DETAILED ACTION
Status of Application
This action is a Non-Final Rejection. This action is in response to the application filed on May 10, 2024.
Claims 1-20 are pending and rejected.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 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.
Claim Objections
Claims 14-20 are objected to for the following reason: The preambles of these claims recite “[t]he controller of claim 12.” However, claim 12 is a method claim. It appears that these preambles should instead state “[t]he controller of claim 13.” Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation 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 5 and 15 are 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 pre-AIA the applicant regards as the invention.
Claims 5 and 15 recite “a set of long-term notes….” The term “long-term” is a relative term which renders the claims indefinite. The term “long-term” is not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Applicant should either amend these claims or show where the Specification defines this term.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 and 12-19 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03)
Yes, with respect to claims 1-9 and 12, which recite a method and, therefore, are directed to the statutory class of process.
Yes, with respect to claims 13-19, which recite a “controller for a virtual assistant (VA) system” and, therefore, are directed to the statutory class of machine or manufacture. Although it appears that the claimed “processing system” may be hardware or software according to the specification, the claimed “memory” is interpreted as hardware.
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a))
The following claims (Claims 1-9 and 12 are representative) identify the limitations that recite the abstract idea in regular text and that recite additional elements in bold:
1. A method performed by a controller for a virtual assistant (VA) system, comprising:
assigning a set of personality traits to the VA system based on one or more interactions between the VA system and a user;
receiving input data via one or more input sources associated with the VA system;
generating a prompt based at least in part on the received input data and the set of personality traits assigned to the VA system; and
inferring a response to the prompt based on a natural language processing (NLP) model.
2. The method of claim 1, wherein the set of personality traits is associated with one or more characteristics of the response.
3. The method of claim 2, wherein the one or more characteristics include a conciseness of the response.
4. The method of claim 1, further comprising: updating the set of personality traits based on the response.
5. The method of claim 1, further comprising:
storing a set of long-term notes associated with past interactions between the VA system and the user, the prompt being further generated based on the set of long-term notes; and
updating the set of long-term notes based on the received input data and the response.
6. The method of claim 1, further comprising:
determining one or more relevant interactions based on the received input data; and
searching a set of past interactions between the VA system and the user for the one or more relevant interactions, the prompt being further generated based on the one or more relevant interactions.
7. The method of claim 1, wherein the prompt is further generated based on a schedule of tasks to be completed by the user and absent any input from the user.
8. The method of claim 1, wherein the input data includes audio received via a microphone, the method further comprising:
detecting speech in the received audio;
determining that the speech matches a voice identifier (ID) associated with the user; and
converting the speech to text, the prompt being further generated based on the voice ID and the text converted from the speech.
9. The method of claim 1, wherein the input data includes an image received via a camera, the method further comprising:
detecting an object of interest in the received image;
determining that the object of interest matches a person identifier (ID) associated with the user; and
inferring contextual information associated with the object of interest based on the NLP model, the prompt being further generated based on the person ID and the contextual information.
12. The method of claim 1, wherein the response includes a text completion associated with the prompt, the method further comprising:
converting the text completion to speech; and
outputting the speech via a speaker associated with the VA system.
Yes. But for the recited additional elements as shown above in bold, the remaining limitations of the claims recite mental processes. For example, claim 1 recites assigning a set of personality traits (evaluation and judgment), receiving input data (observation), generating a prompt (evaluation and judgment), and inferring a response (judgment and opinion). Thus, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d))
No. The claims as a whole merely use a computer as a tool to perform the abstract idea. The computing components (i.e., additional elements that are in bold above) are recited at a high level of generality and are merely invoked as a tool to implement the steps. For example, only a programmed general purpose computing device is needed to implement the claimed process, i.e., assign a set of personality traits, receive input, generate a prompt, and infer a response. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Furthermore, the abstract idea is merely being linked to a particular technological environment, i.e., a computing/VA/NLP environment. Employing existing technology within a computing/VA/NLP environment to execute the abstract idea, even when limiting the use of the abstract idea to this environment, does not integrate the exception into a practical application or add significantly more. Additionally, there is no improvement to the functioning of a computer or technology. Therefore, the abstract idea is not integrated into a practical application.
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05)
No. As discussed with respect to Step 2A, Prong 2, the additional elements in the claims, both individually and in combination, amount to no more than tools to perform the abstract idea. Merely performing the abstract idea using a computer cannot provide an inventive concept. Therefore, the claims do not provide an inventive concept.
As such, the claims are not patent eligible.
Note: Claims 10, 11, and 20 are not rejected under 35 U.S.C. 101 because they have limitations of physically controlling the input sources based on implementing the abstract idea.
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, 2, 4-7, and 12-17 are rejected under 35 U.S.C. 103 as being unpatentable over Spohrer, U.S. Patent Application Publication No. 2022/0174153 A1 and Munro, Katherine. “How Your Digital Personal Assistant Understands What you Want (And Gets it Done),” https://katherineamunro.medium.com/how-your-virtual-assistant-knows-what-you-want-and-gets-it-done-8de4b0845614 (Nov. 7, 2020).
Claim 1:
Spohrer teaches:
assigning a set of personality traits to the VA system based on one or more interactions between the VA system and a user (see at least Spohrer, paragraph 0112 (“k) A personality—how a virtual assistant responds to a user. For example, a virtual assistant may act cheerful (e.g., uses predetermined positive language, speaks in a predetermined upbeat tone, etc.), angry (e.g., speaks above a volume threshold, accents particular words, etc.), depressed (e.g., speaks below a word velocity threshold), and so on. In one instance, a virtual assistant may be configured to emulate or mimic how a user interacts with the virtual assistant (e.g., if the user talks fast, the virtual may speak fast; if the user uses text to input, the virtual assistant may output responses in text; etc.).”)).
receiving input data via one or more input sources associated with the VA system (see at least Spohrer, paragraph 0062 (“The input processing module 208 may be configured to perform various techniques to process input received from a user. For instance, input that is received from the user 106 during a conversation with a virtual assistant may be sent to the input processing module 208 for processing. If the input is speech input, the input processing module 208 may perform speech recognition techniques to convert the input into a format that is understandable by a computing device, such as text. Additionally, or alternatively, the input processing module 208 may utilize Natural Language Processing (NLP) to interpret or derive a meaning and/or concept of the input. The speech recognition and/or NLP techniques may include known or new techniques.”)).
inferring a response to the prompt based on a natural language processing (NLP) model (see at least Spohrer, paragraph 0062 (“The input processing module 208 may be configured to perform various techniques to process input received from a user. For instance, input that is received from the user 106 during a conversation with a virtual assistant may be sent to the input processing module 208 for processing. If the input is speech input, the input processing module 208 may perform speech recognition techniques to convert the input into a format that is understandable by a computing device, such as text. Additionally, or alternatively, the input processing module 208 may utilize Natural Language Processing (NLP) to interpret or derive a meaning and/or concept of the input. The speech recognition and/or NLP techniques may include known or new techniques.”); paragraph 0063 (“The task and response module 210 may be configured to identify and/or perform tasks and/or formulate a response to input. As noted above, users may interact with virtual assistants to cause tasks to be performed by the virtual assistants. In some instances, a task may be performed in response to explicit user input, such as playing music in response to “please play music.” In other instances, a task may be performed in response to inferred user input requesting that that the task be performed, such as providing weather information in response to “the weather looks nice today.” In yet further instances, a task may be performed when an event has occurred (and possibly when no input has been received), such as providing flight information an hour before a flight, presenting flight information upon arrival of a user at an airport, and so on.”)).
Spohrer does not explicitly teach:
generating a prompt based at least in part on the received input data and the set of personality traits assigned to the VA system.
Spohrer, however, teaches:
Input received from the user is input into a NLP. See at least Spohrer, paragraph 0062 (“The input processing module 208 may be configured to perform various techniques to process input received from a user. For instance, input that is received from the user 106 during a conversation with a virtual assistant may be sent to the input processing module 208 for processing. If the input is speech input, the input processing module 208 may perform speech recognition techniques to convert the input into a format that is understandable by a computing device, such as text. Additionally, or alternatively, the input processing module 208 may utilize Natural Language Processing (NLP) to interpret or derive a meaning and/or concept of the input. The speech recognition and/or NLP techniques may include known or new techniques.”); paragraph 0112).
Munro, however, teaches:
Tokenizing is part of NLP. See at least Munro, page 6 (“NLP can involve steps like: … Tokenizing: means splitting the input text into individual words, aka ‘tokens’. (Purpose for ML: most algorithms take their input as a series of individual tokens).”). Note that paragraph 0023 of the Specification describes “prompt” as follows: “As described above, many virtual assistants rely on natural language processing to interpret user queries and generate suitable responses. For example, a virtual assistant may detect user speech and convert the speech into a sequence of input tokens (collectively referred to as a “prompt”) that can be processed by a natural language processor (NLP).”
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate this feature with Spohrer’s virtual assistant conversations. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of providing the NLP with data in a format that is compatible with the NLP.
Claim 2:
Spohrer further teaches:
wherein the set of personality traits is associated with one or more characteristics of the response (see at least Spohrer, paragraph 0112 (“k) A personality—how a virtual assistant responds to a user. For example, a virtual assistant may act cheerful (e.g., uses predetermined positive language, speaks in a predetermined upbeat tone, etc.), angry (e.g., speaks above a volume threshold, accents particular words, etc.), depressed (e.g., speaks below a word velocity threshold), and so on. In one instance, a virtual assistant may be configured to emulate or mimic how a user interacts with the virtual assistant (e.g., if the user talks fast, the virtual may speak fast; if the user uses text to input, the virtual assistant may output responses in text; etc.).”)).
Claim 4:
Spohrer further teaches:
updating the set of personality traits based on the response (see at least Spohrer, paragraph 0112 (“k) A personality—how a virtual assistant responds to a user. For example, a virtual assistant may act cheerful (e.g., uses predetermined positive language, speaks in a predetermined upbeat tone, etc.), angry (e.g., speaks above a volume threshold, accents particular words, etc.), depressed (e.g., speaks below a word velocity threshold), and so on. In one instance, a virtual assistant may be configured to emulate or mimic how a user interacts with the virtual assistant (e.g., if the user talks fast, the virtual may speak fast; if the user uses text to input, the virtual assistant may output responses in text; etc.).”)).
Claim 5:
Spohrer further teaches:
storing a set of long-term notes associated with past interactions between the VA system and the user, the prompt being further generated based on the set of long-term notes; and updating the set of long-term notes based on the received input data and the response (see at least Spohrer, paragraph 0066 (“The user characteristic learning module 212 may be configured to observe user activity and attempt to learn characteristics about a user. The user characteristic learning module 212 may learn any number of characteristics about the user over time, such as user preferences (e.g., likes and dislikes), track patterns (e.g., user normally reads the news starting with the sports, followed by the business section, followed by the world news), behaviors (e.g., listens to music in the morning and watches movies at night, speaks with an accent, prefers own music collection rather than looking for new music in the cloud, etc.), and so on. To observe user activity and learn a characteristic, the user characteristic learning module 212 may access a user profile, track a pattern, monitor navigation of the user, and so on. Learned user characteristics may be stored in a user characteristic data store 226.”)).
Claim 6:
Spohrer further teaches:
determining one or more relevant interactions based on the received input data; and searching a set of past interactions between the VA system and the user for the one or more relevant interactions, the prompt being further generated based on the one or more relevant interactions (see at least Spohrer, paragraph 0067 (“As an example of learning a user characteristic, consider a scenario where a user incorrectly inputs “Cobo” or a speech recognition system incorrectly recognized the user input as “Cobo”. Once the user corrects this to say “Cabo”, the user characteristic learning module 212 can record this correction from “Cobo” to “Cabo” in the event that a similar situation arises in the future. Thus, when the user next speaks the phrase “Cabo San Lucas”, and even though the speech recognition might recognize the user input as “Cobo”, the virtual assistant service 108 will use the learned correction and make a new assumption that the user means “Cabo” and respond accordingly. As another example, if a user routinely asks for the movie “Crazy”, the user characteristic learning module 212 will learn over time that this is the user preference and make this assumption. Hence, in the future, when the user says “Play Crazy”, the virtual assistant service 108 will make a different initial assumption to begin play of the movie, rather than the original assumption of the song “Crazy” by Willie Nelson.”)).
Claim 7:
Spohrer further teaches:
wherein the prompt is further generated based on a schedule of tasks to be completed by the user and absent any input from the user (see at least Spohrer, paragraph 0068 (“The context module 214 may be configured to identify (e.g., determine) one or more pieces of contextual information. Contextual information may be used in various manners. For instance, contextual information may be used by the input processing module 208 to determine an intent or meaning of a user's input. In addition, after identifying the user's intent, the same or different contextual information may be taken into account by the task and response module 210 to determine a task to be performed or a response to provide back to the user. Further, contextual information may be used by the user characteristic learning module 212 to learn characteristics about a user. Moreover, contextual information may be used by the virtual assistant characteristic module 218 to customize a virtual assistant team. Additionally, or alternatively, contextual information may be used by the virtual assistant output module 220 to manage output of virtual assistants to users and/or by the virtual assistant communication module 222 to control conversations between virtual assistants.”); paragraph 0074 (“e) Calendar information describing one or more events of a user (e.g., a scheduled flight, a work meeting, etc.).”)).
Claim 12:
Spohrer further teaches:
wherein the response includes a text completion associated with the prompt, the method further comprising: converting the text completion to speech; and outputting the speech via a speaker associated with the VA system (see at least Spohrer, paragraph 0046 (“Each virtual assistant of the virtual assistant team 102 may be configured for multi-modal input/output (e.g., receive and/or respond in audio or speech, text, touch, gesture, etc.)….”)).
Claim 13:
Claim 13 is rejected using the same rationale that was used for the rejection of claim 1.
Claim 14:
Claim 14 is rejected using the same rationale that was used for the rejection of claim 2.
Claim 15:
Claim 15 is rejected using the same rationale that was used for the rejection of claim 5.
Claim 16:
Claim 16 is rejected using the same rationale that was used for the rejection of claim 6.
Claim 17:
Claim 17 is rejected using the same rationale that was used for the rejection of claim 7.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Spohrer, U.S. Patent Application Publication No. 2022/0174153 A1; Munro, Katherine. “How Your Digital Personal Assistant Understands What you Want (And Gets it Done),” https://katherineamunro.medium.com/how-your-virtual-assistant-knows-what-you-want-and-gets-it-done-8de4b0845614 (Nov. 7, 2020); and Petrov et al., U.S. Patent Application Publication Number 2022/0108685 A1.
Claim 3:
Spohrer does not explicitly teach, but Petrov, however, does teach:
wherein the one or more characteristics include a conciseness of the response (see at least Petrov, paragraph 0007 (Concise answers from an intelligent personal assistant may be desired.)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Petrov’s method of an IPA outputting concise responses with Spohrer’s virtual assistant conversations. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of providing information to a user that can quickly be understood. It would be obvious that a user would have a preference related to conciseness because the user may desire a quick response that is easy to understand.
Claims 8, 9, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Spohrer, U.S. Patent Application Publication No. 2022/0174153 A1; Munro, Katherine. “How Your Digital Personal Assistant Understands What you Want (And Gets it Done),” https://katherineamunro.medium.com/how-your-virtual-assistant-knows-what-you-want-and-gets-it-done-8de4b0845614 (Nov. 7, 2020); and Burton et al., U.S. Patent Application Publication Number 2021/0134286 A1.
Claim 8:
Spohrer further teaches:
wherein the input data includes audio received via a microphone, the method further comprising: detecting speech in the received audio (see at least Spohrer, paragraph 0124 (“one or more microphones 310”); paragraph 0126 (“The client application 318 may receive any type of input from a user, such as audio or speech, text, touch, or gesture input received through a sensor of the smart device 104.”)).
converting the speech to text, the prompt being further generated based on the voice ID and the text converted from the speech (see at least Spohrer, paragraph 0046 (“Each virtual assistant of the virtual assistant team 102 may be configured for multi-modal input/output (e.g., receive and/or respond in audio or speech, text, touch, gesture, etc.)….”)).
Spohrer does not explicitly teach, but Burton, however, does teach:
determining that the speech matches a voice identifier (ID) associated with the user (see at least Burton, paragraph 0046 (“Although not illustrated in FIG. 2, in some implementations, processing environment 230 can include a user identification module that is configured to identify the user requesting the services of the VA based on, for example, voice recognition and/or facial recognition.”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Burton’s VA that performs voice recognition with Spohrer’s virtual assistant conversations. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of efficiently and securely identifying the user so that user context information and characteristics can be determined.
Claim 9:
Spohrer further teaches:
wherein the input data includes an image received via a camera, the method further comprising: detecting an object of interest in the received image (see at least Spohrer, paragraph 0124 (“one or more cameras 306”); paragraph 0126 (“The client application 318 may receive any type of input from a user, such as audio or speech, text, touch, or gesture input received through a sensor of the smart device 104.”)).
inferring contextual information associated with the object of interest based on the NLP model, the prompt being further generated based on the person ID and the contextual information (see at least Spohrer, paragraphs 0070 – 0098 (Examples of contextual information are discussed.)).
Spohrer does not explicitly teach, but Burton, however, does teach:
determining that the object of interest matches a person identifier (ID) associated with the user (see at least Burton, paragraph 0046 (“Although not illustrated in FIG. 2, in some implementations, processing environment 230 can include a user identification module that is configured to identify the user requesting the services of the VA based on, for example, voice recognition and/or facial recognition.”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Burton’s VA that performs facial recognition with Spohrer’s virtual assistant conversations. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of efficiently and securely identifying the user so that user context information and characteristics can be determined.
Claim 18:
Claim 18 is rejected using the same rationale that was used for the rejection of claim 8.
Claim 19:
Claim 19 is rejected using the same rationale that was used for the rejection of claim 9.
Claims 10, 11, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Spohrer, U.S. Patent Application Publication No. 2022/0174153 A1; Munro, Katherine. “How Your Digital Personal Assistant Understands What you Want (And Gets it Done),” https://katherineamunro.medium.com/how-your-virtual-assistant-knows-what-you-want-and-gets-it-done-8de4b0845614 (Nov. 7, 2020); and Fowler, Geoffrey A. “Amazon’s New Rotating, Follow-you camera is useful – and Invasive | Echo Show Review,” https://www.seattletimes.com/business/technology/amazons-new-rotating-follow-you-camera-is-useful-and-invasive/ (Feb. 27, 2021).
Claim 10:
Spohrer does not explicitly teach, but Fowler, however, does teach:
detecting movements of the user based on the received input data; and adjusting a position of at least one of the one or more input sources based on the detected movements (see at least Fowler (This reference discusses the Echo Show, which has a camera and screen that physically move to follow a user.)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Fowler’s device camera that physically moves to follow a user with Spohrer’s virtual assistant conversations. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of allowing camera input to be received even when the user has moved to a different location. This provides convenience to the user and accuracy to the sensing.
Claim 11:
Spohrer does not explicitly teach, but Fowler, however, does teach:
wherein the received input data includes a command to follow the user and the response causes the VA system to activate one or more motors that propel the one or more input sources in a direction of the user (see at least Fowler (This reference discusses the Echo Show, which has a camera and screen that physically move to follow a user.)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Fowler’s device camera that physically moves to follow a user with Spohrer’s virtual assistant conversations. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of allowing camera input to be received even when the user has moved to a different location. This provides convenience to the user and accuracy to the sensing.
Claim 20:
Claim 20 is rejected using the same rationale that was used for the rejection of claim 10.
Relevant Prior Art
The following references are relevant to Applicant’s invention:
Das et al., U.S. Patent Application Publication Number 2019/0361655 A1. This reference teaches a method of providing an intelligent response on an electronic device.
Emma et al., U.S. Patent Application Publication Number 2019/0156222 A1. This reference teaches an AI platform with improved conversational ability and personality development.
Email Communications
Per MPEP 502.03, Applicant may authorize email communications by filing Form PTO/SB/439, available at https://www.uspto.gov/sites/default/files/documents/sb0439.pdf, via the USPTO patent electronic filing system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH H ROSEN whose telephone number is (571) 270-1850 and email address is elizabeth.rosen@uspto.gov. The examiner can normally be reached Monday - Friday, 10 AM ET - 7 PM ET.
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/ELIZABETH H ROSEN/Primary Examiner, 3693