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 .
Status of Claims
Claims 1-20 are pending in this application.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-2, 8-10 and 16-18 are rejected on the ground of nonstatutory double patenting over claims 1-2, 8-9, and 15-16 of Co-Pending Application No. 18/749,517. Although the claims at issue are not identical, they are not patentably distinct from each other because adding inherent and/or unnecessary limitations/step and rearranging the claims would be within the level of one of ordinary skill in the art. It is well settled that the insertion of an element, e.g. “storing rules and policies referencing a plurality of authorized communication operations”, and its function is an obvious expedient if the remaining elements perform the same function as before. In re Karlson, 136 USPQ 184 (CCPA 1963). Also note Ex parte Rainu, 168 USPQ 375 (Bd. App. 1969). Insertion of a reference element or step whose function is not needed would be obvious to one of ordinary skill in the art.
Instant Application No. 18/749,510
Co-Pending Application No. 18/749,517
1. An apparatus, comprising:
a memory operable to store:
a machine learning algorithm configured to evaluate data in accordance with one or more machine learning models; and
a processor communicatively coupled to the memory and configured to:
obtain first audio data from a user device;
in response to receiving the first audio data, execute the machine learning algorithm to:
transcribe the first audio data into first text data;
summarize the first text data into a first request summary, the first request summary being representative of a first predicted purpose associated with the first audio data;
in response to summarizing the first text data, determine a first target operation based on the first request summary, the first target operation being a first determined intent to perform a first communication operation; and
map the first target operation to a first suggestion, the first suggestion comprising a first plurality action items to complete the first target operation; and
presenting the first suggestion to a workspace device.
2. The apparatus of claim 1, wherein:
the processor is further configured to:
prior to obtaining the first audio data from the user device, identify a communication exchange between the user device and the workspace device; and
in the communication exchange, the user device is authenticated by the workspace device as being entitled to access one or more services.
8. The apparatus of claim 7, wherein the processor is further configured to:
generate an overall communication summary comprising a plurality of datapoints indicating of the first request summary in relation to a first plurality of words identified in the first text data, the second request summary in relation to a second plurality of words identified in the second text data, the first suggestion corresponding to the first target operation, the second suggestion corresponding to the second target operation, and the third suggestion corresponding to the second target operation;
in response to generating the overall communication summary, execute the machine learning algorithm to structure the plurality of datapoints to train the one or more machine learning models; and
train the one or more machine learning models in accordance with a structured version of the plurality of datapoints.
1. An apparatus, comprising:
a memory operable to store:
a machine learning algorithm configured to evaluate data in accordance with one or more machine learning models; and
one or more rules and policies referencing a plurality of authorized communication operations by a workspace device interfacing with the apparatus; and
a processor communicatively coupled to the memory and configured to:
obtain first audio data from a user device configured to perform a plurality of communication operations with the workspace device;
in response to receiving the first audio data, execute the machine learning algorithm to:
transcribe the first audio data into first text data;
summarize the first text data into a first request summary, the first request summary being representative of a first predicted purpose associated with the first audio data;
determine a first target operation based on the first request summary, the first target operation being a first determined intent to perform a first communication operation; and
determine whether the first communication operation at least partially matches the plurality of authorized communication operations; and
in response to determining that the first communication operation at least partially matches the plurality of authorized communication operations, present the first request summary as a first reset point to train the one or more machine learning models.
2. The apparatus of claim 1, wherein:
the processor is further configured to:
prior to obtaining the first audio data from the user device, identify a communication exchange between the user device and the workspace device; and
in the communication exchange, the user device is authenticated by the workspace device as being entitled to access one or more services.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A: The independent claim 9 recites “obtaining first audio data from a user device; in response to receiving the first audio data, executing a machine learning algorithm to perform one or more operations comprising: transcribing the first audio data into first text data; summarizing the first text data into a first request summary, the first request summary being representative of a first predicted purpose associated with the first audio data; in response to summarizing the first text data, determining a first target operation based on the first request summary, the first target operation being a first determined intent to perform a first communication operation; and mapping the first target operation to a first suggestion, the first suggestion comprising a first plurality action items to complete the first target operation; and present the first suggestion to a workspace device”. These activities reflect perceiving information, analyzing and extracting meaning, deciding or selecting an action based on that meaning, and generating a recommended set of actions.
For example:
Transcribing speech into text is the conversion of verbal content to written form—a task humans routinely perform mentally or with conventional tools.
Summarizing text to infer a purpose or intent is an activity of comprehension and extraction of meaning, i.e., a cognitive process.
Determining a target operation and mapping it to action items are decision-making and planning steps that are mental processes.
Accordingly, the claims are directed to the judicial exception of a mental process.
Step 2: Claims Do Not Recite an Inventive Concept That Transforms the Mental Process into Patent-Eligible Subject Matter.
This judicial exception is not integrated into a practical application. In particular, the claims add generic, well-understood computer components (memory, processor, and presenting to a workspace device) and broadly recite use of “a machine learning algorithm” and “one or more machine learning models” without describing any specific, unconventional structure, algorithmic detail, data structure, or system architecture that provides a concrete technical improvement in computer functionality.
Applying Alice step two and relevant Federal Circuit precedent:
The recitation of conventional computer components (memory and processor) performing routine functions does not supply an inventive concept.
The mere invocation of “machine learning” or “machine learning models” without particularity does not demonstrate an unconventional machine or technique or a specific improvement in computer technology.
The claims recite high-level, result-oriented steps (e.g., “summarize,” “determine,” “map”) that describe mental processes rather than specific technical means for performing those processes.
Because the claims lack limitations that tie the mental-process steps to a particular way of achieving a technological improvement (for example, a novel model architecture, specialized data representation, unique training regimen that yields demonstrable technical performance gains, a specialized streaming/decoding pipeline that reduces latency by a quantifiable amount, or hardware/software co-design), the additional elements do not transform the mental processes into significantly more. Accordingly, there additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer amounts to no more than mere instructions to apply an exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
With respect to claims 1 and 17, the claim is similar to claim 9 and claims 1 and 17 recite additional element of “memory” and “processor”. The processor and memory are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions and being used as an applying) such that it amounts no more than mere instructions to apply the exception using a generic computer component as well. These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to dependent claims 2-8, 10-16, and 18-20, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, claims 1-20 are rejected
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)(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 1-4, 9-12 and 17-20 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Darla et al., (US Pub. 2024/0386883 filed on 2023-05-16).
Regarding claim 1, Darla discloses: an apparatus, comprising:
a memory operable to store:
a machine learning algorithm configured to evaluate data in accordance with one or more machine learning models (Fig. 1, elements 130, 132, 134, [0054] evaluating data using a machine learning model); and
a processor communicatively coupled to the memory and configured to:
obtain first audio data from a user device (Fig. 1, [0039][0040] obtaining voice input from a customer 102);
in response to receiving the first audio data, execute the machine learning algorithm to:
transcribe the first audio data into first text data (Fig. 1, [0040][0041] transcribing into text data by speech-to-text engine 122);
summarize the first text data into a first request summary, the first request summary being representative of a first predicted purpose associated with the first audio data (Fig. 1, item 124, Fig. 2, step 210, [0042]-[0044] tokenizing the text data and removing stop words, suffixes and prefixes that do not contribute to semantic content);
in response to summarizing the first text data, determine a first target operation based on the first request summary, the first target operation being a first determined intent to perform a first communication operation (Fig. 1, item 130, 140 and Fig. 2, steps 220-250, [0045]-[0048] vectorizing the utterance tokens and identifying a predicted intent using a Machine Learning Engine and determining a query including the predicted intent); and
map the first target operation to a first suggestion, the first suggestion comprising a first plurality action items to complete the first target operation (Fig. 2, step 260, receiving an artifact form the content repository based on the query; Fig. 1, [0048]-[0050][0063] “receive intents from ML engine 130 and query engine 142 may use the intents as a lookup parameter to retrieve content from content repository 150… artifacts stored in content repository 150 may be indexed by intents”; [0033] “artifacts may include institutional knowledge such as instructions and content on how to accomplish tasks related to customer inquiries”); and
presenting the first suggestion to a workspace device (Fig. 1, interface 144, Fig. 2, step 270, [0056][0060][0063] displaying the artifact for an agent’s use via an interface).
Regarding claim 2, Darla discloses the apparatus of claim 1, and Darla further discloses: wherein: the processor is further configured to:
prior to obtaining the first audio data from the user device, identify a communication exchange between the user device and the workspace device; and in the communication exchange, the user device is authenticated by the workspace device as being entitled to access one or more services (Fig. 1, contact management platform 110, [0039][0058][0059] receiving incoming contact from a customer and identifying authenticate and/or verify the customer).
Regarding claim 3, Darla discloses the apparatus of claim 1, and Darla further discloses: wherein: the processor is further configured to:
obtain second audio data and third audio data from the user device (Fig. 1, [0039][0040][0062][0063] obtaining voice input, e.g., ‘I have lost my credit card’, ‘have lost’ is indicative of “second audio data” and ‘my credit card’ is indicative of “third audio data”);
in response to receiving the second audio data and the third audio data, execute the machine learning algorithm to:
transcribe the second audio data into second text data; transcribe the third audio data into third text data (Fig. 1, [0040][0041][0062] transcribing into text data by speech-to-text engine 122);
summarize the second text data and the third text data into a second request summary, the second request summary being representative of a second predicted purpose associated with the second audio data (Fig. 1, item 124, Fig. 2, step 210, [0042]-[0044][0063] tokenizing and processing with an intent model, e.g., ‘lost’ and ‘credit card’ may be indicative of “a second request summary”);
in response to summarizing the second text data and the third text data, determine a second target operation based on the second request summary, the second target operation being a second determined intent to perform a second communication operation (Fig. 2, steps 220-250, [0045]-[0048][0063] determining the intent from the intent model, e.g., ‘lost payment product’ which is indicative of “a second target operation”); and
map the second target operation to a second suggestion, the second suggestion comprising a second plurality action items to complete the second target operation (Fig. 2, step 260, receiving an artifact form the content repository based on the query; Fig. 1, [0048]-[0050][0063] “The intent management platform may query a content repository using the intent as a lookup parameter and retrieve related content, such as KB artifacts. Content in the content repository may be indexed by intent”); and
present the second suggestion to the workspace device (Fig. 1, interface 144, Fig. 2, step 270, [0056][0060][0063] displaying for an agent's use via an interface).
Regarding claim 4, Darla discloses the apparatus of claim 1, and Darla further discloses: wherein: the processor is further configured to:
obtain second audio data and third audio data from the user device (Fig. 1, [0039][0040][0062][0063] obtaining voice input, e.g., ‘I have lost my credit card’, ‘have lost’ is indicative of “second audio data” and ‘my credit card’ is indicative of “third audio data”);
in response to receiving the second audio data and the third audio data, execute the machine learning algorithm to:
transcribe the second audio data into second text data; summarize the second text data into a second request summary, the second request summary being representative of a second predicted purpose associated with the second audio data; transcribe the third audio data into third text data; summarize the third text data into a third request summary, the third request summary being representative of a third predicted purpose associated with the third audio data (Fig. 1, [0040][0041][0062] transcribing into text data by speech-to-text engine 122; Fig. 2, step 210, [0042]-[0044][0063] tokenizing and processing with an intent model, e.g., ‘lost’ and ‘credit card’ may be indicative of “a second request summary”);
in response to summarizing the second text data and the third text data, determine a second target operation based on the second request summary and the third request summary, the second target operation being a second determined intent to perform a second communication operation (Fig. 2, steps 220-250, [0045]-[0048][0063] determining the intent from the intent model, e.g., ‘lost payment product’ which is indicative of “a second target operation”); and
map the second target operation to a second suggestion, the second suggestion comprising a second plurality action items to complete the second target operation (Fig. 2, step 260, receiving an artifact form the content repository based on the query; Fig. 1, [0048]-[0050][0063] “The intent management platform may query a content repository using the intent as a lookup parameter and retrieve related content, such as KB artifacts. Content in the content repository may be indexed by intent”); and
present the second suggestion to the workspace device (Fig. 1, interface 144, Fig. 2, step 270, [0056][0060][0063] displaying for an agent's use via an interface).
Regarding claims 9-12, Claims 9-12 are the corresponding method claims to system claims 1-4. Therefore, claims 9-12 are rejected using the same rationale as applied to claims 1-4 above.
Regarding claims 17-20, Claims 17-20 are the corresponding medium claims to system claims 1-4. Therefore, claims 17-20 are rejected using the same rationale as applied to claims 1-4 above.
Claim Rejections - 35 USC § 103
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.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 5-8 and 13-16 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Darla et al., (US Pub. 2024/0386883 filed on 2023-05-16) in view of Sindhwani (US Pat. 10102855).
Regarding claim 5, Darla discloses the apparatus of claim 1, and Darla further discloses: wherein: the processor is further configured to: obtain second audio data from the user device (Fig. 1, [0039][0040][0062][0063] obtaining voice input, e.g., ‘I have lost my credit card’, which is indicative of “second audio data”);
in response to receiving the second audio data, execute the machine learning algorithm to:
transcribe the second audio data into second text data (Fig. 1, [0040][0041][0062] transcribing into text data by speech-to-text engine 122);
summarize the second text data into a second request summary, the second request summary being representative of a second predicted purpose associated with the second audio data (Fig. 1, item 124, Fig. 2, step 210, [0042]-[0044][0063] tokenizing and processing with an intent model, e.g., ‘lost’ and ‘credit card’ may be indicative of “a second request summary”);
in response to summarizing the second text data, determine a [second target operation and a third] target operation based on the second request summary, wherein: the second target operation is a second determined intent to perform a second communication operation; and [the third target operation is a third determined intent to perform a third communication operation] (Fig. 2, steps 220-250, [0045]-[0048][0063] determining the intent from the intent model, e.g., ‘lost payment product’ which is indicative of “a target operation”);
map the second target operation to a second suggestion, the second suggestion comprising a second plurality action items to complete the second target operation (Fig. 2, step 260, receiving an artifact form the content repository based on the query; Fig. 1, [0048]-[0050][0063] “The intent management platform may query a content repository using the intent as a lookup parameter and retrieve related content, such as KB artifacts. Content in the content repository may be indexed by intent”); and
present the second suggestion and the [third suggestion] to the workspace device (Fig. 1, interface 144, Fig. 2, step 270, [0056][0060][0063] displaying for an agent's use via an interface).
Darla does not explicitly teach the bracketed limitation however Sindhwani does explicitly teach including the bracketed limitation:
determine a [second target operation and a third] target operation based on the second request summary, wherein: the second target operation is a second determined intent to perform a second communication operation; and [the third target operation is a third determined intent to perform a third communication operation]; and [map the third target operation to a third suggestion, the third suggestion comprising a third plurality action items to complete the third target operation] (Fig. 3A, box 320, 330, 326, 328, Col. 37, line 4 - Col. 38, line 29, determining a recipe speechlet and video speechlet based on the determined user intent and accessing instruction database 280 to obtain suggestion associated with the intent; Fig. 1, Col. 7, lines 11-21, Col. 25, line 33 – Col. 26, line 21,Col. 29, lines 54-64); and
present the second suggestion and the [third suggestion] to the workspace device (Figs. 1 and 3A, Col. 7, lines 22-45, Col. 37, line 4 - Col. 38, line 29 presenting a recipe data and video data which may be received from database).
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to incorporate the systems and methods for intent prediction and usage as taught by Darla with the method of displaying multiple-layers content via requester’s UI as taught by Sindhwani to provide user convenience by allowing users to directly check the results/answers to their requests on their monitors and by providing better suggestions.
Regarding claim 6, Darla in view of Sindhwani discloses the apparatus of claim 5, Sindhwani further discloses: wherein:
the first suggestion, the second suggestion, and the third suggestion are presented to the workspace device via a device interface (Figs. 1 and 3A, Col. 7, lines 22-45, Col. 37, line 4 - Col. 38, line 29 presenting a recipe data and video data which may be received from database);
in response to presenting the first suggestion to the workspace device, the workspace device is configured to perform a first update of a user interface (UI) in the device interface (Col. 29, lines 54-64, accessing instruction database 280 to obtain suggestion associated with the intent, e.g., pizza recipe instructions; Fig. 1, step 162, Col. 7, lines 22-45, presenting a recipe data which may be received from an instructions database); and
in response to presenting the second suggestion and the third suggestion to the workspace device, the workspace device is configured to perform a second update of the UI in the device interface (Figs. 1 and 3A, Col. 7, lines 22-45, Col. 37, line 4- Col. 38, line 29 presenting a recipe data and video data which may be received from database).
The previous motivation statement as in claim 5 is still applied.
Regarding claim 7, Darla in view of Sindhwani discloses the apparatus of claim 6, Sindhwani further discloses: wherein:
the second update comprises replacing the first suggestion with the second suggestion and the third suggestion in the UI (Fig. 3A, Col. 37, line 4 - Col. 38, line 29, updating display screen 212 from recipe content 312 to video content 314).
The previous motivation statement as in claim 5 is still applied.
Regarding claim 8, Darla in view of Sindhwani discloses the apparatus of claim 7, Sindhwani further discloses:
wherein the processor is further configured to: generate an overall communication summary comprising a plurality of datapoints indicating of the first request summary in relation to a first plurality of words identified in the first text data, the second request summary in relation to a second plurality of words identified in the second text data, the first suggestion corresponding to the first target operation, the second suggestion corresponding to the second target operation, and the third suggestion corresponding to the second target operation; in response to generating the overall communication summary, execute the machine learning algorithm to structure the plurality of datapoints to train the one or more machine learning models; and train the one or more machine learning models in accordance with a structured version of the plurality of datapoints (Darla, Fig. 1, [0040][0054] training machine learning models in accordance with record responses to IVR prompts and send the response to transcription platform and suggestions of products and/or services based on a state of a customer).
Regarding claims 13-16, Claims 13-16 are the corresponding medium claims to system claims 5-8. Therefore, claims 13-16 are rejected using the same rationale as applied to claims 5-8 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEONG-AH A. SHIN whose telephone number is (571)272-5933. The examiner can normally be reached 9 AM-3PM.
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Seong-ah A. Shin
Primary Examiner
Art Unit 2659
/SEONG-AH A SHIN/ Primary Examiner, Art Unit 2659