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 .
DETAILED ACTION
This action is in response to the application filed 12/17/2024.
Claims 1 - 20 are pending and have been examined.
Claims 1 - 20 are rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/04/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 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.
Claims 1, 7-9, 15-17, and 20 state “generating automatically integration data to include” clause incorrectly. Examiner requests clarification. All independent claims include this ambiguity and are similarly rejected. All dependent claims are rejected based on dependencies.
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)(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) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Fuxman et al. (US 20180210874; “Fuxman”).
As per claim 1, Fuxman discloses A method comprising: accessing data representing an alphanumeric format with which to generate a text-based electronic message in natural language in accordance with attributes of an entity to form text-based data (Fuxman [0055: “For example, if a first user of two users that have consented to suggestions based on conversation content, sends a message “do you want to grab a bite? How about Italian?” a response may be suggested to the second user, e.g., “@assistant lunch, italian, table for 2.””]); receiving event data from distributed data sources to derive a context with which to generate an electronic message including the text-based electronic message (Fuxman [0053: “For example, messaging application 103a/103b may implement machine learning, e.g., a deep learning model, that can enhance user interaction with messaging application 103.” . . . “For example, when users provide consent, suggested responses may be customized based on the user's prior activity, e.g., earlier messages in a conversation, messages in different conversations, etc.”]);
accessing entity objective data configured to adapt the electronic message in accordance with the entity objective data (Fuxman [0042: user info; 0053: Context info; 0060: User data use]);
applying the data representing the alphanumeric format and the entity objective data to a large language model (“LLM”) at a computing platform including a processor and memory (Fuxman [0053: “For example, messaging application 103a/103b may implement machine learning, e.g., a deep learning model, that can enhance user interaction with messaging application 103.” . . . “For example, when users provide consent, suggested responses may be customized based on the user's prior activity, e.g., earlier messages in a conversation, messages in different conversations, etc.”]); deriving content based on an output of the large language model (Fuxman [0021: “A particular conditioned language model can be selected for determining the suggested responses for the image based on a detected language of a message conversation in which the first message is received.” . . . 0038: “For example, sample data may include received messages and responses that were sent to the received messages.”]; [0038, 0042: Using language model for deriving content for response.]);
correlating image data with the output of the large language model to generate the electronic message to form image data to incorporate into the electronic message; and generating automatically integration data to include the image data and the text- based data to integrate with at least one of the distributed data sources (Fuxman [0057-0060: “In different implementations, suggestions, e.g., suggested responses as described herein, may include one or more of: text (e.g., “Terrific!”), emoji (e.g., a smiley face, a sleepy face, etc.), images (e.g., photos from a user's photo library), text generated based on templates with user data inserted in a field of the template (e.g., “her number is <Phone Number>” where the field “Phone Number” is filled in based on user data, if the user provides access to user data), links (e.g., Uniform Resource Locators), message stickers, etc. In some implementations, suggested responses may be formatted and/or styled, e.g., using colors, fonts, layout, etc.”]).
As per claim 2, rejection for claim 1 is incorporated and further Fuxman discloses The method of claim 1 wherein applying the data representing the alphanumeric format and the entity objective data to the large language model further comprises: applying persona data to the large language model (Fuxman [0042: user info; 0053: Context info; 0060: User data use]; [0104: “In some implementations, the scores can be based on a determined probability that the suggested response is relevant to the image, where the probability can be based on, e.g., frequency of occurrence of the response in historical message data (and/or in training data as described above). Historical message data may be data from prior conversations where participants in the conversation have provided consent for use of such data to implement suggested response features. Historical message data is not used if users have not provided permission for such use.”]).
As per claim 3, rejection for claim 1 is incorporated and further Fuxman discloses The method of claim 1 wherein accessing the data representing an alphanumeric format comprises: generating the text-based data in accordance with a writing style associated with the entity (Fuxman [0053: “For example, such activity may be used to determine an appropriate suggested response for the user, e.g., a playful response, a formal response, etc. based on the user's interaction style. In another example, when the user specifies one or more preferred languages and/or locales, messaging application 103a/103b may generate suggested responses in the user's preferred language. In various examples, suggested responses may be text responses, images, multimedia, etc.”]).
As per claim 4, rejection for claim 1 is incorporated and further Fuxman discloses The method of claim 1 wherein accessing the data representing the alphanumeric format comprises: generating the text-based data in accordance with a tone of voice associated with the entity, the tone of voice being associated with data as a set of rules with which to generate the electronic message (Fuxman [0053: “For example, such activity may be used to determine an appropriate suggested response for the user, e.g., a playful response, a formal response, etc. based on the user's interaction style. In another example, when the user specifies one or more preferred languages and/or locales, messaging application 103a/103b may generate suggested responses in the user's preferred language. In various examples, suggested responses may be text responses, images, multimedia, etc.”]).
As per claim 5, rejection for claim 1 is incorporated and further Fuxman discloses The method of claim 1 wherein receiving the event data comprises: receiving moment data as a function of the entity objective data (Fuxman [0053: “For example, such activity may be used to determine an appropriate suggested response for the user, e.g., a playful response, a formal response, etc. based on the user's interaction style. In another example, when the user specifies one or more preferred languages and/or locales, messaging application 103a/103b may generate suggested responses in the user's preferred language. In various examples, suggested responses may be text responses, images, multimedia, etc.”]).
As per claim 6, rejection for claim 1 is incorporated and further Fuxman discloses The method of claim 1 wherein correlating the image data comprises: generating the image data as a function of the text-based data (Fuxman [0053: “For example, such activity may be used to determine an appropriate suggested response for the user, e.g., a playful response, a formal response, etc. based on the user's interaction style. In another example, when the user specifies one or more preferred languages and/or locales, messaging application 103a/103b may generate suggested responses in the user's preferred language. In various examples, suggested responses may be text responses, images, multimedia, etc.”]; [0064: “The conditioned language model can be provided an image feature vector as an input as well as previous predicted words (e.g., word sequences), and can determine the probabilities of all words in a vocabulary to be the next word in the generated response.”] Also see claim 1 for images.).
As per claim 7, rejection for claim 1 is incorporated and further Fuxman discloses The method of claim 1 wherein generating automatically the integration data to include the image data and the text-based data comprises: generating automatically the integration data to conform with a targeted distributed data source (Fuxman [0068: “For example, the image and/or its metadata can be sent to content server 154, which can determine whether the image content is included in particular predefined classifications for which message suggestions are not to be provided.”]; [0088-0089, 0154: “In some examples, if the previous image is determined to depict a first type of object (e.g., article of clothing such as a shirt), and the current image also depicts a same type of object (e.g., based on image object recognition techniques, image tags or other metadata, etc.), then the generated suggested responses can include “I liked the other one better” and/or “I like this one better.””]).
As per claim 8, rejection for claim 7 is incorporated and further Fuxman discloses The method of claim 7 wherein generating automatically the integration data further comprises: generating automatically data representing an advertisement including the image data and the text-based data (Fuxman [0028, 0070-0073: Image description.]).
Claims 9-16 are the systems claims of method claims 1-8, claims 17-20 are the non-transitory computer readable medium claims of method claims 1-2 and 6-7. Method and medium are taught in claims 1 and 15.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fedoryszak et al. (US 20200250249) – Teaches deriving context from streams of messages.
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Taelor Kim whose telephone number is (571) 270-7166. The examiner can normally be reached on Monday-Thursday (11AM-5PM) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached on 571-272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-270-8166.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Taelor Kim
Primary Patent Examiner
Art Unit 2156
/TAELOR KIM/
Primary Examiner, Art Unit 2156