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
Claims 1-20 are pending. Claims 1, 11, and 17 are independent.
Claims 2-10 depend from Claim 1.
Claims 12-16 depend from Claim 11.
Claims 18-20 depend from Claim 17.
This Application was published as U.S. 2026/0093738.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6 May 2025 and 14 May 2025 are 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 § 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 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of the claimed “computer-readable storage medium,” consistent with a conclusion reached by one skilled in the art based on both the specification disclosure and the state-of-the-art, is that the full scope covers transitory “signal” embodiments. The state-of-the-art at the time the invention was made included signals, carrier waves and other wireless communication modalities (e.g. RF, infrared, etc.) as media on which executable code was recorded and from which computers acquired such code. Thus, the full scope of the claim covers “signals” and their equivalents, which are non-statutory per se. (In re Nuijten).
The applicant describes a tangible computer-readable storage medium as including open ended language (“tangible computer-readable storage medium also can be non-transitory in nature” (Par [0147])) and thus it is reasonable to interpret it to include transitory mediums. The words "storage" and/or "recording" are insufficient to convey only statutory embodiments to one of ordinary skill in the art absent an explicit and deliberate limiting definition or clear differentiation between storage media and transitory media in the disclosure.
In order to overcome this rejection, the following language is suggested:
“17. (currently amended) A computer program product comprising code stored in a non-transitory
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, 4-5, 7-8, 11-12, 14-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Alperin (US 12124966 hereinafter Alperin).
With regards to claim 1, Alperin teaches:
A computer-implemented method comprising: selecting, by a user device, [Alperin Fig 1 teaches processor that “may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone.” (Col 3 lines 19-21)]
a plurality of contextual information associated with a user [Alperin Fig teaches “Processor 104 May be configured to receive contextual data 108. For the purposes of this disclosure, a “contextual data” is a representation of information and/or data associated with a user.” (Col 5 lines 8-11)]
generating a plurality of training samples from the plurality of prior communications and the plurality of contextual information; training a machine learning model using the plurality of training samples; [Alperin Fig 1 teaches “a tonal adjustment machine learning model 128 may include a large language model (LLM). A … Large language model may be trained on large sets of data” (Col 17 lines 53-59) such as training samples or “training sets may include a variety of subject matters, such as, as nonlimiting examples, medical report documents, electronic health records, entity documents, business documents, inventory documentation, emails, user communications, advertising documents, newspaper articles, and the like. In some embodiments, training sets of LLM 132 may include a plurality of contextual data 108” (Col 17 line 63 to Col 18 line 3)]
receiving a query from an entity, the query being directed to the user; [Alperin Fig 1 teaches generating a query (112) and receiving a response (116) from the user. (Col 12 lines 24-54)]
using the machine learning model to generate a response to the query on behalf of the user; and [Alperin Fig 1 teaches “processor 104 is configured to generate a return 120 as a function of the query response 116” (Col 12 lines 55-57) where processor (104) executes machine learning model (128) which includes LLM (132)]
providing the response to the entity. [Alperin Fig 1 teaches “processor 104 may be configured to display return 120 using display device 136. As used in the current disclosure, a “display device” is a device that is used to display a plurality of data and other digital content. A display device 136 may include a user interface” (Col 26 lines 13-17)]
With regards to claim 1, Alperin fails to teach:
a plurality of prior communications of the user based on one or more pre-configured data privacy settings;
With regards to claim 1, Alperin teaches:
a plurality of prior communications of the user based on one or more pre-configured data privacy settings; [Alperin Fig 1 teaches prior communications of the user such as, “emails, user communications, advertising documents, newspaper articles, and the like.” (Col 17 line 63 to Col 18 line 3) While Alperin does not specifically state the communications are based on one or more pre-configured data privacy settings, Alperin does teach “processor 104 may be configured to anonymize the non-user specific training data using an anonymization process.” (Col 16 lines 44-46)
This data can be user data, and pre-configured by the processor because it would be obvious to anonymize all data, not just non-user data, and to pre-configure the processor with these data privacy settings in advance because Alperin teaches configuring the processor to comply with privacy regulations “such as GDPR or HIPAA” (Col 16 lines 48-51) Furthermore, the modifications wouldn’t depart “from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments” (Col 46 lines 21-28)
The motivation to combine Alperin with Alperin is because Alperin teaches “Anonymizing training data may be a crucial step in preserving privacy and ensuring compliance with data protection regulations such as GDPR or HIPAA” (Col 16 lines 48-51) which increase the capabilities of Alperin to provide better user responses, while protecting all user’s data.]
With regards to claim 2, Alperin teaches:
All the limitations of claim 1
wherein the plurality of prior communications of the user correspond to a plurality of entities, and the method further comprising: for each respective entity of the plurality of entities: generating a respective secondary training dataset that comprises a respective set of words used in a respective subset of the prior communications that are with the respective entity; and [Alperin teaches generating a secondary training datatset to train the model “by using previous inputs and outputs from the tonal adjustment machine learning model 128 as second training data to then train a second machine learning model or a second iteration of the tonal adjustment machine learning model 128” (Col 23 lines 5-9) where previous inputs correspond to entity that provided the prior communication of the user such as, “emails, user communications, advertising documents, newspaper articles, and the like.” (Col 17 line 63 to Col 18 line 3) and previous inputs comprise a subset of the prior communications with the respective entity because the subset of a set includes the set itself]
re-training the machine learning model using the respective secondary training dataset; and [Alperin teaches model may be re-trained and “updated by using previous inputs and outputs from the tonal adjustment machine learning model 128 as second training data to then train a second machine learning model or a second iteration of the tonal adjustment machine learning model 128” (Col 23 lines 5-10)]
using the machine learning model and the one or more pre-configured data privacy settings to generate a response for a query received from one of the plurality of entities. [Alperin Fig 1 teaches using machine learning model (128) teaches generating a response (120) (Col 12 lines 55-57) using anonymized data (Col 16 lines 44-46) and pre-configured data privacy settings as previously discussed]
With regards to claim 4, Alperin teaches:
All the limitations of claim 2
wherein the one or more pre-configured data privacy settings comprise a respective subset of configurable data privacy settings for each respective entity among the plurality of entities. [Alperin teaches “processor 104 may be configured to anonymize the non-user specific training data using an anonymization process.” (Col 16 lines 44-46) where configurable data privacy settings include the subsets of anonymizing user and non-user data for each entity]
With regards to claim 5, Alperin teaches:
All the limitations of claim 1
wherein the plurality of contextual information comprises contextual information retrieved from one or more user accounts of the user and one or more applications on the user device. [Alperin teaches contextual information retrieved from multiple “third-party sources including the user's inventory records, financial records, human resource records, past contextual data 108, sales records, user notes and observations, and the like” (Col 5 lines 43-47)
With regards to claim 7, Alperin teaches:
All the limitations of claim 1
wherein the plurality of prior communications and the plurality of contextual information is stored on the user device. [Alperin Fig 1 teaches processor 104 may be implemented on user device such as a telephone or smartphone (Col 3 lines 19-21) which stores contextual data and “past or present versions of any data disclosed herein may be stored within the user database” (Col 5 lines 51-53)]
With regards to claim 8, Alperin teaches:
All the limitations of claim 1
wherein generating each training sample of the plurality of training samples comprises: removing one or more portions of the training sample if the one or more portions contain private information of the user; and [Alperin teaches anonymizing training data which “involves removing or obfuscating personally identifiable information (PII) and sensitive data while retaining the utility and quality of the data for model training” (Col 16 lines 52-55)]
performing sentiment analysis on each training sample and excluding a respective training sample from the plurality of training samples if the respective training sample is determined to have one or more pre-specified sentiments. [Alperin teaches sentiment analysis such as “Likert scale surveys, open-ended feedback, binary response options and the like ... A Likert scale survey may permit users to response to statements related to the responses using a scoring system that may consist of responses like strongly agree, agree, neutral, disagree, strongly disagree, and the like. (Col 14 lines 22-32) and Alperin teaches sanitizing data by removing outliers that for example are “more than a threshold number of standard deviations away from an average, mean, or expected value” (Col 29 lines 23-25) which is an example of one or more pre-specified sentiment]
With regards to claim 11, Alperin teaches:
A device comprising: a memory; and a processor configured to: [Alperin Fig 1 teaches “Processor 104 includes a processor communicatively connected to a memory” (Col 2 lines 51-53)]
select a plurality of contextual information associated with a user and [Alperin Fig teaches “Processor 104 May be configured to receive contextual data 108. For the purposes of this disclosure, a “contextual data” is a representation of information and/or data associated with a user.” (Col 5 lines 8-11)]
generate a plurality of training samples from the plurality of prior communications and the plurality of contextual information; train a machine learning model using the plurality of training samples; [Alperin Fig 1 teaches “a tonal adjustment machine learning model 128 may include a large language model (LLM). A … Large language model may be trained on large sets of data” (Col 17 lines 53-59) such as training samples or “training sets may include a variety of subject matters, such as, as nonlimiting examples, medical report documents, electronic health records, entity documents, business documents, inventory documentation, emails, user communications, advertising documents, newspaper articles, and the like. In some embodiments, training sets of LLM 132 may include a plurality of contextual data 108” (Col 17 line 63 to Col 18 line 3)]
receive a query from an entity, the query being directed to the user; [Alperin Fig 1 teaches generating a query (112) and receiving a response (116) from the user. (Col 12 lines 24-54)]
use the machine learning model to generate a response to the query on behalf of the user; and [Alperin Fig 1 teaches “processor 104 is configured to generate a return 120 as a function of the query response 116” (Col 12 lines 55-57) where processor (104) executes machine learning model (128) which includes LLM (132)]
provide the response to the entity. [Alperin Fig 1 teaches “processor 104 may be configured to display return 120 using display device 136. As used in the current disclosure, a “display device” is a device that is used to display a plurality of data and other digital content. A display device 136 may include a user interface” (Col 26 lines 13-17)]
With regards to claim 11, Alperin fails to teach:
a plurality of prior communications of the user based on one or more pre-configured data privacy settings;
With regards to claim 11, Alperin teaches:
a plurality of prior communications of the user based on one or more pre-configured data privacy settings; [Alperin Fig 1 teaches prior communications of the user such as, “emails, user communications, advertising documents, newspaper articles, and the like.” (Col 17 line 63 to Col 18 line 3) While Alperin does not specifically state the communications are based on one or more pre-configured data privacy settings, Alperin does teach “processor 104 may be configured to anonymize the non-user specific training data using an anonymization process.” (Col 16 lines 44-46)
This data can be user data, and pre-configured by the processor because it would be obvious to anonymize all data, not just non-user data, and to pre-configure the processor with these data privacy settings in advance because Alperin teaches configuring the processor to comply with privacy regulations “such as GDPR or HIPAA” (Col 16 lines 48-51) Furthermore, the modifications wouldn’t depart “from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments” (Col 46 lines 21-28)
The motivation to combine Alperin with Alperin is because Alperin teaches “Anonymizing training data may be a crucial step in preserving privacy and ensuring compliance with data protection regulations such as GDPR or HIPAA” (Col 16 lines 48-51) which increase the capabilities of Alperin to provide better user responses, while protecting all user’s data.]
Claim 12 is a device claim with limitations corresponding to the limitations of method Claim 2 and is rejected under similar rationale.
Claim 14 is a device claim with limitations corresponding to the limitations of method Claim 4 and is rejected under similar rationale.
Claim 15 is a device claim with limitations corresponding to the limitations of method Claim 5 and is rejected under similar rationale.
Claim 16 is a device claim with limitations corresponding to the limitations of method Claim 8 and is rejected under similar rationale.
With regards to claim 17, Alperin teaches:
A computer program product comprising code stored in a tangible computer-readable storage medium, the code comprising: [Alperin teaches computer-readable storage medium (Col 4 lines 36-42)]
code to select, by a user device, a plurality of contextual information associated with a user and [Alperin Fig teaches “Processor 104 May be configured to receive contextual data 108. For the purposes of this disclosure, a “contextual data” is a representation of information and/or data associated with a user.” (Col 5 lines 8-11)]
code to generate a plurality of training samples from the plurality of prior communications and the plurality of contextual information; code to train a machine learning model using the plurality of training samples; [Alperin Fig 1 teaches “a tonal adjustment machine learning model 128 may include a large language model (LLM). A … Large language model may be trained on large sets of data” (Col 17 lines 53-59) such as training samples or “training sets may include a variety of subject matters, such as, as nonlimiting examples, medical report documents, electronic health records, entity documents, business documents, inventory documentation, emails, user communications, advertising documents, newspaper articles, and the like. In some embodiments, training sets of LLM 132 may include a plurality of contextual data 108” (Col 17 line 63 to Col 18 line 3)]
code to receive a query from an entity, the query being directed to the user; [Alperin Fig 1 teaches generating a query (112) and receiving a response (116) from the user. (Col 12 lines 24-54)]
code to use the machine learning model to generate a response to the query on behalf of the user; and [Alperin Fig 1 teaches “processor 104 is configured to generate a return 120 as a function of the query response 116” (Col 12 lines 55-57) where processor (104) executes machine learning model (128) which includes LLM (132)]
code to provide the response to the entity. [Alperin Fig 1 teaches “processor 104 may be configured to display return 120 using display device 136. As used in the current disclosure, a “display device” is a device that is used to display a plurality of data and other digital content. A display device 136 may include a user interface” (Col 26 lines 13-17)]
With regards to claim 17, Alperin fails to teach:
a plurality of prior communications of the user based on one or more pre-configured data privacy settings;
With regards to claim 17, Alperin teaches:
a plurality of prior communications of the user based on one or more pre-configured data privacy settings; [Alperin Fig 1 teaches prior communications of the user such as, “emails, user communications, advertising documents, newspaper articles, and the like.” (Col 17 line 63 to Col 18 line 3) While Alperin does not specifically state the communications are based on one or more pre-configured data privacy settings, Alperin does teach “processor 104 may be configured to anonymize the non-user specific training data using an anonymization process.” (Col 16 lines 44-46)
This data can be user data, and pre-configured by the processor because it would be obvious to anonymize all data, not just non-user data, and to pre-configure the processor with these data privacy settings in advance because Alperin teaches configuring the processor to comply with privacy regulations “such as GDPR or HIPAA” (Col 16 lines 48-51) Furthermore, the modifications wouldn’t depart “from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments” (Col 46 lines 21-28)
The motivation to combine Alperin with Alperin is because Alperin teaches “Anonymizing training data may be a crucial step in preserving privacy and ensuring compliance with data protection regulations such as GDPR or HIPAA” (Col 16 lines 48-51) which increase the capabilities of Alperin to provide better user responses, while protecting all user’s data.]
Claim 18 is a device claim with limitations corresponding to the limitations of method Claim 2 and is rejected under similar rationale.
Claim 20 is a device claim with limitations corresponding to the limitations of method Claim 4 and is rejected under similar rationale.
Claims 3, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Alperin (US 12124966) in further view of Bethancourt et al. (US12613948 hereinafter Bethancourt)
With regards to claim 3, Alperin teaches:
All the limitations of claim 2
wherein the prior communications of the user comprise textual messages communicated by the user to the plurality of entities and [Alperin teaches prior communications such as “emails, user communications, advertising documents, newspaper articles, and the like.” (Col 17 line 63 to Col 18 line 3) where emails and user communications can be textual messages communicated by a plurality of entities]
With regards to claim 3, Alperin fails to teach:
textual transcripts of telephonic conversations of the user with the plurality of entities.
With regards to claim 3, Bethancourt teaches:
textual transcripts of telephonic conversations of the user with the plurality of entities. [Bethancourt teaches authentication of a user using “transcript of the phone call can be performed by one or more appropriately trained machine learning models or generative artificial intelligence (AI) models, such as large language models (LLMs)” (Col 4 line 6 to Col 5 line 1) where the data is extracted from the transcript to be provided “for an entity (e.g., a user, the authentication platform 102, a service provider).” (Col 22 lines 3-4)
It would be obvious to one of ordinary skill in the art to combine the model that uses prior communications to generate a response with the textual transcripts as taught by Bethancourt. The motivation to combine Alperin with Bethancourt is because Bethancourt teaches “Authentication of the user with the IVR system, appropriate navigation of the IVR system to access targeted account data, and extraction of targeted account data from a transcript of the phone call” (Col 4 lines 62-65) which increase the capabilities of Alperin to provide better user security through authentication.]
Claim 13 is a device claim with limitations corresponding to the limitations of method Claim 3 and is rejected under similar rationale.
Claim 19 is a computer program produce claim with limitations corresponding to the limitations of method Claim 3 and is rejected under similar rationale.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Alperin (US 12124966) in further view of Gruber et al. (US2012/0016678 hereinafter Gruber)
With regards to claim 6, Alperin teaches:
All the limitations of claim 5
With regards to claim 6, Alperin fails to teach:
wherein the one or more applications on the user device comprises a navigation, a calendar, and a contact application.
With regards to claim 6, Bethancourt teaches:
wherein the one or more applications on the user device comprises a navigation, a calendar, and a contact application. [Gruber teaches intelligent automated assistant implemented on a user electronic device (Par [0008]) and “assistant 1002 can obtain information stored in a calendar application ("app"), contacts, and/or other sources” (Par [0086]) and “at least one intelligent automated assistant system embodiment disclosed herein may be configured or designed to include functionality for enabling … navigation (maps and directions) (Par [0021]) It would be obvious to one of ordinary skill in the art to combine the model that uses prior communications to generate a response with the intelligent automated assistant as taught by Gruber. The motivation to combine Alperin with Gruber is because Gruber teaches facilitating “user interaction with a device, and to help the user more effectively engage with local and/or remote services” (Par [0008]) which increase the capabilities of Alperin to provide regulatory mechanisms to comply with
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Alperin (US 12124966) in further view of Jensen et al. (US2025/0140125 hereinafter Jensen)
With regards to claim 9, Alperin teaches:
All the limitations of claim 8
With regards to claim 9, Alperin fails to teach:
wherein the private information of the user comprises one or more user attributes pre-specified by the user.
With regards to claim 9, Jensen teaches:
wherein the private information of the user comprises one or more user attributes pre-specified by the user. [Jensen teaches data security method of anonymized personal data and “Provide users with control over their data, including options to view, modify, or delete their personal information” (Par [0307]) which are user attributes pre-specified by the user.
It would be obvious to one of ordinary skill in the art to combine the model that uses anonymized data as taught by Alperin with the method of anonymizing personal data as taught by Jensen. The motivation to combine Alperin with Jensen is because Jense teaches complying with regulatory policies like “regulations like GDPR and CCPA” (Par [0300]) by establishing a “compliance framework adhering to relevant regulations, with regular audits and assessments” (Par [0309]) which increase the capabilities of Alperin to provide better compliance with regulatory requirements and improve user data security]
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Alperin (US 12124966) in further view of Bayardelle (US2025/0300950 hereinafter Bayardelle)
With regards to claim 10, Alperin teaches:
All the limitations of claim 8
With regards to claim 10, Alperin fails to teach:
wherein the one or more pre-specified sentiments comprises at least one of anger, frustration, or resentment.
With regards to claim 10, Bayardelle teaches:
wherein the one or more pre-specified sentiments comprises at least one of anger, frustration, or resentment. [Bayardelle teaches large language model (LLM) that performs sentiment analysis “which results in a determination of a numerical value for a predetermined set of emotion and tone parameters, such as emotions of happiness, sadness, anger, cheerful, etc.” (Par [0100])
It would be obvious to one of ordinary skill in the art to combine the model that uses sentiment analysis as taught by Alperin with the sentiment analysis performed by LLM that uses predetermined set of emotions as taught by Bayardelle. The motivation to combine Alperin with Bayardelle is because Bayardelle teaches “multi-level memory architecture enables the AI platform to balance processing efficiency with comprehensive context awareness, preserving the most relevant information at each time scale” (Par [0020]) which increase the capabilities of Alperin to provide better user context information.]
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph J Yamamoto whose telephone number is (571)272-4020. The examiner can normally be reached M-F 1000-1800 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached at 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOSEPH J. YAMAMOTO
Examiner
Art Unit 2656
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656
Prosecution Insights