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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/1/2026 has been entered.
Response to Arguments
Applicant's arguments filed 10/1/2026 have been fully considered but they are not persuasive. The applicant argues the following:
Nagar describes using machine learning to select a persona and to rank potential behaviors or actions for presentation by a virtual agent. See Nagar [0087]-[0092]. The cited portions do not teach or suggest a personal recall application that receives a query concerning a prior activity, experience, capability, task, preference or knowledge of the user; retrieves, in response to that query, information from output data associated with the user ID tag and based on content data associated with the user ID tag; generates a response based on the retrieved information; and provides the response to the user or the authorized third party. In particular, Nagar' s user-specific personalization output is used to determine how the virtual agent interacts with the user, rather than to retrieve information from user-specific output data associated with a user ID tag in response to a recall query concerning the user's prior activity, etc.
This argument is not persuasive for a number of reasons. First, none of the claims actually cover the actions of the personal recall application. The properties of what the personal recall application is configured to do not limit the device of claim 1, the medium of claim 12, or the method of claim 17. Second, the applicant did not disclose the language of the claim regarding the query or the two-step process of retrieving information from the output data and generating a response with this retrieved information. Third, the “behaviors and actions” presented by the virtual agent of Nagar read on the breadth of the generated response. The fact that Nagar also presents a persona does not affect how Nagar generates content which reads on the breadth of the applicant’s response, as illustrated by steps 711 and 713 of Figure 7A of Nagar. The applicant has not disclosed any technology related specific to how the “machine learning application” generates output data or how the “personal recall application” generates responses to queries that can be found patentable over the applied teachings of Nagar.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4, 6-12, 17, and 19-28 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Written Description Issue #1
Claims 1 and 12 feature the following limitation:
sending the content data associated with the user ID tag to a machine learning application; and
receiving, by the user activity monitor server and from the machine learning application, output data associated with the user ID tag, wherein the output data comprises artificial intelligence data specific to the user and representing at least one of knowledge, experiences, capabilities, expertise, preferences, or historical activities of the user;
Claim 12 features the following limitations
sending the content data associated with the user ID tag to a machine learning application; and
receiving, from the machine learning application, output data associated with the user ID tag, wherein the output data comprises artificial intelligence data specific to the user and representing at least one of knowledge, experiences, capabilities, expertise, preferences, or historical activities of the user;
Claim 17 features the following limitation:
sending, by the processing system, the content data associated with the user ID tag to a machine learning application; and
receiving, by the processing system and from the machine learning application, output data associated with the user ID tag, wherein the output data comprises artificial intelligence data specific to the user and representing at least one of knowledge, experiences, capabilities, expertise, preferences, or historical activities of the user;
Section 2163.03(V) of the MPEP states:
V. ORIGINAL CLAIM NOT SUFFICIENTLY DESCRIBED
While there is a presumption that an adequate written description of the claimed invention is present in the specification as filed. In re Wertheim, 541 F.2d 257, 262, 191 USPQ 90, 96 (CCPA 1976), a question as to whether a specification provides an adequate written description may arise in the context of an original claim. An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc). The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. "Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement."Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002).
See MPEP § 2163, subsection I.A and MPEP § 2163, subsection II.A.3(a) for further discussion of rebutting the presumption of adequate written description for originally presented claims.
The applicant is claiming the function of generating artificial intelligence data specific to a user based on content data by using a “machine learning application” but the applicant has not provided any technical disclosure of how this generation step is performed. Paragraph 80 of the applicant’s disclosure is clear that the applicant has not actually invented any machine learning algorithm for carrying out the applicant’s invention. Instead, paragraph 80 describes that a large number of techniques could be used to implement the applicant’s invention. Those of ordinary skill will recognize that this is not a solution, as such techniques may be known in the art, but how these techniques are applied to implement successful machine learning algorithms would need to be explained to achieve the claimed output data, with each technique requiring different training and setups.
The applicant’s disclosure of machine learning completely trivializes the techniques of machine learning and treats them as interchangeable magical solutions for solving complex problems. For example, the applicant’s example of how their invention works, in paragraph 83, allows an application to submit a question “How did I back this cake before?”. Paragraph 84 states that the applicant’s personal recall application somehow has knowledge about baking cakes based on content implicitly or explicitly content data and also uses the generated AI data to “create or supplement” the response to the user. There is absolutely no disclosure of how the machine learning algorithm and/or personal recall application would take all of the data collected from a user, that may or may not have to do with a previous cake baking example, and somehow glean knowledge that will allow the machine learning algorithm and/or personal recall application to provide a coherent answer to the question “How did I back this cake before?”.
This is something that even a human, watching and listening to cakes being made before and reading content about how a cake was baked, would necessarily know the nuances of baking a cake from. For example, without explicit instructions and experience one would not know exactly how the grease the pan. If a custard was involved in the batter how long to let the custard cool before adding it to the batter. If a meringue is involved, how to precisely separate the eggs and whip the egg whites. Those of ordinary skill in the art would recognize that the applicant’s disclosure does not provide a technical solution for answering these details and the thousands of other possible details regarding “baking a cake” even if the process is observed. Baking requires intelligence and trial and error to comprehend, something the probability-based solutions disclosed in paragraph 80 of the applicant’s disclosure do not address.
The cake baking example is only one possible scenario that could be covered by these claims and it is incomplete. Clearly, all other examples of requests to the personal recall application and generation of AI data are not described in any way that described how the AI data is generated. Those of ordinary skill in the art when the invention was filed would recognize that large language models at the time of the filing, such as those from OpenAI, Anthropic, and Google, could answer such general questions in terms of text against their generally trained models but even these extremely complex “state of the art” models wouldn’t cover the scenario where the model itself generates specific AI data about the user’s own implicit and explicit content. Those of ordinary skill would recognize that the applicant’s invention would require a massively complex technical solution that uses large language and imaging models as well as specific information about the user’s implicit and explicit content to solve. Paragraph 80 clearly does not address the enormous complexity of such a solution.
Section 2162 of the MPEP states:
To obtain a valid patent, a patent application must contain a full and clear description of the invention for which a patent is sought in the manner prescribed by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph. The requirement for an adequate written description ensures that the public receives something in return for the exclusionary rights that are granted to the inventor by a patent. The grant of a patent helps to foster and enhance the development and disclosure of new ideas and the advancement of scientific knowledge. Upon the grant of a patent in the U.S., information contained in the patent becomes a part of the information available to the public for further research and development, subject only to the patentee’s right to exclude others during the life of the patent.
In this case the public does not receive any description of how the applicant’s claimed machine learning algorithm generates artificial intelligence data specific to the user based on content data.
Written Description Issue #2
Claims 1, 12, and 17 feature the following limitation:
wherein the personal recall application is configured to receive, from the user or an authorized third party, a query concerning a prior activity, experience, capability, task, preference, or knowledge of the user,
The applicant is arguing this feature renders the claim patentable over the Nagar reference on page 9 of 12 of their 10/1/2026 response. The original disclosure did not define any “query concerning a prior activity, experience, capability, task, preference, or knowledge of the user”. Paragraph 83 provides an example of asking a Personal Recall App “How did I bake this cake before?” but this does not define the query received by the Personal Recall App as broadly covering questions concerning any of “a prior activity, experience, capability, task, preference, or knowledge of the user”.
In particular, there is no implication in the cake baking question that a “capability”, “preference”, or “knowledge of the user” is conveyed in the language of the question. The applicant has disclosed that the Personal Recall App may consider “preferences” and “capability” when it formulates an answer but there is no disclosure that it receives “a query concerning capability” or “a query concerning preferences” or anything about the “knowledge of a user”.
Written Description Issue #3
The applicant has amended the claims to cover the following features:
retrieve, in response to the query, information from the output data associated with the user ID tag based on the content data associated with the user ID tag,
generate a response based on the retrieved information,
First, there is no disclosure that the applicant’s invention performs the two steps of “retrieving, in response to the query information from the output data” and then “generating a response based the retrieved information. The applicant simply did not disclose such a two-step process there the output data is somehow parsed or subdivided in a “retrieval” step to form “retrieved information” and then that “retrieved information” is used to generate a response.
The applicant does not disclose how the Personal Recall App actually generates a response.
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, 4, 6-12, 17, and 19-28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are:
In claim 1, the applicant is claiming “a device”. The applicant is claiming that the device sends content data to a “machine learning application” and then receives output data from the “machine learning application”. This output data is then claimed as essential to how the personal recall application provides responses to a user or authorized third party because the responses are “based on retrieved information” which is retrieved “from the output data”. Therefore the “machine learning application” and its actions to generate the “output data” are essential to how the applicant performs the generation of a response to a query by the personal recall application. Claim 1 does not cover the “machine learning application”.
Additionally, claim 1 features language which specifies what the “personal recall application” is “configured” to do but the personal recall application is not actually covered by claim 1. The device of claim 1 interacts with the personal recall application, but it is not claimed as comprising the personal recall application.
Claim 12 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are:
In claim 12, the applicant is claiming “a machine readable medium” which stored instructions that are executed by a processing system to facilitate the performance of operations. The applicant is claiming that the processing system sends content data to a “machine learning application” and then receives output data from the “machine learning application”. This output data is then claimed as essential to how the personal recall application provides responses to a user or authorized third party because the responses are “based on retrieved information” which is retrieved “from the output data”. Therefore the “machine learning application” and its actions to generate the “output data” are essential to how the applicant performs the generation of a response to a query by the personal recall application. Claim 12 does not cover the “machine learning application”.
Additionally, claim 12 features language which specifies what the “personal recall application” is “configured” to do but the personal recall application is not actually covered by claim 12. The processing system of claim 12 interacts with the personal recall application, but it is not claimed as comprising the personal recall application.
Claim 17 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential steps, such omission amounting to a gap between the steps. See MPEP § 2172.01. The omitted steps are:
In claim 17, the applicant is claiming a method executed mostly the a “processing system”. The applicant is claiming that the “processing system” sends content data to a “machine learning application” and then receives output data from the “machine learning application”. This output data is then claimed as essential to how the personal recall application provides responses to a user or authorized third party because the responses are “based on retrieved information” which is retrieved “from the output data”. Therefore the “machine learning application” and its actions to generate the “output data” are essential to how the applicant performs the generation of a response to a query by the personal recall application. Claim 17 does not cover this essential step of generating the output data by the “machine learning application”.
Additionally, claim 17 features language which specifies what the “personal recall application” is “configured” to do but the actions performed by personal recall application are not actually covered by the method of claim 17. If the applicant wants to claim the actions of the personal recall application, they should be claimed as method steps.
Claim 20 recites the limitation "the query". There is insufficient antecedent basis for this limitation in the claim. Claim 17 states that a personal recall application is configured to receive a query but it does not reference any specific query that can be referred to with the article “the”.
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.
Claim(s) 1, 4, 6-12, 17, 19-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat Application Publication Number 2023/0376328 by Nagar et al. in view of U.S. Patent Application Publication Number 2020/0020328 by Gordon et al. and U.S. Patent Number 9,477,574 to Tumanov et al.
As to claim 1, Nagar teaches a device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: collecting, at a user activity monitor server (ref. no. 501), content data that is associated with a user, wherein the content data includes explicitly generated content generated by the user (paragraph 73, external data sources read on the claimed “explicitly” generated content) and implicitly generated content generated by monitoring activities of the user through sensors (paragraphs 73 and 77, IoT data is generated by sensors monitoring the user), wherein the monitoring activities of the user through the sensors is performed according to monitoring preferences for the user (paragraph 72); associating, at the user activity monitor server, a user ID tag with the content data (paragraphs 72 and 73, data is stored as user-specific manner and there for must be tagged by user ID in order to be stored with respect to each user); storing the content data in a content database (paragraphs 72 and 73); receiving the monitoring preferences for the user and storing the monitoring preferences in a user profiles database (paragraph 72); receiving, at the user activity monitor server, a request for the content data associated with the user ID tag (paragraph 78); retrieving, by the user activity monitor server, the content data associated with the user ID tag (paragraph 78); and sending the content data associated with the user ID tag to a machine learning application (paragraph 81); receiving, by the user activity monitor server and from the machine learning application, output data associated with the user ID tag (paragraphs 81-92), wherein the output data comprises artificial intelligence data specific to the user and representing at least one of knowledge, experiences, capabilities, expertise, preferences, or historical activities of the user (paragraphs 81-92 describe various AI data generation techniques “representing” the information of the user claimed); and providing access to the output data through a personal recall application (paragraphs 81-82, output data is provided to virtual agent 603 so that user 601 can utilize the output data), wherein the personal recall application is configured to receive, from the user or an authorized third party, a query concerning a prior activity, experience, capability, task, preference, or knowledge of the user (paragraph 19, virtual agent can answer questions), retrieve, in response to the query, information form the output data associated with the user ID tag based on the content data associated with the user ID tag (steps 705-715), generate a response based on the retrieved information (steps 729 or 727), and provide the response to the user or the authorized third party (step 731); however Nagar does not explicitly teach that the monitoring preferences include temporal settings specifying when the activities of the user can be monitored.
Gordon teaches allowing a user to specify monitoring preferences temporal settings specifying when the implicit activities of the user can be monitored by sensors (paragraphs 29 and 106).
Tumanov teaches allowing a user to specify monitoring preferences temporal settings specifying when the explicit activities of the user can be monitored (claims 1-3 of Tumanov).
It would have been obvious to one of ordinary skill in the user device management art at the time of filing to combine the teachings of Nagar regarding collecting user data for processing for AI for providing to a personalized application with the teachings of Gordon and Tumanov for allowing a user to specify temporal setting for monitoring because the privacy concerns discussed in Gordon and Tumanov about the need for schedule management would apply directly to the data collected and stored by Nagar.
As to claims 4 and 20, any user could use app 603 of Nagar.
As to claims 6-9, see paragraph 17 of Nagar.
As to claim 10, see paragraph 72 of Nagar.
As to claim 11, see Gordon and Tumanov.
As to claim 19, see Figure 7A of Nagar.
As to claims 12-20, they correspond to limitations in claims 1-11 and are rejected for the same reasoning as claims 1-11.
As to claim 21, see paragraph 17 of Nagar, social media posts and profiles are understood in the art to be capable of including images, video, and audio content.
As to claim 22, see paragraph 72 of Nagar, the sensors capture an “activity history”.
As to claim 23, see paragraphs 29 and 106 of Gordon which, the particular types of events are different types of activities.
As to claim 24, see paragraphs 17 and 73 of Nagar, the social media apps can be considered digital photography applications and video recording applications. The Examiner notes that the applicant has not described anything meaningful about the claimed types of applications.
As to claim 25, see paragraphs 29 and 106 of Gordon, the smart speaker can has an onboard microphone and captures activity. Paragraph 133 of Gordon states the speaker system can have a camera and paragraph 101 states that such cameras can be controlled for monitoring.
As to claim 26, see Figures 7A and 7B and corresponding text of Nagar.
As to claim 27, see paragraph 77 of Nagar which references subsequent retrieval.
As to claim 28, see paragraph 73 of Nagar.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS B BLAIR whose telephone number is (571)272-3893. The examiner can normally be reached Monday-Friday 9am-5pm.
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/DOUGLAS B BLAIR/Primary Examiner, Art Unit 2454
