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) 1-7, 9-12, and 14-21 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 generative language model, a prompt including the textual representation of the utterances and a request, wherein the request to determine (i) an emotion in the textual representation of the utterances and (ii) a characteristic of the software application to which the emotion corresponds (paragraphs 70-71. Paragraph 20 discloses use of a large language model. It is noted by examiner that with respect to “prompt”, the definition of prompt is “an instruction or a set of criteria provided to an artificial intelligence system that establishes the content it will generate”, as such, Lyons discloses an instruction given to machine learning model (which is a prompt), to determine an emotion of the textual representation of the utterances, along with a characteristic of the software application to which the emotion corresponds and other information, to engineer a (second) prompt, and to input that second prompt into the machine learning model in order to cause the software application to operate in accordance with a second set of events.)
Receiving, from the generative 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 generative language model, or an image analysis model, a prompt including the digital image and a request, wherein the request to determine an emotion in the textual representation of the utterances and a characteristic of the software application to which the emotion corresponds corresponds (paragraphs 70-71. Paragraph 20 discloses use of a large language model. It is noted by examiner that with respect to “prompt”, the definition of prompt is “an instruction or a set of criteria provided to an artificial intelligence system that establishes the content it will generate”, as such, Lyons discloses an instruction given to machine learning model (which is a prompt), to determine an emotion of the textual representation of the utterances, along with a characteristic of the software application to which the emotion corresponds and other information, to engineer a (second) prompt, and to input that second prompt into the machine learning model in order to cause the software application to operate in accordance with a second set of events.)
Receiving, from the generative 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, wherein the entertainment service (fig. 7) 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 (paragraph 35, 48) 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 82)
In claim 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 generative 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)
In claim 21, Lyons discloses the emotion and the characteristic are received from the generative language model in a programmatically structured format, and wherein the operations further comprise: parsing the programmatically structured format to identify elements representing the emotion and the characteristic (paragraphs 70-71 discloses a prompt token, which is “programmatically structured” as it is a structure used for being deciphered by a machine learning program. The machine learning program then identifies the emotion and characteristic from the prompt token, and uses it to change a math model associated with the gaming content as per paragraph 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.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
Applicant argues that “Lyons relies on the recognized emotion to construct a prompt, whereas Applicant’s claims constructs a prompt to determine an emotion”. Examiner disagrees. The invention of Lyons provides instructions and information to a machine learning model to determine an emotion. The definition of a “prompt” is merely ” an instruction or a set of criteria provided to an artificial intelligence system that establishes the content it will generate”, as such, the invention of Lyons simply discloses a two step machine learning model, wherein instructions (prompt) is fed to a machine learning model to determine the emotions of the user and to determine a characteristic of the software application to which the emotion corresponds, in order to engineer a (second) prompt which is then automatically fed into a machine learning model and change the gaming machine.
With respect to claim 21, paragraphs 70-71 discloses a prompt token, which is “programmatically structured” as it is a structure used for being deciphered by a machine learning program. The machine learning program then identifies the emotion and characteristic from the prompt token, and uses it to change a math model associated with the gaming content as per paragraph 71
Conclusion
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 THOMAS HAYNES HENRY whose telephone number is (571)270-3905. The examiner can normally be reached M-F 10-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Vasat can be reached at 571-270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THOMAS H HENRY/ Primary Examiner, Art Unit 3715
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