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 .
Claims 1-20 are presented for examination.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “514” has been used to designate both a chatbot and Prompt #1. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “516” has been used to designate both Prompt #1 and Prompt #2. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character not mentioned in the description: 628. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to because Prompt Response #1 should be 516; see Figure 5A. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to because it appears Prompt Response #2 should be 517; see Figure 5A. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Starting with paragraph 100, the paragraph number incrementation does not follow the 4-digit pattern and turns into 5 digits.
In paragraph 0038, line 2, “back end” should read “back-end”.
Appropriate correction is required.
Claim Objections
Claims 3, 4, 7, 9, 10, 11, 16, 17, and 18 are objected to because of the following informalities:
Claims 3, 4 and 7 recite ‘processor is configured to’; however, it should recite - -processor is further configured to - -.
Claims 10, 11, 17, and 18 recite ‘executing comprises’; however, it should recite - - executing further comprises - -.
Appropriate corrections are required.
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 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claim 15, the claim recited “a computer-readable storage medium”. However, the usage of the phrase “computer readable storage medium” is broad enough to include both “non-transitory” and “transitory” media. The specification did not define what is considered as storage media or exclude carrier wave signal as being computer readable storage medium. When the specification is silent, the BRI of a CRM and a computer readable storage media/device (CRSM) in view of the state of the art covers a signal per se. Carrier wave signal is non-statutory subject matter. Therefore, claim 8 is directed to a non-statutory subject matter.
Claims 16-20 are rejected for failing to cure the deficiency from their respective parent claim by dependency.
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 1-14 and 16-20 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.
Regarding claim 1, claim 1 recites the limitation “the plurality of vectorized responses” in lines 8-9. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 8, claim 8 recites the limitation “the plurality of vectorized responses” in lines 6-7. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 16, claim 16 recites the limitations “the converting” in line 1; “the vector” and “the vectorized response” in line 3. There is insufficient antecedent basis for these limitations in the claim.
Regarding claim 17, claim 17 recites the limitations "the vector" in line 2; “the plurality of vectorized responses” in lines 2-3; “the vectorized response” in line 3; “the vectorized response” in line 4. There is insufficient antecedent basis for these limitations in the claim.
Regarding claim 18, claim 18 recites the limitations “the vector” in line 3; “the vectorized response” in line 2. There is insufficient antecedent basis for these limitations in the claim.
Regarding claim 19, claim 19 recites the limitations "the vector" in line 3. There is insufficient antecedent basis for these limitations in the claim.
Regarding claim 20, claim 20 recites the limitations “the text response” in line 2; “the LLM on the vector” and “the text response” in line 3; “the additional text content” in lines 3-4. There is insufficient antecedent basis for these limitations in the claim.
The following claims are indefinite:
Regarding claim 2, lines 3-4, it recites “identify the vectorized response based on an aggregation of the received input, the previous responses from the user, and the previous outputs by the chatbot”. It is not clearly understood what the relationship is between the “identify the vectorized response based on an aggregation of the received input, the previous responses from the user, and the previous outputs by the chatbot” as recited in claim 2 with the large language model (LLM) of claim 1. According to claim 1, lines 7-8, the “identify a vectorized response” is obtained based on “execute a large language model (LLM) on a vector and a database of vectorized response”. For the purpose of examination, the examiner will interpret the “identify the vectorized response” as recited in claim 2 as “identify another vectorized response” unrelated to the “identify a vectorized response” of claim 1.
Regarding claim 9, lines 1-3, it recites “wherein the converting comprises converting previous responses…”. It is not uncertain which converting step of claim 8 that the converting step of claim 9 referred to (i.e. there are two converting steps in claim 8). For the purpose of examination, the examiner will interpret the converting step of claim 9 as the converting step of claim 8 line 4 (i.e. converting text content).
Regarding claims 2-7 and 12-14, claims 2-7 and 12-14 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for depending on indefinite parent claims.
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.
Claims 1-14 and 16-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Guinn et al. (US 20240412030 A1, filed 06/11/2023), hereinafter Guinn.
Regarding claim 1, Guinn teaches an apparatus comprising:
a memory (Guinn; [0039], Briefly described, a memory subsystem); and
a processor coupled to the memory, the processor configured to: (Guinn; [0045],
Briefly described, a computation engine such as a processor, being able to access stored information using the memory engine):
receive an input from a user during a conversation that includes a plurality of
prompts between the user and a chatbot within a chat window of a software application (Guinn Figs. 5 and 6; [0023] and [0024], The figures illustrate a conversation that includes a plurality of prompts between the user and a chatbot within a chat window of a software application; [0045], Briefly described, a prompt received as input during a digital conversation between the user and the chatbot):
convert text content within the received input into a vector (Guinn Fig. 9 (912
-920); The figure illustrates the conversion process from a user input to a vector; [0102], Briefly described, text content is transformed into match text, such as a vectorized representation; [0126], Briefly described, converting text, described generally as a user input, to a vector):
- execute a large language model (LLM) on a vector and a database of vectorized responses to identify a vectorized response to output from among the plurality of vectorized responses within the database (Guinn; [0102], Briefly described, converting text into a vector may ensure an effective search to identify the output from the database; [0129], Briefly described, a database of embedded vectors containing vectorized and ranked responses may be accessed; [0132], Briefly described, the execution of the LLM uses the database of ranked vectors to be pulled [0123], Briefly described, an LLM for use):
- convert the vectorized response into a text response (Guinn Fig 6; [0024], The figure illustrates a quote describing the vectorized embedding pulled from the database to respond to the user in a conversation; Fig. 9 (926-946); [0138], The figure illustrates the process by which an embedded vector turns into text output as under dialogue output; [0097], Briefly described, a response from the AI may be output as a text response that a user can understand and respond to):
- display the text response output by the chatbot within the chat window of the software application (Guinn Figs. 5 and 6; [0023] and [0024], The figures illustrate a display of a conversation containing text response output by the chatbot within a chat window of a software application; Fig. 9 (946), The figure illustrates a step in the process by which the output is a displayed text response; [0055], Briefly described, software can be used to execute a web browser or other application tool to display the chatbot; [0069], Briefly described, the output may be displayed as text; [0093], Briefly described, software applications like web pages or websites may be used for conversation with the chatbot; [0096], Briefly described, a web page or website can host conversational interaction with the chatbot).
Regarding claim 2, Guinn teaches the apparatus of claim 1 wherein the processor is further configured to convert previous responses from the user and previous outputs by the chatbot within the chat window into the vector, and identify the vectorized response based on an aggregation of the received input, the previous responses from the user, and the previous outputs by the chatbot (Guinn Figs. 5 and 6; [0023] and [0024], The figures illustrate a conversation consisting of previous responses from the user and previous outputs by the chatbot within a chat window; [0044], Briefly described, a prompt received to be used as input may include a query, such as a dialogue during a conversation between the user and the chatbot; [0102], Briefly described, the text or prompts received are vectorized; [0126], Briefly described, the vectorized embedding is identified for response using text-similarity).
Regarding claim 3, Guinn teaches the apparatus of claim 1 wherein the processor is configured to compare the vector to a plurality of vectors corresponding to the plurality of vectorized responses in the vector space, and identify the vectorized response based on a distance between the vector and a corresponding vector of the vectorized response (Guinn; [0129], Briefly described, a vectorized embedding is compared to the entries of other vectorized embeddings in the database using cosine similarity, a distance algorithm; [0130], Briefly described, the vectors when compared to the database are programed to match what closely resembles the user input; [0131], Briefly described, ranking is based on matches, indicating a method for sorting to identify a vectorized response; [0133], Briefly described, a user input is compared with other vectorized responses to match a corresponding vector).
Regarding claim 4, Guinn teaches the apparatus of claim 3 wherein the processor is configured to execute a cosine similarity of the vector and the plurality of vectors and identify the vectorized response based on the execution of the cosine similarity (Guinn; [0129], Briefly described, the ranking of a vectorized embedding may be ranked using a cosine similarity).
Regarding claim 5, Guinn teaches the apparatus of claim 1 wherein the processor is further configured to store a mapping between the vector and the vectorized response within the database of vectorized responses (Guinn Fig. 9 (928); The figure illustrates a chart following the step of embedding the user input into a vector into 928, where the mapped vector and vectorized response are stored and ranked in the database of vectorized responses).
Regarding claim 6, Guinn teaches the apparatus of claim 1 wherein the processor is further configured to generate additional text content for the text response based on execution of the LLM on the vector, and display the text response with the additional text content (Guinn Figs. 5 and 6; [0023 and [0024], The figures illustrate a conversation between a user and a chatbot displayed with additional text content and responses generated by the chatbot; Fig. 9 (946); The figure illustrates an output 946 comprising displayed text as a response following the execution of the LLM on the vector; [0069], Briefly described, the processor may provide output to be displayed to the user in the form of text).
Regarding claim 7, Guinn teaches the apparatus of claim 1 wherein the processor is configured to execute the LLM on the vector and the database of vectorized responses to identify a prompt, and output the prompt by the LLM via the chat window (Guinn Figs. 5 and 6; [0096], The figures illustrate the output of prompts by the LLM via a chat window; [0093], Briefly described, a web page or website opened in a window can be used as a means of having he conversation with the chatbot; [0102], Briefly described, an LLM is executed on a vectorized embedding and the database of vectorized responses to be stored and used searched for output; [0129], Briefly described, the LLM can access the database of vectorized embeddings to be identified.
Regarding claims 8-14, they are method claims that correspond to apparatus claims 1-7. Therefore, they are rejected for the same reason as claims 1-7 above.
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 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Guinn et al. (US 20240412030 A1, filed 06/11/2023), hereinafter Guinn, and in view of Yuan et al. (US 20250077799 A1, filed 08/31/2023), hereinafter Yuan.
Regarding claim 15, Guinn teaches: A computer-readable storage medium comprising instructions stored therein which when executed by a processor cause a computer to perform: (Guinn; [0015], Briefly described, a computer-readable storage medium containing programming instructions for use in conjunction with the computer system; [0045], Briefly described, the use of a processor by the computer):
executing the LLM to generate a sequence of prompts which are output to a user
via a chatbot within a chat window of a software application (Guinn Figs. 5 and 6; [0023], [0024], and [0096], The figures illustrate a software application utilizing a chat window disclosing a sequence of prompts between a user and a chatbot; [0069], Briefly described, output to a first individual/user; [0093], Briefly described, a web page or website to host a chatbot within a chat window; [0132], Briefly described, the execution of the LLM outputs the vectorized embeddings to be pulled and output in the form of prompts to the user):
receiving responses to the sequence of prompts for the user via the chat window
of the software application (Guinn Figs 5 and 6; [0023], [0024], and [0096], The figures illustrate a chat window of a software application disclosing responses and prompts between the user and the chatbot; [0044], Briefly described, a communication engine designed to receive prompts as input to generate response prompts for the user using a software application on an electronic device; [0093], Briefly described, a web page or website used to host a chatbot to receive responses from the user for conversation):
However, Guinn fails to expressly teach - training a large language model (LLM) to learn credit card data via execution of the LLM on content from one or more credit card documents; and – retraining the LLM model to further learn credit card data via execution of the LLM on a combination of the sequence of prompts and the received responses.
In the same field of endeavor, Yuan teaches:
training a large language model (LLM) to learn credit data via execution of the
LLM on content from one or more credit card documents (Yuan; [0044], Briefly described, account/card numbers to be stored into an account to be accessed through a web browser application and/or dedicated software application executed by a computing device to engage in computing services; [0047], Briefly described, service providers like a database where account/card data can be stored, the data stored being used for similarity scores and clusters in large language models; [0051], Briefly described, similarities and clusters derived from training the data on an LLM; [0052], Briefly described, training utilizes the similarities and clusters in the form of prompts generated for further training and similarity detection):
retraining the LLM model to further learn credit card data via execution of the
LLM on a combination of the sequence of prompts and the received responses (Yuan; [0044], Briefly described, account/card numbers to be stored inro an account to be accessed through a web browser application and/or dedicated software application executed by a computer device to engage in computing services; [0047], Briefly described, service providers like a database where account/card data can be stored, the data stored being used for similarity scores and clusters in large language models; [0051], Briefly described, similarities and clusters derived from training the data on an LLM; [0052], Briefly described, training utilizes the similarities and clusters in the form of prompts generated for further training/retraining and similarity detection).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated – training a large language model (LLM) to learn credit card data via execution of the LLM on content from one or more credit card documents; and – retraining the LLM model to further learn credit card data via execution of the LLM on a combination of the sequence of prompts and the received responses as suggested by Yuan into Guinn. Doing so would be desirable because authentication credentials for financial information like card numbers can be stored by the database which an LLM can be utilized on. A user using a chatbot would receive prompt response outputs by the chatbot. This is accomplished from the vectorization of the user input and the prompt responses by the chatbot being executed by the LLM. Vectorization occurs so the LLM can accurately compare this vector to the other vectorized prompt responses in the database. The database in which the LLM pulls from can store credit card data from the combination of Guinn and Yuan, so the vectorized prompt responses in the database may incorporate this data depending on the user input and conversation state. The credit card data is stored from the training and retraining of the LLM on these credit card documents. This provides a user with a sense of personalization and convenience to have a conversation with a chatbot specifically for their credit card information.
Regarding claim 16, the combination of Guinn and Yuan teaches the invention as claimed in claim 15 above including wherein the converting comprises converting previous responses from the user and previous outputs by the chatbot within the chat window into a vector, and identifying the vectorized response based on an aggregation of the received input, the previous responses from the user, and the previous outputs by the chatbot (Guinn Figs. 5 and 6; [0023] and [0024], The figures illustrate a conversation consisting of previous responses from the user and previous outputs by the chatbot within a chat window; [0044], Briefly described, a prompt received to be used as input may include a query, such as a dialogue during a conversation between the user and the chatbot; [0102], Briefly described, the text or prompts received are vectorized; [0126], Briefly described, the vectorized embedding is identified for response using text-similarity).
Regarding claim 17, the combination of Guinn and Yuan teaches the invention as claimed in claim 15 above including wherein the executing comprises comparing the vector to a plurality of vectors corresponding to the plurality of vectorized responses in vector space, and identifying the vectorized response based on a distance between the vector and a corresponding vector of the vectorized response (Guinn; [0129], Briefly described, a vectorized embedding is compared to the entries of other vectorized embeddings in the database using cosine similarity, a distance algorithm; [0130], Briefly described, the vectors when compared to the database are programed to match what closely resembles the user input; [0131], Briefly described, ranking is based on matches, indicating a method for sorting to identify a vectorized response; [0133], Briefly described, a user input is compared with other vectorized responses to match a corresponding vector).
Regarding claim 18, the combination of Guinn and Yuan teaches the invention as claimed in claim 17 above including wherein the executing comprises executing a cosine similarity of the vector and the plurality of vectors, and identifying the vectorized response based on the execution of the cosine similarity (Guinn; [0129], Briefly described, the ranking of a vectorized embedding may be ranked using a cosine similarity).
Regarding claim 19, the combination of Guinn and Yuan teaches the invention as claimed in claim 15 above including wherein the computer is further configured to perform storing a mapping between the vector and the vectorized response within the database of vectorized responses (Guinn Fig. 9 (928); The figure illustrates a chart following the step of embedding the user input into a vector into 928, where the mapped vector and vectorized response are stored and ranked in the database of vectorized responses).
Regarding claim 20, the combination of Guinn and Yuan teaches the invention as claimed in claim 15 above including wherein the computer is further configured to perform generating additional text content for the text response based on the execution of the LLM on the vector, and displaying the text response with the additional text content (Guinn Figs. 5 and 6; [0023 and [0024], The figures illustrate a conversation between a user and a chatbot displayed with additional text content and responses generated by the chatbot; Fig. 9 (946); The figure illustrates an output 946 comprising displayed text as a response following the execution of the LLM on the vector; [0069], Briefly described, the processor may provide output to be displayed to the user in the form of text).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Al-Qasem et al. (arXiv:2306.05827v1 [cs.CL], 9 Jun 2023) teaches in Page 7, Conclusion paragraph 2, a conversion technique called vectorization using LlamaIndex, where it vectorizes a text data input that the chatbot can utilize.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROLANDO PATRICK VIRREIRA whose telephone number is (571)270-1570. The examiner can normally be reached Monday – Friday, 8:30AM-5PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached on (571)272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROLANDO PATRICK VIRREIRA/Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
Prosecution Insights