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 .
This action is responsive to the Application filed on June 9, 2023. Claims 1-20 are pending in the case. Claims 1, 8, and 15 are the independent claims.
Specification
The disclosure is objected to because of the following informalities: paragraph [0009] “provide memories to users based a process” should read “provide memories to users based on a process”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 4 is rejected under 35 U.S.C. 112(b) second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding dependent claim 4, the term “the importance” is a relative term which renders the claim indefinite. The term “the importance” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is not clear how the importance will be measured in regard of the biometric data point, and how it will turn into a weight of the node.
Claim 6 is rejected under 35 U.S.C. 112(b), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding dependent claim 6, it mentions “wherein providing a memory”, and claim 1 also states “and providing a memory”, so it’s not clear if the two appearances of the word “memory” is the exactly same concept or can be different.
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.
Examiner’s note: claim 15 recites “one or more computer-readable tangible storage media…” but does not explicitly recite that such media is non-transitory. However, paragraph [0015] of the specification of the instant application further directs that “A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media.” Therefore, when interpreted in view of the specification, the recited “computer-readable tangible storage media” does not include transitory signals, and claims 15-20 are not rejected as reciting transitory signals under 35 USC 101 at this time.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. This judicial exception is not integrated into a practical application because the additional elements amount to implementing the abstract idea on a generic computer process. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding independent claims 1, 8, and 15, and relying on the evaluation flowchart in MPEP 2106:
Step 1 (Is the claim to a process, machine, manufacture, or composition of matter?): Yes. Claim 1 is a method (process). Claim 8 is a computer system (machine). Claim 15 is a computer program product (manufacture).
Step 2a Prong One (Does the claim recite an abstract idea?): Yes. Claims 1, 8, and 15 recite:
building a graph based on the collected biometric data (a mental process, such as a human determining an appropriate data representation for collected biometric data, including by using mathematical calculations and a physical aid such as pen and paper);
Under the broadest reasonable interpretation, these steps may be performed mentally, using mental observation, calculation and mental determination, including by a human using a physical aid such as pen and paper, including a human mentally performing observations on the biometric equipment readings and mentally performing mathematical calculations, and therefore correspond to the Mental Processes grouping.
Step 2a Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?): No. Claims 1, 8, and 15 additionally recite:
the method is processor-implemented (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
the computer system comprising: one or more processors, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more tangible storage media for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
the computer program product comprising: one or more computer-readable tangible storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor capable of performing a method, the method comprising (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
collecting biometric data from a subject in response to an event (insignificant extra-solution activity as discussed in MPEP 2106.05(g) with respect to collecting data; field of use and technological environment: generally linking the use of a judicial exception to a particular technological environment as discussed in MPEP 2106.05(h) with respect to the data being biometric data from a subject);
training a machine learning model based on the built graph (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
providing a memory to a user based on the machine learning model (insignificant extra-solution activity as discussed in MPEP 2106.05(g) with respect to providing the memory to the user; mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to the providing being based on the machine learning model).
Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity, combined with implementing the abstract idea using generic computer components.
Step 2b (Does the claim recite additional elements that amount to significantly more than the judicial exception): No. Relying on the same analysis as Step 2a Prong Two (see MPEP 2106.05.I.A: Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:…Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP 2106.05(f));…Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception...; Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g);…)), claims 1, 8, and 15 do not recite any additional elements that amount to significantly more than the abstract idea. As discussed above, Claims 1, 8, and 15 recite:
the method is processor-implemented (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
the computer system comprising: one or more processors, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more tangible storage media for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
the computer program product comprising: one or more computer-readable tangible storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor capable of performing a method, the method comprising (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
collecting biometric data from a subject in response to an event (insignificant extra-solution activity as discussed in MPEP 2106.05(g) with respect to collecting data, such as mere data gathering and outputting, reevaluated in Step 2b to include well-understood, routine, and conventional activity such as receiving or transmitting data as discussed in MPEP 2106.05(d); field of use and technological environment: generally linking the use of a judicial exception to a particular technological environment as discussed in MPEP 2106.05(h) with respect to the data being biometric data from a subject);
training a machine learning model based on the built graph (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
providing a memory to a user based on the machine learning model (insignificant extra-solution activity as discussed in MPEP 2106.05(g) with respect to providing the memory to the user, such as mere data gathering and outputting, reevaluated in Step 2b to include well-understood, routine, and conventional activity such as receiving or transmitting data as discussed in MPEP 2106.05(d); mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to the providing being based on the machine learning model).
The additional elements as discussed above, in combination with the abstract idea, are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea.
Regarding dependent claims 2, 9, and 16:
Step 2a Prong One: incorporates the rejection of claims 1, 8, and 15.
Step 2a Prong Two: the claims additionally recite wherein the subject is a domain expert in relation to the event (field of use and technological environment: generally linking the use of a judicial exception to a particular technological environment as discussed in MPEP 2106.05(h)).
Step 2b: the claims additionally recite wherein the subject is a domain expert in relation to the event (field of use and technological environment: generally linking the use of a judicial exception to a particular technological environment as discussed in MPEP 2106.05(h)).
Regarding dependent claims 3, 10, and 17:
Step 2a Prong One: incorporates the rejection of claims 1, 8, and 15.
Step 2a Prong Two: the claims additionally recite wherein a node on the graph is a biometric data point (field of use and technological environment: generally linking the use of a judicial exception to a particular technological environment as discussed in MPEP 2106.05(h)).
Step 2b: the claims additionally recite wherein a node on the graph is a biometric data point (field of use and technological environment: generally linking the use of a judicial exception to a particular technological environment as discussed in MPEP 2106.05(h)).
Regarding dependent claims 4, 11, and 18:
Step 2a Prong One: incorporates the rejection of claims 3, 10, and 17. The claims additionally recite assigning a weight to the node signifying the importance of the biometric data point in remembering the event (a mental process of determination, such as a human mentally determining a weight that should be assigned to a node of the graph corresponding to a mentally determined importance of the associated biometric datapoint in remembering the event).
Step 2a Prong Two: the claims recite no additional limitations.
Step 2b: the claims recite no additional limitations.
Regarding dependent claims 5, 12, and 19:
Step 2a Prong One: incorporates the rejection of claims 1, 11, and 18.
Step 2a Prong Two: the claim additional recites wherein providing the memory is performed in response to a biometric input (insignificant extra-solution activity as discussed in MPEP 2106.05(g) with respect to providing data in response to an input; field of use and technological environment: generally linking the use of a judicial exception to a particular technological environment as discussed in MPEP 2106.05(h) with respect to the data being a memory and the input being a biometric input)
Step 2b: the claim additional recites wherein providing the memory is performed in response to a biometric input (insignificant extra-solution activity as discussed in MPEP 2106.05(g) with respect to providing data in response to an input, such as mere data gathering and outputting, reevaluated in Step 2b to include well-understood, routine, and conventional activity such as receiving or transmitting data as discussed in MPEP 2106.05(d); field of use and technological environment: generally linking the use of a judicial exception to a particular technological environment as discussed in MPEP 2106.05(h) with respect to the data being a memory and the input being a biometric input)
Regarding dependent claims 6, 13, and 20:
Step 2a Prong One: incorporates the rejection of claim, 1, 8, and 15.
Step 2a Prong Two: the claim additional recites wherein providing a memory is performed by providing a stimulus to trigger the memory (insignificant extra-solution activity as discussed in MPEP 2106.05(g)).
Step 2b: the claim additional recites wherein providing a memory is performed by providing a stimulus to trigger the memory (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting, reevaluated in Step 2b to include well-understood, routine, and conventional activity such as receiving or transmitting data as discussed in MPEP 2106.05(d)).
Regarding dependent claims 7 and 14:
Step 2a Prong One: incorporates the rejection of claim1.
Step 2a Prong Two: the claim additionally recites wherein the machine learning model is a neural network (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Step 2b: the claim additionally recites wherein the machine learning model is a neural network (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
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.
The factual inquiries 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 1-3, 5-10, 12-17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mars (US 20240362260 A1) in view of Skorheim (US 20180068581 A1).
Regarding claims 1, 8, and 15, Mars teaches: a computer system, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more tangible storage media for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing the method steps; a computer program product, the computer program product comprising: one or more computer-readable tangible storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor capable of performing the method steps. ([0083] system and methods of preferred embodiment and variations thereof implemented as machine configured to receive computer readable medium storing computer readable instructions which are executed by computer executable components integrated with the system; computer readable medium stored on any suitable computer readable media; computer executable component includes processor, etc.); and the processor-implemented method, the method comprising:
collecting biometric data from a subject in response to an event (e.g. [0034], collecting or receiving memory input data related to one or more memory instances, relating to a particular memory of a user or subscriber which may be defined or represented by memory input data; users or subscribers providing memory input data to input collection engine; in addition, input collection engine is operable in communication with sources of memory input data including textual, audio, and streaming or real-time data sources, as well as external data repositories, etc.; [0045], collecting primary memory image data, including image data, video data, digital images, digital documents, etc. related to memories to be stored and analyzed including memories of users or subscribers relating to past experiences, events, etc.; user uploading image to system and system collecting and receiving memory input data; [0046], collecting conversational memory input data including textual, audio, speech utterance, image, and other types of inputs; [0050], receiving memory input data via user interfaces including via GUI, VUI, etc.; Fig. 2, collecting primary image input data S205 and collecting conversational memory input data S210; compare with paragraph 0033, of the specification of the instant application, indicating that biometric data may be any data providing insight into a subject’s physical or mental state, and may be collected by any sensor including a camera or microphone; i.e. collected user information including image and audio data, in response to an event such as user input, appears to be analogous to collection of biometric data in response to an event under the broadest reasonable interpretation of the claims in view of the specification, since the specification indicates that biometric data of the subject may include data collected via camera or microphone);
building a graph based on the collected memory input data ([0036] The dynamic graph construction engine 120…, preferably functions to dynamically construct and/or modify a graphical data structure for representing and storing memory input data; [0052], constructing dynamic memory graph based on the collected memory data, such as a hierarchical graphical data structure for dynamically representing and storing memory input data, having one or more graphical nodes and one or more graphical edges as shown in Fig. 3; Fig. 2, constructing a dynamic memory graph S220);
training a machine learning model based on the built graph ([0018], constructing target memory graph includes machine learning model generating contextual metadata for tokens of memory input data; [0038], any suitable model, including machine learning models, implemented in described systems and methods; [0056], Fig. 2, constructing dynamic memory graph S220 implementing one or more machine learning algorithms/models; [0057], S220 implementing semantic extraction machine learning models to extract semantic memory data; [0061] Fig. 2 S230, assimilating dynamic memory graph into a semantic nexus including one or more other memory graphs; [0065] In some embodiments, S230 may function to implement a semantic matching model that may comprise one or more machine learning models to determine whether the semantic data element values in two distinct semantic nodes (e.g., semantic nodes in two different memory graphs) meet or exceed a similarity threshold or matching criterion; i.e., the machine learning model is trained on the input memory graphs to calculate similarity threshold or matching criterion; [0073] Fig. 2 synthesizing a mnemonic narrative S240 of target memory graphs based on identifying semantic data stored in one or more memory graphs; S240 implementing narrative construction machine learning models to construct the mnemonic narrative; i.e. the system includes at least one machine learning model which is trained to process constructed memory graph information, and which are therefore trained based on constructed memory graphs);
providing a memory to a user based on the machine learning model ([0073] In such preferred embodiments, S240 may function to implement one or more narrative construction algorithms and/or one or more narrative construction machine learning models to construct the mnemonic narrative. Here the mnemonic narrative is the memory provided from machine learning model; synthesizing mnemonic narrative including collecting image data, audio data, etc. stored in nodes of the memory graphs, the mnemonic narrative constructed to include the collected data; [0075] constructing mnemonic narrative based on user input, such as to construct a narrative of memories related to the user input; [0076], constructing mnemonic narratives automatically based on identified/target semantic values; [0078], Fig. 2 deploying the mnemonic narrative S250, such as by outputting or surfacing the narrative to subscribers via user interface, etc.; [0079], outputting constructed mnemonic narrative to subscribers/users via a variety of user interfaces/computing devices; [0080], displaying primary memory images/artifacts, outputting narrative summaries in visual or audio format, surfacing to output any memory input data to user).
Assuming arguendo that Mars does not teach collecting biometric data in the method, Skorheim teaches: collecting biometric data from a subject in response to an event ([0069] During waking experience 300, when a user 301 is about to experience an event that must be remembered accurately, data recording 302 is initiated either by some automated decision system or by the user 301 [0070] A physiological measurement module 316 is included to obtain physiological measurements based on biometric sensor data (e.g., biometric data) from the subject. Fig. 3, the physiological measurement module 316 is part of collected data 302 entered into the cognitive model 304).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Mars and Skorheim in front of him/her to have modified the teachings of Mars (directed to Collecting And Processing Memory-Related Data And Synthesizing Memory-Based Narratives), to incorporate the teachings of Skorheim (directed to a Closed-Loop Intervention Control System for memory consolidation in a subject) to collect memory related input data via corresponding proper biometric measurements for providing a memory to a user. One of ordinary skill would have been motivated to perform such a modification in order to improve the effectiveness and efficiency as described in Skorheim ([0065] this disclosure provides a cognitive model-based predictive controller (or intervention control system) that improves the effectiveness and efficiency of interventions that produce replay of specific memories. The model is personalized to simulate a particular individual subject based on biometric data from the subject; ([0069] For example, for visual items, an eye tracker can be used to decide what the user is looking at; e.g., an image chip is formed around visual fixations averaged over a short (1 sec) time window).
Regarding claims 2, 9, and 16, Mars in view of Skorheim teaches all of the limitations of claims 1, 8, and 15 as previously discussed, and Mars and Skorheim both further teach wherein the subject is a domain expert in relation to the event (Mars [0034], collecting or receiving memory input data related to one or more memory instances, relating to a particular memory of a user or subscriber; Skorheim [0065] The intervention control system incorporates a model of the way the human brain encodes and consolidates memories of events and skills during waking experience and sleep; i.e., the collected memory is related to an event in the user’s own lived experience or relating to the user/subject’s own personal skills, such that the user/subject may be considered to be a domain expert in relation to the experiences and events in their own lives).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Mars and Skorheim in front of him/her to have modified the teachings of Mars (directed to Collecting And Processing Memory-Related Data And Synthesizing Memory-Based Narratives), to incorporate the teachings of Skorheim (directed to a Closed-Loop Intervention Control System for memory consolidation in a subject) to acquire input from the domain expert in relation to the event. One of ordinary skill would have been motivated to perform such a modification in order to better consolidate memories as described in Skorheim (discussion of claim 1, 8, 15 and in addition [0065] The intervention control system incorporates a model of the way the human brain encodes and consolidates memories of events and skills during waking experience and sleep. [0069] During waking experience 300, when a user 301 is about to experience an event that must be remembered accurately, [0071] At the end of a day in which a memory of a specific event was trained and/or tested. I.e. the subject here is trained on a specific skill and this invention intents to give quicker improvement on this skill).
Regarding claims 3, 10, and 17, Mars in view of Skorheim teaches all of the limitations of claims 1, 8, and 15 as previously discussed, and Mars further teaches wherein a node on the graph is a biometric data point ([0005] wherein the target memory graph comprises a plurality of graphical nodes representing a semantic illustration of the memory input data; [0034], collecting or receiving memory input data related to one or more memory instances, relating to a particular memory of a user or subscriber which may be defined or represented by memory input data; users or subscribers providing memory input data to input collection engine; in addition, input collection engine is operable in communication with sources of memory input data including textual, audio, and streaming or real-time data sources, as well as external data repositories, etc.; [0036] The dynamic graph construction engine 120, preferably functions to dynamically construct and/or modify a graphical data structure for representing and storing memory input data; [0052], constructing dynamic memory graph based on the collected memory data, such as a hierarchical graphical data structure for dynamically representing and storing memory input data, having one or more graphical nodes and one or more graphical edges as shown in Fig. 3; graphical node functions to store memory input data; [0073], image data, audio data, etc. stored in the nodes of the memory graphs; compare with paragraph 0033, of the specification of the instant application, indicating that biometric data may be any data providing insight into a subject’s physical or mental state, and may be collected by any sensor including a camera or microphone; i.e. collected user information including image and audio data appears to be analogous to collection of biometric data in response to an event under the broadest reasonable interpretation of the claims in view of the specification, since the specification indicates that biometric data of the subject may include data collected via camera or microphone; where such memory input data is stored and represented in the memory input graph, one or more nodes of the memory graph would include such data, e.g. as a biometric data point (as defined in the specification of the instant application, i.e. a node storing input image or audio memory data of a user)).
Assuming arguendo that Mars fails to teach, biometric data, Skorheim teaches: biometric data ([0069] During waking experience 300, when a user 301 is about to experience an event that must be remembered accurately, data recording 302 is initiated either by some automated decision system or by the user 301 [0070] A physiological measurement module 316 is included to obtain physiological measurements based on biometric sensor data (e.g., biometric data) from the subject. Fig. 3, the physiological measurement module 316 is part of collected data 302 entered into the cognitive model 304).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Mars and Skorheim in front of him/her to have modified the teachings of Mars (directed to Collecting And Processing Memory-Related Data And Synthesizing Memory-Based Narratives), to incorporate the teachings of Skorheim (directed to a Closed-Loop Intervention Control System for memory consolidation in a subject) to collect memory related input data via corresponding proper biometric measurements for providing a memory to a user. One of ordinary skill would have been motivated to perform such a modification in order to improve the effectiveness and efficiency as described in Skorheim ([0065] this disclosure provides a cognitive model-based predictive controller (or intervention control system) that improves the effectiveness and efficiency of interventions that produce replay of specific memories. The model is personalized to simulate a particular individual subject based on biometric data from the subject; ([0069] For example, for visual items, an eye tracker can be used to decide what the user is looking at; e.g., an image chip is formed around visual fixations averaged over a short (1 sec) time window).
Regarding claims 5, 12, and 19, Mars in view of Skorheim teaches all of the limitations of claims 1, 11, and 18 as previously discussed, and Skorheim further teaches wherein providing the memory is performed in response to a biometric input ([0015] In another aspect, the simulated memory changes are based on increases in levels of skill in the memory due to training and replays and on biometric data on the subject when the data correlates with the subject's performance of the skill. [0016] In yet another aspect, simulating memory changes includes encoding and consolidation of specific memory. [0072], employing system during sleep phase to associate cue like odor, sound, or electrical stimulation with the memory of interest during waking, and reapplying during sleep or quiet waking as a cue to trigger a recall of the specific cued memory; [0072], detecting sleep phase via EEG analyzer module; [0073], controlling intervention by turning the intervention on or off based on prediction of the intervention’s effect; [0082] At times other than task-relevant training and testing, biometric parameters identify memory-relevant physiological states and replay parameters that change the model's mode of operation during periods of waking, quiet waking, and the stages of sleep; i.e., whether the intervention control system is turned on or not to provide user a memory is based on the biometric input, such as detecting a subject has entered a sleep or quiet waking phase via EEG input).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Mars and Skorheim in front of him/her to have modified the teachings of Mars (directed to Collecting And Processing Memory-Related Data And Synthesizing Memory-Based Narratives), to incorporate the teachings of Skorheim (directed to a Closed-Loop Intervention Control System for memory consolidation in a subject) to provide the memory in response to a biometric input. One of ordinary skill would have been motivated to personalize the memory in response to a subject’s biometric input as Skorheim describes ([0082] The cognitive model 304 is personalized by incorporating biometrics 403 measured by prior art techniques, including measurements of the subject's fatigue, stress, and attention during waking. These inputs are used to modulate the initial activation level of the memories when they are learned or trained (the time of memory encoding)).
Regarding claims 6, 13, and 20, Mars in view of Skorheim teaches all of the limitations of claims 1, 8, and 15 as previously discussed, and Skorheim further teaches wherein providing a memory is performed by providing a stimulus to trigger the memory ([0071] The system includes an intervention module 310 employed in the sleep phase 306 that associates a cue like an odor, a sound, or electrical stimulation with the memory of interest daring waking, and reapplies it during sleep or quiet waking as a cue to trigger a recall of the specific cued memory).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Mars and Skorheim in front of him/her to have modified the teachings of Mars (directed to Collecting And Processing Memory-Related Data And Synthesizing Memory-Based Narratives), to incorporate the teachings of Skorheim (directed to a Closed-Loop Intervention Control System for memory consolidation in a subject) to include audio/visual mnemonic narrative into stimulus of memory triggering. One of ordinary skill would have been motivated to enhance the Mars’ invention of memory triggering by stimulus as Skorheim describes ([0062] As a non-limiting example, the model-based predictive controller of the present invention can be utilized with the transcranial current stimulation memory intervention systems (having electrodes) … The controller could also be used with the audio or odor memory interventions used in university laboratories).
Regarding claims 7 and 14, Mars in view of Skorheim teaches all of the limitations of claims 1 and 8 as previously discussed, and Mars further teaches wherein the machine learning model is a neural network ([0038] In such embodiments, semantic extraction module 125 may employ any suitable machine learning including one or more of: supervised learning (e.g., using logistic regression, using back propagation neural networks,)).
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Mars in view of Skorheim, further in view of Park et al. (US 20220335026 A1).
Regarding claims 4, 11, and 18, Mars in view of Skorheim teaches all of the limitations of claims 3,10, and 17 as previously discussed.
Assuming arguendo that Mars fails to teach that the data point is biometric data, Skorheim teaches biometric data ([0069] During waking experience 300, when a user 301 is about to experience an event that must be remembered accurately, data recording 302 is initiated either by some automated decision system or by the user 301 [0070] A physiological measurement module 316 is included to obtain physiological measurements based on biometric sensor data (e.g., biometric data) from the subject. Fig. 3, the physiological measurement module 316 is part of collected data 302 entered into the cognitive model 304).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Mars and Skorheim in front of him/her to have modified the teachings of Mars (directed to Collecting And Processing Memory-Related Data And Synthesizing Memory-Based Narratives), to incorporate the teachings of Skorheim (directed to a Closed-Loop Intervention Control System for memory consolidation in a subject) to collect memory related input data via corresponding proper biometric measurements for providing a memory to a user. One of ordinary skill would have been motivated to perform such a modification in order to improve the effectiveness and efficiency as described in Skorheim ([0065] this disclosure provides a cognitive model-based predictive controller (or intervention control system) that improves the effectiveness and efficiency of interventions that produce replay of specific memories. The model is personalized to simulate a particular individual subject based on biometric data from the subject; ([0069] For example, for visual items, an eye tracker can be used to decide what the user is looking at; e.g., an image chip is formed around visual fixations averaged over a short (1 sec) time window).
Mars and Skorheim do not explicitly disclose assigning a weight to the node signifying the importance of the biometric data point in remembering the event. However, Park teaches assigning a weight to the node signifying the importance of the biometric data point in remembering the event. (e.g. [0026], establishing links between memory content items in various dimensions, including identified sounds, identified emotions, etc., in a graph; graph data structure for memory content items; links between memory pairs weighted based on relevance of match to a central concept; [0059], collecting moment content items, such as images, videos, audio recordings, etc.; [0060], assigning tags/semantic identifiers to moment content items, including indication of emotions, etc.; [0062], generating memory hierarchy by arranging memory content items into ordered set of nodes; [0070]-[0071], tag/semantic identifier for moment content item including based on emotion detection such as of voice or face and body language in the content item; emotion engines analyzing voice tone, expression, body language, etc. to identify emotion of person in the content item; [0080], generating match value/score for moment content items, such as based on assigned tags; each assigned tag type can have a weight taking into account the importance of each match type; clustering moment content items into memory when match score is above threshold; [0082], user gaze metadata for moment content items, where this gaze information can be used to determine if content items are related or if a subset relate to a change in focus of the user; determining to keep less relevant moment content items in memory based on amount of time user’s focus is away from the focus of the memory; for example, if a user’s focus switches from watching her son ride a bike to watching a car on the road, excluding moment content items of the car due to the car being above a threshold distance from her son riding a bike; [0107], as new memories are formed, they are put into memory graph; [0111], determining associations between memory content items in various dimensions, including identified emotions, etc.; [0112], adding graph edge between memory pairs with threshold match level for selected dimension; [0113], weighting links/edges between memory pairs based on level of match, relevance to central concept, etc.; i.e. the graph may be implemented using a set of nodes, each node representing a memory, where each memory/node includes at least one biometric data point (i.e. a clustered moment content item comprising the memory), such as a photograph, video, audio, etc. of an event along with corresponding context information such as identified emotions (based on vocal tone, body language, facial expression, etc.) and user gaze information, where the biometric data point (and therefore the memory/node which ultimately represents it) is assigned various weight measures signifying the importance of the biometric data with respect to the event/memory, such as a weighting corresponding to tagged emotion data, a measure of relevance to the event/memory based on gaze/focus information, etc., along with higher-level weight measures, such as weighting/association strength between the memory/node and other memories/nodes and/or relation to a higher level memory or other associated concept information).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Mars, Skorheim, and Park in front of him/her to have modified the teachings of Mars (directed to Collecting And Processing Memory-Related Data And Synthesizing Memory-Based Narratives) and Skorheim (directed to a Closed-Loop Intervention Control System for memory consolidation in a subject), to incorporate the teachings of Park (directed to a Automated memory creation and retrieval from moment content items) to assign a weights to the nodes signifying the importance of the biometric data points in remembering the event, such as by assigning a weight corresponding to an importance of a particular type of biometric data matching to the memory/event (such as a weight corresponding to a matching emotion based on detected vocal tone, facial expression, or body language in a corresponding content item), a weight/relevance score corresponding to relevance of the content item to the event (such as based on corresponding gaze information associated with the content item), or a weight corresponding to matching with other memories/concepts in the graph structure (such as a weighting associated with a link/edge between the node and other nodes, which may each be associated with a higher level concept/memory associated with the event). One of ordinary skill would have been motivated to perform such a modification in order overcome problems associated with conventional content organization techniques, providing the user with greater ability to search for specific memories, receive related memories for a search or a current context, etc., in a manner that significantly increases efficiency and more effective communication as described in Park ([0030].
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain,” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting in re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (GCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co, v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert, denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F,3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir, 2005): Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
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/JEREMY L STANLEY/Examiner, Art Unit 2127
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