative model.” [These elements are additional elements besides the abstract idea, but they constitute no more than mere instructions to apply the judicial exception using generic computer functions (MPEP § 2106.04(d)(I)), namely the generic computer function of a generative model. These additional elements merely invoke the use of generic machine learning as a tool to apply an abstract idea.]
Claim 5:
“wherein the content class comprises audience, tone, purpose, size, function, or demographic.” [This limitation merely further defines the mental process recited in the parent claim and is therefore considered to be part of the mental process of the parent claim. This claim does not recite any non-abstract additional elements for purposes of Step 2A Prong Two and Step 2B analysis.]
Claim 6:
"further comprising: determining a subsequent interest score for the subsequent content component; selecting, based on the subsequent interest score, a next subsequent content type; and selecting a subsequent layer for a subsequent content component having the next subsequent content type.” [These further limitations are mental processes that can be performed by observation, evaluation, judgment and opinion. This claim does not recite any non-abstract additional elements for purposes of Step 2A Prong Two and Step 2B analysis.]
Claim 7:
“wherein the content component is generated by providing a base content component and a default type to a generative model.” [These elements are additional elements besides the abstract idea, but they constitute no more than mere instructions to apply the judicial exception using generic computer functions (MPEP § 2106.04(d)(I)), namely the generic computer function of a generative model. These additional elements merely invoke the use of generic machine learning as a tool to apply an abstract idea.]
Claim 8:
“further comprising: determining a user type associated with the user interface; and generating the content component.” [These further limitations are mental processes that can be performed by observation, evaluation, judgment and opinion.]
“using a generative model by providing to the generative model a base content component and an initial content type defined by the user type” [These elements are additional elements besides the abstract idea, but they constitute no more than mere instructions to apply the judicial exception using generic computer functions (MPEP § 2106.04(d)(I)), namely the generic computer function of a generative model. These additional elements merely invoke the use of generic machine learning as a tool to apply an abstract idea.]
The above analysis is also applied to claims 10-16, which recite limitations that are the same or substantially the same as those of claims 2-8.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 5-6, 9-10, and 13-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Johnson (US 2023/0336823 A1).
As to claim 1, Johnson teaches a method for generating content for user interfaces comprising:
determining a user interest parameter for a content component of content output by a user interface, the content component being of a content type for a content class; [[0036]: “This system uses various techniques to record user cues, which reflect their experience and sentiment.” [0041]: “To deduce sentiment from user engagement patterns, a sequence of user cues {x1, x2, ..., xn} is first gathered, where n represents the total number of user interactions recorded.” That is, the user cues are a user interest parameter because they reflect sentiment, which in this context refers to “the user's engagement” (see [0037], last sentence). See also [0042] for examples of cues, which are also referred to as user interactions. The cues are for generated content. See [0079]: “The adaptive cycle proceeds from the initial observation of user cues and begins content generation, at the completion of which user cues are observed again and used to decide which generated continuation to present to the user. This cycle ensures that the media displayed to the user is never disrupted as its display period happens simultaneously with the generation of the next continuation of the media.” Here, “displayed” to the user and the various cues such as “click patterns” and “click durations” (see [0042]), teaches a user interface. The content is generally of a media type, such as “various content forms, including tutorials, news articles, and explainer videos, based on the user's preferences and sentiments” ([0010]).]
receiving an interest score for the content component from an interest model in response to providing the user interest parameter as input to the interest model; [[0042]: “These cues are encoded into high-dimensional vectors using an appropriate embedding function E: x→E(x), which transforms each cue into a corresponding vector in the embedding space.” That is, the encoding process is an interest model, and the encoded vector constitutes an interest score, noting that the claim does not require the score to be in any specific format.]
determine, based on the interest score and a threshold interest score, a different content type for the content class; [[0006]: “By continuously monitoring and analyzing reactions through the detectable data points displayed by the user (cues), the system intelligently adjusts the content, providing a highly personalized and engaging experience.” [0010]: “the system generates various content forms, including tutorials, news articles, and explainer videos, based on the user's preferences and sentiments. The invention adjusts based on computational modelling of users' cognitive preferences by analyzing their sentimental responses to previously attempted communicatory methods. The system adapts the presentation of information according to users' comprehension and content-based preferences.” Note that since the “type” is not specifically defined, pre-adaptation and post-adaptation content can be regarded as different types. In regards to the use of a threshold interest score, see [0082]: “This method entails contrasting the user's existing sentiment vector with a desired sentiment vector, as shown in FIG. 3. The relationship between these vectors signifies the necessary modification in sentiment. In one embodiment, the system calculates the Euclidean distance between these vectors, an approach particularly effective in instances where the sentiment space exhibits a linear and symmetrical structure.” That is, the desired sentiment vector is a threshold interest score. See also [0083]: “If the system effectively mirrors the user's desired sentiment, it persists in selecting media with congruent sentiment labels. Conversely, if there's a misalignment, it deliberately opts for media with divergent sentiment labels.”] and
selecting a layer for a subsequent content component of the content having the different content type. [Content generation is performed in cycles, as described in [0079]: “The adaptive cycle proceeds from the initial observation of user cues and begins content generation, at the completion of which user cues are observed again and used to decide which generated continuation to present to the user.” Furthermore, the content described in this reference (e.g., [0079]) has a format, noting that the term “selecting a layer” does not require selection from multiple different possible layers, but only the selection of at least one layer.]
As to claim 2, Johnson teaches the method of claim 1, wherein the interest model comprises a machine learning model trained to generate an interest score for a content component of a user interface based on user interest parameters. [Johnson, [0043]: “Speech, serving as another embodiment, can be transformed into high-dimensional embeddings using deep learning techniques, based on MFCCs or spectrogram data. For textual cues, in another embodiment, embedding functions such as Word2Vec, GloVe, or FastText can be employed.”]
As to claim 5, Johnson teaches the method of claim 1, wherein the content class comprises audience, tone, purpose, size, function, or demographic. [Johnson, [0006]: “By continuously monitoring and analyzing reactions through the detectable data points displayed by the user (cues), the system intelligently adjusts the content, providing a highly personalized and engaging experience.” That is, the content that is generated is based on the user, and thus the content class comprises audience.]
As to claim 6, Johnson teaches the method of claim 1, further comprising:
determining a subsequent interest score for the subsequent content component; [As shown in the figure on Sheet 5, the process repeats between state t and t+1. Therefore, the sentiment estimation “sentiment is estimated” step in this figure, teaches a subsequent interest score.]
selecting, based on the subsequent interest score, a next subsequent content type; [As shown in the figure on Sheet 5, each cycle has a step of “content is selected from state t+1,” which corresponds to the selection of a content. Note that the “subsequent content type” is not particularly defined and is met by the selection of the subsequent content. See also [0079]: “The adaptive cycle proceeds from the initial observation of user cues and begins content generation, at the completion of which user cues are observed again and used to decide which generated continuation to present to the user. This cycle ensures that the media displayed to the user is never disrupted as its display period happens simultaneously with the generation of the next continuation of the media.”] and
selecting a subsequent layer for a subsequent content component having the next subsequent content type. [Content generation is performed in cycles, as described in [0079], quoted above. Furthermore, the content described in this reference (e.g., [0079]) has a format, noting that the term “selecting a layer” does not require selection from multiple different possible layers, but only the selection of at least one layer.]
As to claims 9-10 and 13-14, these claims are directed to a system for performing the same or substantially the same operations as those of claims 1-2 and 5-6, respectively. Therefore, the rejections made to claims 1-2 and 5-6 are applied to claims 9-10 and 13-14, respectively.
Furthermore, Johnson teaches “A system for generating content for user interfaces, comprising: a memory having executable instructions stored thereon; one or more processors configured to execute the executable instructions to cause the system to perform a method, the method comprising” [Since Johnson teaches that its method is for “generative artificial intelligence, sentiment analysis and natural language processing, personalization and recommendation algorithms, and human-computer interaction” ([0001]), and methods that use machine learning (see [0031]: “Within ML, reinforcement learning (RL) has emerged as a promising approach for teaching computers to learn from their interactions with the environment.”) and computational models ([0007]: “a continually updated computational model of the user's media perception based on their interactions and responses”), the instant limitations which are generic computer components are implied by the disclosure of John.]
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
1. Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson in view of Sampat (US 2025/0224918 A1).
As to claim 3, Johnson teaches the method of claim 1, but does not teach the further limitations of the instant dependent claim.
Sampat teaches “wherein the user interest parameter comprises a scroll rate, a click bar position, or a reading speed.” [[0025]: “By integrating eye-tracking technology or other methods for monitoring, determining, or estimating reading speed, the computing system may adapt in real time to the reader's pace and focus within the book. For example, the computing device may adjust the music to enhance the emotional impact of a particularly intense or emotional passage in response to determining that the reader is lingering on that passage”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Johnson with the teachings of Sampat by Sampat by using reading speed as a user cue, so as to arrive at the claimed invention. The motivation for doing so would have been to use a metric that enables content to be adapted to a reader's pace, as suggested by Sampat (see part quoted above).
As to claim 11, the further limitations of this claims are the same or substantially the same as those of claim 3. Therefore, the rejection made to claim 3 is applied to claim 11.
2. Claims 4, 7-8, 12, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson in view of Tamayo et al. (US 2025/0209311 A1) (“Tamayo”).
As to claim 4, Johnson teaches the method of claim 1, but does not teach the further limitations of the instant dependent claim.
Tamayo teaches “wherein the content comprises a plurality of content components having a plurality of layers generated by a generative model, the plurality of layers corresponding to content types provided to the generative model.” [[0020]: “For example, to generate the alternate versions, the generative system may input the content into the AI language model. Moreover, the generative system may input a prompt for the AI language model indicating the content styles that are to be used to generate the alternate versions.” [0021]: “Accordingly, the alternate versions may have different content styles from each other (e.g., different content styles corresponding to the preferred content styles indicated by the user profiles). For example, the composition of each alternate version may be different from the composition of any other alternate version due to the use of the different content styles. In this way, the alternate versions convey the same information as the content, but are in different content styles that appeal to different users (e.g., different consumers of the content).” That is, the content types in the form of different content styles is taught, and the content styles are indicated by the prompt.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Johnson with the teachings of Tamayo by implementing the technique of generating content of different styles, so as to arrive at the claimed invention of the instant dependent claim. The motivation would have been to prepare different versions from which a style that conforms to a user’s preference can be selected, as suggested by Tamayo (see parts quoted above and [0015]: “In some implementations, the preferred content style for the user may be a preference or setting indicated for the user (e.g., via an indication transmitted from the user device and received by the content system). Alternatively, the content system may determine the preferred content style for the user based on data associated with the user.”).
As to claim 7, Johnson teaches the method of claim 1, wherein the content component is generated by providing a base content component […] to a generative model.
Johnson does not teach the limitation of also providing “a default type.”
Tamayo teaches “a default type” [[0026]: “In some implementations, the content system may select an alternate version associated with a default content style if the user's preferred content style is unknown (e.g., the user is a new user or the user is not logged in).”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Johnson with the teachings of Tamayo by implementing the teaching of Tamayo by providing a “default type” as claimed, so as to arrive at the claimed invention of the instant dependent claim. The motivation would have been to provide a default type of content when the user’s preference is not known, as suggested by Tamayo (see parts quoted above).
As to claim 8, Johnson teaches the method of claim 1, but does not teach the further limitations of the instant dependent claim.
Tamayo teaches “further comprising: determining a user type associated with the user interface” [[0025]: “As shown by reference number 145, the content system may retrieve the user's user profile from the data structure using the user identifier associated with the user. As described herein, the user profile may indicate a preferred content style of the user. In some implementations, the content system may retrieve the user's preferred content style from a cookie or a session variable set by the content system.” That is, a preferred content style defines a user type. The limitation of “association with the user interface” is met because the user device (see FIG. 1 ad [0028]) has an interface.] and “generating the content component using a generative model by providing to the generative model a base content component and an initial content type defined by the user type.” [[0020]: “For example, to generate the alternate versions, the generative system may input the content into the AI language model. Moreover, the generative system may input a prompt for the AI language model indicating the content styles that are to be used to generate the alternate versions.” [0021]: “Accordingly, the alternate versions may have different content styles from each other (e.g., different content styles corresponding to the preferred content styles indicated by the user profiles). For example, the composition of each alternate version may be different from the composition of any other alternate version due to the use of the different content styles. In this way, the alternate versions convey the same information as the content, but are in different content styles that appeal to different users (e.g., different consumers of the content).” That is, the “information” corresponds to a base content component” and the style corresponds to the “initial content type.” Note that the instant claim does not require the determination of the user type to be performed in an order of operation that precedes the generation of the content.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Johnson with the teachings of Tamayo by implementing the technique of generating content of different styles, so as to arrive at the claimed invention of the instant dependent claim. The motivation would have been to prepare different versions from which a style that conforms to a user’s preference can be selected, as suggested by Tamayo (see parts quoted above and [0015]: “In some implementations, the preferred content style for the user may be a preference or setting indicated for the user (e.g., via an indication transmitted from the user device and received by the content system). Alternatively, the content system may determine the preferred content style for the user based on data associated with the user.”).
As to claims 12 and 15-16, the further limitations of this claims are the same or substantially the same as those of claim 4 and 7-8. Therefore, the rejection made to claims 4 and 7-8 are applied to claims 12 and 15-16.
3. Claims 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tamayo et al. (US 20250209311 A1) (“Tamayo”) in view of Rodgers (US 2022/0229529 A1).
As to claim 17, Tamayo teaches a method of training a machine learning model, [[0015]: “the content system may determine the preferred content style for the user using a machine learning model trained to output the preferred content style in response to an input of the data associated with the user.” [0030]: “The content system may transmit the data to the generative system to facilitate training, re-training, and/or adjustment (e.g., hyperparameter adjustment) of the AI model.”] comprising:
receiving a corpus of text; [[0010]: “An electronic document, such as a web page, may contain content that is presented to a user that accesses the electronic document. Generally, the content is static, such that each user that accesses the electronic document is presented the same content.”]
generating a plurality of labeled text components from the corpus of text, the plurality of labeled text components including a label for a content type of a content class; [[0011]: “When the content is detected, the system may cause alternate versions of the content to be generated (e.g., using artificial intelligence (AI)). Each of the alternate versions may employ a different content style.” [0019]: “To generate the alternate versions, the generative system may use one or more algorithms and/or one or more templates to dynamically modify the content according to the content styles indicated by the request.” As to the limitations of “labeled” and “label,” noting that the instant claim does not require any specific use of the label, these limitations are met because the alternate versions are stored (see [0023]: “the content system may cache the alternate versions of the content.”) and are selected based on content style (see, e.g., [0011]: “In response to a request for the electronic document made by a user, the system may select one of the alternate versions that employs a content style preferred by the user.”). Therefore, the “content style” used for selection of a specific version is considered to be labels for a content type.]
Tamayo does not explicitly teach the remaining limitations of “generating training data using the labeled text component by recording an interest level and a read speed attribute for the labeled text component; and training a machine learning model, through a supervised learning process using the training data to output a present interest level for a present text component based on a present read speed attribute.”
Rodgers teaches “generating training data using the labeled text component by recording an interest level and a read speed attribute for the labeled text component;” [[0040]: “The training data set 156 may include, for example, historical data regarding content attributes and historical engagement scores assigned to historical content data.” [0072]: “A machine learning engine obtains historical data including prior indicators of user engagement, including: prior items of content displayed in a viewport and classifications for the items of content, historical user scrolling behavior, a user selection of an interactive element in a viewport, a user reaction to content in a viewport, third party data specifying regions of a viewport in which users typically engage content, data from devices tracking eyeball positions of users as the users engage content in a viewport, and historical data of the types of content a particular user has engaged (Operation 302).” [0074]: “The historical data also includes historical engagement scores for the historical items of content displayed in the viewport.” Note that “historical user scrolling behavior” includes read speed attribute under a definition in accordance with dependent claim 18 of the instant application, since it includes “scroll speed” and “scroll position” (see claim 18: “scroll speed while the particular content item was displayed in a viewport, a scroll speed while the particular content item was displayed at a particular vertical position within the viewport”; [0057]: “whether the user stopped scrolling while the content was in the viewport”; [0089]: “user scrolled quickly past the content item 404”). Furthermore, the context includes “text” ([0035]).] and “training a machine learning model, through a supervised learning process using the training data” [[0040]: “The machine learning engine 141 includes training logic 142 to train one or more machine learning models 143. The training logic 142 trains the machine learning models 143 based on a training data set 156 stored in the data repository 150. The training data set 156 may include, for example, historical data regarding content attributes and historical engagement scores assigned to historical content data…” Training is described in more detail in [0071]-[0082]. The limitation of “supervised” is disclosed in [0077]: “The machine learning engine generates engagement scores based on the executed algorithm. The machine learning engine compares the engagement scores with target values for the engagement scores and adjusts parameters of the machine learning algorithm based on differences between the engagement scores and the target values.”] to output a present interest level for a present text component based on a present attribute. [Abstract: “A system generates a user engagement score based on the user's scrolling behavior. The system detects one scrolling event that moves content into a viewport and another scrolling event that moves the content out of the viewport. The system calculates a user engagement score based on the duration of time the content was in the viewport.” The limitation of “present” is met because the method, which is illustrated in FIG. 2, is performed responsible to the display of the content (step 202).]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Tamayo with the teachings of Rodgers by implementing a model that determines engagement (interest) level, so as to arrive at the instant dependent claim. Doing so would have enabled “measuring a user's level of interest in content in an electronic document” (Rogers, abstract).
As to claim 18, the combination of Tamayo and Rodgers teaches the method of claim 17, as set forth above.
Rodgers further teaches “wherein the read speed attribute comprises a selection from a read speed; a read speed delta; a scroll rate, or a scroll position.” [The alternatives of “scroll speed” and “scroll position” are taught. See claim 18: “scroll speed while the particular content item was displayed in a viewport, a scroll speed while the particular content item was displayed at a particular vertical position within the viewport”; [0057]: “whether the user stopped scrolling while the content was in the viewport”; [0089]: “user scrolled quickly past the content item 404.”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Tamayo with the teachings of Rodgers so as to arrive at the instant dependent claim. The motivation for doing so is covered by the motivation given for Rodgers in the rejection of the parent claim.
As to claim 20, the combination of Tamayo and Rodgers teaches the method of claim 17, wherein the present text component and the corpus of text use a common format. [As shown in FIG. 1C of Tamayo, both the content (corpus) and a present text component that is used for the user use a common format, namely being text format.]
4. Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Tamayo in view of Rodgers, and further in view of Yadav (US 2020/0104648 A1).
As to claim 19, the combination of Tamayo and Rodgers teaches the method of claim 17, as set forth above, but does not teach the method further comprising “processing the training data to remove outliers or to clean noise.”
Yadav teaches “processing the training data to remove outliers or to clean noise.” [[0002]: “Apparatuses, methods and systems consistent with the present disclosure relate generally to detecting and removing outliers, and more particularly, to apparatuses, methods and systems that detect outliers from a text corpus using dynamically determined sensitivity score.” [0004]: “Outliers and anomalies (hereinafter collectively called as “outliers”) appear in various steps of processing a dataset (e.g., a text corpus), which can significantly reduce the accuracy of computerized processing. For example, outlier entries in a dataset may skew average results or prevent the identification of an otherwise prevalent trend. Therefore, outliers in a dataset may result in less-usable computational results.”]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of the references combined thus far with the teachings of Yadav by implementing the method to further comprising “processing the training data to remove outliers or to clean noise.” The motivation would have been to remove data that “may skew average results or prevent the identification of an otherwise prevalent trend” (Yadav, [0004], as quoted above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following documents depict the state of the art.
Sinha et al. (US 2022/0366299 A1) teaches training and using a model that determines user-engagement levels, similar to the concepts recited in claim 17 of the instant application.
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/Y.D.H./Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124
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