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 § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-12, 14-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lyons (US 20240362968).
In claims 1 and 18, Lyons discloses
One or more processors (figure 3 #342)
Memory storing instructions that are executable by the one or more processors to perform operations comprising: (figure 3 #346)
Receiving audio inputs that contain utterances related to a software application (paragraph 70 discloses using a microphone to capture sound data of a player, paragraph 53 discloses detecting of user voice, i.e. utterances for prompts, as does paragraph 56 and 90.), wherein the software application is operating in accordance with a first set of events respectively associated with a first set of probabilities and a first set of results (paragraph 71 changing a math model associated with the gaming content, paragraph 24 discloses that this dynamic generation occurs “during game runtime (e.g., while play occurs)”, which would mean that the utterances would be captured during the gameplay before the change of the math model, which would be the first of two sets of events associated with the first of two sets of probability and a first of two sets of results)
Determining, by a speech to text engine that receives the audio input, a textual representation of the utterances (paragraph 53 speech-to-text conversion)
Providing, to a natural language model, a request to determine an emotion in the textual representation of the utterances and a characteristic of the software application to which the emotion corresponds (paragraphs 70-71. Paragraph 20 discloses use of a large language model)
Receiving, from the natural language model, the emotion and the characteristic, and based on the emotion and the characteristic, (paragraphs 70-71) causing the software application to operate in accordance with a second set of events respectively associated with a second set of probabilities and a second set of results (paragraph 71 changing a math model associated with the gaming content. paragraph 24 discloses that this dynamic generation occurs “during game runtime (e.g., while play occurs)”, which would mean that after the emotion is captured, and the math model is changed, the gameplay and odds after the change of the math model would be the second of two sets of events associated with the second of two sets of probability and a second of two sets of results)
In claim 17, Lyons discloses
One or more processors (figure 3 #342)
Memory storing instructions that are executable by the one or more processors to perform operations comprising: (figure 3 #346)
Receiving a digital image, wherein the digital image is of a user of a software application (paragraph 70 discloses using a camera to capture images of a player), wherein the software application is operating in accordance with a first set of events respectively associated with a first set of probabilities and a first set of results (paragraph 71 changing a math model associated with the gaming content, paragraph 24 discloses that this dynamic generation occurs “during game runtime (e.g., while play occurs)”, which would mean that the images would be captured during the gameplay before the change of the math model, which would be the first of two sets of events associated with the first of two sets of probability and a first of two sets of results)
Providing, to a natural language model, or an image analysis model, a request to determine an emotion in the textual representation of the utterances and a characteristic of the software application to which the emotion corresponds (paragraphs 70-71. Paragraph 20 discloses use of a large language model as well as other AI analysis models)
Receiving, from the natural language model or the image analysis model, the emotion, and based on the emotion, (paragraphs 70-71) causing the software application to operate in accordance with a second set of events respectively associated with a second set of probabilities and a second set of results (paragraph 71 changing a math model associated with the gaming content. paragraph 24 discloses that this dynamic generation occurs “during game runtime (e.g., while play occurs)”, which would mean that after the image is captured, and the math model is changed, the gameplay and odds after the change of the math model would be the second of two sets of events associated with the second of two sets of probability and a second of two sets of results)
In claim 2, Lyons discloses prior to receiving the audio input, the software application is configured to generate the first set of events in accordance with the respective probabilities of the first set of probabilities and wherein the first set of events produce respective results of the first set of results (paragraph 71 changing a math model associated with the gaming content, paragraph 24 discloses that this dynamic generation occurs “during game runtime (e.g., while play occurs)”, which would mean that the utterances would be captured during the gameplay before the change of the math model, which would be the first of two sets of events associated with the first of two sets of probability and a first of two sets of results)
In claim 3, Lyons discloses after causing the software application to operate in accordance with the second set of events, the software application is configured to generate the second set of events in accordance with respective probabilities of the second set of probabilities, and wherein the second set of events produce respective results of the second set of results (paragraph 71 changing a math model associated with the gaming content. paragraph 24 discloses that this dynamic generation occurs “during game runtime (e.g., while play occurs)”, which would mean that after the emotion is captured, and the math model is changed, the gameplay and odds after the change of the math model would be the second of two sets of events associated with the second of two sets of probability and a second of two sets of results)
In claim 4, Lyons discloses the second set of events is identical to the first set of events, and wherein a particular event is associated with at least one of a different probability or a different result in the first set of events and the second set of events (paragraphs 70-71, both set of events are results of gaming content such as the spins of the slot machine of fig. 7)
In claim 5, Lyons discloses the request indicates that the emotion is to be selected form a plurality of pre-defined emotions or that the characteristic is to be selected from a plurality of pre-defined characteristics (paragraphs 70-71)
In claim 6, Lyons discloses determining, based on the emotion or the characteristic, an actions, and based on the action, causing the software application to operate in accordance with the second set of events (paragraph 70-71, the action is changing the math model)
In claim 7, Lyons discloses the software application relates to an entertainment service (fig. 7)
In claims 8 and 19, Lyons discloses the entertainment service involves a game of chance, wherein the first set of events are random outcomes of the game of chance occurring in accordance with respective probabilities of the first set of probabilities and wherein the first set of events respectively provide payouts in accordance with the first set of results (paragraph 49, figure 7)
In claim 9, Lyons discloses providing, to the natural language model, a further request to generate dialog for the character based on state of the entertainment service and properties of the character, receiving from the natural language model, a further response containing the dialog, and providing the dialog as being spoke by the avatar of the character (paragraph 73 discloses an avatar with a voice that mimics that of the player. See further paragraph 77)
In claim 10, Lyons discloses the audio input is received by way of a microphone that is positioned such that, when activated, it detects the utterances, and wherein a user associated with the microphone has opted-in to sharing the audio input (paragraphs 53, 56, 70, 90. The microphone is necessarily positioned in such a way, as the utterances are detected, and with respect to “opted in”, under BRI this is taught by the player being at the machine which is recording the audio)
In claims 11 and 20, Lyons discloses receiving a digital image, providing the natural language model or an image analysis model, a second request to identify objects within the digital image, receiving from the natural language model or the image analysis model, a list of identified objects within the digital image, wherein the emotion is also determined based on the identified objects (paragraph 70 discloses facial expressions and taps, for example, which would be the face object and the finger object)
In claim 12, Lyons discloses the second request indicates that, for any of the objects that are identified as human faces, the human faces are to be associated with one or more emotions detected therein (paragraph 70)
In claim 14, Lyons discloses providing to a prompt pre processor, the text representation of the utterances, modifying the textual representation of the utterances into a natural language model prompt and providing to the natural language model, the natural language model prompt (paragraph 20, 53)
In claim 15, Lyons discloses receiving from the natural language model, a natural language model response containing a representation of the emotion and the characteristic, parsing natural language model response to obtain the emotion and the characteristic (paragraph 20, 53, 70, 71)
In claim 16, Lyons discloses causing the software to operate in accordance with the second set of events is based on one or more of a user profile, historical data, or application data relating to the software (paragraphs 70-71)
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.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyons in view of Som (US 20250124352)
In claim 13, Lyons discloses claimed invention except the natural language model comprises a neural network architecture including: a plurality of transformer layers, each layer with a self-attention network, and a position wise feed forward network, an input layer configured to receive and tokenize natural language phrases into input tokens, an embedding mechanism to map input tokens to vectors n a multi dimensional space, and an output layer configured to transform the vectors as processed from a final transformer layer into natural language text, however Som discloses the natural language model comprises a neural network architecture including: a plurality of transformer layers, each layer with a self-attention network, and a position wise feed forward network, an input layer configured to receive and tokenize natural language phrases into input tokens, an embedding mechanism to map input tokens to vectors n a multi dimensional space, and an output layer configured to transform the vectors as processed from a final transformer layer into natural language text (paragraph 57). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Lyons with Som in order to allow for the invention of Lyons to use a preferred machine learning model of the operator to optimize results.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS HAYNES HENRY whose telephone number is (571)270-3905. The examiner can normally be reached M-F 10-6.
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/THOMAS H HENRY/ Primary Examiner, Art Unit 3715