DETAILED ACTION
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 .
Claims 1-20 are pending.
Claims 1, 7, 8, 11, 14, and 15 are amended.
Claim 21 is new
Claim 10 is cancelled
Claim Rejections - 35 USC § 103
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, 3-8 and 10-20 are rejected under 35 U.S.C. 103 as being anticipated by Krishnamurthy et al. (US 20220357914) and Saigh et al. (US 20190054347), Yuan et al. (US 20220027784) and Carter et al. (US 20190222776).
Regarding claim 1:
Krishnamurthy teaches:
A method comprising (Krishnamurthy [0004]: Accordingly, a method includes receiving text such as from speech conversion and processing the text using at least one neural network to render a two dimensional (2D) image of a computer simulation asset. The method also includes converting the 2D image to a three dimensional (3D) asset. The method includes presenting the 3D asset in at least one computer simulation.):
receiving, by a computing system, a prompt to generate a data sticker for a sporting event and a corresponding sticker type, (Krishnamurthy [0040], [0054]: Moving to block 602, if desired a description also may be received of only part of an asset which does not apply to the entire asset. If the description is received as voice input, it is converted to text at block 604.),
parsing, by the computing system, the prompt to identify individual components of the prompt and the sticker type (Krishnamurthy [0003]: Present principles allow content creators to describe the asset they want as a natural language input, and create a 2D or 3D asset from that (voice) input.);
and generating, by the computing system, an image file comprising the data sticker (Krishnamurthy [0054]: An AI engine such as a generative adversarial network (GAN) may be used at block 606 to generate a 2D image based on the asset descriptions and locations received previously).
Krishnamurthy does not teach:
the data sticker comprising one or more graphical representations of sports analytics data;
wherein the sticker type includes at least one of: a scorinq play data sticker type and a heat map data sticker type;
one or more predictions based on the individual components of the prompt, wherein the one or more predictions correspond to a player performance or a team performance of the sporting event
the data sticker that includes the one or more graphical representations corresponding to the one or more predictions
SAIGH teaches:
the data sticker comprising one or more graphical representations of sports analytics data (SAIGH [0153], [0082] FIG. 19 is an illustration of exemplary graphical representations of data collection generated by the sports analytics system;);
Krishnamurthy and Yuan teach:
generating, by the computing system using a generative artificial intelligence model, one or more predictions based on the individual components of the prompt, wherein the one or more predictions correspond to a player performance or a team performance of the sporting event (Krishnamurthy [0054]: An AI engine such as a generative adversarial network (GAN) may be used at block 606 to generate a 2D image based on the asset descriptions and locations received previously) (Yuan [0020] In another example, the input data may include various information about players on a basketball team and the intended output may be to predict how many wins the basketball team may have this season. In this example, the intended output of predicted wins, a category of the data such as sports and/or basketball, and other information may be analyzed in order to match the data to a machine learning model capable of predicting wins for a basketball team.);
creating, by the computing system using the generative artificial intelligence model, the data sticker that includes the one or more graphical representations corresponding to the one or more predictions (Krishnamurthy [0054]: An AI engine such as a generative adversarial network (GAN) may be used at block 606 to generate a 2D image based on the asset descriptions and locations received previously) (Yuan [0020] In another example, the input data may include various information about players on a basketball team and the intended output may be to predict how many wins the basketball team may have this season. In this example, the intended output of predicted wins, a category of the data such as sports and/or basketball, and other information may be analyzed in order to match the data to a machine learning model capable of predicting wins for a basketball team.);
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Krishnamurthy with Saigh, Carter, and Yuan. Having graphical representation of a sports analytics data and using ai to get prediction data, as in Saigh, Carter, and Yuan, would benefit the Krishnamurthy teachings by allowing for the specific data to be used in the graphics that are generated. Additionally, this is the application of a known technique, using AI to generate graphics that a user requests using prediction data, to yield predictable results.
Carter teaches:
wherein the sticker type includes at least one of: a scoring play data sticker type and a heat map data sticker type (Carter [0006] FIG. 1B depicts a graphical representation of a heat map corresponding to FIG. 1A generated from an analysis of the video data.);
Regarding claims 3, 10 and 17:
Krishnamurthy, Yuan, Carter, and Saigh teach:
The method of claim 1 (as shown above),
Saigh does not teach:
wherein the prompt is a voice-based prompt
Krishnamurthy teaches:
wherein the prompt is a voice-based prompt (Krishnamurthy [0054]: If the description is received as voice input, it is converted to text at block 604.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Krishnamurthy with Saigh, Carter, and Yuan. Having graphical representation of a sports analytics data and using ai to get prediction data, as in Saigh, Carter, and Yuan, would benefit the Krishnamurthy teachings by allowing for the specific data to be used in the graphics that are generated. Additionally, this is the application of a known technique, using AI to generate graphics that a user requests using prediction data, to yield predictable results.
Regarding claims 4, 11 and 18:
Krishnamurthy, Yuan, Carter, and Saigh teach:
The method of claim 3 (as shown above),
Saigh does not teach:
further comprising: converting, by the computing system, the voice-based prompt into a text-based prompt.
Krishnamurthy teaches:
further comprising: converting, by the computing system, the voice-based prompt into a text-based prompt (Krishnamurthy [0054]: If the description is received as voice input, it is converted to text at block 604.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Krishnamurthy with Saigh, Carter, and Yuan. Having graphical representation of a sports analytics data and using ai to get prediction data, as in Saigh, Carter, and Yuan, would benefit the Krishnamurthy teachings by allowing for the specific data to be used in the graphics that are generated. Additionally, this is the application of a known technique, using AI to generate graphics that a user requests using prediction data, to yield predictable results.
Regarding claims 5 and 12:
Krishnamurthy, Yuan, Carter, and Saigh teach:
The method of claim 1 (as shown above), further comprising:
determining, by the computing system, that at least one individual component of the individual components requires an application programming interface (API) call to retrieve prediction data generated by one or more prediction models;
and based on the determining, generating, by the computing system, the API call to the one or more prediction models to obtain the prediction data.
SAIGH teaches:
determining, by the computing system, that at least one individual component of the individual components requires an application programming interface (API) call to retrieve prediction data generated by one or more prediction models (SAIGH [0096]: The analytics platform 102 is capable of utilizing the information obtained from the one or more sensors and having functions including, but not limited to, data storage, data retrieval, data synthesis, alert programs, data management, characterization, filtering, transformation, sorting, processing, modeling, mining, inspecting, investigation, retrieval, integrating, dissemination, qualitative, quantitative, normalizing, clustering, correlations, computer derived values and ranges, simple or complex mathematical calculations and algorithms, statistical, predictive, integrative, interpretative, exploratory, abnormality seeking, data producing, comparative, historical or previous from same or different individual or team, visualizing or presentation development platforms.);
and based on the determining, generating, by the computing system, the API call to the one or more prediction models to obtain the prediction data (Saigh [130]: In one example, the analytics platform 102 is configured to obtain or transmit information, analysis or alerts customized to meet the specific needs of the developer, consumer or user via an API 115 executing on the server system 104.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Krishnamurthy with Saigh, Carter, and Yuan. Having graphical representation of a sports analytics data and using ai to get prediction data, as in Saigh, Carter, and Yuan, would benefit the Krishnamurthy teachings by allowing for the specific data to be used in the graphics that are generated. Additionally, this is the application of a known technique, using AI to generate graphics that a user requests using prediction data, to yield predictable results.
Regarding claims 6, 13 and 19:
Krishnamurthy, Yuan, and Saigh teach:
The method of claim 5 (as shown above),
wherein generating, by the computing system using the generative artificial intelligence model, the one or more graphical representations based on the individual components of the prompt comprises (as shown above):
generating a graphical representation of the prediction data received from the one or more prediction models (SAIGH [0082]: FIG. 19 is an illustration of exemplary graphical representations of data collection generated by the sports analytics system;).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Krishnamurthy with Saigh, Carter, and Yuan. Having graphical representation of a sports analytics data and using ai to get prediction data, as in Saigh, Carter, and Yuan, would benefit the Krishnamurthy teachings by allowing for the specific data to be used in the graphics that are generated. Additionally, this is the application of a known technique, using AI to generate graphics that a user requests using prediction data, to yield predictable results.
Regarding claims 7, 14 and 20:
Krishnamurthy, Yuan, Carter, and Saigh teach:
The method of claim 5 (as shown above),
wherein generating, by the computing system using the generative artificial intelligence model, the one or more graphical representations based on the individual components of the prompt comprises (as shown above):
merging … with the one or more graphical representations to generate the data sticker (Krishnamurthy [0075]: Encoder-decoder models may be adapted to incorporate additional encodings (for example, texture encodings) to transform the 3D objects to meet the specs in the description.).
the prediction data received form the one or more prediction models (SAIGH [0082]: FIG. 19 is an illustration of exemplary graphical representations of data collection generated by the sports analytics system;)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Krishnamurthy with Saigh, Carter, and Yuan. Having graphical representation of a sports analytics data and using ai to get prediction data, as in Saigh, Carter, and Yuan, would benefit the Krishnamurthy teachings by allowing for the specific data to be used in the graphics that are generated. Additionally, this is the application of a known technique, using AI to generate graphics that a user requests using prediction data, to yield predictable results.
Regarding claim 8:
Krishnamurthy teaches:
A non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by a processor, causes a computing system to perform operations comprising (Krishnamurthy [0006]: In another aspect, a device includes at least one computer memory that is not a transitory signal and that in turn includes instructions executable by at least one processor):
receiving, by a computing system, a prompt to generate a data sticker for a sporting event (Krishnamurthy [0054]: Moving to block 602, if desired a description also may be received of only part of an asset which does not apply to the entire asset. If the description is received as voice input, it is converted to text at block 604.),
parsing, by the computing system, the prompt to identify individual components of the prompt (Krishnamurthy [0003]: Present principles allow content creators to describe the asset they want as a natural language input, and create a 2D or 3D asset from that (voice) input.);
Krishnamurthy and Saigh do not teach:
the data sticker comprising one or more graphical representations of sports analytics data;
wherein the sticker type includes at least one of: a scoring play data sticker type and a heat map data sticker type
one or more predictions based on the individual components of the prompt, wherein the one or more predictions correspond to a player performance or a team performance of the sporting event
representations corresponding to the one or more predictions
SAIGH teaches:
the data sticker comprising one or more graphical representations of sports analytics data (SAIGH [0082] FIG. 19 is an illustration of exemplary graphical representations of data collection generated by the sports analytics system;);
Krishnamurthy and Yuan teach:
generating, by the computing system using a generative artificial intelligence model, one or more predictions based on the individual components of the prompt, wherein the one or more predictions correspond to a player performance or a team performance of the sporting event (Krishnamurthy [0054]: An AI engine such as a generative adversarial network (GAN) may be used at block 606 to generate a 2D image based on the asset descriptions and locations received previously) (Yuan [0020] In another example, the input data may include various information about players on a basketball team and the intended output may be to predict how many wins the basketball team may have this season. In this example, the intended output of predicted wins, a category of the data such as sports and/or basketball, and other information may be analyzed in order to match the data to a machine learning model capable of predicting wins for a basketball team.);
creating, by the computing system using the generative artificial intelligence model, the data sticker that includes the one or more graphical representations corresponding to the one or more predictions (Krishnamurthy [0054]: An AI engine such as a generative adversarial network (GAN) may be used at block 606 to generate a 2D image based on the asset descriptions and locations received previously) (Yuan [0020] In another example, the input data may include various information about players on a basketball team and the intended output may be to predict how many wins the basketball team may have this season. In this example, the intended output of predicted wins, a category of the data such as sports and/or basketball, and other information may be analyzed in order to match the data to a machine learning model capable of predicting wins for a basketball team.);
Carter teaches:
wherein the sticker type includes at least one of: a scoring play data sticker type and a heat map data sticker type (Carter [0006] FIG. 1B depicts a graphical representation of a heat map corresponding to FIG. 1A generated from an analysis of the video data.);
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Krishnamurthy with Saigh, Carter, and Yuan. Having graphical representation of a sports analytics data and using ai to get prediction data, as in Saigh, Carter, and Yuan, would benefit the Krishnamurthy teachings by allowing for the specific data to be used in the graphics that are generated. Additionally, this is the application of a known technique, using AI to generate graphics that a user requests using prediction data, to yield predictable results.
Regarding claim 15:
Krishnamurthy teaches:
A system comprising: a processor; and a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations comprising (Krishnamurthy [0006]: In another aspect, a device includes at least one computer memory that is not a transitory signal and that in turn includes instructions executable by at least one processor):
receiving, by a computing system, a prompt to generate a data sticker for a sporting event (Krishnamurthy [0054]: Moving to block 602, if desired a description also may be received of only part of an asset which does not apply to the entire asset. If the description is received as voice input, it is converted to text at block 604.),
parsing, by the computing system, the prompt to identify individual components of the prompt (Krishnamurthy [0003]: Present principles allow content creators to describe the asset they want as a natural language input, and create a 2D or 3D asset from that (voice) input.);
Krishnamurthy and Saigh do not teach:
the data sticker comprising one or more graphical representations of sports analytics data;
one or more predictions based on the individual components of the prompt, wherein the one or more predictions correspond to a player performance or a team performance of the sporting event
graphical representations corresponding to the one or more predictions
wherein the sticker type includes at least one of: a scoring play data sticker type and a heat map data sticker type
SAIGH teaches:
the data sticker comprising one or more graphical representations of sports analytics data (SAIGH [0082] FIG. 19 is an illustration of exemplary graphical representations of data collection generated by the sports analytics system;);
Krishnamurthy and Yuan teach:
generating, by the processor using a generative artificial intelligence model, one or more predictions based on the individual components of the prompt, wherein the one or more predictions correspond to a player performance or a team performance of the sporting event (Krishnamurthy [0054]: An AI engine such as a generative adversarial network (GAN) may be used at block 606 to generate a 2D image based on the asset descriptions and locations received previously) (Yuan [0020] In another example, the input data may include various information about players on a basketball team and the intended output may be to predict how many wins the basketball team may have this season. In this example, the intended output of predicted wins, a category of the data such as sports and/or basketball, and other information may be analyzed in order to match the data to a machine learning model capable of predicting wins for a basketball team.);
creating, by the processor using the generative artificial intelligence model, the data sticker that includes the one or more graphical representations corresponding to the one or more predictions (Krishnamurthy [0054]: An AI engine such as a generative adversarial network (GAN) may be used at block 606 to generate a 2D image based on the asset descriptions and locations received previously) (Yuan [0020] In another example, the input data may include various information about players on a basketball team and the intended output may be to predict how many wins the basketball team may have this season. In this example, the intended output of predicted wins, a category of the data such as sports and/or basketball, and other information may be analyzed in order to match the data to a machine learning model capable of predicting wins for a basketball team.);
Carter teaches:
wherein the sticker type includes at least one of: a scoring play data sticker type and a heat map data sticker type (Carter [0006] FIG. 1B depicts a graphical representation of a heat map corresponding to FIG. 1A generated from an analysis of the video data.);
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Krishnamurthy with Saigh, Carter, and Yuan. Having graphical representation of a sports analytics data and using ai to get prediction data, as in Saigh, Carter, and Yuan, would benefit the Krishnamurthy teachings by allowing for the specific data to be used in the graphics that are generated. Additionally, this is the application of a known technique, using AI to generate graphics that a user requests using prediction data, to yield predictable results.
Regarding claim 21:
The method of claim 1,
wherein creating the data sticker based on the sticker type comprises: receiving, by the computing system, additional field data based on the sticker type from a user device (Carter 0064] FIG. 4A depicts a frame of event video 402 that includes bounding boxes 404 and 406 identifying detected locations of race cars, while FIG. 4B graphically represents corresponding heat map data 412 determined by the computing system.) (Krishnamurthy [0003]: Present principles allow content creators to describe the asset they want as a natural language input, and create a 2D or 3D asset from that (voice) input.);
and retrieving, by the computing system, the data sticker based on the additional field data and the sticker type from a data sticker system (Carter 0064] FIG. 4A depicts a frame of event video 402 that includes bounding boxes 404 and 406 identifying detected locations of race cars, while FIG. 4B graphically represents corresponding heat map data 412 determined by the computing system.) (Krishnamurthy [0003]: Present principles allow content creators to describe the asset they want as a natural language input, and create a 2D or 3D asset from that (voice) input.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Krishnamurthy with Saigh, Carter, and Yuan. Having graphical representation of a sports analytics data and using ai to get prediction data, as in Saigh, Carter, and Yuan, would benefit the Krishnamurthy teachings by allowing for the specific data to be used in the graphics that are generated. Additionally, this is the application of a known technique, using AI to generate graphics that a user requests using prediction data, to yield predictable results.
Claims 2 and 9 are rejected under 35 U.S.C. 103 as being anticipated by Krishnamurthy et al. (US 20220357914) in view of Saigh et al. (US20190054347) and Kaltwang et al. (WO2019219949), Yuan et al. (US 20220027784) and Carter et al. (US 20190222776).
Regarding claims 2 and 9:
Krishnamurthy, Yuan, and Saigh teach:
The method of claim 1 (as shown above),
Krishnamurthy, Yuan, and Saigh do not teach:
wherein the prompt is received responsive to a partially generated data sticker, wherein the prompt is a request to complete the partially generated data sticker using the generative artificial intelligence model.
Kaltwang teaches:
wherein the prompt is received responsive to a partially generated data sticker, wherein the prompt is a request to complete the partially generated data sticker using the generative artificial intelligence model (Kaltwang [pg. 11: 2nd para.]: The generative graphical model may be used in the processing of partly complete images to generate probable image data of parts or subparts of the image by recurringly modifying the latent variable of the root node.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Krishnamurthy with Saigh, Yuan, Carter, and Kaltwang. Finishing a partially generated image, as in Kaltwang, would benefit the Krishnamurthy teachings by allowing for the image to be finished if it is only partly generated. Additionally, this is the application of a known technique, using AI to generate graphics that are partially already created, to yield predictable results.
Response to Arguments
Applicant's arguments filed 10/30/2025 have been fully considered but they are not persuasive.
Applicant has amended claims 1, 8, and 15. Applicants arguments state that the claims now include the limitations “wherein the sticker type includes at least one of: a scoring play data sticker type and a heat map data sticker type;”
However, In light of the amendments, the 103 rejection for independent claims 1, 8, and 15 has been updated to include Carter (US 20190222776). Carter teaches “Carter [0006] FIG. 1B depicts a graphical representation of a heat map corresponding to FIG. 1A generated from an analysis of the video data.” It would make sense for a sticker indicating performance to include a heat map
Therefore in light of the amendments, claims 1, 8, and 15 are rejected by 35 U.S.C. 103. All dependent claims have also been updated.
Claim 21 was also added, it would be obvious to have “additional field data” in which the “additional field data” can be referred to as practically anything regarding the scores or even the heat map.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENIS VASILIY MINKO whose telephone number is (571)270-5226. The examiner can normally be reached Monday-Thursday 8:30-6:00 EST.
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/DENIS VASILIY MINKO/Examiner, Art Unit 2612
/Said Broome/Supervisory Patent Examiner, Art Unit 2612