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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/02/2025, 04/11/2025, and 04/02/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings were submitted on 07/22/2024. These drawings are reviewed and accepted by the examiner.
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)(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.
Claim(s) 1-10, 12, and 14-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Visser et al. (US 20210011887 A1).
Regarding claim 1, Visser teaches:
“receiving physiological data for a user from a system, the system including a physiological monitor worn by the user and the physiological monitor providing the physiological data” (par. 0024, fitness tracker, health monitor; ‘In a particular aspect, one or more of the device 102, the device 104, the device 106, or the device 108 includes at least one of a portable electronic device, a home appliance, factory equipment, a security device, a vehicle, a car, an internet-of-things (IoT) device, a television, an entertainment device, a navigation device, a fitness tracker, a mobile device, a health monitor, a communication device, a computer, a virtual reality device, an augmented reality device, or a device controller.’);
“receiving a message from the user, wherein the message includes a question from the user in a natural language form” (par. 0045; ‘In a particular aspect, the query 185 includes a natural language query.’);
“creating one or more prompts for use with large language models” (par. 0047; ‘In a particular aspect, the query response system 138 generates the query response 187 by using a memory network architecture, a language model based on bidirectional encoder representations from transformers (BERT), a bi-directional attention flow (BiDAF) network, or a combination thereof.’), the one or more prompts including:
“a request for a code block that encodes a response to the question and is processable by one or more components of the system to generate one or more component specific representations of the response” (par. 0048; ‘In a particular aspect, the language model is trained using a masked language model (MLM). The query response system 138 uses the trained language model to identify a portion of the activity log 107 as an answer 191 for the query 185.’),
“a language schema for code within the code block, the language schema defining at least a syntax, functions, and operators for a programming language used to express the code block, an object schema, the object schema specifying programming objects available for use in the code block” (par. 0048; ‘In a particular aspect, a BERT architecture includes a multi-layer bidirectional transformer encoder. In a particular aspect, the language model is trained using a masked language model (MLM).’ The bidirectional transformer encoder analyzes text to capture language scheme.), and
“a context block relating to user-specific data” (par. 0048; ‘The query response system 138 uses the trained language model to identify a portion of the activity log 107 as an answer 191 for the query 185.’).
“transmitting the one or more prompts to one or more large language models” (par. 0048; ‘In a particular aspect, the query response system 138 generates the query response 187 by using a language model based on BERT.’)
“receiving the response containing the code block from the one or more large language models” (par. 0048; ‘In a particular aspect, the query response system 138 generates the query response 187 by using a language model based on BERT.’); and
“transmitting the code block that encodes the response to a component of the system for use in providing the response to the user” (par. 0050; ‘In a particular aspect, the query response system 138 provides the query response 187 to the device 108. Alternatively, or in addition, the query response system 138 provides the query response 187 to a display device coupled to the device 102.’).
Regarding claim 2 (dep. on claim 1), Visser further teaches:
“processing the code block on a component of the system to provide the response to the user in a component dependent and user specific manner” (par. 0051; ‘In this example, a user 101 (e.g., a vehicle owner, such as a parent or an employer) can send a query 185 to the query response system 138 requesting information regarding the vehicle (e.g., “what speed was the user 105 driving the vehicle?”). The query response system 138 generates a query response 187 (e.g., indicating the particular speed) by analyzing the activity log 107 based on the query 185 and provides the query response 187 to a display, the device 108, or both.’).
Regarding claim 3 (dep. on claim 1), Visser further teaches:
“wherein the context block includes one or more template tokens to be replaced with predetermined user-specific data when presenting the response to the user” (par. 0048; ‘In a particular aspect, the language model is trained using a masked language model (MLM). The query response system 138 uses the trained language model to identify a portion of the activity log 107 as an answer 191 for the query 185.’ Masked language models use replaceable tokens.).
Regarding claim 4 (dep. on claim 1), Visser further teaches:
“wherein the context block includes an identification of the user-specific data, and wherein the request for the code block includes a request for creation of a query to retrieve the user-specific data from the system when providing the response to the user” (par. 0057; ‘The user detector 114 generates (or updates) textual label data 145 associated with the sensor data 143 to indicate the user ID 173.’).
Regarding claim 5 (dep. on claim 1), Visser further teaches:
“wherein the context block includes a portion of the physiological data for the user” (par. 0051; ‘To illustrate, the sensor(s) 142 generate the sensor data 143 including image data indicating that the user 105 occupied the driver's seat of the vehicle at a particular time, image data indicating that the user 103 occupied a passenger seat of the vehicle, sensor data indicating that the user 103 increased a volume of a music player of the vehicle, location data indicating a particular location of the vehicle, and vehicle status data indicating that the vehicle was travelling at a particular speed at the particular time.’).
Regarding claim 6 (dep. on claim 1), Visser further teaches:
“wherein the request for the code block includes a request to rephrase the physiological data for presentation in the response to the user” (par. 0069; ‘In a particular aspect, the query response system 138, in response to determining that the query 304 indicates a particular user (e.g., “Max”) and is requesting a user emotion (e.g., “How” and “feeling”) and that a most recent entry (e.g., “Max is relaxed”) of the activity log 107 that is associated with the particular user indicates a particular user emotion (e.g., “relaxed”), generates the answer 306 indicating the particular user emotion (e.g., “relaxed”).’).
Regarding claim 7 (dep. on claim 1), Visser further teaches:
“wherein the context block includes at least one of a topic for the question, dynamic data related to the topic for the question, and static data related to the topic for the question” (par. 0069; ‘In a particular aspect, the query response system 138, in response to determining that the query 304 indicates a particular user (e.g., “Max”) and is requesting a user emotion (e.g., “How” and “feeling”) and that a most recent entry (e.g., “Max is relaxed”) of the activity log 107 that is associated with the particular user indicates a particular user emotion (e.g., “relaxed”), generates the answer 306 indicating the particular user emotion (e.g., “relaxed”).’).
Regarding claim 8 (dep. on claim 1), Visser further teaches:
“further comprising code that performs the step of presenting a preliminary prompt to one of the one or more large language models to generate a second code block based on the message operable to obtain at least a portion of the context block used in the one or more prompts” (par. 0049; ‘In a particular aspect, a BiDAF network includes a hierarchical multi-stage architecture for modeling representations of context at different levels of granularity.’).
Regarding claim 9 (dep. on claim 1), Visser further teaches:
“further comprising code that performs the step of presenting a preliminary request to one of the one or more large language models to identify one or more topics for the question” (par. 0049; ‘In a particular aspect, a BiDAF network includes a hierarchical multi-stage architecture for modeling representations of context at different levels of granularity.’).
Regarding claim 10 (dep. on claim 1), Visser further teaches:
“further comprising code that performs the step of identifying one or more topics in the question, and mapping at least one of the one or more topics to a portion of the physiological data for the user” (par. 0049; ‘BiDAF includes character-level, word-level, and phrase-level embeddings, and uses bi-directional attention flow for query-aware context representation.’).
Regarding claim 12 (dep. on claim 1), Visser further teaches:
“wherein the one or more prompts include an instruction to rephrase the context data in a natural language form for use in generating the response” (par. 0069; ‘In a particular aspect, the query response system 138, in response to determining that the query 304 indicates a particular user (e.g., “Max”) and is requesting a user emotion (e.g., “How” and “feeling”) and that a most recent entry (e.g., “Max is relaxed”) of the activity log 107 that is associated with the particular user indicates a particular user emotion (e.g., “relaxed”), generates the answer 306 indicating the particular user emotion (e.g., “relaxed”).’).
Regarding claim 14 (dep. on claim 1), Visser further teaches:
“wherein the question is an implicit question from the user including a request for a creation of user-specific content” (par. 0055; ‘An utterance of the user 105 is detected by the sensor(s) 142 of the device 104 of FIG. 1. For example, the user 105 speaks (e.g., “Play some music”) within a coverage area of the sensor(s) 142 (e.g., a microphone). The sensor(s) 142 generate sensor data 143 (e.g., the audio data 171) corresponding to the utterance of the user 105.’).
Regarding claim 15 (dep. on claim 1), Visser further teaches:
“further comprising code that performs the step of processing the code block on an Internet-of-Things device in the system to provide a functional response to the message from the user” (par. 0024; ‘In a particular aspect, one or more of the device 102, the device 104, the device 106, or the device 108 includes at least one of a portable electronic device, a home appliance, factory equipment, a security device, a vehicle, a car, an internet-of-things (IoT) device, a television, an entertainment device, a navigation device, a fitness tracker, a mobile device, a health monitor, a communication device, a computer, a virtual reality device, an augmented reality device, or a device controller.’).
Regarding claim 16, Visser teaches:
“a physiological monitor used to acquire physiological data from a user” (par. 0024, fitness tracker, health monitor; ‘In a particular aspect, one or more of the device 102, the device 104, the device 106, or the device 108 includes at least one of a portable electronic device, a home appliance, factory equipment, a security device, a vehicle, a car, an internet-of-things (IoT) device, a television, an entertainment device, a navigation device, a fitness tracker, a mobile device, a health monitor, a communication device, a computer, a virtual reality device, an augmented reality device, or a device controller.’);
“one or more computing devices associated with the user” (par. 0022; ‘The system 100 (e.g., an activity query response system) includes a device 102 communicatively coupled, via an interface 134, to a device 104, a device 106, a device 108, one or more additional devices, or a combination thereof. In a particular aspect, the device 104, the device 106, the device 108, or a combination thereof, can enter or leave a communication range of the device 102 at various times.’); and
“a query module, the query module executing on one or more processors and configured by non-transitory computer executable code to perform the steps of” (par. 0022; ‘The system 100 (e.g., an activity query response system) includes a device 102 communicatively coupled, via an interface 134, to a device 104, a device 106, a device 108, one or more additional devices, or a combination thereof. In a particular aspect, the device 104, the device 106, the device 108, or a combination thereof, can enter or leave a communication range of the device 102 at various times.’):
“receiving a message from the user, wherein the message includes a question from the user in a natural language form” (par. 0045; ‘In a particular aspect, the query 185 includes a natural language query.’);
“creating one or more prompts for use with large language models” (par. 0047; ‘In a particular aspect, the query response system 138 generates the query response 187 by using a memory network architecture, a language model based on bidirectional encoder representations from transformers (BERT), a bi-directional attention flow (BiDAF) network, or a combination thereof.’) the one or more prompts including:
“a request for a code block that encodes a response to the question and is processable by the one or more computing devices to generate one or more component specific representations of the response” (see claim 1),
“a language schema for code within the code block, the language schema defining at least a syntax, functions, and operators for a programming language used to express the code block, an object schema, the object schema specifying programming objects available for use in the code block” (see claim 1), and
“a context block relating to user-specific data for the user based on the physiological data” (par. 0048; ‘The query response system 138 uses the trained language model to identify a portion of the activity log 107 as an answer 191 for the query 185.’).;
“transmitting the one or more prompts to one or more large language models” (see claim 1); and
“receiving the code block that encodes the response from one of the large language models” (par. 0050; ‘In a particular aspect, the query response system 138 provides the query response 187 to the device 108. Alternatively, or in addition, the query response system 138 provides the query response 187 to a display device coupled to the device 102.’).
Regarding claim 17 (dep. on claim 16), Visser further teaches:
“the query module further configured to perform the step of transmitting the response containing the code block to one of the computing devices of the system, wherein the one of the computing devices processes the code block to provide the response for the user” (par. 0050; ‘In a particular aspect, the query response system 138 provides the query response 187 to the device 108. Alternatively, or in addition, the query response system 138 provides the query response 187 to a display device coupled to the device 102.’).
Regarding claim 18 (dep. on claim 16), Visser further teaches:
“further comprising a context service configured to process the message to identify at least one topic for the question, map the at least one topic to dynamic and static content stored by the system, and retrieve the dynamic and static content for use in the prompt” (par. 0069; ‘In a particular aspect, the query response system 138, in response to determining that the query 304 indicates a particular user (e.g., “Max”) and is requesting a user emotion (e.g., “How” and “feeling”) and that a most recent entry (e.g., “Max is relaxed”) of the activity log 107 that is associated with the particular user indicates a particular user emotion (e.g., “relaxed”), generates the answer 306 indicating the particular user emotion (e.g., “relaxed”).’).
Regarding claim 19, Visser teaches:
“obtaining a portion of a natural language message received from a user of a system, wherein the portion of the natural language message relates to a request from the user” (par. 0045; ‘In a particular aspect, the query 185 includes a natural language query.’);
“providing, to a first large language model (LLM), a prompt operable to cause the first LLM to output a code block that encodes a response to the request and is processable by one or more components of the system to generate one or more component specific representations of the response, wherein the prompt comprises a predetermined instruction, a context block based on the portion of the natural language message, a language schema for code within the code block, and an object schema that defines objects usable within the code block, wherein the object schema is related to the request from the user” (par. 0048; ‘In a particular aspect, a BERT architecture includes a multi-layer bidirectional transformer encoder. In a particular aspect, the language model is trained using a masked language model (MLM).’ The bidirectional transformer encoder analyzes text to capture language scheme.); and
“obtaining the code block from the first LLM” (par. 0048; ‘In a particular aspect, a BERT architecture includes a multi-layer bidirectional transformer encoder. In a particular aspect, the language model is trained using a masked language model (MLM). The query response system 138 uses the trained language model to identify a portion of the activity log 107 as an answer 191 for the query 185.’).
Regarding claim 20 (dep. on claim 19), Visser further teaches:
“wherein the system is a physiological monitoring system” (par. 0024, fitness tracker, health monitor; ‘In a particular aspect, one or more of the device 102, the device 104, the device 106, or the device 108 includes at least one of a portable electronic device, a home appliance, factory equipment, a security device, a vehicle, a car, an internet-of-things (IoT) device, a television, an entertainment device, a navigation device, a fitness tracker, a mobile device, a health monitor, a communication device, a computer, a virtual reality device, an augmented reality device, or a device controller.’).
Allowable Subject Matter
Claims 11 and 13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The Examiner deems the prior art of record, whether taken alone or in combination, fails to teach, inter alia, “wherein identifying one or more topics includes presenting the question to one of the large language models with a list of candidate topics and requesting a selection of the one or more topics for the question from the list of candidate topics” nor “further comprising code that performs the steps of monitoring a responsiveness of the large language model and optimizing a use of the large language model by varying a length limit for text-based responses according to the responsiveness” in combination with the other claimed features.
Conclusion
Other pertinent prior art are cited in the PTO-892 for the applicant's consideration.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK VILLENA whose telephone number is (571)270-3191. The examiner can normally be reached 10 am - 6pm EST Monday through Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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MARK . VILLENA
Examiner
Art Unit 2658
/MARK VILLENA/ Examiner, Art Unit 2658
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