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 .
Procedural Summary
This is responsive to the claim amendments filed 12/23/2024.
Claims 24-44 are pending.
The Drawings filed 10/10/2024 are noted.
Continuation application
This is a continuation of U.S. Patent Application No.: 17/770,946, now U.S. Patent No.: 12,138,541 B2.
Applicant’s priority claim is acknowledged.
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 24-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
The claims are drawn to process, apparatus and CRM categories.
Thus, initially, under Step 1 of the analysis, it is noted that the claims are directed towards eligible categories of subject matter
Step 2A:
Prong 1: Does the Claim recite an Abstract idea, Law of Nature, or Natural Phenomenon?
Representative Claim 24 is analyzed below, with italicized limitations indicating recitations of an abstract idea, noting that independent Claims 34 & 44 recite substantially similar limitations but being drawn to different statutory classes.
Claim 24: “A method comprising: recording, by a processing system, representations in an event log of application events transpiring during execution of an application; ranking, by the processing system, multiple potential application actions based on context information, the context information comprising two or more of the event log, an indication of a target user experience, and metadata associated with the multiple potential application actions; and selecting, by the processing system and during the execution of the application, to initiate at least one application action of the multiple potential application actions based on the ranking of the multiple potential application actions.”
The italicized limitations fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG1:
“Certain Methods Of Organizing Human Activity”: managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The claims are drawn to gathering, analyzing and organizing data. The claims require recording events, ranking potential actions based on contexts and selecting an action. The claims represent managing interactions between people, wherein the interactions are represented by potential actions in applications. The recording, ranking and selections are merely analyzing and organizing data which represents how personal behavior is managed. It also represents following rules/instructions defining how to rank and select potential actions.
Prong 2: Does the Claim recite additional elements that integrate the exception into a practical application of the exception?
Although the claims recite additional limitations, these limitations do not integrate the exception into a practical application of the exception. For example, the claims require additional limitations drawn to a computing system with a processor and memory, (a GUI).
These additional limitations:
Do not represent an improvement to the functioning of a computer, or to any other technology or technical field, (MPEP 2106.05(a));
Fail to recite an improved way of training a machine learning model that protected the model’s knowledge about previous tasks while allowing it to effectively learn new tasks, and do not recite improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams2;
Do not apply the exception using a particular machine, (MPEP 2106.05(b)) and
Fail to effect a transformation. (MPEP 2106.05(c)).
Rather, these additional limitations amount to an instruction to “apply” the judicial exception using a computer as a tool to perform the abstract idea.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they amount to conventional computer implementation.
For example, as pointed out above, the claimed invention recites additional elements facilitating implementation of the abstract process. However, these elements viewed individually and as a whole, are indistinguishable from conventional computing elements known in the art. Therefore, the additional elements fail to supply additional elements that yield significantly more than the underlying abstract idea.
Regarding the Berkheimer decision, see U.S. Pub. No.: 2019/0251603 A1 showing the conventionality of GUIs coupled to server systems using machine-learning. These elements fail to supply additional elements that yield significantly more than the underlying abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Additionally, Applicant’s Specifications acknowledge that generic devices including desktop computers are used to implement the claimed invention.3
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions provide conventional computer implementation of an abstract process.
Moreover, the claims do not recite improvements to another technology or technical field. Nor, do the claims improve the functioning of the underlying computer itself -- they only recite generic computing elements. Furthermore, they do not effect a transformation of a particular article to a different state or thing: the underlying computing elements remain the same.
Concerning preemption, the Federal Circuit precedent controls4:
The Supreme Court has made clear that the principle of preemption is the basis for the judicial exceptions to patentability. Alice, 134 S. Ct at 2354 (“We have described the concern that drives this exclusionary principal as one of pre-emption”). For this reason, questions on preemption are inherent in and resolved by the § 101 analysis. The concern is that “patent law not inhibit further discovery by improperly tying up the future use of these building blocks of human ingenuity.” Id. (internal quotations omitted). In other words, patent claims should not prevent the use of the basic building blocks of technology—abstract ideas, naturally occurring phenomena, and natural laws. While preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility. In this case, Sequenom’s attempt to limit the breadth of the claims by showing alternative uses of cffDNA outside of the scope of the claims does not change the conclusion that the claims are directed to patent ineligible subject matter. Where a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot. (Emphasis added.)
For these reasons, it appears that the claims are not patent-eligible under 35 USC §101.
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) 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 www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 24-44 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No.: 12,138,541 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claims substantially disclose the pending claim limitations. For example, see the claim chart below:
Pending Claims:
Patented Claims:
24. A method comprising: recording, by a processing system, representations in an event log of application events transpiring during execution of an application; ranking, by the processing system, multiple potential application actions based on context information, the context information comprising two or more of the event log, an indication of a target user experience, and metadata associated with the multiple potential application actions; and selecting, by the processing system and during the execution of the application, to initiate at least one application action of the multiple potential application actions based on the ranking of the multiple potential application actions.
25. The method of claim 24, wherein ranking the multiple potential application actions comprises ranking the multiple potential application actions using a language processing machine learning algorithm.
26. The method of claim 25, wherein ranking the multiple potential application actions comprises ranking the multiple potential application actions based at least in part on a degree to which the recorded representations of application events match the indicated target user experience.
27. The method of claim 24, wherein recording the representations of the application events comprises storing textual descriptions of the application events in the event log.
28. The method of claim 27, further comprising: mapping, by the processing system, a set of application events to a corresponding set of one or more textual descriptions.
29. The method of claim 24, wherein ranking the multiple potential application actions comprises generating one or more scores for each of the multiple potential application actions in accordance with the indicated target user experience.
30. The method of claim 29, further comprising: modifying at least one generated score of the one or more generated scores based on an association of the representation in the event log with an alternative score indicated by at least one rule.
31. The method of claim 30, wherein the generating of the at least one generated score is performed using a language processing machine learning algorithm, and wherein the modifying of the at least one generated score is performed contrary to at least one conventional association indicated by a corpus used to train the language processing machine learning algorithm.
32. The method of claim 24, further comprising: receiving at least some of the context information via a programmatic interface of the processing system.
33. The method of claim 24, further comprising providing information indicative of the ranking of at least some of the multiple potential application actions via a programmatic interface of the processing system.
34. A non-transitory computer-readable medium embodying a set of executable instructions, the set of executable instructions to manipulate at least one processor to: record representations in an event log of application events transpiring during execution of an application; rank multiple potential application actions based on context information, wherein the context information comprises two or more of the event log, an indication of a target user experience, and metadata associated with the multiple potential application actions; and initiating at least one application action of the multiple potential application actions during the execution of the application based on the ranking of the multiple potential application actions.
35. The non-transitory computer-readable medium of claim 34, wherein to rank the multiple potential application actions comprises ranking the multiple potential application actions using a language processing machine learning algorithm.
36. The non-transitory computer-readable medium of claim 35, wherein to rank the multiple potential application actions comprises ranking the multiple potential application actions based at least in part on a degree to which the recorded representations of application events match the indicated target user experience.
37. The non-transitory computer-readable medium of claim 34, wherein to record the representations of the application events comprises storing textual descriptions of the application events in the event log.
38. The non-transitory computer-readable medium of claim 37, wherein the set of executable instructions are further to manipulate the at least one processor to map a set of application events to a corresponding set of one or more textual descriptions.
39. The non-transitory computer-readable medium of claim 34, wherein to rank the multiple potential application actions comprises generating one or more scores for each of the multiple potential application actions in accordance with the indicated target user experience.
40. The non-transitory computer-readable medium of claim 39, wherein the set of executable instructions are further to manipulate the at least one processor to modify at least one generated score of the one or more generated scores based on an association of the representation in the event log with an alternative score indicated by at least one rule.
41. The non-transitory computer-readable medium of claim 40, wherein to generate the one or more scores comprises generating the at least one score using a language processing machine learning algorithm, and wherein the modifying of the at least one generated score is performed contrary to at least one conventional association indicated by a corpus used to train the language processing machine learning algorithm.
42. The non-transitory computer-readable medium of claim 34, wherein the set of executable instructions are further to manipulate the at least one processor to receive at least some of the context information via a programmatic interface.
43. The non-transitory computer-readable medium of claim 34, wherein the set of executable instructions are further to manipulate the at least one processor to provide information indicative of the ranking of at least some of the multiple potential application actions via a programmatic interface.
44. An apparatus, comprising: a storage element configured to store an executable language processing machine learning algorithm; and at least one processor configured to: rank multiple potential application actions using the language processing machine learning algorithm based on context information that comprises two or more of a log of application events transpiring during execution of an application, an indication of a target user experience for the application, and metadata associated with the multiple potential application actions; and select at least one application action of the multiple potential application actions to initiate during the execution of the application based on the ranking of the multiple potential application actions.
1. A method comprising: executing a video game application at a video game processing system; in response to a game event during execution of the video game application, recording, by the video game processing system, a text string that represents the game event in a text log that comprises a sequence of text strings that represent game events that have transpired during a portion of the execution of the video game application; generating, by the video game processing system using a semantic natural language processing (NLP) machine learning (ML) algorithm, scores for labeled actions or content based on information provided via an application programming interface (API) call, the provided information indicating a context indicated by the text log, a curve that represents a target player experience as a function of progress through the game, an indication of a type of the curve, and metadata indicating labels for the labeled actions or content; returning, via the API call, a ranked list of the metadata; and selecting, by the video game processing system, the at least one of the labeled actions or content to be served based on the ranked list.
2. The method of claim 1, further comprising: mapping, by the video game processing system, a set of game events to a corresponding set of text strings.
3. The method of claim 2, further comprising: adding, by the video game processing system, a text string of the set of text strings to the text log in response to occurrence of a corresponding one of the set of game events.
4. The method of claim 1, further comprising: labeling, by the video game processing system, a set of actions or content with a corresponding set of text strings to generate the labeled actions or content.
5. The method of claim 1, further comprising: serving, by the video game processing system to a display associated with the video game processing system, at least one of the labeled actions or content that is selected based on the scores.
6. The method of claim 1, wherein the semantic NLP ML algorithm uses the text log to rank the labeled actions or content based on how closely they match the target player experience indicated by the curve of the type indicated by the indicator of the type of the curve.
7. The method of claim 1, further comprising: returning, via the API call, at least one of the scores generated by the semantic NLP ML algorithm for the metadata or an actual curve representing an actual player experience indicated by the text log.
8. The method of claim 1, further comprising: modifying the scores generated by the semantic NLP ML algorithm based on alternate associations of the text strings in the text log with labels of the labeled actions or content, wherein the alternate associations are indicated by at least one rule.
9. The method of claim 8, wherein the at least one rule indicates at least one association between a text string and a label that is different than, or contrary to, at least one conventional association indicated by a corpus that is used to train the semantic NLP ML algorithm.
10. An apparatus, comprising: a storage element configured to store executable a semantic natural language processing (NLP) machine learning (ML) algorithm; and at least one processor configured to, in response to a game event during execution of a video game application: record a text string that represents the game event in a text log that comprises a sequence of text strings that represent game events that have transpired during a portion of the execution of the video game application; generate, using the semantic NLP ML algorithm, scores for labeled actions or content based on information provided via an application programming interface (API) call, the provided information indicating a context indicated by the text log, a curve that represents a target player experience as a function of progress through the game, an indication of a type of the curve, and metadata indicating labels for the labeled actions or content; returning, via the API call, a ranked list of the metadata; and selecting, by the video game processing system, the at least one of the labeled actions or content to be served based on the ranked list.
11. The apparatus of claim 10, wherein the processor is configured to map a set of game events to a corresponding set of text strings, and wherein the mapping is stored in the storage element.
12. The apparatus of claim 11, wherein the processor is configured to add one of the set of text strings to the text log in response to occurrence of a corresponding one of the set of game events.
13. The apparatus of claim 10, wherein the processor is configured to serve, to a display associated with the video game application, at least one of the labeled actions or content that is selected based on the scores.
14. The apparatus of claim 10, wherein the processor is configured to: return, via the API call, a ranked list of the metadata; and select the at least one of the labeled actions or content to be served based on the ranked list.
15. The apparatus of claim 14, wherein the semantic NLP ML algorithm uses the text log to rank the labeled actions or content based on how closely they match the target player experience indicated by the curve of the type indicated by the indicator of the type of the curve.
16. The apparatus of claim 14, wherein the processor is configured to return, via the API call, at least one of the scores generated by the semantic NLP ML algorithm for the metadata or an actual curve representing an actual player experience indicated by the text log.
17. The apparatus of claim 10, wherein the processor is configured to modify the scores generated by the semantic NLP ML algorithm based on alternate associations of the text strings in the text log with labels of the labeled actions or content, wherein the alternate associations are indicated by at least one rule.
18. The apparatus of claim 17, wherein the at least one rule indicates at least one association between a text string and a label that is different than, or contrary to, at least one conventional association indicated by a corpus that is used to train the semantic NLP ML algorithm.
19. A non-transitory computer readable medium embodying a set of executable instructions, the set of executable instructions to manipulate at least one processor to: in response to a game event during execution of a video game application, record a text string that represents the game event in a text log that comprises a sequence of text strings that represent game events that have transpired during a portion of the execution of the video game application; generate, using a semantic natural language processing (NLP) machine learning (ML) algorithm, scores for labeled actions or content based on information provided via an application programming interface (API) call, the provided information indicating a context indicated by the text log, a curve that represents a target player experience as a function of progress through the game, an indication of a type of the curve, and metadata indicating labels for the labeled actions or content; returning, via the API call, a ranked list of the metadata; and selecting, by the video game processing system, the at least one of the labeled actions or content to be served based on the ranked list.
Conclusion
Additional Relevant References: See 892
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/OMKAR A DEODHAR/Primary Examiner, Art Unit 3715
1 See MPEP 2106
2 Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential)
3 Specifications: [0025] The cloud-based system 200 includes one or more processing devices 230 such as a computer, set-top box, gaming console, and the like that are connected to the server 205 via the network 210. In the illustrated embodiment, the processing device 230 includes a transceiver 235 that transmits signals towards the network 210 and receives signals from the network 210. The transceiver 235 can be implemented using one or more separate transmitters and receivers. The processing device 230 also includes one or more processors 240 and one or more memories 245. The processor 240 executes instructions such as program code stored in the memory 245 and the processor 240 stores information in the memory 245 such as the results of the executed instructions. The transceiver 235 is connected to a display 250 that displays images or video on a screen 255 and a game controller 260. Some embodiments of the cloud-based system 200 are therefore used by cloud-based game streaming applications. (Emphasis Added.)
4: Ariosa Diagnostics, Inc., V. Sequenom, Inc., (Fed Cir. June 12, 2015)
Prosecution Insights